esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review on Computer Vision Technology for Physical Exercise Monitoring
## 3 Discussion about Articles
### Blood Pressure
#### 3.3.1 Photoplethysmography and Pulse Arrival Time  
The Pulse Arrival Time (PAT) is the time between the peak of the electrocardiogram (ECG) and the arrival of the pulse detected by the PPG. [PERSON] [51; 52] investigated the correlation between image-based photoplethysmography pulse arrival time (iPPG-PAT) and diastolic BP (DBP) during one-minute seated rest and three minutes of isometric handgrip exercise. The video was recorded from the face using a standard web camera and estimates were compared to a ground-truth device. It was found that the beat-to-beat iPPG-PAT and DBP were negatively correlated.
# Simultaneous Transmission of Photonic Services over One Fiber with an ITU 100 GHz Grid
###### Abstract  
The increasing interest in distributed sensors and the decreasing price of optical components have led to leveraging the use of existing fiber in deployments over optical networks and more application possibilities (from seismic activity measurement to perimeter protection and tunnel fire detection). Because of the possibility of data interference in single fibers, dark fibers are used. On the one hand, optical networks are able to transfer popular services, such as streaming and data transmission, and on the other hand, special advanced services such as an accurate time, a stable frequency, and high-power optical sensor signals can be provided. In our work, we address the simultaneous transmission of an accurate time, 100 G data, and a high-power optical sensor based on Phase-sensitive optical time domain reflectometer (\(\Phi\)-OTDR). The measurement setup consists of the optical fiber G.652 (\(7\,\mathrm{km}\)), G.653 (\(7\,\mathrm{km}\)), and G.655 (\(10\,\mathrm{km}\)) and a combination of G.652D + G.653 (\(14\,\mathrm{km}\)). Moreover, we also provide results for their combination. The services were transferred in single fiber with an ITU 100 GHz channel spacing grid. We performed a set of measurements with an evaluation of the BER value for data transmission affected by a high-power sensor system and accurate time values. The results confirmed our assumptions that 100 GHz spacing is not large enough, especially with the increasing power level of the sensor system. The main aim of the article is to determine whether data are disturbed with normal 100 GHz channel spacing.  
accurate time; data transmission; optical fibers; \(\Phi\)-OTDR; photonic services; pulse duration; simultaneous transmission; wavelength grid +
Footnote †: journal: Sensors
# Experimental Study of Radiated Emission Due to Secondary Common-Mode Current in Buck Converters
## 5 Conclusions  
In this article, the generation mechanism of radiated emission was experimentally investigated for the simple experimental system consisting of the buck converter. The contribution of this article is to clarify that the SCM noise is the radiated emission source in power converters with ground wires, and that the PCM current and the unbalance of the power supply side impedances experimentally cause the SCM noise.  
The radiated emission generation mechanism demonstrated in this article is widely applicable to power converters with ground wires or shielded cables. By applying effective radiated emission suppression techniques, based on the result of this article, the time and cost of suppressing radiated emission are expected to be reduced. For example, clamp-type ferrite cores have been widely used to suppress radiated emissions from power cables. Based on the results obtained in this article, it is difficult to suppress the radiated emission at frequencies where the SCM current is the node when the cable, including the ground wire, is clamped together near the PCM noise source. In other words, when using clamp-type ferrite cores, it is essential to consider the appropriate insertion position for each noise propagation mode. Future studies will investigate effective CMI placement and supply side impedance balancing to suppress radiated emissions.  
This research received no external funding.  
Not applicable.  
The author would like to thank [PERSON] of Hokkaido University, for supporting this article through providing his expertise in the electromagnetic compatibility of power electronics.  
The author declares no conflict of interest.
# Diffusion Model as a Base for Cold Item Recommendation
## 5 Result Analysis
### Overall Performance (RQ1)  
Table 4 shows the overall performance. DMCIR, the recommendation that directly used the collaborative embeddings generated by the DM-based generator, shows decent performance. Only Heater in ML25M and CCFC in CiteULike outperform or match DM-CIR. The observation highlights the diffusion model's superior capability in generating high-quality outputs. DMCIR+, recommended with the Refiner-generated collaborative embeddings from DMCIR's results, achieved the best performance among all other comparison methods over all datasets. This observation suggests that certain collaborative information is challenging to infer solely from the items' side information and that some can  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**Common**} & \multicolumn{4}{c}{**DMCIR**} & \multicolumn{2}{c}{**DMCIR+**} \\ \cline{2-9}  & \(d_{cf}\) & \(d_{coud}\) & \(\beta_{s}\) & \(\beta_{e}\) & \(T\) & \(|E|\) & **Optimizer (Lr)** & **Optimizer (Lr)** \\ \hline CiteULike & 200 & 300 & 0.0001 & 0.05 & 400 & 3 & Adam (0.001) & Momentum SGD (0.005) \\ ML25M & 128 & 400 & 0.0001 & 0.05 & 300 & 5 & Adam (0.001) & Momentum SGD (0.005) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hyperparameters of DMCIR/DMCIR+.  
be effectively captured through a refinement process incorporating user-side collaborative embeddings.  
Among the comparison methods, those that incorporate explicit collaborative embedding training guidance into their objective functions--namely Heater and CCFC--consistently outperform the others in most cases. This observation indicates that providing predictive guidance on collaborative embeddings during training effectively enhances the performance of cold item recommendation. DMCIR+ leverages the output of DMCIR as prediction guidance and achieves the best performance.  
The elapsed time for cold item recommendation is shown in Table 5. It demonstrates how quickly the system can incorporate new items into the top recommendations for relevant users. \"Item Gen\" refers to the time required by the DM-based generator to generate item embeddings. \"Item Refine\" and \"User Refine\" indicate the time taken by the Refiner to refine item and user embeddings, respectively. As shown, generating about 17,000 item embeddings takes approximately 40 seconds, and the Refiner completes both refinement steps in less than one second. The calculation of recommendation item lists (\"Rec\" column) for ML25M (162,540 users and 13,816 candidate items) is also completed in under one second. Since the generation and refinement steps are performed only once per item, the \"Rec\" process dominates the actual recommendation cost. We argue that achieving recommendations for approximately 160,000 users in under one second is fast enough for deployment in commercial recommender systems.
## 4 Impossibility Results
### Distributed Graph Problems  
#### 4.2.1 Spanning Tree Construction (ST)  
The ST problem is specified as follows.  
* **Safety specification of ST** states that a spanning tree (having \(n-1\) edges and an acyclic sub-graph) is selected.
* **Liveness specification of ST** states that some process should eventually be able to detect that the spanning tree construction is completed.  
Theorem 8: _In a system with even one Byzantine process, the distributed ST construction problem is subject to the same limitations (exposure to false positives and false negatives) as the CD problem, resulting in safety and liveness violations._  
Proof: Solving ST requires satisfying  
\[\Phi_{ST}(e_{i}^{\hat{y}})\stackrel{{ def}}{{=}}\bigwedge_{h}(e_{h}^{x_{h}} \to e_{i}^{y_{h}}|_{E}=e_{h}^{x_{h}}\to e_{i}^{y_{h}}|_{F}=1),\]  
where \(e_{h}^{x_{h}}\) is an event where \(p_{h}\) has selected its incident spanning tree edges (of an actual spanning tree), \((\forall h)\,y_{h}\leq\hat{y},e_{i}^{\hat{y}}\) is an event where \(p_{i}\) determines that the distributed spanning tree determination is complete, and \(\to\) is defined on spanning tree algorithm messages.  
As \(e_{h}^{x_{h}}\) is an internal event, from Theorem 1 for the CD problem, detecting \(e_{h}^{x_{h}}\to e_{i}^{y_{h}}\) is susceptible to false positives and false negatives. Thus, it cannot be guaranteed that the predicate \(e_{h}^{x_{h}}\to e_{i}^{y_{h}}|_{E}=e_{h}^{x_{h}}\to e_{i}^{y_{h}}|_{F}\) in the formula \(\Phi_{ST}\) can be satisfied. Hence, ST cannot be solved.  
A false positive of the CD problem is a safety violation--a cycle or a non-tree sub-graph is created instead of a spanning tree--in the ST problem. A false negative of the CD problem is a liveness violation--completion of the distributed spanning tree construction is not detectable to any \(p_{i}\)--in the ST problem.
# Borrow from Source Models: Efficient Infrared Object Detection with Limited Examples
###### Abstract  
Recent deep models trained on large-scale RGB datasets lead to considerable achievements in visual detection tasks. However, the training examples are often limited for an infrared detection task, which may deteriorate the performance of deep detectors. In this paper, we propose a transfer approach, Source Model Guidance (SMG), where we leverage a high-capacity RGB detection model as the guidance to supervise the training process of an infrared detection network. In SMG, the foreground soft label generated from the RGB model is introduced as source knowledge to provide guidance for cross-domain transfer. Additionally, we design a Background Suppression Module in the infrared network to receive the knowledge and enhance the foreground features. SMG is easily plugged into any modern detection framework, and we show two explicit instantiations of it, SMG-C and SMG-Y, based on CenterNet and YOLOv3, respectively. Extensive experiments on different benchmarks show that both SMG-C and SMG-Y achieve remarkable performance even if the training set is scarce. Compared to advanced detectors on public FLIR, SMG-Y with 77.0% mAP outperforms others in accuracy, and SMG-C achieves real-time detection at a speed of 107 FPS. More importantly, SMG-Y trained on a quarter of the thermal dataset obtains 74.5% mAP, surpassing most state-of-the-art detectors with full FLIR as training data.  
infrared object detection; limited training examples; knowledge transfer +
Footnote †: journal: Applied Sciences
## 5 Experimental Validation
### Case 3: Rotor Oil-Whip Fault Detection  
Figure 20a,b shows the waveform and FFT spectrum of the rotor oil-whip signal with a length of 1024, respectively. As depicted in Figure 20b, the oil-whip frequency \(f_{w}\) of 30 Hz and rotating frequency \(f_{w}\) of 75 Hz can be clearly seen in the FFT spectrum, but the amplitudes at a high frequency are weak. Besides, the FFT spectrum is short of time-varying frequency contents.  
Analogously, five methods (i.e., IAVMD, OVMD, LMD, EMD, and WT) are utilized to deal with the oil-whip signal. Figure 20c-g plots, respectively, the TFR obtained by the five methods. The inside parameters (\(\hat{\alpha}\) = 1987 and \(K\) = 4) of IAVMD are determined by using GWO. Two key parameters of OVMD are determined as \(\hat{\alpha}\) = 1175 and \(K\) = 4 by using PSO. As shown in Figure 20c, the oil-whip frequency (i.e., \(f_{w}\) = 30 Hz) and rotating frequency (i.e., \(f_{3}\) = 75 Hz) are very obvious. Moreover, the associated frequency components 120 Hz (i.e., \(f_{3}\) + 1.5 \(f_{w}\)) and 150 Hz (i.e., 2 \(f_{3}\)) are also extracted, and the aggregation along the time-frequency trajectory is very explicit. This indicates that the oil-whip fault can be detected accurately by using the proposed method. As shown in Figure 20d, only three frequencies (i.e., \(f_{w}\) = 30 Hz, \(f_{3}\) = 75 Hz, and the relevant frequency 120 Hz) can be found, but the harmonic frequency of 150 Hz is almost invisible due to the discrete distribution of high frequency points. Besides, there is an end effect problem in Figure 20d. Seen from Figure 20e,f, the TFR derived from LMD and EMD can also extract the oil-whip frequency \(f_{w}\) = 30 Hz, but the mode mixing phenomenon is very serious, and the time-frequency trajectory at higher frequency is discrete and blurry. The oil-whip frequency \(f_{w}\) = 30 Hz can be seen in Figure 20g, but the resolution of the WT-based TFR is worse than that in our approach. Hence, according to the above comparison results, compared with the other methods, our approach is more effective for detecting rotor oil-whip. In short, we can know from Figures 18-20 that the proposed method is once again validated as being valuable in analyzing multi-component signals.
## 7 Conclusions  
1. The traditional gate outlet flow formula cannot be applied to the flow calculation in the measurement box of the integrated measurement and control gate. By optimizing the weights and thresholds of BP neural networks using the genetic algorithm (GA) and particle swarm optimization (PSO), GA-BP and PSO-BP neural network models suitable for the integrated measurement and control gate were established. The established model has good self-adaptability for three flow patterns: free flow with gate control, submerged flow with gate control, and weir flow.
2. Both GA and PSO algorithms can be used to optimize the initial weights and thresholds of BP neural networks, resulting in better initial network parameters. Compared to the BP model, the GA-BP and PSO-BP models have enhanced robustness and generalization capabilities and reduced the risk of the model falling into the local minimum. The prediction accuracy of the two models was also improved. In the case of a combination of the three flow patterns, the MAPE of the GA-BP model increased by 30.53% and that of the PSO-BP model increased by 34.52%. Compared to the GA-BP model, the PSO-BP model better improves the error distribution of the model and has shorter run times and higher prediction accuracy under single and combined flow patterns.
3. Under the premise of known upstream and downstream water levels and gate openings, the PSO-BP model can be used to preliminarily predict the flow of the integrated measurement and control gate and determine the change range of gate flow. By comparing the predicted flow value with the calculated value of the flow velocity in the measurement box of the integrated measurement and control gate, the outlier causedby the shielding of the probe in the measurement box or the complex flow pattern can be effectively eliminated. This prediction method can be used as an efficient and fast method for the flow calibration of the integrated measurement and control gate.  
Conceptualization, [PERSON] and J.N.; methodology, [PERSON] and J.Z.; data curation, J.N. and L.L.; writing--original draft preparation, J.N.; writing--review and editing, J.Z.; visualization, L.L.; supervision, J.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Ningxia Natural Science Foundation (grant no. 2022 AAC03726).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
Author [PERSON] was employed by Hydrology and Water Resources Monitoring and Early Warning Center of Ningxia Hui Autonomous Region. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Best Relay Selection Strategy in Cooperative Spectrum Sharing Framework with Mobile-Based End User
## 5 Conclusions  
In this end, the performance of cooperative spectrum sharing networks using AF relays is investigated for mobile end users using the best relay selection strategy. A system that combines cooperative communication technique with cognitive radio scheme is proposed, in which all channels between the stationary nodes (SU source, SU relays, and PU) are modelled using Rayleigh fading model and all channels connected with the mobile end user (SU destination) are modelled using Nakagami-\(m\) fading.  
The signal-to-noise ratio and outage probability are investigated for the proposed system model under several scenarios to study the impact of various crucial agents that influence the system performance. In particular, the OPs are evaluated under different numbers of SU relays, different numbers of the fading severity parameter of Nakagami-\(m\) channels, different numbers of PU locations, and different speeds of the mobile end user. Furthermore, the simulation results are provided using Monte Carlo simulation to verify the analytical results. Overall, the obtained results confirmed that the use of a cooperative communication technique with cognitive radio networks can improve the system performance. Moreover, several factors can affect the performance of cooperative spectrum sharing networks with the mobile end user, such as the channel fading, the number of SU relays, the fading severity parameter, the PU location, and the speed of the mobile end user. Such factors are considered crucial for service providers in order to design their networks. A proper mobile network must take into account these important factors when it is designed and put into operation.
## 4 Discussion
### Effects of the GGP on the ESV  
Land-use change has a quantifiable impact on the ESV. The implementation of the GGP effectively changed the ESV of farmland, forestland, and grassland in the region [5; 40]. Table 8 shows that, as the project was implemented, the area of farmland was gradually reduced, the ESV was considerably lowered, and it shifted from 0.18% before project implementation to 7.36% after project implementation. The ESV rose dramatically as the forestland area increased, and the dynamic degree changed from 0.42% before project implementation to 4.25% after project implementation. The ESV of grassland changed little, both before and after the implementation of the project. The main reason for this was more mutual transformation between grassland and other land uses, and the overall change was small.  
Combining Tables 5 and 8, the implementation of the GGP had an observed impact on the change in the ESV in Shaanxi Province. Although the total value of Shaanxi Province  
\begin{table}
\begin{tabular}{c c c c} \hline
**Year** & **Farmland** & **Forestland** & **Grassland** \\ \hline
1990–2000 & 0.02 & 0.04 & 0.08 \\
2000–2020 & \(-\)0.37 & 0.21 & 0.04 \\ \hline \end{tabular}
\end{table}
Table 7: Dynamic degree of cultivated land, forestland, and grassland in Shaanxi Province from 1990 to 2020 (in %).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Year** & **Farmland** & **Forestland** & **Grassland** \\ \hline
1990–2000 & 0.18 & 0.42 & 0.81 \\
2000–2020 & \(-\)7.36 & 4.25 & 0.75 \\ \hline \end{tabular}
\end{table}
Table 8: Dynamic degree of the EVs of cultivated land, forestland, and grassland in Shaanxi Province from 1990 to 2020 (in %).  
changed little during the study period, the ESVs of farmland and grassland were effectively improved. With the implementation of the project and the accelerating process of urbanization and industrialization in China, a part of the farmland has been consumed and occupied, and the area of built-up land has increased, resulting in a decrease in the regional ESV. Therefore, to ensure the benefits of project implementation, basic farmland should be protected in the future so as to safeguard the amount of farmland while avoiding the expansion of built-up land.
# Dynamic Network Formation for FSO Satellite Communication
###### Abstract  
Satellite network optimization is essential, particularly since the cost of manufacturing, launching and maintaining each satellite is significant. Moreover, classical communication optimization methods, such as Minimal Spanning Tree, cannot be applied directly in dynamic scenarios where the satellite constellation is constantly changing. Motivated by the rapid growth of the Star-Link constellation that, as of Q4 2021, consists of over 1600 operational LEO satellites with thousands more expected in the coming years, this paper focuses on the problem of constructing an optimal inter-satellite (laser) communication network. More formally, given a large set of LEO satellites, each equipped with a fixed number of laser links, we direct each laser module on each satellite such that the underlying laser network will be optimal with respect to a given objective function and communication demand. In this work, we present a novel heuristic to create an optimal dynamic optical network communication using an Ant Colony algorithm. This method takes into account both the time it takes to establish an optical link (acquisition time) and the bounded number of communication links, as each satellite has a fixed amount of optical communication modules installed. Based on a large number of simulations, we conclude that, although the underlying problem of bounded-degree-spanning-tree is NP-hard (even for static cases), the suggested ant-colony heuristic is able to compute cost-efficient solutions in semi-real-time.  
FSO; satellite laser communication; ant colony methodology; dynamic network optimization; satellite constellation interlink optimization