esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Electric transportation, energy storage, renewable energy systems, hybrid electric vehicles (HEV), and fuel cell vehicles require bidirectional power electronics for power processing [1; 2; 3; 4]. A DC-DC front-end converter is the major part of a fuel cell inverter, which boosts the low-voltage fuel cell stack's voltage to the peak of utility line alternating current (AC) voltage [5]. Among various categories of high-frequency (HF) soft-switching isolated DC-DC converters, a current-fed converter is popular in fuel cell applications. Fuel cells produce low-voltage direct current (DC), which has a wide variation with load current and requires a high voltage conversion ratio [6; 7; 8; 9; 10]. Current-fed topologies offer high voltage gain with a stiff DC input current [11]. The historical problem with current-fed converters has been the necessity of a passive snubber [12; 13] or an active-clamping circuit [14; 15; 16] to clamp voltage overshoot across the semiconductor devices at their turn-off. Passive snubbers lead to poor efficiency because the energy stored by the clamping capacitor is later dissipated into the resistor. However, better efficiency is obtained, along with zero voltage switching (ZVS) of the semiconductor devices, with an active-clamping circuit [14; 15; 16], at the cost of additional footprints of floating active device(s) and a large HF clamp capacitor for accurate and effective voltage clamping. Additionally, the boost factor of the converter is reduced as well as the high peak, and circulating currents can be observed at light load. In [17], a new modulation technique of high-voltage side devices was proposed to solve the voltage spike problem of primary devices, eliminating the requirement for an external snubber. A current-fed half-bridge DC-DC converter was proposed as shown in Figure 1 with a new modulation,which was studied in [17] to achieve zero current switching (ZCS) commutation of semiconductor devices along with natural device voltage clamping.  
Steady-state analysis, power circuit design, and steady-state experimental performance results of the converter have previously been presented in [17]. However, its small-signal modeling, controller design, and dynamic performance results have not yet reported. The authors reported the small-signal modeling and derived the transfer functions in [18]. Controller design and simulation results were presented to show initial results and investigation on the transient performance of the converter. This paper presents systematic small-signal analysis and experimental results to demonstrate the closed-loop dynamic performance of the converter.
## 3 Description of Problem Phenomenon and Theoretical Analysis  
### Description of Phenomenon 1  
A certain MEO orbit satellite exhibits small-amplitude fluctuations in solar array current over a short period, which is particularly evident during high-power switching operations of payloads, leading to significant changes in load current (with an amplitude of approximately 28.89 A). When the satellite performs high-power switching, drastic changes in load current trigger voltage fluctuations in the Main Error Amplifier (MEA) of the power supply system and adjustments in the number of shunt stages. The specific changes in telemetry data are shown in Figures 3 and 4 below.  
Figure 3: Trend diagram of load, solar array current, shunt current, and MEA.  
Figure 2: Principal block diagram of the shunt regulation circuit.
# Adopting Signal Processing Technique for Osteoporosis Detection Based on CT Scan Image
## 2 Literature Review
### Application of AI/ML- in Medical Image Processing  
AI/ML based on medical imaging modalities has been widely used to solve medical problems. It was used as a tool for the early detection of various diseases. Machine learning based on ultrasound images has been used to diagnose breast cancer. In this application, machine learning was trained to identify breast cancer based on time-dependent feature extraction and contourlet transformation techniques by classifying data into three classes: benign, malignant, and healthy control test samples [12]. It has not only been applied to solve the problem based on images; AI/ML has also been used to solve signal-based problems. Human brain activities will change as a response to a stimulus. Movement intention identification is an example of utilizing AI/ML upon the real-time electro en en en en en en en en (EEG) signal. Automating movement intention identification is very important for controlling the movement of intelligent prosthetics to help patients after amputation. An algorithm satisfying any movement scenario must be developed as accurately as possible. In this case, the different classes of movement, such as resting state, right or left hand movement, left or right foot movement, and others, must be defined accurately. A convolutional neural network (CNN) network has been successfully applied to identify the movement intention based on EEG signal [13].  
The disease is related to a specific human organ or body part appearance. For example, by recognizing the face patient, doctors can diagnose a particular type of disease, such as beta-thalassemia, hyperthyroidism, Down's syndrome, and leprosy. It is challenging for engineering to automate face recognition to identify specific conditions; however, this may  
Figure 1: Identification of bone quality based on the HU value of a CT scan. (**A**) axial; (**B**) sagittal, from abdominal CT scan with a moderate compression fracture (arrow). (**C**) pelvic bone; and (**D**) T-score from DEXA data where L1-L4 are a vertebral levels. [11].  
help doctors/practitioners to determine diseases automatically. Deep transfer learning architectures were successfully applied in recognizing deep facial diagnoses based on facial images. Transfer learning can identify 2D face images and classify them into certain diseases related to bone deformities, endocrine disorders, Down's syndrome, and mycobacterium diseases [14].
###### Abstract  
Frequency allocations in the L band suitable for global navigation satellite system (GNSS) services are getting crowded and system providers face an ever tougher job when they try to bring in new signals and services while maintaining radio frequency compatibility. With the successive opening of the S and C bands to GNSS service, the multi-band combined navigation is predicted to become a key technology for future high-precision positioning navigation systems, and a single modulation scheme satisfying the requirements in each band is a promising solution for reducing user terminal complexity. A universal modulation scheme based on the continuous phase modulation (CPM) family suitable for the above bands' demands is proposed. Moreover, this paper has put forward two specific CPM signals for the S and C bands, respectively. Then the proposed modulation schemes, together with existing candidates, are comprehensively evaluated. Simulation results show that the proposed CPM signals can not only satisfy the constraint condition of compatibility in different bands well and reduce user terminal complexity, but also provide superior performance in terms of tracking accuracy, multi-path mitigation and anti-jamming compared to other candidate modulation schemes.  
Keywords:multi-band; combined navigation; modulation signal; CPM
## 4 Materials and Methods
### Schemes for Recovery of Optical Parameters
#### 4.4.2 Extraction of Free Optical Parameters  
The inversion scheme minimizes the error between the synthetic SRR tabulated in MC-LUT and the measured SRR from the unknown sample, and the minimizer thus obtained is the extracted optical parameter of interest.  
Figure 7: Comparison of simulated data and experimental data (**a**) for 53 outer ring fibers from 1 to 58 (measurement was not taken for fibers 24, 25, 26, 40 and 46) and (**b**) for all fibers of inner ring from 59 to 77, and (**c**) epithelial sensitivity measurement for inner ring fibers.  
**Problem-I:** The first minimization problem we solve is to recover the absorption coefficient of the top layer \(\mu_{a}^{t}\), if the top layer scattering coefficient \(\tilde{\mu}_{s}^{t}\), and both the optical parameters of the bottom layer (\(\tilde{\mu}_{a}^{b},\tilde{\mu}_{s}^{b}\)) are known, that is  
\[\tilde{\mu}_{a}^{t}=\underset{\mu_{a}^{t}}{\arg\min}\ \epsilon(\mu_{a}^{t})=|| \underline{R}^{meas}-\underline{R}^{pred}(\mu_{a}^{t},\tilde{\mu}_{s}^{t}, \tilde{\mu}_{a}^{b},\tilde{\mu}_{s}^{b})||_{2}^{2} \tag{4}\]  
where \(\underline{R}^{meas}=(R_{1}^{meas},R_{2}^{meas},...R_{N}^{meas})\), and \(\underline{R}^{pred}=(R_{1}^{pred},R_{2}^{pred},...R_{N}^{pred})\) represent the measured and predicted SRR values, respectively, at \(N\) detection fibers. While carrying out numerical validations (Section 5.1), \(\underline{R}^{meas}\) refers to synthetic SRR measurements from the sample with unknown optical properties and in case of experimental studies (Section 5.2) it refers to the experimentally measured SRR.  
**Problem-II:** Next, we aim to recover the scattering coefficient of the top layer \(\mu_{s}^{t}\), given the top layer absorption coefficient \(\tilde{\mu}_{a}^{t}\) and both the optical parameters of the bottom layer (\(\tilde{\mu}_{a}^{b},\tilde{\mu}_{s}^{b}\)), by solving the minimization problem:  
\[\hat{\mu}_{s}^{t}=\underset{\mu_{s}^{t}}{\arg\min}\ \epsilon(\mu_{s}^{t})=|| \underline{R}^{meas}-\underline{R}^{pred}(\tilde{\mu}_{a}^{t},\mu_{s}^{t}, \tilde{\mu}_{a}^{b},\tilde{\mu}_{s}^{b})||_{2}^{2} \tag{5}\]  
**Problem-III:** In order to recover both the optical parameters of the top layer (\(\mu_{a}^{t},\mu_{s}^{t}\)) for known optical parameters of the bottom layer (\(\tilde{\mu}_{a}^{b},\tilde{\mu}_{s}^{b}\)), the following minimization problem needs to be solved:  
\[\hat{\mu}_{a}^{t},\hat{\mu}_{s}^{t}=\underset{\mu_{a}^{t},\mu_{s}^{t}}{\arg \min}\ \epsilon(\mu_{a}^{t},\mu_{s}^{t})=||\underline{R}^{meas}-\underline{R}^{ pred}(\mu_{a}^{t},\mu_{s}^{t},\tilde{\mu}_{a}^{b},\tilde{\mu}_{s}^{b})||_{2}^{2} \tag{6}\]
# Antiference: New Concept for Evolutive Mitigation of RFI to GNSS +
## 5 Antiference Testbed  
The testbed is designed and configured to facilitate the verification and validation of the Antiference system as well as for the benchmarking of the results in relation to the two COTS receivers which have state-of-the-art RFI mitigation techniques implemented in them. Figure 2 shows the Antiference testbed architecture. As can be seen in the image, the COTS receivers can be used to process the incoming signals with or without their internal mitigation mechanisms tuned on, or to process the mitigated signal using the Antiference signal. The testbed has the following sections:  
Signal simulator: The signal simulator can be used for the simulation of clean, clean and RFI, or only RFI signals in the GNSS frequency bands. In this project, OHB's GNSS simulator XPLORA was used for the simulation of all simulated signals. Analogue signals recorded through the GNSS antenna can be directly fed into the COTS receivers (to be directly processed by them) or be converted to digital signals to be fed into the Antiference system. Antiference only works in the digital domain. Analogue to Digital Convertor (ADC): in this project, a GNSS frontend based on a 8-bit ADC was used (TeleOrbit MGSE). Two COTS receivers are used for the benchmarking of the results: (1) a low-cost receiver (Rec01) and (2) a mid-range receiver (Rec02) suitable for the automotive domain. SDR (software defined receiver): OHB Digital Solutions' GNSS SDR was used in this project, to process the results of the Antiference, and verify if any residual RFI could be detected using the built-in RFI detectors. Digital to Analogue Convertor (DAC): For the conversion of digital to analogue signals, the built-in convertor in TeleOrbits' MGSE (Multi-GNSS  
Figure 2: Antiference testbed architecture.  
Simulation and Test Environment) was used for the broadcasting of digital signals for processing using the COTS receivers. The logging manager (or the collection of logs created by each of the elements): to be stored for analysis, signal by signal or in batches. Analysis tools: developed in MATLAB, these are used to analyze the results for the final validation and assessment of the performance
# Beacon-Based Hybrid Routing Protocol for Large-Scale Unmanned Vehicle Ad Hoc Network
## 2 Beacon-Based Multi-Agent Network Hybrid Routing Algorithm
### Pulse Message  
In the beacon-based hybrid routing protocol for LUVAN, a pulse cycle was the basic unit. As shown in Figure 3, a pulse cycle was divided into four phases, each of which was executed in a fixed time and the cost controlled by the routing protocol was only related to the time allocated in the first three phases.  
Power on Reset (POR): In order to avoid the time error of different nodes, nodes can start receiving in advance and wait for receiving pulse messages.  
Pulse Propagation: Pulse node periodically broadcasted pulse messages, other nodes received pulse messages and established the optimal path tree (OPT) to the pulse node.  
Pulse Reply: For the routing request node, after receiving the pulse message, it would reply to the message in the form of unicast pulse along the OPT to the pulse node and request the path to the pulse node.  
Data Sending: After the optimal path from pulse node to the destination node was established, the pulse node sent data along the OPT and sent the data to the destination node through forwarding to the nodes along the OPT.  
In the beacon-based routing protocol, the non-beacon node would actively maintain the OPT pointing to the beacon node and establish the path to the destination node through the OPT.  
When the non-beacon nodes received the pulse messages sent by the beacon node, they would create an OPT from non-beacon nodes to the beacon node. Because the beacon node sent pulse messages periodically, the non-beacon nodes could maintain an OPT to the beacon node in real time to ensure the real-time effectiveness of the OPT.  
Figure 3: Schematic diagram of pulse unit.  
The beacon node sent pulse messages in a fixed period. Pulse messages included time information for time synchronization of non-beacon nodes.  
The non-beacon node determined the time of the next FOR state according to the pulse interval received last time and entered the receiving state at the specified time while remaining in the inactive state at other times. This node could enter the hibernation state to save power.  
After receiving the pulse message, the non-beacon node would judge it according to the serial number and link metric of the pulse message. When the pulse number was the latest or the pulse number was the same as the stored pulse number but the link measure was small, the path was saved and the pulse message was forwarded at the same time. At this time, the node would establish an optimal path data to reach the beacon node.  
After the beacon node sent a pulse message, all nodes in the network would create a path tree to the beacon source node, as shown in Figure 4.
## 2 Description of the Noise Certification Procedure
However, it is also expected that the efforts on VNRS development for supersonic aircraft, such as the automatic control of high-lift devices, will lead to wider implementation of VNRS for subsonic aircraft as well, including propeller-driven aircraft. Since there seems to be no accepted interpretation of how the certification point should be defined for a VNRS-equipped aircraft, for the purpose of this study, the lateral point K\({}_{4}\) is defined as the point on the extended runway centerline, corresponding to the flight altitude of 650 m. Thus, by analogy with the other position of the lateral certification point (K\({}_{2}\)), the requirement of full takeoff power (the maximum available thrust) is excluded from the definition. However, to make the comparison of points K\({}_{2}\) and K\({}_{4}\) more representative, the study will limit their consideration to the case when the distance \(X_{4}\) from the point of  
Figure 2: Takeoff reference profile of subsonic aircraft for noise certification, after [29]. brake release to point K4 is less than the distance \(X_{D}\) to Point D, where the pilot-initiated cutback is initiated:  
\[X_{4}<X_{D} \tag{1}\]  
It allows the VNRS efficiency for cumulative noise reduction at these points to be compared in a consistent manner.
# A Parallel Approach for Multi-GNSS Ultra-Rapid Orbit Determination
## 1 Introduction
In the following sections, the principle and main steps of the modified parallel multi-GNSS obit determination approach will be introduced. Then, some processing strategies are presented. Afterward, using MGEX/IGS data and related products, the improvement of computational efficiency of the new approach is analyzed. At last, the accuracy of multi-GNSS obit using the proposed approach is evaluated, and the results indicate that the proposed parallel approach is viable.
# Detection and Classification of Human Activity for Emergency Response in Smart Factory Shop Floor
## 2 Related Works and Background Information
### Summary of Related Works and Motivation  
From the instance above, it is evident that there is a clear difference between safety and security works in smart factory. Examples of security researches are AI-enabled anomalous detection, intrusion detection, and vulnerability detection of the cyber physical systems. However, few research works exist in AI-enabled safety detection of smart-factory floor workers. Existing works are mainly devoted to smart homes for elderly or assisted living [47,48]. In this work, we proposed a CNN model for the emergency detection in the smart factory shop floor, as well as developed a simple testbed for the purpose of providing dataset for future research direction in this area. In addition, the use of CNN for non-image dataset is a novel area that is now attracting research attention, such as it is with this work.
## 3 Results
### Effect of Parameters on Extraction Efficiency
#### 3.4.2 Effect of Extraction Temperature  
In the HS-SPME technique, the temperature of the sample solution can affect the extraction rate and equilibrium. The increase in temperature accelerates the transfer of the analyte between phases and affects the extraction efficiency through partition coefficients, i.e., \(K_{\text{hs/s}}\) and \(K_{\text{ffhs}}\) (Equations (1) and (2)), in such a way that improves the former and worsens the latter.  
\[K_{\text{hs/s}}=\frac{C_{\text{hs}}}{C_{\text{os}}} \tag{1}\]  
\[K_{\text{ffhs}}=\frac{C_{\text{ff}}}{C_{\text{hs}}} \tag{2}\]  
where \(K_{\text{hs/s}}\), \(K_{\text{ffhs}}\) are the partition coefficients of the analyte in sample/headspace, and headspace/fiber, respectively; and \(C_{\text{hs}}\), \(C_{\text{s}}\), and \(C_{\text{f}}\) show the concentrations of analytes in the headspace, sample solution, and nanofibers, respectively. The influence of temperature on the extraction efficiency was studied in the range of 20-60 \({}^{\circ}\)C. Figure 5B shows that the extraction efficiency increased from 20-40 \({}^{\circ}\)C and declined at higher temperatures. The increase in efficiency in the first region is due to the increase in the concentration of analytes in the headspace, but the decrease in efficiency in the second region can be attributed to the decrease of \(K_{\text{ffhs}}\)[13]. Thus, 40 \({}^{\circ}\)C was selected as the optimum temperature for the subsequent experiments.
# Non-Cooperative Spacecraft Pose Measurement with Binocular Camera and TOF Camera Collaboration
###### Abstract  
Non-cooperative spacecraft pose acquisition is a challenge in on-orbit service (OOS), especially for targets with unknown structures. A method for the pose measurement of non-cooperative spacecrafts based on the collaboration of binocular and time-of-flight (TOF) cameras is proposed in this study. The joint calibration is carried out to obtain the transformation matrix from the left camera coordinate system to the TOF camera system. The initial pose acquisition is mainly divided into feature point association and relative motion estimation. The initial value and key point information generated in stereo vision are yielded to refine iterative closest point (ICP) frame-to-frame registration. The final pose of the non-cooperative spacecraft is determined through eliminating the cumulative error based on the keyframes in the point cloud process. The experimental results demonstrate that the proposed method is able to track the target spacecraft during aerospace missions, which may provide a certain reference value for navigation systems.  
space traffic management; binocular camera; TOF camera; joint calibration; non-cooperative spacecraft; pose estimation +
Footnote †: journal: Applied
## 4 Results and Analysis
### Results of Simultaneous Equations Model
This shows that after fully considering the endogeneity of the two-way causal relationship between air pollution and industrial development, PM2.5 concentration does have a significant impact on the proportion of secondary and tertiary industries. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & **(1)** & **(2)** & **(3)** & **(4)** & **(5)** & **(6)** \\ \hline
**2S** & & & & & & \\ _PM2.5_ & 0.092 & 0.075 & 0.155 *** & 0.141 *** & 0.284 *** & 0.264 *** \\  & (1.22) & (0.99) & (4.22) & (4.02) & (10.26) & (9.76) \\ _FA_ (\(ln\)) & \(-\)1.437 *** & \(-\)1.364 *** & 3.250 *** & 3.348 *** & 3.490 *** & 3.501 *** \\  & (\(-\)2.97) & (\(-\)2.79) & (6.34) & (6.52) & (6.65) & (6.78) \\ _Innovation_ (\(ln\)) & & & \(-\)4.937 *** & \(-\)4.948 *** & \(-\)5.886 *** & \(-\)5.862 *** \\  & & & (\(-\)12.00) & (\(-\)11.96) & (\(-\)12.30) & (\(-\)12.44) \\ _Open_ & & & & & 0.762 *** & 0.741 *** \\  & & & & & (4.09) & (4.03) \\ _\_cons_ & 65.214 *** & 65.056 *** & 20.690 *** & 20.063 *** & 13.941 ** & 14.858 *** \\  & (11.20) & (11.04) & (3.57) & (3.44) & (2.33) & (2.52) \\
**PM2.5** & & & & & & \\ _2S_ & \(-\)3.288 \({}^{*}\) & 12.657 *** & 0.484 \({}^{*}\) & 0.339 & 0.466 ** & 0.501 ** \\  & (\(-\)1.90) & (5.96) & (1.89) & (1.30) & (1.99) & (2.21) \\ _Pop_ (\(ln\)) & 11.978 *** & 3.290 & 8.172 *** & 8.013 *** & 3.484 *** & 3.677 *** \\  & (3.23) & (1.08) & (5.56) & (5.43) & (2.84) & (2.97) \\ _FDI_ (\(ln\)) & & 20.786 *** & \(-\)3.406 *** & \(-\)0.615 & 3.642 *** & 3.940 *** \\  & & (4.76) & (\(-\)2.83) & (\(-\)0.38) & (2.70) & (2.86) \\ _Trans_ (\(ln\)) & & & 15.874 *** & 13.752 *** & 24.422 *** & 22.722 *** \\  & & & (7.85) & (6.15) & (9.38) & (7.93) \\ _Enviro_ & & & \(-\)0.644 *** & \(-\)0.717 *** & \(-\)0.677 *** & \(-\)0.636 *** \\  & & & (\(-\)3.65) & (\(-\)3.93) & (\(-\)4.71) & (\(-\)4.36) \\ _Open_ & & & & \(-\)1.841 *** & \(-\)1.680 *** & \(-\)1.821 *** \\  & & & & (\(-\)2.81) & (\(-\)3.04) & (\(-\)3.29) \\ _UIL_ & & & & & \(-\)1.233 *** & \(-\)1.255 *** \\  & & & & & (\(-\)6.22) & (\(-\)6.58) \\ _CEI_ & & & & & & \(-\)1.401 ** \\  & & & & & & (\(-\)2.05) \\ _\_cons_ & 123.130 & \(-\)831.274 *** & \(-\)10.890 & \(-\)23.566 & \(-\)31.028 & \(-\)27.949 \\  & (1.54) & (\(-\)5.66) & (\(-\)0.44) & (\(-\)0.94) & (\(-\)1.51) & (\(-\)1.41) \\ _R-sq_ & \(-\)2.516 & \(-\)18.023 & 0.426 & 0.415 & 0.550 & 0.573 \\ \(N\) & 234 & 234 & 234 & 234 & 234 & 234 \\ \hline \end{tabular} Note: The numbers in brackets are t statistics; ***, **, and \({}^{*}\) represent their significance at 1%, 5%, and 10% levels, respectively.  
\end{table} 
Table 4: Estimated results of the simultaneous equations for secondary industry and air pollution.