esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
* A rotation-invariant module and a rotated feature fusion module are proposed to enhance the adaptability of FCOS to detect rotated objects, which can better counter the interference of complex backgrounds on foreground objects. Figure 1: The overall architecture of FCOS. \(C_{i}\) denotes the feature maps from backbone network at stage \(i\) and \(P_{i}\) denotes the feature level used for final prediction. The decoupled detection head consists of separate branches for classification, bounding-box regression, and centerness prediction, where the centerness branch outputs a score that helps suppress low-quality predictions. * The dual-aspect RIoU loss function is introduced to improve detection performance by integrating both rotational and horizontal information, addressing the complexities introduced by arbitrary orientations. * Extensive experiments conducted on the DIOR-R [14] and HRSC2016 [15] datasets validate the superiority of the proposed method.
# Development of Radio-Frequency Sensor Wake-Up with Unmanned Aerial Vehicles as an Aerial Gateway
## 6 Experimentation and Results  
In the following we evaluate the wake-up range as well as the energy consumption for both out-band methods (RF and IR), then compare the energy costs with the traditional duty-cycle method, all based on the same hardware setup.
## 4 Conclusions  
In this paper, we proposed the novel design structure of a monopulse antenna system that could operate with only a single radiator to reduce the aperture size. The complicated waveguide comparator was also replaced by the simple two-port SIW feeding network that could obtain the ratio of the sum and difference patterns. The two ports in the SIW feeding network transmitted the TE\({}_{10}\) and TE\({}_{20}\) modes to the single patch antenna. To verify the feasibility, the proposed antenna was fabricated, and its antenna characteristics were measured in a full anechoic chamber. The measured reflection coefficients of the SIW for both the TE\({}_{10}\) and TE\({}_{20}\) modes were \(-18.8\) dB and \(-11.5\) dB at \(4.85\) GHz. The measured and simulated mutual couplings were maintained below \(-30\) dB from \(4.5\) GHz to \(5.5\) GHz. The maximum gains of \(3.1\) dBi and \(-2.2\) dBi for the TE\({}_{10}\) mode and TE\({}_{20}\) mode were observed at \(\theta=0^{\circ}\) and \(\theta=45^{\circ}\), respectively. The accuracy of the DOA estimation was further improved by applying the derived calibration function to the resulting sum and difference ratio. Through this calibration, the RMSE value of \(0.07\) was obtained, which was \(0.1\) lower than that of the uncalibrated result. For the future work, we will increase the efficiency of the proposed monopulse system using low loss material such as Duroid and Teflon that can increase the efficiency up to \(82\%\) for the proposed design based on electromagnetic (EM) simulation results.  
Figure 12: Simulated and measured \(\Delta/\Sigma\) of the proposed antenna.  
Figure 13: Root mean square error (RMSE) in terms of the incident angle \(\theta\).  
**Author Contributions:** Conceptualization, S.Y., T.H.L. and H.C.; methodology, S.Y. and T.H.L.; software, S.Y.; validation, S.Y., T.H.L. and H.C.; formal analysis, S.Y., T.H.L. and B.-j.J.; investigation, S.Y. and T.H.L.; writing--original draft preparation, S.Y. and T.H.L.; writing--review and editing, S.Y. and H.C.; visualization, S.Y.; supervision, H.C.; project administration, H.C.; funding acquisition, H.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Samsung Research Funding Center of Samsung Electronics under project number SRFC-IT1801-06.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Integrating Machine Learning Techniques for Enhanced Safety and Crime Analysis in Maryland
## 7 Disclosure: Crime Reporting Bias and Ethical Considerations  
### Data Integrity and Bias Mitigation  
Our research utilizes extensive crime and socioeconomic datasets which, like any large-scale data, may inherently include biases. These can stem from the under-reporting of crimes in certain areas, differences in law enforcement practices, or the over-policing of specific demographics. Acknowledging these challenges, we conducted a thorough review of the data collection and reporting methodologies to identify and mitigate such biases wherever possible. This included assessing regional discrepancies in reporting, validating demographic coverage, and excluding highly collinear or non-informative features to prevent skewed model behavior.
# Assessment of Coating Properties in Car Body by Ultrasonic Method
## 2 Materials and Methods
### Ultrasonic Coating Width Testing Methodology  
The goal of this part of the research is to evaluate the suitability of using the ultrasonic Rayleigh wave to test the width of a putty coating applied to a body sheet. The coating was applied in varying widths to the body steel sheet. Car putty coating is still the most commonly used material in the construction of motor vehicle bodies, mainly in car body repair process. Universal putty coating was applied to 0.8 mm thick body sheet. Before the application process of the coating, the surface of the specimens was treated with sandpaper and then cleaned. The roughness of the surface not only affects the adhesion of the coating to the substrate but also affects the dispersion of the ultrasonic wave [24]. Therefore, it is important that the values of its basic parameters (R\({}_{\mathrm{a}}\), R\({}_{\mathrm{z}}\)) are similar over the entire surface of the sample. The next step was the preparation and application of putty on the steel sheet. The finished putty was applied to two steel sheets: on the first one in a width of 1 to 6 mm and on the second one from 7 to 13 mm, with a coating thickness of 0.7 mm (Figure 2). The applied coating was left for a period of about 30 min until it was completely set with the substrate.  
Figure 1: The consecutive stages of the conducted research.  
Figure 2: View of samples with putty coating; (**a**) coating width from 1 to 6 mm, (**b**) coating width from 7 to 13 mm.  
Ultrasonic testing was carried out using a USM 35 XS flaw detector from Krautkramer (GE, Boston, MA, USA) and two MWB 90-4 surface wave transducers with a frequency of 4 MHz from the same manufacturer. The tests were performed using the pass-through technique, where one head transmitted an ultrasonic wave pulse (transmitting head) and the other received this pulse (receiving head). Conducting the tests required maintaining invariable, experimentally determined settings of the flaw detector as follows:  
- Pulse gain: 58 dB;  
- Ultrasonic wave speed: 4000 m/s;  
- Run-up: 0 \(\upmu\)s;  
- Distance between heads: 110 mm;  
- Constant coupling between the head and the surface of the substrate.  
The observed ultrasonic parameter during the tests was the pulse amplitude of the ultrasonic Rayleigh wave. An important parameter during the implementation of the study was the use of a fixed distance of ultrasonic head placement and their parallel positioning so that the transmitted pulse was received by the other head. Therefore, a holder was designed and fabricated to ensure constant placement of the heads regardless of the width of the coating on the sample. A schematic diagram of the implementation of ultrasonic coating width measurements by the ultrasonic surface wave is shown in Figure 3.
# Routing Schemes in FANETs: A Survey
## 5 Routing Schemes in FANETs
### Reactive
#### 5.3.2 Enhancing Route Stability Using Link Delay  
[PERSON] et al. [126] propose a reactive routing scheme, which is based on AODV [129], that enables UAVs to establish routes by accumulating a route stability factor, which is based on link delay and mobility. The main objectives are to enhance route stability (O.1) as well as to enhance routing performance and QoS (O.3) in FANETs. The routing scheme addresses two challenges, namely high dynamicity (X.1) of network topology and cost (X.2), particularly routing overhead which increase energy consumption and computational complexity. The routing scheme is designed for hop-by-hop (C.1.1) and source routing (C.1.2) approaches.  
During the neighbor and route discoveries stage, each node periodically broadcasts hello messages to form a neighbor set comprised of two-hop neighboring nodes. The hello message contains the sending time, geographical location (M.3) (i.e., GPS coordinates), mobility metric (M.1) (i.e., the distance with and average speed of neighboring nodes), and one-hop neighboring nodes. Using the sending time information and the reception time at a neighboring node, the link delay can be estimated. Subsequently, the source node broadcasts RREQ messages to accumulate the route stability factor, which is based on the link delay and mobility metric along potential routes [130; 131]. During the route selection stage, the destination nodes, which receive RREQ messages via different routes, select a route that provides the least accumulated route stability factor in order to increase route stability and to reduce delay. The destination node sends a RREP message to the source node using the same route that the RREQ message traverses. During the route maintenance stage when a link disconnection occurs, an intermediate node performs a local link repair so that the rest of the route can still be used and identifying a new route is not necessary. Specifically, the intermediate node does the following:  
* stores the affected packets;
* broadcasts RREQ to identify a next-hop node located closer to the destination node, which is based on the greedy geographic forwarding (GGF) [132];
* selects the next-hop node to form a new link in order to repair the broken route;
* forwards packets along the new link. Nevertheless, if steps broadcasting RREQ and selecting the next-hop node are unsuccessful, then the node drops the packets in its storage.  
The routing scheme is compared with a state-of-the-art scheme that prefers the route with the least number of hops during route discovery. Random waypoint mobility model (A.2) is used in simulation. The routing scheme has been shown to increase QoS (P.1) (i.e., higher packet delivery rate and lower routing overhead).
## 2 System Model  
For the SAEC configuration (see Figure 1), we refer to the acoustic channels as the _left_ (L\({}_{\rm c}\)) and the _right_ one (R\({}_{\rm c}\)). We employ the discrete time index \(n\) to denote the input (loudspeaker) signals as \(x_{\rm L_{c}}(n)\) and \(x_{\rm R_{c}}(n)\). They can be used to express two \(L\times 1\) vectors comprising the most recent \(L\) input samples for each channel as [4]:  
\[\mathbf{x}_{\rm L_{c}}(n)=[x_{\rm L_{c}}(n)\quad x_{\rm L_{c}}(n-1)\quad \cdots\quad x_{\rm L_{c}}(n-L+1)]^{T}, \tag{1}\]  
\[\mathbf{x}_{\rm R_{c}}(n)=[x_{\rm R_{c}}(n)\quad x_{\rm R_{c}}(n-1)\quad \cdots\quad x_{\rm R_{c}}(n-L+1)]^{T}. \tag{2}\]  
Moreover, we combine the four loudspeaker-to-microphone echo path impulse responses of length \(L\) (denoted by the \(L\times 1\) vectors \(\mathbf{h}_{\rm t,L_{c}}\), \(\mathbf{h}_{\rm t,L_{c}R_{c}}\), \(\mathbf{h}_{\rm t,R_{c}L_{c}}\), and \(\mathbf{h}_{\rm t,R_{c}R_{c}}\)) to write the echo contributions corresponding to the stereophonic channels [4]:  
\[y_{\rm L_{c}}(n)=\mathbf{h}_{\rm t,L_{c}L_{c}}^{T}\mathbf{x}_{\rm L_{c}}(n)+ \mathbf{h}_{\rm t,R_{c}L_{c}}^{T}\mathbf{x}_{\rm R_{c}}(n), \tag{3}\]  
\[y_{\rm R_{c}}(n)=\mathbf{h}_{\rm t,L_{c}R_{c}}^{T}\mathbf{x}_{\rm L_{c}}(n)+ \mathbf{h}_{\rm t,R_{c}R_{c}}^{T}\mathbf{x}_{\rm R_{c}}(n), \tag{4}\]  
where superscript \(T\) denotes the transpose operator. Consequently, the microphone signals can be expressed as [4]:  
\[d_{\rm L_{c}}(n)=y_{\rm L_{c}}(n)+w_{\rm L_{c}}(n), \tag{5}\]  
\[d_{\rm R_{c}}(n)=y_{\rm R_{c}}(n)+w_{\rm R_{c}}(n), \tag{6}\]  
where \(w_{\rm L_{c}}(n)\) and \(w_{\rm R_{c}}(n)\) represent environmental noise signals, which are uncorrelated with \(y_{\rm L_{c}}(n)\) and \(y_{\rm R_{c}}(n)\). The WL model combines the previously introduced real random variables into fewer CRVs in order to improve the handling of the SAEC setup. The signals associated with the inputs and the outputs of the unknown echo paths can be paired using the notation \(j=\sqrt{-1}\) into:  
\[x(n)=x_{\rm L_{c}}(n)+jx_{\rm R_{c}}(n), \tag{7}\]  
\[y(n)=y_{\rm L_{c}}(n)+jy_{\rm R_{c}}(n). \tag{8}\]  
Moreover, the four unknown impulse responses can be combined into  
Figure 1: The WL model for SAEC systems.
# Automated Defect Detection Using Threshold Value Classification Based on Thermographic Inspection
## 4 Results and Discussion  
### Lock-In Signal Images  
After excitation on the specimen of a halogen lamp, which is an external heat source, the thermal response of the surface was obtained by converting it into a 2D image. Then, amplitude and phase images were acquired; Figure 5 shows the amplitude and Figure 6 shows the phase image. Each image was scaled for qualitatively clear defect detection. The use of two halogen lamps caused non-uniform thermal responses of the specimen surface. Therefore, it can be visually confirmed that there was a lot of noise.  
The optimum frequencies of amplitude and phase images were calculated at the excitation frequency set in this study. To calculate the optimum frequency, two ROIs of 5 \(\times\) 5 pixels (one at the center of defective area and another in the adjacent sound area) were considered. Then, contrast evaluation was performed using Equation (7) [28]:  
\[\Delta\mathrm{c}=\varnothing\mathrm{DROI}_{\mathrm{mean}}-\varnothing\mathrm{ SROI}_{\mathrm{mean}} \tag{7}\]  
where \(\varnothing\mathrm{DROI}_{\mathrm{mean}}\) and \(\varnothing\mathrm{SROI}_{\mathrm{mean}}\) are the average values of the defective area and sound area, respectively.  
Figure 5: Amplitude images from the lock-in signal processing: (**a**) 0.01 Hz, (**b**) 0.02 Hz, (**c**) 0.03 Hz, (**d**) 0.04 Hz, (**e**) 0.05 Hz, (**f**) 0.06 Hz, (**g**) 0.07 Hz, (**h**) 0.08 Hz, (**i**) 0.09 Hz and (**j**) 0.1 Hz.  
Figure 6: Phase images from the lock-in signal processing: (**a**) 0.01 Hz, (**b**) 0.02 Hz, (**c**) 0.03 Hz, (**d**) 0.04 Hz, (**e**) 0.05 Hz, (**f**) 0.06 Hz, (**g**) 0.07 Hz, (**h**) 0.08 Hz, (**i**) 0.09 Hz and (**j**) 0.1 Hz.  
Figure 7 shows the ROI contrasts of amplitude and phase for each excitation frequency. An amplitude of 0.02 Hz and a phase of 0.01 Hz were evaluated. In both images, it can be seen that the higher the frequency, the lower the contrast. This can be regarded as a result of insufficient heat being supplied because the excitation frequency is higher. It can be seen that the higher the excitation frequency, the lower the defect detectability.
# Effects of Boundary Condition Models on the Seismic Responses of a Container Crane
## 2 Description of a Numerical Model of the Container Crane
### Dynamic Analysis of the Container Crane  
In this study, the time history analysis (THA) method is selected to determine the dynamic response of the container crane under seismic excitation. The fundamental dynamic equilibrium equations of THA can be expressed as Equation (5) by SAP2000 [20; 21]:  
\[\mathbf{K}\mathbf{u}(t)+\mathbf{C}\mathbf{\dot{u}}(t)+\mathbf{M}\mathbf{\ddot{u}}(t)=\mathbf{ r}(t) \tag{5}\]where **K**, **C**, and **M** are the stiffness matrix, damping matrix, and mass matrix, respectively; **u**, \(\dot{u}\), and \(\ddot{\mathbf{u}}\) are the displacements, velocities, and accelerations of the structure; and \(\mathbf{r}\) is the applied load.  
There are several options for solving Equation (5), i.e., linear and nonlinear, modal, and direct integration. In this study, the intensity of recorded ground motion is increased to evaluate the uplift and derailment events of the container crane. In this study, the maximum mean of acceleration spectra of ground motions (\(S_{u}=0.9\) g) is much larger than the elastic acceleration spectra, according to KDS 2016; as a result, the dynamic response of the container crane is expected to deform past the limit point for elastic behavior. Therefore, the nonlinear analysis and direct-integration solution are applied to solve Equation (5). In addition, the properties of the plastic hinges are defined in ASCE/SEI 41-13 [22]. The plastic hinge mechanism is a model that is appropriate for the maximum moment that occurs at the end of members [23; 24], and beam-hinge and column-hinge mode were assigned for the portal frame and legs, as in Figure 3a. Assigning plastic hinges has been found to be suitable in some previous research, such as that recommended in PIANC [25]; in fact, in the 1995 Kobe earthquake, the extensive damage to the container crane was local buckling of the legs and derailment [26].  
The fundamental period of the Korean container crane was analyzed by the Ritz vectors method. Twelve natural frequencies of the main vibration modal characteristics were studied, in which the fundamental period of the PIN and GAP were approximately 1.35 s for the primary bending mode (portal sway), while that of the FC is 1.36 s. Figure 3b shows the fundamental period of the PIN.
# Vegetation Productivity Losses Linked to Mediterranean Hot and Dry Events
## 2 Materials and Methods
### Remote Sensing Data  
#### 2.2.1 Vegetation Productivity and Soil Moisture  
Gross Primary Production (GPP) from MOD17A2 Collection 6 was used to monitor vegetation activity. As described in [37], in the MOD17 algorithm, GPP is calculated based on radiation conversion efficiency. This concept requires the estimation of the absorbed PAR (APAR), which in MOD17A2 relies on satellite-derived FPAR (Fraction of PAR) and IPAR (incident PAR) from MOD15 collection 6 and estimations of PAR provided by surface meteorological fields from GMAO/NASA. Also, information related to specific biome-type radiation-use efficiency is extracted from a biome properties look-up table to determine the radiation conversion efficiency parameter \(\varepsilon\). GPP is then calculated as:  
\[\text{GPP}=\varepsilon\ \times\ \text{(FPAR}\times\text{IPAR)}=\varepsilon\ \times\ \text{APAR} \tag{1}\]  
Collection 6 of MOD17A2 includes updates on global biome properties descriptions and meteorological inputs, as described in [38; 39]. Various studies analysed the ability of MOD17A2 to reproduce the variability, both seasonal and interannual, across a broad range of different biomes; the results are quite accurate, especially in mid-latitudes, including the detection of hot and dry impacts on vegetation [18; 38; 39; 40; 41]. A detailed description of the product and algorithm can be found in [37]. The dataset is provided at 500 m spatial resolution on original projection (sinusoidal) and constitutes a daily cumulative composite of 8 days, expressed in kg C/m\({}^{2}\)/8 days.  
Soil water availability was monitored through the European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM, hereafter SM). The product has been recognized as a high-quality monitoring satellite product, useful for climate change and variability with meteorological and hydrological applications and land-atmosphere interaction studies [35; 42; 43; 44; 45]. In this work, the combined dataset, obtained from the combination of different active and passive datasets, is used with a spatial resolution of 0.25\({}^{\circ}\) and daily temporal resolution. It is described in volumetric units [m\({}^{3}\)m\({}^{-3}\)] and is representative of soil moisture in the top soil layer [35]. The detailed algorithm is described in [43]. The SM data were resampled to MODIS GPP resolution data for each study area by using the nearest neighbour interpolation scheme, as our main focus is to link the vegetation's productivity disturbances in ecologically stressed areas to hot and dry events.
## 2 Development of Identification Procedure
### Defining the Debonding Damage Map  
To construct a dataset (debonding damage map) for identifying debonding damage based on natural frequency change characteristics, the various natural frequencies corresponding to various debonding damage configurations were obtained by performing a series of modal analyses. To do so, the FE model shown in Figure 2 was constructed in the ABAQUS according to the Sandia report [21]. Here, the authors used the 4 node S4R shell elements and their number of elements and nodes which were 92,234 and 94,709, respectively. The mechanical properties are summarized in Table 1.  
Figure 1: Flow chart for identifying debonding damage in a wind turbine blade.  
Figure 2: Composite wind turbine blade model.  
In particular, to model debonding damage at the spar cap-shear web joints, the multi-point constraint (MPC) technique was applied to prevent stress concentrations and consider the joints' structural behavior [22]. In addition, although there are numerous configurations of damage that can occur in blades composed of skin, a spar cap, and a shear web, a limited set of governing variables were selected in the study to realize a more efficient and less time-consuming analysis. As shown in Figure 3, the debonding damage configurations were defined by varying the single/multiple damages, start location, and length of debonding damage at the specific joint. The natural frequencies for each of these damage configurations were then obtained by performing modal analysis.
## 3 Material and Methods
### Dataset and Preprocessing
Notably, there exists a pronounced positive correlation amongst 'Actualpower', 'Windspeed', and 'Thpower', which implies robust positive linear interrelations between these variables. 'Thpower', or the Theoretical Power Curve, elucidates the maximum power generation potential of wind turbines under ideal conditions. However, actual conditions or the specific focus of this study may deviate from these theoretical estimations. Consequently, if the analysis is concentrated on the actual energy yield, incorporating theoretical values could potentially lead to misconceptions. Therefore, such theoretical values were omitted from this research. Subsequently, the dataset was refined to omit records wherein wind speed was between 3 and 26 m/s and the corresponding 'LV ActivePower' was recorded as 0 kW. The refined dataset is portrayed in a matrix combining scatter plots and histograms, as depicted in Figure 3.  
Figure 3 shows the positive relationship between Wind Speed and Actual Power. There is no clear relationship between Wind Speed and Wind Direction. However, it can provide important information for applications such as choosing appropriate locations for wind  
Figure 3: Matrix consisting of scatter plot and histogram. turbines, energy production forecasts, and adjusting the direction of the turbine. For this reason, the wind rose graph is given in Figure 4.  
Figure 4 shows the direction (in degrees) and speed (in meters/seconds) of the wind. The wind vane has sections extending outward from the center, indicated by different colors for different wind speeds. Each slice represents the frequency and speed at which the wind blows from that direction. Figure 4 shows that the wind blows most frequently from the northeast (N-E) and southeast (S-E). The highest wind speeds (e.g., over 20.2 m/s) were observed in northeasterly winds.