esa-sceva/satcom-chunk-collection huggingface dataset:
# DGANet: Dynamic Gradient Adjustment Anchor-Free Object Detection in Optical Remote Sensing Images
## 3 Proposed Approach
### United Bounding Regression Function  
The regression loss function of the bounding box in CenterNet is defined as \(L_{regs}=\lambda_{size}L_{size}+\lambda_{off}L_{off}\), where \(L_{size}\) denotes the loss function of the bounding box size by using the L1 loss and \(L_{off}\) is introduced to inhibit the discretization error of the center-point caused by the output stride. However, there are two drawbacks to this approach. First, the size and location of the bounding box are regarded as two independent variables, which violated the fact that the size and location of an object are highly correlated. Second, this method introduces weight parameters \(\lambda_{size}\) and \(\lambda_{off}\), which need to be optimized. We select the CIoU as the bounding box regression function and its schematic diagram is shown in Figure 4.  
The united regression function is defined as follows:  
\[L_{CIoU}=1-IoU+\frac{\rho^{2}(b^{pred},b^{gt})}{c^{2}}+\kappa\
u \tag{5}\]  
\[\kappa=\frac{\
u}{(1-IoU)+\
u} \tag{6}\]  
\[\
u=\frac{4}{\pi^{2}}(\arctan\frac{wb^{gt}}{h^{gt}}-\arctan\frac{w}{h})^{2} \tag{7}\]  
where \(b^{pred}\) and \(b^{gt}\) denote the center-points of predicted bounding box and ground truth bounding box, respectively. \(IoU\) is the maximal overlap between the predicted bounding box and the ground truth box. \(\rho(\cdot)\) is the Euclidean distance of two points and \(c\) is the diagonal length of the smallest enclosing box that covers the predicted box and ground truth box. \(\kappa\) is a positive trade-off parameter and \(\
u\) measures the consistency of box aspect ratio. Besides, Distance IoU (DIoU) is defined similarly to CIoU, which doesn't consider the effect of aspect ratio. Since the experiment part involves the comparison of both of the methods, the expression of DIoU is given here:  
\[L_{DIoU}=1-IoU+\frac{\rho^{2}(b^{pred},b^{gt})}{c^{2}} \tag{8}\]  
Figure 4: Illustrations of complete-intersection over union (CIoU) loss for bounding box regression. c is the diagonal length of the smallest enclosing box covering bbox1 and bbox2, and d is distance of center-points of two boxes. Besides, additional is the parameter of aspect ratio.  
After a series of optimizations to the classification function and regression function, the overall training objective is:  
\[L_{\text{det}}=\lambda_{cls}L_{DGA}+\lambda_{regs}L_{CloU} \tag{9}\]  
\(\lambda_{cls}\) and \(\lambda_{regs}\) are equal to 1 in our experiments. DGANet is used to predict the center-point, offset, and size of objects. All output branches share a common fully convolution backbone network as shown in Figure 2.
# PFC Inductor Design Considering Suppression of the Negative Effects of Fringing Flux
## 5 Conclusions  
In this study, the investigation of the influence of fringing flux on the operational frequency of the PFC converter was evaluated, together with an experimental analysis of the construction of magnetic components reflecting suppression of the fringing flux effect. For the developed PFC converter, the alternatives of power inductors were designed, while the focus was to observe optimization of the operational parameters of the converter. Dealing with the fringing flux effect, when delimitated air gap of the magnetic core was provided, the suppression of the eddy current effect was achieved, which was reflected in better thermal performance of the component. It was also found that with proper distribution of the inductor air gap, the higher stability of the inductance value was achieved as well. Based on the results, it was found that the fringing flux effect was also dependent on the arrangement of the winding, while one-layer winding provided better eddy current suppression compared to multi-layer windings. Various alternatives of power inductor design were experimentally verified during the investigation of the efficiency performance of the PFC converter. The original design utilized alloy core inductors and operation was defined for 41 kHz of switching frequency. With the optimized alternatives of power inductors (delimitated airgap), better performance was achieved considering efficiency values, while the operational frequency was increased in the first experiment by up to 3 times. For nominal operation (4 kW) of PFC, the efficiency increase was 0.5%. Consequently, other experiments were carried out considering distributed airgap, for which the main goal was to achieve higher operational frequencies (200 kHz) and to maintain high-efficiency performance. Compared to the original design, the efficiency increase was 0.5%, whereby a 5-times-higher operational frequency enabled to significantly reduce component sizes.  
Conceptualization, M.F.; methodology, M.P.; formal analysis, M.Z.-M.; writing--original draft preparation, D.A.; writing--review and editing, M.F.; visualization, supervision, M.Z.-M.; funding acquisition, M.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Grant Agency Vega, grant number 1/0063/21, and by National Grant Agency APVV for project funding APVV-20-500.  
Figure 22: Comparison of the efficiency performance in dependency on the output power of converter for original inductor and inductors with the distributed air gap.
## Nomenclature  
\begin{tabular}{l l} \(C_{p}\) & specific heat, J kg\({}^{-1}\) K\({}^{-1}\) \\ \(D_{\omega}\) & cross-diffusion term, kg m\({}^{-3}\) s\({}^{-2}\) \\ \(G_{k}\) & production of turbulence kinetic energy, kg m\({}^{-1}\) s\({}^{-3}\) \\ \(G_{\omega}\) & generation of specific dissipation rate, kg m\({}^{-3}\) s\({}^{-2}\) \\ \(p\) & pressure, Pa \\ \(R\) & gas constant, J K\({}^{-1}\) mol\({}^{-1}\) \\ \(T\) & temperature, K \\ \(T_{\rm ini}\) & initial temperature, K \\ \(T_{\rm amb}\) & ambient temperature, K \\ \(V\) & molar volume, m\({}^{3}\) mol\({}^{-1}\) \\ \(X\) & design factor for regression equation \\ \(Y_{k}\) & dissipation of turbulence kinetic energy, kg m\({}^{-1}\) s\({}^{-3}\) \\ \(Y_{\omega}\) & dissipation of specific dissipation rate, kg m\({}^{-3}\) s\({}^{-2}\) \\ \(Z\) & result of regression equation \\ \(a\) & constant that corrects for attractive potential of molecules \\ \(b\) & constant that corrects for volume of molecules \\ \(g\) & gravity, m s\({}^{-2}\) \\ \(k\) & turbulence kinetic energy, m\({}^{2}\) s\({}^{-2}\) \\ \(k_{c}\) & thermal conductivity, W m\({}^{-1}\) k\({}^{-1}\) \\ \(t\) & time, s \\ \(t_{chr}\) & charging time, s \\ \(u\) & velocity, m s\({}^{-1}\) \\ \(\beta\) & coefficient for each terms of regression equation \\ \(\Gamma_{k}\) & effective diffusivity for the turbulence kinetic energy, kg m\({}^{-1}\) s\({}^{-1}\) \\ \(\Gamma_{\omega}\) & effective diffusivity for the specific dissipation rate, kg m\({}^{-1}\) s\({}^{-1}\) \\ \(\gamma\) & heat capacity ratio \\ \(\mu\) & viscosity, kg m\({}^{-1}\) s\({}^{-1}\) \\ \(\rho\) & density, kg m\({}^{-3}\) \\ \(\omega\) & specific dissipation rate, m\({}^{2}\) s\({}^{-3}\) \\ \end{tabular}
## 3 Antenna Performance Enhancement Techniques
### Performance Enhancement Using Metamaterials  
Artificial materials are composed up of periodic material structures and specific dielectric whose electromagnetic properties in terms of electric permittivity as well as magnetic permeability could be redesign to a level higher than the nature of the available  
Figure 14: Antenna configuration: (**a**) 3D view, (**b**) top view, (**c**) front view, (**d**) Equivalent circuit [39].  
materials. Materials having negative permittivity and negative permeability do not exist in nature, and metamaterial is the one and only source. If we replace conventional patch antenna with MMTs, which are commonly used with DNG double-negative characteristics, it will lead to an excellent shift to higher frequencies, and thus will definitely increase bandwidth [59; 60]. In [61], a modified patch with diagonal slot and a layer of metasurface is demonstrated in low profile with circular polarization. A patch with a diagonally positioned slot in the center of the patch to achieve wide bandwidth as well as circular polarization is proposed. The metasurface is designed without generating any gap on the patch surface to make provision for miniaturization. A wide-band low-profile antenna with circularly polarized metasurface-based antenna for realization of performance enhancement in terms of high gain, wide, bandwidth, high efficiency, and circular polarization is presented, as well as a wide bandwidth of 37.4% (24-34.1 GHZ) for the value of S11 \(<-\)10 dB with a high value of gain 11 dBic. The gain response is stable at 9.5-11 dBic. CP in the left hand is achieved. At 11 dBic the same Antenna is tuned. Large axial ratio as well as high bandwidth and high efficiency is achieved. Similarly, in the antenna in [62], a circularly polarized structure with wide band and bidirectional metasurface-based antenna is proposed. A metasurface composed up of two identical periodic metallic layers as a metal surface is used. In this case, the metasurfaces convert the linear polarized radiation to circular polarized radiation which was produced by the rectangular slot. In total, 14.3% bandwidth is achieved (5.2-6.0 GHz). The antenna offers state-of-the-art operating bandwidths compare with simple planar antennas, shown in Figure 15.
## 6 Experiments
### Experiment V: Comparison with Three Combined Models (Two Objective Functions)
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Combined Model \({}^{*}\)**} & \multicolumn{3}{c}{**Combined Model \({}^{**}\)**} & \multicolumn{3}{c}{**Combined Model \({}^{***}\)**} & \multicolumn{3}{c}{**Combined Model**} \\ \cline{2-13}  & **MAX** & **MIN** & **AVE** & **STD** & **MAX** & **MIN** & **AVE** & **STD** & **MAX** & **MIN** & **AVE** & **STD** \\ \hline Monday & 11.47\% & 6.55\% & 7.60\% & 0.7837 & 11.08\% & 5.94\% & 7.43\% & 0.7540 & 11.12\% & 6.30\% & 7.82\% & 0.7138 & 8.83\% & 5.01\% & 5.86\% & 0.6240 \\ Tuesday & 5.92\% & 4.38\% & 4.86\% & 0.2528 & 5.83\% & 3.96\% & 4.81\% & 0.2772 & 5.99\% & 3.93\% & 4.86\% & 0.2263 & 4.80\% & 3.64\% & 4.05\% & 0.0203 \\ Wednesday & 6.27\% & 4.65\% & 5.25\% & 0.3012 & 6.34\% & 4.37\% & 5.21\% & 0.3227 & 6.62\% & 4.32\% & 5.42\% & 0.2811 & 5.69\% & 3.46\% & 3.93\% & 0.2775 \\ Thursday & 18.12\% & 6.73\% & 8.60\% & 1.6167 & 6.69\% & 6.19\% & 8.41\% & 1.6433 & 14.05\% & 7.03\% & 8.95\% & 1.63\% & 4.91\% & 6.64\% & 1.5877 \\ Friday & 7.98\% & 5.75\% & 6.60\% & 0.4075 & 8.07\% & 5.41\% & 6.55\% & 0.4319 & 8.33\% & 5.30\% & 6.60\% & 0.3179 & 6.67\% & 5.16\% & 5.90\% & 0.3199 \\ Saturday & 8.03\% & 5.67\% & 6.62\% & 0.3858 & 7.75\% & 5.51\% & 6.55\% & 0.3903 & 8.32\% & 5.36\% & 6.69\% & 3.0555 & 6.33\% & 4.94\% & 5.63\% & 0.2820 \\ Sunday & 6.08\% & 4.37\% & 4.94\% & 0.3127 & 6.16\% & 4.10\% & 4.88\% & 0.3043 & 6.38\% & 3.94\% & 4.90\% & 0.3148 & 4.70\% & 3.37\% & 3.82\% & 0.2345 \\ \hline \hline \end{tabular}  
* Combined Model with the objective functions: TIC (\(\dot{\bf Y}\), \(\bf Y\)) and RMSE (\(\dot{\bf Y}\), \(\bf Y\)). \({}^{**}\) Combined Model with the objective functions: TIC (\(\dot{\bf Y}\), \(\bf Y\)) and MAPE (\(\dot{\bf Y}\), \(\bf Y\)). \({}^{**}\) Combined Model with the objective functions: RMSE (\(\dot{\bf Y}\), \(\bf Y\)) and MAPE (\(\dot{\bf Y}\), \(\bf Y\)).  
\end{table} 
Table 8: Mean absolute percentage error (MAPE) of the results for the combined model compared with combined model with two objective functions.
## 5 Experimentation Process
### Clustering  
In this phase of the experimentation, another non-supervised learning approach was tested on the transactions' data: the k-means clustering algorithm. A clustering algorithm aims to group elements of the dataset into different clusters based on the similarity of their features.  
For this specific case, splitting the set of transactions into different clusters can help distinguish between fraudulent and legal transactions depending on the cluster they belong to. In this manner, the results of different experiments were analyzed by contrasting each transaction's cluster with its label (licit or illicit). This analysis could be summarized in the corresponding contingency matrix. Additionally, to evaluate the quality of the generated clusters, a silhouette score analysis was carried out for each experiment to measure the cohesion of each cluster.  
To handle the large number of points in the dataset, the experiments were carried out using a specific implementation of the algorithm called the mini-batch k-means, which carries out the creation of batches with a minimal impact on the results. After tweaking several parameters, including the number of clusters \((K)\), some interesting results were obtained with the values of \(K=7\), \(K=33\) and \(K=49\). Figures 15-17 show their respective contingency matrices. In addition, Figure 18 shows the silhouette diagram for the \(K=33\) case.  
Figure 15: Contingency matrix of k-means \((K=7)\).  
Figure 16: Contingency matrix of k-means (\(K=33\)).  
Figure 17: Contingency matrix of k-means (\(K=49\)).  
Figure 18: Silhouette score diagram of k-means (\(k=33\)). Each color represents a different cluster.  
While the results obtained with \(K=7\) showed a cluster with a remarkable number of illicit transactions (4129), that same cluster also contained the majority of licit transactions (24,113), making it useless for the task of distinguishing between both labels. Conversely, both experiments conducted with \(K=33\) and \(K=49\) revealed a cluster with a large number of illicit transactions while keeping the number of licit ones low, as illustrated by clusters 10 and 30 in Figures 16 and 17, respectively. The outcome of these experiments is significantly more relevant for the proposed task, as features shared by the transactions in those clusters might indicate a fraudulent tendency.  
On the other hand, Figure 18 shows a very poor performance in terms of the silhouette analysis, with an average silhouette score close to 0. This indicates a low cohesion of the generated clusters and a generally bad clustering quality, suggesting that the features of the data, and especially the high dimensionality, hindered their clusterization.
## 2 A Prototype Design of the Smart Photovoltaic System Blind
### Properties 1: PV panel
#### 2.1.2 PV Panel's Design Variables Applicable to the SPSB  
To determine the PV panel design applicable to the SPSB, the optimal size capable of maximizing the electricity generation of PV panels should be considered. Towards this end, three factors were considered in this study: (i) the length of the PV panel; (ii) the width of the PV panel; and (iii) the distance between the centerline of the SPSB and the exterior window. This is because the influence of the shading effect caused by the slat of the SPSB should be minimized to maximize the electricity generation of the SPSB.  
1. Length of the PV panel: According to previous studies, the length of the PV panel has little effect on the electricity generation per unit area. However, the length of the PV should be considered as a factor affecting the range of tracking [27].
2. Width of the PV panel: According to previous studies, the width of the PV panel is a factor that has a great effect on the electricity generation per unit area. This is because as the width of the PV panel increases, the shading effect caused by the upper slat increases. Thus, there is a need to determine the optimal width of the PV panel in order to maximize electricity generation [27].
3. Distance between the centerline of the SPSB and the exterior window: According to previous studies, the distance between the centerline of the SPSB and the exterior window is a factor that has an effect on the electricity generation per unit area [47]. This is because a farther distance between the SPSB and exterior window leads to reduced solar radiation, resulting in the reduction of electricity generation [27].
# Dynamics Investigation on Axial-Groove Gas Bearing-Rotor System with Rod-Fastened Structure
## 1 Introduction
[26; 27] investigated dynamic characteristics of the rod-fastened rotor bearing system, considering the rub-impact effect and influence of rotating velocities and radial stiffnesses of stator. Based on the D'Alembert principle, a nonlinear dynamic hybrid model of the rod-fastened rotor was built by considering different rub-impact and initial permanent deflection; the effects of rotating speed and the length of initial permanent deflection on the dynamic behaviors of the journal bearing rod-fastened rotor system were analyzed. A dynamic mathematical model of a rotor system with a rod-fastened structure supported on three-axial-groove gas bearings is formulated in this report. The time-based dependency-compressible Reynolds equation in the gas bearing lubrication nonlinear system is solved by using the differential transformation method, and the nonlinear gas film forces of a three-axial-groove gas bearing are obtained. The eight degrees of freedom dynamic motion equation of the rod-fastened rotor-gas bearing system is solved by the modified Newmark-\(\beta\) method, which considers the disturbance compensation. The dynamic behaviors of the rod-fastened rotor-bearing nonlinear system are analyzed efficiently, and the dynamic unbalance responses of the rod-fastened rotor and continuous integral rotor, three-axial-grooved and two-axial-grooved gas bearings are compared. Furthermore, the effects of the position angle of the pad on the nonlinear dynamic behavior are also investigated.
# Microwave Planar Resonant Solutions for Glucose Concentration Sensing: A Systematic Review
## 2 Microwave Planar Resonant Glucose Sensors: Fundamentals and Classification
### Proposal of Classification
Therefore, according to the classification scheme in Figure 2, the next sections will review the most significant works on \(f_{\text{r}}\)-based, insertion/return loss-based, \(Q_{\text{u}}\)-based and phase-based planar resonant glucose sensors while applying the \(RS(G)\) concept. It is important to note that the \(RS(G)\) cannot be calculated for the specific case of phase-based sensors because of the difficulty in defining the \(SP_{0}\) value. For example, a candidate parameter for this value could be the unwrapped phase of the sensor (absolute phase shift between the input and output of the device). However, it is not available in the references, and even if it was, it is still unclear if this would be a relevant calculation.
# MALDI-TOF Mass Spectrometry Applications for Food Fraud Detection
## 2 MALDI Applications
### Oils
Furthermore, high energy collision-induced dissociation (CID) MALDI-TOF/TOF analysis of TAGs was suggested as a mean that could correlate oil TAGs to the geographical origin, analyzing a high number of samples that can provide statistically significant data. A multi-instrumental analysis (MALDI for TAGs, GC-MS for fatty acids, and NIRS for non-selective analysis) coupled to PCA and partial least square-discriminant analysis (PLS-DA) was also used [78] for geographical origin sample grouping evaluation and for testing of predictive capabilities of measured variables on accurate classification of EVOO regional category. A synergic MALDI-TOF/GC-MS approach was proposed to study the different components of pomergnate oil [79] and of several nut oil varieties [80], respectively. In particular, MALDI confirmed its ability and reliability to profile the TAG component, showing unique fingerprints that allows to differentiate pomergnate from most edible oils and nut oils exhibiting quite similar fatty acid composition (hazelnut, pistachio, and beech oil) between each other. Unique TAGs profiles were also displayed [81] by different Amazonian oils and fats, characterized by MALDI-TOF without prior separation. The variable combinations of fatty acids provided detailed information that could permit the development of a database from spectra to be used for fast and reliable typification, screening, and quality control. The triacylglycerglycerol content of shea butter fat, palm kernel oil, and peanut oil was also rapidly characterized [82]. The potential of the method as a tool for quality control of the matrices under study was demonstrated by the detection, in addition to intact and specific triacylglycerlycerols, of oxygenated and fragmented TAGs, likely arising from poor handling and production processes.
## 3 Results and Discussion
### Backstress Increments  
Figure 9 presents the evolution of backstress as a function of temperature for both the SA508 and SA333 steel alloys under different element structures. The numerical results obtained for the nonlinear brick elements and nonlinear tetrahedral elements were compared by the analytical (A-V) method. Across all element types, SA508 consistently exhibited higher backstress levels than SA333 due to its greater resistance to plastic deformation. A decreasing trend in backstress was observed as the temperature increased from 298 K to 898 K, which corresponds to the typical thermal softening behavior of steels. However, a slight elevation in the backstress magnitude was identified within the intermediate temperature range, suggesting the influence of dynamic strain aging (DSA). This temporary increase is attributed to solute dislocation interactions that restrict dislocation motion, requiring higher backstress to maintain cyclic plasticity. At temperatures below 473 K, solute atoms do not diffuse rapidly enough to influence dislocation dynamics, and DSA remains inactive. Above 873 K, dislocations move too quickly, and dynamic recovery dominates, causing DSA effects to disappear. The difference in backstress evolution between the two materials is also influenced by their microstructures and alloying content. SA508, with higher carbon and alloying element contents, typically forms a finer martensitic or bainitic structure that resists dislocation movement more effectively than the ferritic-pearilic microstructure of SA333. As a result, SA508 maintains a higher backstress level under similar thermal and loading conditions. Overall, the predicted values using different element types showed close agreement, confirming the robustness of the hardening model and its implementation in various mesh schemes.  
Figure 8: Distribution of total strain throughout the sample during cyclic loading.  
Figure 9: Initial backstress increments over a range of temperatures for different element types at different stress levels.