esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Verification and Analysis
### Steady-State Operational Validation
#### 4.2.1 GSMMC AC-Side Single-Phase Ground Fault  
To analyze the transient characteristics of a single-phase ground fault, a phase A ground fault with a duration of 0.5 s was set at \(t=4.5\) s. The simulation results are shown in Figure 7. Figure 7a,b show the voltage and current waveforms on the AC side of the GSMMC. After the fault occurred, the AC voltage of the fault phase was reduced to 3% of the normal voltage, and the AC short-circuit current surged to about 7 times the rated current. In a transient analysis, the MMC can be equated to a controllable voltage source. After a fault occurred, the system voltage dropped instantaneously. However, due to the dynamic response delay in the adjustment of the MMC output voltage, it was not  
Figure 6: Steady-state results. (**a**) The output power of the GSMMC and wind farms; (**b**) DC line voltage.  
possible to quickly follow the system voltage drop, resulting in a sudden increase in the AC-side current.  
Figure 7c shows the transmission power waveform of the GSMMC. It can be observed that before the fault occurred, the system's grid-connected power remained stable at around 280 MW, with no significant fluctuations in power transmission. After the fault occurred, due to the sudden voltage drop, the power instantly dropped to nearly 0 MW. Although the converter attempted to regulate the power output during the fault period, the power continued to oscillate violently within the range of 50-300 MW since the short circuit had not been cleared. After the fault was cleared, the power recovery time was approximately 0.1 s. During the fault, the power control of the WFMMC did not fail, resulting in the continuous injection of energy into the GSMMC by the power of the wind farms. The DC voltage of the MMC-HVDC system increased by more than 5%.
## 1 Introduction
### Applications for Opportunistic Networks  
These kinds of networks (sometimes called delay- or disruption-tolerant networks--DTNs) have multiple applications. For example, sensor networks are composed of widely distributed low-cost devices that collect environmental data. This application is an important component of smart cities and Internet of Things (IoT) research [1]. The networking component is tasked with collecting all the produced data to be processed. An ON can transmit data without depending on external infrastructure, sometimes under very adverse conditions [2].  
Opportunism can be used in conventional networks to reduce the load on the infrastructure and better use network devices' computational, storage and communication resources. Device2 Device (D2D) functionality allows LTE network terminals, or smartphones, to download directly from neighboring terminals instead of doing it through the cellular network [3]. That can significantly reduce latency, power consumption, and infrastructure costs [4].  
There are many applications wherein vehicles want to exchange information within a vehicle fleet: Cars could warn other vehicles of a dangerous event using a vehicular ad hoc network (VANET); A team of fire-fighting robots might coordinate tasks and providecommunication infrastructure while searching for survivors in a building on fire; a swarm of UAVs could communicate to keep formation; etc.
# High-Resolution Multi-Scale Feature Fusion Network for Running Posture Estimation
## 3 Method
### RFSP Module  
The design of RFSP is inspired by Receptive Field Block (RFB) [18], Spatial Pyramid Pooling (SPP) [19], and residual connections. The processing flow and internal structure are illustrated in Figure 3. RFSP leverages the RFB module to simulate the receptive fields of human vision and enhance the capability of the network for feature extraction. Subsequently, it employs the spatial pyramid pooling method to fuse multi-scale features, thus improving the model's predictive ability for occluded joints. During this process, integrating shallow features can enhance the network's accuracy in localizing edge joints while mitigating the issue of gradient vanishing.  
Figure 2: The integrated framework for RHPNet involves the utilization of an improved HRNet backbone and an RFSP module to process input color images.  
The Population Receptive Field (pRF) represents the receptive range of specific neurons in the visual cortex for visual stimuli. Some evidence from neuroscience studies suggests that the size of the pRF increases with increasing retinal eccentricity [20]. Inspired by the structure of the human visual system, RFBNet [18] introduced the Receptive Field Block (RFB) module. It utilizes multiple branches of pooling with different kernels corresponding to varying receptive field sizes and employs dilated convolutional layers to control the eccentricity of the receptive fields. Finally, these features are reassembled to generate the ultimate feature representation. The RFB-S module is a variation of the RFB module that simulates the smaller pRF found in the shallow layers of the human visual system. It incorporates additional branches and smaller convolutional kernels. The deployment of RFB-S followed by RFB in the RFSP module has been identified as the optimal combination through ablation experiments, which will be further described in the subsequent experimental Section 4.6.2.  
Spatial Pyramid Pooling (SPP) converts feature maps of varying scales into a unified scale, allowing for the fusion of multi-scale features. Building upon SPP, the authors of YOLOv5 introduced the Spatial Pyramid Pooling-Fast (SPPF) module, which features faster computation speed while maintaining the same output. After refinement, the feature maps are processed by the RFB-S and RFB modules. Subsequently, we merge them with shallow-level feature maps. This fusion serves the purpose of preserving contextual information and mitigating the issue of gradient vanishing. The fused feature maps then are processed by the spatial pyramid pooling module to produce the final joint heatmaps. In order to reduce the parameter count and enhance computational speed, we adopt the Simplified SPPF (SimSPPF) [21] module in this work.
# Acoustic Backscatter Communication and Power Transfer for Batterless Wireless Sensors
## 4 Evaluation  
### Wireless Energy Transfer  
This section first analyzes the potential for acoustic energy transfer through GWC over several meters. We establish the maximum power point for the piezoelectric transducers and then evaluate the extractable power and efficiency over different distances and frequencies. Afterward, the efficiency of the presented harvester device is evaluated.
## 4 Risk Analysis for Current Protocols
### NMEA 2000
#### 4.3.2 Spoofing/Packet Sniffing  
Spoofing and sniffing has been shown to be trivial against the standard [20]. For example, GPS col can be spoofed with GPS simulation software, such as \"LabSat 3 GPS Simulator\" or \"NMEAsoft GPS Simulator\" [34, 35].
# Evaluating the Effectiveness of Designs for Low-Cost Digital Manufacturing Systems
## 5 Evaluation of Designs
### Other Cases in Manufacturing System Designs
#### 5.2.1 A Low-Cost Design of a Demand-Response Application  
PROSA was originally developed for demand-response applications, such as a customer order management solution [50]. Specifically, PROSA decouples logistical aspects from technical ones and allows for more advanced hybrid control algorithms. These capabilities are not required for the low-cost digital systems considered in this study, and thus, they have not been captured by the selected design scenarios. Hence, we investigate how Shoestring could be adopted to design such a demand-response application.  
As an example we consider a packaging cell for gift boxes, which consists of a robot arm for picking up boxes from a buffer and packaging them, and a gantry robot to move packed boxes to a shelf [54]. A Shoestring design could be as follows: both robots, including the control functions, are controlled via actuation service modules. The control commands are triggered by sensors wrapped as data collection service modules. In contrast, PROSA would encapsulate the robots into resource holons and represent the current state of a given box by a product holon. Order holons would characterise a specific customer order and exchange relevant process information between the resource and product holons. Compared to PROSA, Shoestring handles customer orders as events and is unaware of the product state at any given time, which impedes the scheduling and optimisation of the process. Additionally, more architectural elements are required for Shoestring, yielding a more complex system. We expect that PROSA entities are better equipped for such scenarios [55]. Specifically, Shoestring would need an additional scheduling service module,while holons can negotiate their schedule by themselves. Holons are more adaptable since they find a schedule locally, while the scheduling service module of the Shoestring design would need to solve this problem globally.
# A Classification-Based Blockchain Architecture for Smart Home with Hierarchical PoW Mechanism
## 5 Performance Evaluations
### Latency and Security Evaluation  
In this section, we evaluate the performance of the hierarchical PoW mechanism while comparing it with the traditional PoW. Generally, the running time of PoW is a linear positive correlation with the difficulty. Both the original and hypergraph-based blockchain architecture use traditional PoW so that they can only set one kind of difficulty for all data categories. In order to ensure safety, they have to set all categories of data to the highest difficulty. However, our architecture with hierarchical PoW mechanism can set the appropriate difficulty to each data category. As shown in Table 2, we compare the running time of generating a certain amount of blocks among three architectures. The results reflect the running time of ours is almost 50% less than other architectures, due to our proposed hierarchical PoW mechanism. That means our scheme has less latency than the others. Moreover, in our experimental test, the difficulty of each security grade is not high, and the average block time of all security grades is only about 3 s.  
Figure 5: The used memory comparison among three architectures in real smart home scenario.  
To further evaluate our safety performance, we conducted several attacks on the system, tampering with data with malicious nodes to affect integrity. As shown in Figure 6, in our experiment, both the safety factor and average storage of each node are proportional to the \(maxn\) of a VS (i.e., the maximum number of participated devices in a sub-network). Obviously, the security level is determined by the maximum size of the VS. The greater the \(maxn\) of a VS is, the higher the security, and vice versa. In other words, the specific number of participating devices determines the safety factor of the system. Since smart homes usually contain dozens of devices, the systems have a high safety factor, and the cost of hacking is quite high. Consequently, the safety factor can be improved by increasing the number of participating devices depending on the specific scenarios. In summary, our scheme has a good integrity performance.
## 4 Results
### Performance
#### 4.2.1 Memory Bandwidth
The data from Figure 5 allow us to draw a general conclusion that, for dense data layouts, the Taichi language brings in a small, usually negligible overhead. However, sparse layouts (bitmask and pointer) reduce available bandwidth by at least about 40% for fine-grained resolution. It should be noticed that, for all data layouts available in Taichi, we were trying to find parameters giving the highest bandwidth, although we did not conduct the exhausting measurements for all available combinations. We could have then missed some significantly better settings, although this seems unlikely.
# Proteomics in Forensic Analysis: Applications for Human Samples
## 2 Proteomics Methods: Sample Preparation Techniques, Data Acquisition, and Data Analysis
### Data Acquisition  
After preparation, the samples are injected into an LC-MS/MS system for analysis. Peptides are separated using a column, typically a reversed-phase column (e.g., C18). The mobile phase usually includes a gradient of solvents (e.g., 0.1% formic acid in water and 0.1% formic acid in 80-90% acetonitrile) with an increasing percentage of the organic solvent. In reverse-phase LC, peptides are separated based on their hydrophilicity. Hydrophilic peptides are eluted earlier, followed by those with increased hydrophobicity. The analysis time can be several minutes (for targeted proteomics) or 1-3 h (for untargeted proteomics) [104].  
Peptides eluted from the column are analyzed using a mass spectrometer. Depending on the type of data acquisition, there are untargeted or targeted proteomics. Untargeted or shotgun proteomics aims to collect as much data as possible. Commonly, peptide masses are measured (precursor scan), and the top 10 or more peptides with the highest intensities are sequentially isolated and fragmented. MS/MS spectra are recorded. Untargeted proteomics is usually used to profile the proteome of samples, which can present a comprehensive view of their protein components [105]. In contrast, targeted proteomics only detects and quantifies a small number of peptides specific for selected proteins. Generally, it is carried out on a triple-quadrupole mass spectrometer using selected reaction monitoring (SRM) or multiple reaction monitoring (MRM). As shown in Figure 1, _m/z_ values of the targeted peptides were set in the system; thus, only peptides with these predetermined _m/z_ values were isolated (in quadrupole Q1) and fragmented (in quadrupole Q2). In quadrupole Q3, only fragment ions with a predetermined _m/z_ value were filtered and allowed to pass through the detector. Double filtering makes SRM and MRM methods highly specific and sensitive. Targeted proteomics is appropriate for precise protein quantitation [106].
# Recognizing and Recovering Ball Motion Based on Low-Frame-Rate Monocular Camera
## 3 Method
### Reconstructing Trajectories
#### 3.4.2 Determining Ball Speed  
The instantaneous speed is usually the average velocity of a moving object over a short time interval, and we observed that each pixel on the streak axis corresponds to a ball's center in space, and the ball image would overlap the pixel for a while. In this time span, the ball would contact the projecting line that goes through the pixel. Therefore, we propose a ball velocity calculation model which estimates the speed of the sphere with the time span and the movement distance.  
We first focus on the time span. The pixel intensity \(T_{xy}\) at pixel \((x,y)\) in the image is a combination of the background pixel intensity \(B_{xy}\) and the sphere pixel intensity \(C\)[12]. Given exposure duration \(t_{1}\) and pixel intensity \(T_{xy}\) and \(B_{xy}\), time span \(t_{2}\) of pixel \((x,y)\) covered by the image of the sphere could be obtained with the following equation:  
Figure 11: Mapping a coupled point pair to a ball in space: (**a**) the overall reconstruction geometry. The coupled point pair formed by \(p_{1}\) and \(p_{2}\) is mapped to the ball centered on \(P_{c}\); (**b**) determination of ball center position. The sight line overlaps with the plane \(T\)  
\[t_{2}=\frac{\left(T_{xy}-B_{xy}\right)t_{1}}{B_{xy}-C} \tag{18}\]  
Exploiting the above equation, we can determine the time span of a pixel on the streak axis overlapped by the ball image.  
At the start and end of the time span \(t_{2}\), the sphere tangents to the projecting line that goes through the pixel \(I_{xy}\). We propose a model to calculate the move distance based on this property. The model is shown in Figure 12. In Figure 12a, projecting line \(L_{2}\) goes through \(I_{xy}\) in the image plane and optical center \(O\) of the camera, intersecting the axis of the ball trajectory at \(P_{t}\). In Figure 12b, \(P_{s}^{\prime}\) and \(P_{e}^{\prime}\) is the image of the ball center, and \(L_{1}^{\prime}\) is the image of \(L_{1}\). In \(t_{2}\), the sphere's image moves from \(P_{s}^{\prime}\) to \(P_{e}^{\prime}\). This means that the sphere in space moves from \(P_{s}\) to \(P_{e}\) along direction \(L_{1}\). \(I_{xy}\) locates at the tangent point of the two circles, which are the images of sphere \(P_{s}\) and sphere \(P_{e}\), so these two spheres also tangent to \(L_{2}\).  
With the ball positions obtained in Section 3.4.1, the sphere's moving direction (\(L_{1}\)) is definite. \(L_{2}\) is also definite according to Equation (16). Thus, the angle \(\beta\) between \(L_{1}\) and \(L_{2}\) can be determined. Then, given the raids R of the sphere, the distance \(S\) from \(P_{s}\) to \(P_{e}\) is \(2R/\sin\beta\). Thus, the speed of the sphere with center \(P_{t}\) is \(S/t_{2}\).
## 3 System and Simulation
The angular measurement accuracy has the following expression:  
\[\sigma=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(\alpha^{\prime}_{AZIi}-\alpha_{ AZI}\right)^{2}} \tag{23}\]  
In the simulation system of the heterodyne coherent angle measurement, the following parameters were observed: the laser wavelength was 1550 nm, the beat signal frequency was 1 MHz, the QD photosensitive surface diameter was 3 mm, the spot diameter was 2 mm, the sampling frequency was 50 MHz, and the number of sampling bits was 12. To accentuate the phase difference between the quadrants, the initial angle deflection of the  
Figure 3: Block diagram of the simulation system for heterodyne coherent angle measurements. azimuth and pitch axes were set to 300 \(\upmu\)rad, and the initial frequency of the phase-locked NCO output signal was also set to 1 MHz. The tracking results of the NCO signals in each quadrant on the phase of the beat frequency signal are shown in Figure 4a. Following the establishment of a locked loop, the current phase information was characterized according to the index of the NCO output signal in each quadrant concurrently. The ensuing results are illustrated in Figure 4b. To facilitate the visualization of the phase differences in the signals within each quadrant, a certain amount of direct flow was superimposed on the signals during the plotting process, which allowed for differentiation of the signals. The initial angular deflections of the azimuth and pitch axes were calculated and measured based on the NCO output signal index of each quadrant. The angle measuring results are shown in Figure 5.  
The experimental results indicate that the accuracy of the heterodyne coherent angle measurements in the azimuth and pitch directions was 2.56 \(\upmu\)rad and 2.45 \(\upmu\)rad, respectively. This suggests that the simulation system functioned properly in each module and was capable of measuring the given angular deflection with high accuracy.
## 3 Results
### Evaluation Measurements  
In order to assess the capability of the RP classifier, the accuracy (_Acc_), sensitivity (_Sen_), precision (_PE_), and Mathews' correlation coefficient (_MCC_) were used as evaluation indexes. They can be described as follows:  
\[Acc=\frac{TP+TN}{TP+FP+TN+FN} \tag{14}\]  
\[Sen=\frac{TP}{TP+FN} \tag{15}\]  
\[PE=\frac{TP}{TP+FP} \tag{16}\]\[MCC=\frac{TP\times TN-FP\times FN}{\sqrt{(TP+FN)\times(TN+FP)\times(TP+FP)\times(TN+ FN)}} \tag{17}\]  
where true positive (_TP_) indicates the count of true samples predicted to interact; false negative (_FN_) is the quantity of interacting samples predicted to not interact; false positive (_FP_) is the count of non-interacting samples predicted to interact; true negative (_TN_) is the number of true samples predicted to not interact.
# Fast-Convergence Reinforcement Learning for Routing in LEO Satellite Networks
## 2 System Model
### LEO Satellite Constellation  
Currently, LEO satellite constellations can be divided into two types based on the number of satellite orbital planes: single-layer and multi-layer constellations. The Iridium system is representative of single-layer constellations, while Starlink is a multi-layer constellation with satellite orbital planes mainly distributed between 300 and 600 km. To facilitate the analysis, this paper uses the Iridium system as the satellite environment.  
As shown in Figure 1, the Iridium constellation consists of multiple satellite orbits that are evenly distributed which intersect at the pole position. The area at the ends of the constellation is known as the polar region, where satellites are prohibited from communicating. The direction of motion of the satellite changes as it passes the pole, and the relative positions between the satellites change, which leads to periodic changes in the topology of the satellite. There are two satellite-to-satellite links within the satellite network. The link with an orbiting satellite is an intra-satellite link, and the link between adjacent orbiting satellites is an inter-satellite link [20]. Thus, each satellite has up to four neighbor nodes with which to communicate, leaving little decision space for the satellite when considering packet forwarding.
## 3 Pseudolite-Based Positioning System for Legacy GNSS Receivers
### System Configuration
#### 3.1.2 Reference Station  
The reference station whose position is accurately known is needed for the clock synchronization of the pseudolites. It includes a reference GPS receiver to collect measurements of the pseudolite signals. The reference station provides the data server with these measurements. Then the data server generates the clock control inputs for each pseudolite.
# A Survey of NFC Sensors Based on Energy Harvesting for IoT Applications
###### Abstract  
In this article, an overview of recent advances in the field of battery-less near-field communication (NFC) sensors is provided, along with a brief comparison of other short-range radio-frequency identification (RFID) technologies. After reviewing power transfer using NFC, recommendations are made for the practical design of NFC-based tags and NFC readers. A list of commercial NFC integrated circuits with energy-harvesting capabilities is also provided. Finally, a survey of the state of the art in NFC-based sensors is presented, which demonstrates that a wide range of sensors (both chemical and physical) can be used with this technology. Particular interest arose in wearable sensors and cold-chain traceability applications. The availability of low-cost devices and the incorporation of NFC readers into most current mobile phones make NFC technology key to the development of green Internet of Things (IoT) applications.  
NFC; energy harvesting; IoT; green electronics +
Footnote †: journal: Sensors
## 3 Results and Discussion
### Results
#### 3.1.2 Evaluation of Toxicity of CQDs and SQDs
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **CQDs** & **SQDs** & **SWCNTs-PEG** \\  & **(0.2–100 mg\(\cdot\)L\({}^{-1}\))** & **(0.2–100 mg\(\cdot\)L\({}^{-1}\))** & **(2.6–13 mg\(\cdot\)L\({}^{-1}\))** \\ \hline \(\lambda\)excitation/\(\lambda\) emission (nm) & 365/450 & 390/650 & - \\ \hline \multirow{2}{*}{Cell localization in fibroblasts} & Intracellular, & Intracellular, & \\  & Absence of cell death & & decondensation Cell & - \\ \hline \% Cell viability 3T3-L1 fibroblasts (-FBS) & 94–100\% & 66–95\% & 85–95\% \\ \hline \% Cell viability 3T3-L1 fibroblasts (+FBS) & 87–99\% & 33–89\% & 95–100\% \\ \hline \% Cell viability HepG2 hepatocytes (+FBS) & 93–98\% & 84–91\% & - \\ \hline Reference & \multicolumn{2}{c}{This work} & [26] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the main toxicological results of our proposal vs. literature. Figure 8: Influence of culture media on fluorescence emission of CQDs and SQDs. Effects of DMEM and Minimum Essential Medium Eagle (MEM) on fluorescence intensity of CQDs (**A**) and SQDs (**B**). Effects of fetal bovine serum (FBS) on fluorescence intensity of CQDs (**C**) and SQDs (**D**). [Nanodots] = 50 mg\(\cdot\)L\({}^{-1}\); slit widths at emission and excitation were set at 3 nm.