esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study
### Data Acquisition and Result
#### 4.2.2 Result of the Development of City-Industry Integration
The Chengdu Plain Economic Zone has decreased in terms of social services and ecology and environment, which shows that this zone is prone to lose coordination efforts on service and ecology while rapidly developing industrial transformation and urban construction. The South Sichuan Economic Zone has decreased in terms of urban construction, while the Northeast Sichuan Economic Zone has decreased in terms of ecology and environment, as well as science and technology. These two economic zones are both eco-cultural tourism zones, with industrial development in southern Sichuan and agricultural development in northeast Sichuan, indicating that both economic zones still need to strengthen their industrial transformation. Although Panzhihua's overall level is high and has increased, it has decreased in three major categories: industry and economy, ecology and environment, and science and technology. This prompted Panzhihua to focus on synergistic development of industrial restructuring and ecological protection when building Vanadium and Titanium New City. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Region**} & \multicolumn{2}{c}{**Chengdu Plain**} & \multicolumn{2}{c}{**South Sichuan**} & \multicolumn{2}{c}{**Northeast Sichuan**} & \multirow{2}{*}{**Panzhihua**} \\  & & **Economic Zone** & & **Economic Zone** & \\ \hline \multirow{2}{*}{Economic industry} & 2010 & 2.8091 & 1.2005 & 1.3908 & 0.4190 \\  & 2019 & 2.9977 & 1.2744 & 1.4264 & 0.4108 \\  & Change & 0.1886 & 0.0739 & 0.0356 & \(-\)0.0082 \\ \hline \multirow{2}{*}{Habitat and employment} & 2010 & 3.5818 & 1.5358 & 2.0731 & 0.3790 \\  & 2019 & 3.6058 & 1.6039 & 2.2388 & 0.4060 \\ \cline{1-1}  & Change & 0.0240 & 0.0681 & 0.1657 & 0.0270 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The development level of each index category. Figure 5: Location of economic zones.
## 1 Introduction  
Tetragonal Heusler compounds have been receiving huge attention in recent years due to their potential applications in spintronic [1; 2; 3; 4; 5] and magnetoelectronic devices [6; 7; 8; 9], such as ultrahigh density spintronic devices [9; 10; 11; 12; 13], spin-transfer torque (STT) [9; 10; 11; 12; 13; 14; 15; 16] and permanent hard magnets [17; 18]. Among the tetragonal Heusler compounds, Mn\({}_{3}\)-based Heusler compounds exhibit very interesting properties. The previous theoretical and experimental studies [4; 19; 20; 21; 22] show that the tetragonal (DO\({}_{22}\)) phase of Mn\({}_{3}\)Ga compound is ferrimagnetic at room temperature and shows a unique combination of magnetic and electronic properties, including low magnetization, high uniaxial anisotropy, high spin polarization, and high Curie temperature. Because of these interesting properties, this material is believed to have potential for nanometer-sized spin transfer torque (STT) -based nonvolatile memories [4; 16; 23]. The first-principles calculations reveal that Mn\({}_{3}\)Z (Z = Ga, Sn and Ge) type Heusler compounds can have three different structural phases, where each phase exhibits different magnetic properties [24]. There are also some other reports about the phase stability and the magnetic properties for these systems [25; 26; 27; 28], but the relation between the phase stability and the magnetic properties of Mn\({}_{3}\)Z tetragonal Heusler alloys has not been investigated in detail.  
In this paper, the relation between the phase stability, magnetic properties, the covalent hybridization effect, and the relative position between atoms of Mn\({}_{3}\)Z (Z = Al, Ga, In, Tl, Si, Ge, Sn, Pb) Heusler alloys has been investigated by using the first-principles calculations. It is found that the atomic radius of Z atoms and the level of distortion have great effects on the degree of the covalent hybridization between atoms in Mn\({}_{3}\)Z system, which plays an important role in the phase stability and the magnetic properties of Mn\({}_{3}\)Z Heusler compounds.
# 3-D SRIM Simulation of Focused Ion Beam
## 3 Application-Oriented Model of Incident Beam  
Coulomb force interaction among ions cannot be neglected, especially in low energy, which deflects the beam current distribution at the sample from the ideal Gaussian distribution. Coulomb force interaction will change under different beam currents and beam current densities. In order to find the ion-solid interaction under different beam energies and beam currents, an application-oriented model was brought forward.  
In this new model, the ion beam current density distribution was obtained by tracing the incident ions in three dimensions [31] from the source to the surface of the sample with lens aberrations and Coulomb force interaction being considered as shown in Figure 2. Three-dimensional ion trajectories were obtained by solving Newton-Lorentz equations of ions with original random conditions at FIB source. For a typical FIB source, its beam current density satisfies Gaussian distribution as shown in Figure 3. Newton-Lorentz equations of ions can be expressed as:  
\[F=ma_{i}=qE_{i}=q(E_{E_{i}}+E_{C_{i}}),\quad for\quad i=1,...,N \tag{3}\]  
\[E_{C_{i}}=\frac{q}{4\pi\epsilon_{0}}\sum_{\begin{subarray}{c}i=1\\ i\
eq j\end{subarray}}^{N}\frac{r_{i}-r_{j}}{\left|r_{i}-r_{j}\right|^{3}}, \tag{4}\]  
where \(m\) is the mass and \(q\) is the charge of Ga\({}^{+}\), \(a_{i}\) is the acceleration, \(E_{i}\) is the field strength, \(E_{E_{i}}\) and \(E_{C_{i}}\) are the electrostatic field and coulomb field at the \(i\)-th ion respectively, \(r_{i}\) and \(r_{j}\) are the positions of \(i\)-th and \(j\)-th ions and \(N\) represents the number of ions in a bunch within which Coulomb forces among ions are considered. \(N\) is dependent on the beam energy, beam current and beam current density [31]. Normally, many bunches of ions were calculated to get a good statistical law and the total number of ions is about 10,000.  
For a 30 keV FIB, the application-oriented model of 50 pA beam current is shown in Figure 4 and its variables such as the lenses and the source originated from the FIB-SEM system in our lab. There are three key parameters to describe the distribution, which are called full width at half maximum (FWHM) values, 20-80% rise distances and 12-88% rise distances. In a Gaussian distribution, 12-88% rise distances can be comparable to FWHM values. Normally, 12-88% rise distances can be used to represent the resolution in charged particle optics. From Figure 4, we can see that the beam current density at the sample is different from a Gaussian distribution. In order to reflect the real incident beam, the application-oriented model was chosen to simulate ion-solid interaction.  
Figure 1: Ion-solid interaction schematic diagram.  
Figure 3: Gaussian distribution with the virtual source diameter being 50 nm.  
Figure 2: Ion motion trajectories and Coulomb forces among ions.
# Grasshopper Algorithmic Modelling: Parametric Design for Product Platform Customisation
## 5 Results
### Comparative Analysis of Case Studies  
The comparative analysis (Table 6) reveals significant differences in the capacity of each platform to generate customised variants through the parametric configuration of models in GH.  
The three case studies allow for the assessment of how effectively the algorithmic model has been structured to enable product customisation while preserving the integrity of the base architecture used in the platform.  
The platform for desk organisers (Case 3) stands out as the most robust in terms of parametric design. The algorithm that was developed makes it possible to generate variants by modifying both geometric and functional aspects, while maintaining a coherent shared structure. This is the only case that incorporates explicit modularity into the architecture of the product, which significantly increases the potential for adaptation to different user needs and contexts of use.  
The platform for outdoor furniture (Case 2) also supports a high level of customisation, especially in formal attributes such as dimensions and proportions. However, it is based on scaling a single product typology without altering its structure. As a result, it does not incorporate a modular approach or enable reorganisation of components. This limitation reflects a design strategy that emphasises scalability over component reconfiguration.  
In the case of perfume bottles (Case 1), although the model allows some variation through adjustments in colour, volume, and finish, the overall capacity for customisation remains limited.  
Among the analysed parameters, dimension appears as the most commonly configurable aspect in all three cases, confirming its relevance as a key variable in parametric modelling. In contrast, decorative pattern has limited presence, possibly because it is not associated with structural or functional differentiation, but rather with purely aesthetic variation.  
In conclusion, the desk organiser platform (Case 3) achieved the highest degree of product variety, supported by a modular architecture that enables both geometric and functional differentiation. The outdoor furniture platform (Case 2) was oriented towards scalability through dimensional variation of a fixed product type. In contrast, the perfume bottle platform (Case 1) exhibited the lowest level of configurability, limited primarily to aesthetic modifications. These findings highlight the importance of early architectural decisions in enhancing platform robustness and enabling the reuse of core design elements across multiple variants.
## 2 Introduction to Digital Watermarking: Trajectory, Techniques, and Applications
### Watermarking Requirements and Characteristics  
Image watermarking must account for specific characteristics, properties, and attributes to achieve both effectiveness and compatibility with intended applications. Five key requirements guide the design of watermarking systems [9].  
Fidelity:  
Watermark fidelity measures the degree of similarity between the original and watermarked images [42]. High fidelity prevents perceptible distortions that might reduce the evidentiary value of the media [10]. Most applications, particularly digital forensics and media distribution, require imperceptibility as a key requirement [10]. A robust watermark must survive typical signal processing operations, including compression, scaling, and filtering [32]. Effective watermarking systems maintain watermark detectability after image processing or transmission and prevent unauthorised removal attempts.
# Augmenting GPS with Geolocated Fiducials to Improve Accuracy for Mobile Robot Applications
## 5 Results and Discussion
### Results: Experiment 3  
Figures 12 and 13 show the results of experiment 3 with the RAW GPS data and the FAGPS compensated data respectively. The computed offsets were as follows: Latitude: 0.0000006667, Longitude: 0.0000140000. Given the relative size of each of the offsets the larger shift in the longitude compared to the latitude shown between the two figures is expected. The corrected values (Figure 13) although showing the same amount of fluctuation (in latitude and longitude) are clearly more closely aligned to the boarder of the grass.  
Figure 11: Results from Experiment 2 with a consistently lower error for the first 30 min.  
Figure 10: RMS error increases over time with respect to drift from the first data point in the sequence.
# Mass Flow Scaling of Gas-Assisted Coaxial Atomizers
## 4 Results and Discussion  
For an illustration of the necessity of liquid mass flow scaling rules, in an initial set of experiments, nozzle N1 (see Table 1) was operated at varying liquid mass flows of \(\dot{M}_{liq}\ =\ 20/35/50\) kg\(\cdot\)h\({}^{-1}\) (i.e., \(v_{liq}\) = 1.7/3.1/4.4 m\(\cdot\)s\({}^{-1}\)). The gas mass flow was kept constant at \(\dot{M}_{gas}\ =\ 14\) kg\(\cdot\)h\({}^{-1}\) (i.e., \(We_{aero}\ =\ 1000\) and gas velocity \(v_{gas}\approx 178\) m\(\cdot\)s\({}^{-1}\)).  
In a second set of experiments, the four mass flow steps at four different \(We_{aero}\) values were investigated. The operating conditions and relevant calculated dimensionless numbers for the respective nozzle are listed in Table 3.  
\begin{table}
\begin{tabular}{l c c} \hline
**Parameters** & **Values** & **Unit** \\ \hline Transmitter focal length \(f_{T}\) & 1000 & mm \\ Receiver focal length \(f_{R}\) & 1000 & mm \\ Beam expander ratio \(E\) & 1 & - \\ Receiver slit width (physical) \(l_{S}\) & 200 & \(\mu\)m \\ Laser wavelength \(\lambda_{L}\) & 561 & nm \\ Laser power (transmitter exit) & 40 & mW \\ Off-axis angle \(\Phi_{R}\) & 70 & \({}^{\circ}\) \\ Frequency shift & 80 & MHz \\ \hline \end{tabular}
\end{table}
Table 2: Evaluated settings of fiber PDA for application in labscale experiments.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Nozzle Number** & \(\dot{M}_{liq}\) **in kg\(\cdot\)h\({}^{-1}\)** & \(GLR\) & \(We_{aero}\) & \(v_{gas}\) **in m\(\cdot\)s\({}^{-1}\)** & \(j\) & \(J\) \\ \hline N1 & 20 & 0.36 & 250 & 88 & 2.99 & 29.80 \\ N2 & 50 & 0.36 & 250 & 70 & 1.98 & 24.27 \\ N3 & 100 & 0.36 & 250 & 59 & 1.38 & 20.23 \\ N4 & 500 & 0.36 & 250 & 40 & 0.62 & 13.54 \\ N1
## Materials and Methods
### Gauss-Levenberg-Marquardt Method  
The Gauss-Levenberg-Marquardt method (GLM) is a blend of gradient descent and Gauss-Newton methods. In the literature, it is also referred to as the \"Levenberg-Marquardt\" algorithm (LMA or just LM), or the \"damped least-squares\" method (DLS) [58]. The parameter estimation is based on Equation (1). When the target function is far from being the smallest, the GLM operates by the steepest gradient method, but around the minimum, the GLM begins to act as a Gauss-Newton method. The Marquardt lambda in Equation (1) is a blending factor, which determines the mix between the gradient descent and the Gauss-Newton methods. \(\lambda\) is the so-called \"Marquardt parameter,\" or synthetic \"Marquardt lambda,\" named after [PERSON] [58], who introduced this methodology. However, the use of this parameter was pioneered by [PERSON] [59]. If the error is increasing, the function is far from its minimum and lambda should be increased approaching the gradient descent method. On the other hand, if the error is decreasing, the approximation is working well, so lambda is decreased. The parameter upgrade vector (\(u\)) is calculated according to the following equation [58; 60]:  
\[u=\left(J^{T}\cdot W\cdot J+\lambda\cdot I\right)^{-1}J^{T}\cdot W\cdot r \tag{1}\]  
where  
\(J\)--a Jacobean matrix, each element is a derivative of one particular observation for each parameter;  
\(W\)--a square diagonal matrix with squared weights attached to each observation;  
\(\lambda\)--Marquardt lambda or damping factor, which is adjusted during each iteration;  
\(r\)--vector of residuals which is minimized during calibration;  
\(I\)--an identity matrix; and  
\(T\)--the matrix transpose operator.  
As it is apparent from Equation (1), the GLM algorithm requires the computation of the Jacobian matrix and matrix inversion at each iteration step as many times as there are adjustable parameters. Matrix inversion is a significant disadvantage. When the matrix is singular, GLM still performs due to the Marquardt lambda. A matrix often becomes singular when columns (or rows) are linearly dependent [61]. An enhanced version of GLM is implemented in PEST_HP to improve computing performance. Each lambda defines a direction in parameter space; lambdas are selected using an enhanced version of the algorithm employed by the traditional PEST [62].  
The GLM algorithm is a standard method for solving nonlinear least-squares problems. The method needs to adjust the parameterized functions to the set of measured data by reducing the sum of the squares of the differences between the measured and computational values [44; 60; 63]. The result of this procedure is not only an optimal set of parameters but also a detailed analysis of the sensitivity of the parameters [11], which tells us what the impact and importance of each parameter are on the modeling itself [64]. Thus, several sets of parameter values that meet the criteria of the objective function with the same degree of adaptation can be obtained [30].
###### Abstract  
This study reports facile synthesis of MnO2 nanoflowers/N-doped reduced graphene oxide (MnO2 NFs/NrGO) composite and its application on the simultaneous determination of dopamine (DA) and uric acid (UA). The microstructures, morphologies, and electrochemical performances of MnO2 NFs/NrGO were studied using X-ray diffraction (XRD), scanning electron microscopy (SEM), cyclic voltammetry (CV), and electrochemical impedance spectroscopy (EIS), respectively. The electrochemical experiments showed that the MnO2 NFs/NrGO composites have the largest effective electroactive area and lowest charge transfer resistance. MnO2 NFs/NrGO nanocomposites displayed superior catalytic capacity toward the electro-oxidation of DA and UA due to the synergistic effect from MnO2 NFs and NrGO. The anodic peak currents of DA and UA increase linearly with their concentrations varying from 0.2 uM to 6.0 uM. However, the anodic peak currents of DA and UA are highly correlated to the Napierian logarithm of their concentrations ranging from 6.0 uM to 100 uM. The detection limits are 0.036 uM and 0.029 uM for DA and UA, respectively. Furthermore, the DA and UA levels of human serum samples were accurately detected by the proposed sensor. Combining with prominent advantages such as facile preparation, good sensitivity, and high selectivity, the proposed MnO2 NFs/NrGO nanocomposites have become the most promising candidates for the simultaneous determination of DA and UA from various actual samples.  
dopamine; uric acid; MnO2 nanoflowers; N-doped reduced graphene oxide; voltammetric sensor +
Footnote †: journal: Nanomaterials
## 2 Related Works and Problem Statement
### Related Works  
In this section, we review related studies that address the challenges caused by the mobility of devices in industrial wireless sensor networks (IWSNs), focusing on neighbor discovery, mobility prediction, and resource allocation strategies.  
Table 1 summarizes the key approaches, their strengths, and limitations discussed in the Related Works section. These insights highlight the gaps in existing studies that our proposed scheme addresses. [PERSON] et al. [16] proposed the LAN-ND (Listen Advertise Network Neighbour Discovery) protocol, which enhances WirelessHART's neighbor de  
tection capabilities by leveraging deterministic scheduling of advertisement links. While effective in improving detection speed and reliability, the protocol faces challenges in energy efficiency under highly dynamic conditions. [PERSON] et al. [18] introduced neighbor discovery protocols for mobile WirelessHART networks, emphasizing the integration of deterministic and probabilistic methods to reduce latency and improve reliability. Their findings highlight the need for efficient discovery mechanisms in dynamic industrial environments with mobile nodes. [PERSON] et al. [19] introduced a machine learning-based mobility support scheme for IWSNs. This approach demonstrated improvements in packet delivery ratio and resource efficiency compared to traditional methods but primarily focused on linear regression and did not fully address non-linear or random mobility patterns. [PERSON] et al. [20] investigated resource allocation strategies in dual-hop cognitive relay networks with amplify-and-forward relays, demonstrating the benefits of best-relay selection (BRS) under spectrum-sharing constraints. Their findings highlight the importance of efficient resource utilization in dynamic environments, which is a significant consideration for industrial networks.  
These studies collectively emphasize the importance of efficient neighbor discovery, mobility prediction, and resource allocation in dynamic IWSN environments. Building on these insights, our proposed scheme leverages decision tree-based classification and regression models to dynamically adapt to diverse mobility patterns. Unlike existing approaches, our proposed scheme improves prediction accuracy, enhances resource efficiency, and ensures reliability in industrial settings with complex mobility dynamics.
## 6 Conclusions  
In summary, we propose a method to calibrate and compensate the gyro drift in a one-axis RINS and experimentally verify our algorithm's effectiveness. In this article, we analyze the influence of several error sources on navigation accuracy. By establishing a relationship between gyro drift and the calculated attitude error, the gyro drift can be calibrated theoretically and experimentally. To verify our theory, we built a one-axis RINS to eliminate the gyro bias influence from two axes perpendicular to the rotational angle. The gyro bias along the azimuth direction is calibrated and compensated, and navigation accuracy was improved from 7 km to less than 1 km. Several individual experiments show that after the calibration and compensation of gyro bias, the improvement of navigation accuracy could be realized.  
Conceptualization, W.L. and Z.W.; methodology, Y.Z.; software, G.Y.; validation, H.M. and J.P.; formal analysis, Y.X.; writing--original draft preparation, Y.X.; writing--review and editing, W.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Natural Science Foundation of China (Grant No. 62388101) and China Postdoctoral Science Foundation (Certificate Number: 2024M/52780).  
Not applicable.  
Figure 11: Longitude, latitude and position error comparisons after compensating for gyro bias.  
Figure 12: Longitude, latitude and position error comparisons after compensating for gyro bias.  
Figure 13: Longitude, latitude and position error comparisons after compensating for gyro bias.  
Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors upon request.  
The authors declare no conflicts of interest.