esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Orientation Compressed Map
### Orientation Compressing Map  
As mentioned above, although the DOT roughly achieves speed requirements, it only considers the dominant orientations, resulting in the unsatisfactory performance of object detection. In order to make the measure more robust to small transformations of object while meeting the speed requirements, orientation compressing map which combines the encoded orientations is proposed in this section.  
In order to reduce computation complexity, a compressing process is implemented after encoding the orientation. The location \(k\) in the compressed image corresponds to a \((2r^{c}+1)\times(2r^{c}+1)\) block of pixels in the original image centered at location \(i\), with:  
\[i=(2r^{c}+1)\cdot k+r^{c}. \tag{1}\]  
As illustrated in Figure 2b,c, \(r^{c}=1\) and a blue \(3\times 3\) block corresponds to a pixel in the compressed image.  
Different from the DOT [32], the proposed compressing map exploits all the encoded orientations in a neighborhood instead of only considering the orientation of the strongest gradient. Define ori\({}^{\mathrm{C}}\) as the encoded orientation of the compressing map, it can combine the orientations efficiently by performing the bitwise OR operation:  
\[\mathrm{ori}^{\mathrm{C}}_{k}(\mathcal{T},r^{c})=\mathrm{ori}^{\mathrm{C}}_{k} (\mathcal{T},r^{c})\Big{|}\mathrm{ori}_{r^{\prime}}(\mathcal{T})\forall i^{ \prime}\in C(i,r^{\prime}), \tag{2}\]  
where \(\mathrm{ori}^{\mathrm{C}}_{k}(\mathcal{T},r^{c})\) is the compressed orientation at location \(k\) in the orientation compressing map of the template image \(\mathcal{T}\), which combines all the orientations in the region \(C(i,r^{\prime})\) centered at location \(i\) in the template image. Specially, to prevent the orientation compressing map from changing largely when shift one or two pixel, the mechanism of two overlapped circle windows is implemented. As shown in Figure 2b, two circular windows centered on blue pixels partially overlap, and their radius can be defined as follows:  
\[r^{\prime}=r^{c}+[r^{c}/2], \tag{3}\]  
\(C(i,r^{\prime})\) indicates the circular window centered at location \(i\) with radius \(r^{\prime}\). The symbol \([\cdot]\) indicates rounding.  
Figure 1: (**a**) A semi-circle is divided into 16 parts which are congruent with each other, and every part has an unique discrete direction; (**b**) The orientation of the gradients and their binary strings.  
Figure 2 shows the process of computing the orientation compressing map of the template image which is shown in Figure 2a. Figure 2b is original quantized gradient orientations in the red box of the template's edge map shown in Figure 2d. Figure 2c is the orientation compressing map of the orientations in Figure 2b, it combines the original quantized orientations efficiently by performing the bitwise OR operation. Figure 2e represents the orientation compressing map of the template image. In Figure 2e, the darker pixel indicates the more different quantized orientations are combined.
# Zero Average Dynamic Controller for Speed Control of DC Motor
## 2 Materials and Methods  
Figure 1 presents the diagram of the ZAD applied to the buck converter when it is used to control a DC motor. This figure shows two main blocks that were broken into the buck-motor system and the controller board. Thus, the buck converter controls the speed of the motor, the sensors measure the signals in the buck-motor system, and the gates receive the signals from the software. The controller system is composed of the blocks such as A/D inputs to obtain the signals, the ZAD controller, the input parameters, the reference signal, and CPWM that transmit the signal output to the buck-motor system.  
The permanent magnet DC motor parameters are as follows: 250 W (nominal power), 42 VDC (input voltage), 6 A (current), and 4000 RPM (speed). The speed of the motor (\(W_{m}\)) is measured with an encoder of 1000 pulses per turn, \(v_{c}\) is the capacitor voltage that feeds the motor, \(i_{L}\) is the inductor current, and \(i_{a}\) is the armature current. The controller was implemented in the dSPACE DS1104 [40; 41]; therefore, the signals are sampled by the card during the real-time operation, and the output of the control is applied to the buck converter.  
Figure 1: Block diagram of the buck-motor system using ZAD control strategy.  
One fast response optocoupler (HCPL-J312) was used to isolate the card from the power grid. This device connects the control output, operates the buck converter switch, and closes the control loop.  
Moreover, \(v_{c}\), \(i_{L}\), and \(i_{a}\) pass through the 12-bit ADC inputs, the controlled variable \(W_{m}\) is sensed by an encoder, and the variables are sensed at a sampling frequency of 6 kHz. The DC motor, buck converter, and ZAD technique control parameters are stored as constants in the control block. The DS1104 card microprocessors generate the duty cycle \(d\) and the corresponding CPWM signal for the solid-state switch S with a resolution of 10 bits during each sampling time.
## 2 General Characteristics of Micro-Doppler Signatures from Birds and Drones
The rotation is deterministic and controlled, and coupled with the fact that these targets have a high RCS, their micro-Doppler signatures are clean, symmetric, and recognizable compared to those of birds. Assuming pure periodic rotation, the micromotion dynamics generate sideband Doppler frequency shifts about the scattered signal. The modulation contains spectral components that depend on the vibration or rotation rate, the vibration amplitude, and the angle between the direction of vibration and the direction of the incident wave [23]. A small asymmetry may be due to the asymmetry of the propellers, as the propeller that is moving towards the radar is curved in the opposite way with respect to the propeller moving away from the radar [24]. The micro-Doppler signatures of drones depend on their size, number of propellers or rotors, rotational frequency, radar frequency, and viewing angle. The unique signatures of birds and drones can be exploited not only to perform bird vs. drone classification but also to classify birds of different sizes and drones of different structures and types [25, 26, 27, 28].
# Stream: Spatial Trajectory Enhanced Attention Mechanism for Abnormal UAV Trajectory Detection
## 1 Introduction
This approach is crucial as data quality can deteriorate due to various external factors, such as noise during data collection or omissions in data transmission and reception. Making predictions with low-quality data as input challenges the accuracy of even the most sophisticated DNN models. Traditional methodologies, based on statistics and probability, often interpolate data collected from sensors by focusing on numerical data. However, when the degree of missingness in data is significant, these traditional methods risk distorting the original data, rendering the interpolation process ineffective. To address this, our system leverages advanced DNN-based interpolation techniques that use images instead of numerical data, filling gaps in the data more effectively. This approach not only mitigates the degradation of data quality but also significantly improves the classification performance of the UAV abnormal maneuver detection model. Secondly, we propose the STEAM, a new and efficient attention module optimized for analyzing trajectory pattern data of UAVs. STEAM adopts a maximum-pooling-based attention strategy to emphasize prominent trajectory features with high precision. This strategy effectively minimizes the number of parameters in the model, thereby reducing model complexity and enhancing interpretability. Our comprehensive experiments demonstrate superior performance of STEAM over traditional convolutional models, especially in UAV trajectory classification tasks, where STEAM shows improved accuracy with fewer computational resources. Our system, using the above proposed methods, facilitates real-time UAV flight trajectory tracking and classification, enabling rapid and accurate evaluation of UAVs for predefined optimal trajectories. The results obtained from this process can pinpoint UAVs requiring flight trajectory modifications and facilitate efficient monitoring for precise task execution. Moreover, the ability to classify flight trajectories allows for the identification of specific tasks being performed by the UAVs. This feature of the system extends its utility beyond mere monitoring, making it applicable to a wide range of unmanned vehicles requiring optimized routing for specific tasks. Our contributions can be summarized as follows:  
* We introduced an effective interpolation technique utilizing partial convolution with a suitable loss function. * The proposed attention-based CNN classifier not only achieves outstanding classification performance but also features a lightweight learning model. * We presented a framework for the validation and assessment of UAV trajectories, integrating a hybrid model selection approach. The remainder of this paper is organized as follows. We introduce related work in Section 2. In Section 3, we provide a detailed explanation about our visualization system, deep interpolation model, and attention mechanism. Section 4 evaluates the performance of the proposed framework. Finally, Section 5 concludes this paper.
## 4 Conclusions  
In this paper, the numerical simulations were carried out on the fluid flow and heat transfer in straight pipes and helical pipes having bow cross-sections with different central angles. Conclusions are drawn as follows:  
1. For a given volume flow rate, by decreasing the central angle \(\alpha\) of the bow cross-sections in the range of 90-180\({}^{\circ}\), \(Re\) and \(Nu\) for both straight pipes and helical pipes increase, meaning that the bow cross-sections can enhance heat transfer in the pipes. However, at the same time, the flow resistance will also increase.
2. Compared with half-pipes with the central angle \(\alpha\) being 180\({}^{\circ}\), the comprehensive performance evaluation factor \(pec\) for both straight pipes and helical pipes having bow cross-sections is greater than 1, meaning that bow cross-sectional pipes have better comprehensive heat transfer performance. Specifically, for the helical pipes, when \(\alpha\) is 90\({}^{\circ}\), the \(pec\) can reach 1.68 times that with \(\alpha\) being 180\({}^{\circ}\).
3. Compared with the half-pipe with \(\alpha\) equal to 180\({}^{\circ}\), less weight of the bow cross-sectional pipe is needed for transferring the same amount of heat. For the helical pipe having a bow cross-section with \(\alpha\) being 90\({}^{\circ}\), the weight of the pipe can be reduced by about 80%, a significant saving of the material or the manufacturing cost;
4. Correlation formulas for \(Nu\) and \(f\) of the whole straight pipe and the helical pipe were modified to include the influence of the central angle \(\alpha\) of the bow cross-sections. With relative errors of less than 10%, the modified formulas can be applied in engineering to construct jackets with bow cross-sectional pipes.  
As mentioned in the Introduction and shown in Figure 1, pipe jackets are manufactured by bending and feeding metal plates into rings rather than cutting from whole pipes. Obviously, a pipe jacket with a bow cross-section with a central angle of less than 180\({}^{\circ}\) is easier to manufacture than a half-pipe jacket. Therefore, together with the higher heat transfer ability and fewer consumed materials found in this study, using bow cross-sectional pipes to construct jackets is more economic compared with half-pipes and should be adopted preferentially in engineering, with the design based on the correlation formulas for \(Nu\) and \(f\) proposed in this paper.  
Conceptualization, X.Y. and C.Q.; methodology, C.Q.; software, W.L.; validation, Z.W.; formal analysis, W.L. and Z.W.; investigation, X.Y. and C.Q.; resources H.L.; data curation, W.L. and Z.W.; writing--original draft preparation, W.L.; writing--review and editing, X.Y. and C.Q.; visualization, Z.W.; supervision, H.L.; project administration, H.L.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
# XBeats: A Real-Time Electrocardiogram Monitoring and Analysis System
###### Abstract  
This work presents XBeats, a novel platform for real-time electrocardiogram monitoring and analysis that uses edge computing and machine learning for early anomaly detection. The platform encompasses a data acquisition ECG patch with 12 leads to collect heart signals, perform on-chip processing, and transmit the data to healthcare providers in real-time for further analysis. The ECG patch provides a dynamically configurable selection of the active ECG leads that could be transmitted to the backend monitoring system. The selection ranges from a single ECG lead to a complete 12-lead ECG testing configuration. XBeats implements a lightweight binary classifier for early anomaly detection to reduce the time to action should abnormal heart conditions occur. This initial detection phase is performed on the edge (i.e., the device paired with the patch) and alerts can be configured to notify designated healthcare providers. Further deep analysis can be performed on the full fidelity 12-lead data sent to the backend. A fully functional prototype of the XBeats has been implemented to demonstrate the feasibly and usability of the proposed system. Performance evaluation shows that XBeats can achieve up to 95.30% detection accuracy for abnormal conditions, while maintaining a high data acquisition rate of up to 441 samples per second. Moreover, the analytical results of the energy consumption profile show that the ECG patch provides up to 37 h of continuous 12-lead ECG streaming.  
remote patient monitoring; electrocardiogram; telemedicine; cardiovascular diseases +
Footnote †: journal: _signals_
# Data-Driven Calibration Estimation for Robust Remote Pulse-Oximetry
###### Abstract  
Pulse-oximetry has become a core monitoring modality in most fields of medicine. Typical dual-wavelength pulse-oximeters estimate blood oxygen saturation (SpO\({}_{2}\)) levels from a relationship between the amplitudes of red and infrared photoplethysmographic (PCG) waveforms. When captured with a camera, the PPG waveforms are much weaker and consequently the measurement is more sensitive to distortions and noises. Therefore, an indirect method has recently been proposed where, instead of extracting the relative amplitudes from the individual waveforms, the waveforms are linearly combined to construct a collection of pulse signals with different pulse signatures, each corresponding to a specific oxygen saturation level. This method has been shown to outperform the conventional ratio-of-ratios based methods, especially when adding a third wavelength. Adding wavelengths, however, complicates the calibration. Inaccuracies in the calibration model threaten the performance of the method. Opto-physiological models have been shown earlier to provide useful calibration parameter estimates. In this paper, we show that the accuracy can be improved using a data-driven approach. We performed 5-fold cross validation on recordings with variations in oxygen saturation and optimized for pulse quality. All evaluated wavelength combinations, also without visible red, meet the required ISO standard accuracy with the calibration from the proposed method. This scalable approach is not only helpful to fine-tune the calibration model, but even allows computation of the calibration model parameters from scratch without prior knowledge of the data acquisition details, i.e., the properties of camera and illumination.  
contactless monitoring; pulse-oximetry; remote photoplethysmography; near-infrared +
Footnote †: journal: Applied Physics Letters
_Applied_  
_sciences_  
_Article_  
**Analyses of the Effect of Cycle Inlet Temperature on the Precooler and Plant Efficiency of the Simple and Intercooled Helium Gas Turbine Cycles for Generation IV Nuclear Power Plants**  
**[PERSON] \({}^{\star}\), [PERSON] and [PERSON]**  
Gas Turbine Engineering Group, Cranfield University, Cranfield MK43 OAL, UK;  
[EMAIL_ADDRESS] (T.N.); [EMAIL_ADDRESS] (P.P.)  
\({}^{\star}\) Correspondence: [EMAIL_ADDRESS]; Tel.: +44-797-353-9110  
Academic Editor: [PERSON]  
Received: 22 January 2017; Accepted: 20 March 2017; Published: 24 March 2017  
**Abstract:** Nuclear Power Plant (NPP) precooler coolant temperature is critical to performance because it impacts the work required to increase the coolant pressure. Variation of the coolant temperature results in varied precooler hot gas temperatures, which are cooled before re-entry. For recirculation, the heat sink (usually sea water), could exit the precooler at unfavourable temperatures and impact the re-entering coolant, if not recirculated properly at the source. The study objective is to analyse the effects of coolant inlet temperature on the heat sink and cycle efficiency. The cycles are Simple Cycle Recuperated (SCR), Intercooled Cycle Recuperated (ICR), and Intercooled Cycle without Recuperation (IC). Results show that the co-current precooler provides favourable outlet heat sink temperatures but compromises compactness. For a similar technology level, the counter-current precooler yields excessive heat sink outlet temperatures due to a compact, robust, and efficient heat transfer design, but could be detrimental to precooler integrity due to corrosion, including the cycle performance, if not recirculated back into the sea effectively. For the counter-current, the ICR has the best heat sink average temperature ratio of 1.4; the SCR has 2.7 and IC has 3.3. The analyses aid the development of Gas Cooled Fast Reactors (GFRs) and Very High Temperature Reactors (VHTRs), where helium is used as the coolant.  
**Keywords:** Gen-IV; efficiency; NPP; cycle; precooler; performance; simple; intercooled; recuperated
# A Modified KNN Algorithm for High-Performance Computing on FPGA of Real-Time m-QAM Demodulators
## 4 Experimental Setup
### Real-Time Implementation of KNN-Based Demodulator on Aws-FPGA  
The migration of the design to a high-performance architecture allows scaling the KNN algorithm implementations. Amazon EC2 F1 provides a platform for design implementation on a Xilinx Virtex UltraScale+ VU9P FPGAs. The device, manufactured with 16 nm technology, has approximately 2.5 million logic cells, more than 6800 Digital Signal Processing (DSP) engines. Additionally, four DDR4 channels providing a total bandwidth of 48 Gb per second. The high speed of the data communication allows emulating the communication channels using the PCI-e as the ADC in a communication system. The sampling frequency of the symbols depends on the numerical format and the maximum reading speed that can be achieved by the implementation in the hardware.
# Boating Tourism and Fishing Interactions: A Social Network Analysis Using AIS Data
## 4 Findings
### Data Analysis  
The data under analysis reports the full years of 2022 and 2023 with AIS data collection on an hourly basis (Table 2). To show some interaction, it was defined--after empirical analysis--that the number of users (social nodes of the network) that demonstrated some regularity in the use of resources should be equal to or greater than 22 records/pings per boat and must have at least one vessel nearby (i.e., up to 1 nautical mile) in at least five of these records.  
A total of 108 vessels were found. Of these, 65 were recreational (4758 pings) and 43 were fishing (30,896 pings). Of the recreational boats, only three met the previous premises (all passenger boats), while of the fishing boats, only one was left out of the social network analysis due to no known interaction with other fishing/recreational activities (the longliner).  
It is worth noting that hourly AIS data have some limitations as they may not cover short-term interactions between vessels. AIS data also does not cover vessels that lack this type of equipment (transponder). For the reasons stated above, it is reiterated that the analysis in the present study only allows for a partial representation of interactions between vessels.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Description** \\ \hline Vessel ID & The identification of the vessel (e.g., MMSI, name). \\ \hline No. Pings & Identified number of AIS data records, cleansing, and selection of the useful ones. \\ \hline Activity type & By crosschecking data, an economic/leisure activity for the vessel is attributed either as fishing or recreational. \\ \hline Area used & From the number of pings and its characterisation, it is possible to identify the area used by that vessel. \\ \hline Resource(s) & Associated with the different areas are some resources that stimulate economic or leisure activities. \\ \hline Interaction & The degree of systematic occurrence of some vessels in a given area in similar time periods. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data collection from AIS pings crosschecked with other sources to be used in the SNA.  
Figure 2: Map showing the case study location off south Portugal and the main areas where maritime–coastal activities can be found. Orangish circles represent marinas where recreational boats are berthed, and bluish ones represent ports for fishing vessels. The red rectangle is the AIS study area for the years 2022/3, whereas the pinkish area is a recently created MPA (access restrictions in the central polygon since 2024) [48].