esa-sceva/satcom-chunk-collection huggingface dataset:
# A Rotating Object Detector with Convolutional Dynamic Adaptive Matching
## 2 Related Work
### Rotating object detection  
The detector [11] based on rotating boxes effectively solves the problem of severe background interference and repeated detection boxes in the field of aerial targets. For rotating target detectors, the current research mainly focuses on three aspects: rotation feature extraction, rotation box generation, and loss function optimization.  
**Rotation feature extraction**: at present, most rotation target detectors use ResNet as the backbone network to extract the angle feature through the design of the improved convolution module. [PERSON] [12] designed a cascaded network that generates rotational features in the backbone network through feature pyramid networks and multi-scale convolution kernels. This makes target direction detection more robust, but it can lead to a complex model structure. [PERSON] [13] designed a lightweight rotation detector and added an enhanced channel attention (ECA) module in each layer to strengthen the representation ability of the model and to improve the detection performance. However, the features of small targets are easily lost during downsampling. RODFormer [14] uses a structured transformer to collect features of different resolutions, which is convenient for detecting targets densely distributed from multiple angles in aerial images.  
**Rotation box generation**: [PERSON] [2] designed a rotation region of interest learner (RRoI Learner) module that adds a small amount of computation to apply spatial transformation to RoIs and generates rotation boxes to solve the problem of misalignment between horizontal boxes and targets. However, this method ignores angle deviation and is prone to missing targets with large aspect ratios. [PERSON] [3] generated a rotation box by sliding four vertices on the horizontal box. This rotation box generation method has a small angle deviation, but it also has the problem of inaccurate boundary box regression and slow inference speed. When the object is in a 3D environment, the quaternion [15] can be used to rotate the target. The quaternion cannot be applied to 2D environments, so we use matrices for rotation transformation.  
**Regression loss function optimization**: [PERSON] [16] uses the Gaussian Wasserstein distance instead of the nondifferentiable rotation IoU to optimize the regression loss function, align model training with measurement standards, and solve the problem of a discontinuous rotation angle range. [PERSON] [17] proposed an approximate SkewIoU loss based on the Gaussian product, which solves the problem of gradient explosion or vanishment during the training of rotation detectors.  
However, the designed models are often too complex and difficult to transplant to different detectors. The variable rotation convolution kernel proposed in this paper serves as a plugin module, which is more convenient to embed into the detector and improves the model's detection performance for rotating targets.
# Possibility of Bridge Inspection through Drive-By Vehicles
## 5 Effect of Road Roughness on Drive-By Bridge Inspection
### Effect of Road Roughness on STFT Damage Detection  
After the road roughness was superimposed onto the bridge deck, a test of damage detection was conducted through the 3D VBI model with embedded damages. Its STFT results are shown in Figures 26-30.  
Figure 28: STFT results of vehicle’s right rear axle acceleration difference between the 60% damage case and the healthy bridge.  
Figure 27: STFT results of vehicle’s right rear axle acceleration difference between the 40% damage case and the healthy bridge.  
Figure 29: STFT results of vehicle’s right rear axle acceleration difference between the 80% damage case and the healthy bridge.  
The STFT results show that there is much roughness noise signal. To reduce the effect of roughness, the vibration differences between the healthy bridge and damaged bridge were used to process the STFT analysis results. A 0.1 m damage with 40%, 60%, and 80% of the original Young's modulus was set at the bridge's mid-span.  
In these three plots, the acceleration differences can not completely offset all roughness effects. However, the frequencies between 20 to 50 Hz have obvious changes at 7.5 m according to different damage levels (Figure 30). The STFT method still can identify different damages of bridges with consideration of the roughness effect.
## 2 Basic Theory
### Establishing Health Assessment Indicators  
A flight control system is usually composed of a main control system and an auxiliary control system. The former uses the elevator, aileron and rudder to complete the pitch, roll and yaw actions, which can be divided into these three channels from the operating aspects of the system. As an important part of the auxiliary control system, the flap system plays a major role in providing lift and maintaining attitude. Therefore, failure mode and effect analysis (FMEA) of the main control system and flap system is selected to filter possible failures. Based on FMEA, all possible impacts on the system are studied to complete characteristic parameters selection.  
The definition of FMEA is as follows: by analyzing the various possible failures of a system, failures are divided into their failure modes, respectively, and by analyzing the influence of each failure mode on the upper module and entire system, the influence level of the occurrence of these failures can be classified.  
As shown in Figure 1, according to the FMEA of the flight control system, the transmission coefficients from the control terminal to the acting surface of the three channels are selected as the characteristic parameters, including the pitch channel, roll channel and yaw channel, which indicate the health status of main control system; meanwhile, the main hydraulic pressure is selected to indicate the health status of flap channel. In detail, each channel contains several parameters to evaluate the health status of the channel. According to the flight control system health assessment process under certain conditions, the entire assessment process is divided into two parts: the first assessment is used to evaluate the health status of each channel, and the second assessment is used to evaluate the health status of the whole flight control system.  
Then, the relative degradation degree method [21] is used to normalize each health indicator to obtain the corresponding health index, \(d(x)\). The relative degradation degree refers to the similarity between the current state of a characteristic parameter and the fault state, and the value range is (0,1). A fault state is indicated by 1, and 0 indicates a healthy state. For the analysis of the characteristic parameters, the intermediate method is used for calculation:  
\[d(x)=\begin{cases}1&x<x_{min}\\ \frac{x-x_{min}}{x_{a}-x_{min}}&x_{min}\leq x\leq x_{a}\\ 0&x_{a}\leq x\leq x_{b}\\ \frac{x-x_{b}}{x_{max}-x_{b}}&x_{b}\leq x\leq x_{max}\\ 1&x>x_{max}\end{cases}, \tag{1}\]  
where \(x_{min}\) represents the minimum value of characteristic parameters, \(x_{max}\) is the maximum value of feature parameters and \(x_{a}\) and \(x_{b}\) represent the optimal intervals of characteristic parameters.
## 2 In-Cavity Pulse Shaping
### Tailored Pulse Waveform Generation
To gain a broader understanding of the laser dynamics, we investigated the effect of net dispersion on the laser response. The results [34] revealed that the potential of tuning the output pulse temporal duration through in-cavity linear chirping is minimal. This stems from the filtering process being used, _i.e._, slicing of a fairly broader spectrum, and indicates that one should act directly on the impulse response function of the filter to control the output pulse width. This result is not surprising, in fact, as a triangular, parabolic or flat-top temporal profile is rather sensitive to group delay. Note that, as we shall see in Section 3, the net dispersion is controlled by adjusting the transfer function of the pulse shaper [29]; therefore, no physical change in the cavity is required in order to change the output from one pulse shape to another, provided that a programmable pulse shaper is used.
## 4 Validation of General ESS-Based VSM Control
### Simulation Test of a Single ESS-Based VSM under Step Load Change
#### 4.1.3 Case 3: Add ESS-Based VSM with Energy Recovery Control  
After adding SoC recovery control to the ESS, the frequency nadir becomes 59.34 Hz at t = 10.38 s, as displayed in Figure 16a, which approximates the results above. However, the SoC of ESS gradually returns to 0.5 after frequency regulated to the normal value. The comparison with Figure 15 shows the impact on frequency performance is almost negligible for the selected SoC control parameters. The settling time for SoC recovery is approximately 100 s, which is close to the bandwidth estimation in Equation (9).  
If the PI parameters are selected without obeying the bandwidth separation rule, for example \(k_{p,e}=1\) and \(k_{i,e}=0.5\), consistent oscillation is observed in the power, frequency and SoC waveforms as Figure 16b, indicating undesirable system stability. The voltage and current exhibits similar shapes, and the system will be forced to shut down.
## 3 System Model
### Impersonation Attack Model (IAM)  
The IAM proposed in our work is illustrated in Figure 1. This model is composed of several mobile users and edge nodes. In a MEC environment, the users (nodes) communicate with each other through wireless networks. We consider \(x\) legitimate nodes (edge node or mobile user) and \(y\) illegitimate nodes (edge node or mobile user). In our proposed network, the total number of nodes (legitimate + illegitimate), including mobile users and edge nodes, is represented by \(N^{*}\). However, \(N^{*}\) is further divided into the set of \(X\) and \(Y\) to differentiate legitimate and illegitimate mobile users and edge nodes, respectively. Hence, the set of all the legitimate nodes including edge nodes and mobile users is represented by \(X\), which is given by  
\[X=\{1,2,3,\ldots,x\},x\in N^{*}, \tag{1}\]  
and the set of all the illegitimate nodes, including edge nodes and mobile users, is represented by \(Y\), which is given by  
\[Y=\{1,2,3,\ldots,y\},y\in N^{*}. \tag{2}\]  
In summary, the illegitimate users/nodes are those users/nodes that pretend to be legal users/nodes and send data to legitimate nodes/users. Hence, the impersonation attack is assumed by such behavior of illegitimate nodes.  
Figure 1: IAD between MEC servers and mobile users to detect authentic and unauthentic transmitter.
## 4 Results
### Modeling and Validation of ET and NEE Estimation
#### 4.1.3 The Stability of the Model in Different Sites  
To assess the spatial robustness of our proposed model, validation was performed across multiple sites. Specifically, observations from a designated site were used as the testing set, while data from the other five sites served as the training set. Utilizing the optimal conditions (RFR with four types of variables together as inputs for both ET and NEE estimation), the results were modeled and are presented in Figure 8 and Table 5. Although the validation results were generally slightly higher to those obtained through five-fold cross-validation for both ET and NEE estimation, the differences were not pronounced. Specifically, for ET estimation, we achieved an R\({}^{2}\) of 0.86 and an RMSE of 1.12 mm, while for NEE estimation, the R\({}^{2}\) was 0.83 and the RMSE was 2.74 gC/m\({}^{2}\). However, there were notable variations in accuracy across different sites. For ET estimation, the R\({}^{2}\) ranged from 0.51 to 0.87, and the RMSE fluctuated between 0.94 and 2.28 mm across the six sites. Similarly, for NEE estimation, the R\({}^{2}\) spanned from 0.35 to 0.80, with the RMSE varying from 1.42 to 5.48 gC/m\({}^{2}\). These discrepancies can be attributed to several factors.  
Figure 6: The accuracy metrics of ET and NEE estimation: (**a**) \(R^{2}\) (**b**) RMSE for ET and (**c**) \(R^{2}\) (**d**) RMSE for NEE. Note: A, B, C and D represents the input variables of vegetation growth, surface moisture, radiative energy, and others.  
Firstly, the spatial accuracy differences inherent in our proposed model contribute to these variations. Secondly, the differing number of samples observed at each site also influences the validation results.  
Figure 8: The validation conducted in different sites: (**a**) ET and (**b**) NEE.  
Figure 7: The box plots of different algorithms for ET and NEE estimation: (**a**) \(R^{2}\); (**b**) RMSE for ET and (**c**) \(R^{2}\); (**d**) RMSE for NEE. The letters “a”, “b” and “c” on the boxes indicate significant differences based on ANOVA (\(p<0.05\)).
# Electronic and Transport Properties of Epitaxial Graphene on SiC and 3C-SiC/Si: A Review
## 3 Transport Measurement Methods
### Raman Spectroscopy  
The values of phonon frequencies of EG on SiC depend on the mechanical strain and charge transfer between SiC and graphene [62]. The Raman G-band and the 2D band involve phonons at the \(K+\Delta K\) points in the Brillouin zone [62; 63; 64]. The influence of the doping on the Raman bands has been studied by [PERSON] et al. [65] and [PERSON] et al. [62] using a gate voltage controlled graphene transferred onto SiO\({}_{2}\)/Si. G-peak frequency showed an upshift up to 20 cm\({}^{-1}\) when the graphene is n-type doped at \(4\times 10^{13}\) cm\({}^{-2}\). The influence of doping on the 2D-peak shift was shown to be weak and is \(\sim\)10-30% compared to the G-peak shift (3-5 cm\({}^{-1}\)) [66] (see Figure 7). The value of 2D to G peak intensity was determined by the carrier concentration [65] and indicates the graphene doping level. [PERSON] et al. [67] reported a method to extract the value of doping from the Raman G and 2D modes of graphene formed on any arbitrary substrates. Furthermore, [PERSON] et al. [68] estimated the temperature dependent doping of monolayer and bilayer graphene on SiO\({}_{2}\)/Si using Raman spectral mapping between 10 K and 300 K. More recently, in Reference [69], [PERSON] et al. estimated the thickness dependent electron doping of EG/SiC by silver films from the red-shift and broadening of the 2D band. The same research group studied the temperature and time dependency of the H-intercalation on the p-type doping of quasi-free standing monolayer graphene on EG/SiC via micro-Raman spectroscopy in Reference [70].
## 2 Materials and Methods
### Blood Laboratory Analysis  
As morphological alterations were only found in PACAP HZ mice, but not PAC1 receptor-deficient mice, blood laboratory analysis was performed only in PACAP HZ mice and compared to WT animals. Blood from young (3 months old) PACAP HZ (\(n=11\)) and WT mice (\(n=11\)) was collected under isoflurane overdose into BD Vacutainer tubes with sodium heparin for plasma analysis or EDTA for routine complete blood count (Becton, Dickinson and Company, NJ, USA). Na\({}^{+}\) and K\({}^{+}\) ions, alkaline phosphatase (ALP), creatinine, cholesterol, triglyceride, and high- and low-density lipoprotein (HDL, LDL) were measured with a COBAS 8000 analyzer (Roche Ltd., Rotkreuz, Switzerland), while blood count was measured with a Sysmex XN-1000-V Multispecies Hematology Analyzer (Sysmex Hungaria, Budapest, Hungary). The following parameters were measured: white blood cell count (WBC), red blood cell count (RBC), hemoglobin concentration (HGB), hematocrit (HCT), mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), platelet count measured with optical method (PLT&Q), red cell distribution width--standard deviation (RDW-SD), red cell distribution width--coefficient of variation (RDW-CV), platelet distribution width (PDW), mean platelet volume (MPV), platelet large cell ratio (P-LCR), plateletcrit (PCT), nucleated red blood cell (NRBC), neutrophil granulocyte count (NEUT), lymphocyte count (LYMPH), monocyte count (MONO), eosinophil granulocyte count (EO), basophil granulocyte count (BASO), reticulocyte count (RET), immature reticulocyte fraction (IRF), reticulocytes with low fluorescence (LFR), reticulocytes with median fluorescence (MFR), reticulocytes with high fluorescence (HFR), and reticulocyte hemoglobin equivalent (RET-HE).  
Statistical analysis of plasma parameters and blood count was performed using two-way ANOVA followed by [PERSON]'s or [PERSON]'s post hoc test, respectively. Differences were considered significant when \(p<0.05\).
## 1 Introduction
In this work, we design an optical delay line based on a single-stage racetrack (ring) resonator equipped with two tuning heaters: One to tune the coupling coefficient and another one to tune the round-trip phase. The design aims at applications for optical and biomedical sensing where the optical signal usually has a narrow linewidth. The application examples include, but are not limited to, the optical phase arrays of LiDAR [26; 27], laser linewidth measurement [28], and interferometry-type biomedical sensing with laser Doppler or swept-source coherent tomography [29; 30]. The delay-bandwidth product (DBP) is limited for a given type of ODL, and the DBP is smaller for resonant-type delay lines, like ring resonators. For the above applications, the linewidth of laser sources is typically less than a few MHz or even on the order of kHz. Therefore, a long delay can be realized with a simple resonator by using a design with a higher quality factor and smaller bandwidth. In such cases, the wavelength of the resonant peak needs to be stabilized and aligned to the laser wavelength. The ODL design demonstrated in this paper can achieve a true delay ranging from tens of picoseconds to hundreds of picoseconds at a stable wavelength.
## 2 Methodology
### Electrical Model  
The power supply circuit of the DTT TF coils and its FDUs have been simulated by means of an object-oriented model developed in OpenModelica [10]. The structure of the electrical circuit is taken from [11]; the eighteen TF coils are split into three groups of six coils each, connected in series, and the three groups are, in turn, connected in series among them and with the three FDUs, alternating between one FDU and one group of coils. Each TF coil is modeled considering the self inductance and mutual inductances of the other  
Figure 2: Logical connections between the three aspect of physics (and sub-blocks) and related results.  
TF coils based on the inductance matrix of the DTT TF system [12], solving the following equation:  
\[\Delta V_{i}=\sum_{j=1}^{N}L_{i,j}\cdot\frac{dI_{j}}{dt}\qquad i\in[1,N], \tag{1}\]  
where \(L\) is the inductance matrix and \(N\) is the number of TF coils in the tokamak (18 in DTT). The inductance matrix \(L\) is square and has the coils self inductances on the main diagonal and the mutual inductances out of the main diagonal.  
The FDU is modeled as the parallel of the dumper and an ideal switch, normally closed; when the fast discharge is triggered, the switch is opened, forcing the current to flow through the dumper. Meanwhile, the power supply, modeled as an ideal current generator (forcing the nominal current of 42.5 kA in DTT [13]) taken from the Modelica Standard Library [7], is disconnected from the circuit so that the current in the loop decreases. The FDU dumper is composed of a collection of varistor disks [12], modeled here as an equivalent single disk whose characteristic is described by the following equation:  
\[\Delta V=K\cdot I^{\beta}. \tag{2}\]  
The two parameters describing the varistor behavior (\(K\) and \(\beta\)) are affected by epistemic uncertainty (i.e., there is a lack of knowledge of their values due to fabrication processes). Indeed, according to the varistor producer, they can be assumed as uniformly distributed in the following ranges [12]:  
\[K\in[8.134,13.05], \tag{3}\]  
\[\beta\in[0.562,0.595], \tag{4}\]  
and have been considered here as statistically independent.  
\(K\) and \(\beta\) directly impact the transport current evolution. The latter influences the eddy currents and Joule power generated by the quench and thus the quench propagation and hotspot temperature. As a result, the very large uncertainty on these input parameters has a non-negligible impact on the transient evolution. When trying to numerically predict the behavior of the TF coil system during an FD, the uncertainty of these parameters is then propagated through the analysis of all the model sub-blocks, requiring an uncertainty propagation analysis.
## 3 Test Results  
### Analysis of Orthogonal Test Results  
The surface texture characteristics, SRNR performance, and mechanical properties indicators of the EAC slab specimens prepared through orthogonal experimental design were tested. The test results are presented in Table 3, and the corresponding range analysis results are shown in Table 4.  
By comparing the range values of the four factors, the influence degrees of each mix design parameter on the six performance indicators of EAC can be ranked from high to low as follows: exposed aggregate depth D > B > C > A; exposed aggregate area ratio D > C > B > A; pendulum value D > C > A > B; noise value D > A > B > C; 28-day compressive strength A > B > C > D; and 28-day flexural strength B > D > A > C. The above results indicate that, for the surface texture characteristics of EAC, the proportion of aggregates > 9.5 mm has the most significant impact on both exposed aggregate depth and area ratio, while the water-binder ratio exerts the minimal influence. Regarding skid resistance performance, the proportion of aggregates > 9.5 mm remains the most influential factor. Both the water-binder ratio and sand ratio contribute marginally to skid resistance, with their range values being less than 2 BPN, indicating a substantial gap compared to the impact of other factors. For noise reduction performance, the proportion of aggregates > 9.5 mm is still the most significant factor, whereas the coarse aggregate volume ratio has the least impact on noise value. In terms of mechanical properties, the water-binder ratio and sand ratio are the most significant factors affecting the 28-day compressive strength and 28-day flexural strength, respectively. The coarse aggregate volume ratio has a negligible impact on the 28-day flexural strength, with a range of only 0.08 MPa.  
Figure 7: Twenty-eight-day compressive and flexural strength test: (**a**) Compressive strength tester. (**b**) Flexural strength tester.