esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Analysis of Heart Rhythm Coherence during the Heart Lock-In  
In order to assess the changes in heart rhythm coherence of the participants within each group before, during and after the HLI, a mean HRV coherence score was calculated for each participant. A coherent heart rhythm is defined as a relatively harmonic (sine wave-like) signal with a very narrow, high-amplitude peak in the low frequency region (typically around 0.1 Hz) of the power spectrum with no major peaks in the other bands [45]. Coherence was assessed by identifying the maximum peak in the 0.04-0.26 Hz range of the HRV power spectrum from all 1 min segments of the HRV time series, calculating the integral in a window 0.030 Hz wide, centered on the highest peak in that region, and then calculating the total power of the entire spectrum. The normalized coherence ratio is formulated as: (Peak Power/Total Power). A number of studies have shown that emotional states tend to be reflected in the patterns of the HRV rhythm independent of changes in the amount of HRV (quantity characterizing the variability in the measurements of inter-beat intervals), although emotional state related changes in the amount of HRV can also occur [45; 46; 47]. Heartfelt positive emotions are associated with a more coherent rhythm, as opposed to emotions such as impatience, anxiety, and anger [46; 47; 48].  
A coherence score below one is considered as low coherence, scores between one and two as medium, and scores above two as high levels of coherence. With practice using the HLI technique, individual coherence scores are typically in the 3 to 6 range. The results of a repeated measures ANOVA test of within-subjects effects is shown in Figure 4 and Table 9.  
Figure 4: The results of a repeated measures (within-subjects) ANOVA test assessing differences in the coherence score for all five groups during 15-min periods before (T1), during (T2) and after (T3) using the Heart lock-In technique (* corresponds to \(p\) < 0.05, *** corresponds to \(p\) < 0.001). Note that a coherence score below one is considered as low coherence, scores between one and two as medium, and scores above two as high levels of coherence. It can be seen, for example, that California’s and New Zealand’s group members’ HRV coherence was significantly higher during the HLI period than before and after the HLI period, etc.  
The California and New Zealand group members' HRV coherence was significantly higher during the HLI period than before (\(p<0.01\)) and after (\(p<0.01\)) the HLI period. In the Lithuanian group, the HRV coherence was significantly higher during the HLI period than the period after it (\(p<0.05\)). There were no significant differences between the three periods in the UK and Saudi Arabia groups HRV coherence although there was a trend of increased coherence during the HLI period, and likely did not reach significance due to the low number of participants (9 and 11, respectively) that had usable data during all three periods.
## 4 Discussion
### Future Implementation and Integration  
The long-term clinical utility of this AI interpretation system depends on seamless integration with existing laboratory information systems (LISs). Based on our preliminary technical assessment, integration could be achieved through three potential approaches:  
1. **API-based integration**: Laboratory information systems could connect to the AI service via secure API calls, automatically sending structured report data for interpretation and receiving explanations that could be appended to standard reports or made available through patient portals. This approach would require minimal modification to existing LIS architecture and could be implemented with industry-standard HL7 FHIR protocols.
2. **Middleware solution**: For laboratories with legacy systems lacking API capabilities, a middleware layer could intercept report generation, process the data through the AI system, and reincorporate the interpretation before final delivery to patients or clinicians.
3. **Standalone patient-facing portal**: As an alternative requiring no direct LIS integration, laboratories could offer a secure portal where patients upload their reports for interpretation, though this approach would place more burden on users and potentially introduce transcription errors.  
A critical consideration for any implementation approach is the mechanism for maintaining up-to-date reference ranges. We envision a dual-update system: (1) scheduled quarterly updates to incorporate systematic changes in laboratory methodologies or reference ranges, and (2) an alert-based system where participating laboratories can flag substantive changes requiring immediate AI system updates. The chatbot's prompts would include timestamp information to ensure transparency about when reference data were last updated.  
Future research should focus on developing standardized formats for laboratory data exchange specifically designed for AI interpretation, potentially through extension of existing standards such as LOINC (Logical Observation Identifiers Names and Codes) to include AI-relevant contextual information. This aligns with [PERSON] and [PERSON]'s [9] vision of machine learning integration in laboratory medicine, where standardized data structures facilitate reliable algorithm performance.
# Cooperative Downloading for LEO Satellite Networks: A DRL-Based Approach  
Hongrok Choi  
School of Electrical Engineering, Korea University, Seoul 02841, Korea  
Sangheon Pack  
School of Electrical Engineering, Korea University, Seoul 02841, Korea
## 4 Simulations  
MATLAB simulation of the proposed control methods for a UBSHR was divided into (1) Case 1, in which the tracking performance and synchronization performance of SC-AFNN were compared with the control method merely considering synchronization errors of active joints (SE-SOSMC) and the control method combining the composite error with first-order SMC (CE-SMC), and (2) Case 2, in which the flexible control performance of SC-AFNN is contrasted with CE-SOSMC method. Set the initial pose as \(\mathbf{q}_{0}=(0.22\,\mathrm{m},0.26\,\mathrm{rad})\). After debugging, the optimal parameters are \(r=3\), \(p=5\), \(q=5\), \(\rho=0.5\), \(\mathbf{\lambda}=\mathrm{diag}(80,80)\), \(\mathbf{\alpha}=\mathrm{diag}(300,300)\), \(\mathbf{\epsilon}=\mathrm{diag}(0.2,0.2)\), \(\mathbf{W}=\mathrm{diag}(20,40)\), \(\delta_{a}=0.3\), and \(\delta_{b}=0.45\).
# AC Fault Ride-Through Strategy of a Transformerless Soft Normally Open Point
## 7 Conclusions  
Through theoretical analysis and formula derivation, this paper reveals the intrinsic mechanism of the DC voltage fluctuation and the non-fault AC voltage fluctuation caused by the zero-sequence component on the AC fault side of SNOP without the connection transformer. The zero-sequence control strategy and the SM improvement strategy of MMC are proposed to suppress the transferring of the zero-sequence component between AC and DC. The following conclusions were drawn:  
1. When asymmetrical faults such as a single-phase grounding occur, the zero-sequence component will be directly superimposed on the positive and negative DC voltage,  
Figure 12: Fault response characteristics of MMC2 after optimization. (**a**) Three-phase AC voltage (black is A-phase, red is B-phase, blue is C-phase). (**b**) AC zero-sequence voltage. (**c**) AC zero-sequence current.  
Figure 13: Fault response characteristics of two-phase grounding fault. (**a**) Three-phase AC voltage of MMC1 (black is A-phase, red is B-phase, blue is C-phase). (**b**) Three-phase AC voltage of MMC2 (black is A-phase, red is B-phase, blue is C-phase). (**c**) AC zero-sequence voltage of MMC1 and MMC2 (black is MMC1, red is MMC2). (**d**) AC zero-sequence current of MMC1 and MMC2 (black is MMC1, red is MMC2). (**e**) Positive and negative DC voltage (black is positive DC voltage, red is negative DC voltage).  
which causes the power frequency fluctuation in positive and negative DC voltage. At the same time, the zero-sequence component is transferred to the non-fault supply area through SNOP, which causes the AC voltage fluctuation of the non-fault system. The voltage characteristics of the non-fault supply area are related to the system parameters, the voltage amplitude and phase difference of the supply areas.
2. Based on the formula derivation, an improved scheme of PR controller based zero-sequence control loop and hybrid MMC topology with added FBSM is proposed. The zero-sequence control loop and hybrid MMC are designed for the particularity of the problem described in this paper. The simulation results show that the proposed method can effectively suppress the DC voltage fluctuation and the non-fault AC voltage fluctuation, and limit the fault effect area to a certain extent, thus effectively improving the reliability of the distribution network.  
Conceptualization, F.X.; Data curation, X.H.; Formal analysis, Y.L.; Investigation, Z.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors gratefully acknowledge the financial support of the Science and Technology Project of the State Grid Zhejiang Electric Power Co., Ltd., named \"Research on medium and low voltage DC networking technology and application considering optimal carbon flow (5211 DS21N003)\".  
The authors declare no conflict of interest.
## 3 Results and Discussion  
Stainless steel of the 316L type with an FCC crystal structure has different microstructural attributes based on the production conditions. The elongated austenitic phase with a high dislocation density is the main attribute of the as-rolled microstructure, and if the cold rolling was extensive, some strain-induced martensite might have occurred.  
High dislocation densities, cellular grain structures with solute segregation along cell walls, and high residual stresses from thermal contraction are some of the features of non-equilibrium microstructures produced by extremely high thermal gradients and fast cooling rates in laser powder bed fusion (LPBF) and other AM processes. Internal energy is stored as a result of these traits. Depending on the treatment parameters, post-processing heat treatment of the as-built condition caused microstructure alterations.  
Compared to the as-built microstructure, annealing at 800 \({}^{\circ}\)C produces slightly coarse cellular austenitic grains. Since 800 \({}^{\circ}\)C is lower than the delta-ferrite dissolution or carbide precipitation temperatures for 316L, no phase transformation takes place. The material starts the recovery mechanism to lower its internal energy at this temperature. Subgrains are created when dislocations reorganize into lower-energy structures. This removes residual stress and lowers the dislocation density.  
However, because of the recrystallization process, holding at 1050 \({}^{\circ}\)C produces equiaxed austenitic grains with a decreased dislocation density and little remaining AM substructure.  
It is worthwhile to mention that achieving these microstructural characteristics typically requires a holding period of at least one hour at the treatment temperature. To avoid the complete recrystallization process and preserve some of the characteristics of the as-built characteristics, the holding time was limited, in the current investigation, to 20 min for both treatments and followed the holding at 1050 \({}^{\circ}\)C by water quenching. This prevents the strength loss brought on by complete recrystallization.  
Since the impact of the current processing conditions was thoroughly examined in earlier research [9; 26; 27; 28], the current study concentrated on how these conditions affected the materials' functional performance.
## 3 Methods
### Kernel Density Analysis  
Hotspot analysis and kernel density analysis were performed using the spatial statistics tools in ArcGIS software to facilitate the rapid identification of large deformation areas [31].  
Hotspot analysis is the basis for the kernel density analysis of deformation points, which calculates the initial clustering degree of all deformation points through hotspot analysis. For a detection position, \(i\), within a given distance range, the ratio of the sum of the feature attribute values of the adjacent positions to the sum of the feature attribute values of all positions is used to calculate whether \(i\) and the surrounding adjacent position features are high-value clustered or low-value clustered. The equation is as follows:  
\[G_{i}(d)=\frac{\sum\limits_{j-1}^{n}w_{ij}(d)x_{j}}{\sum\limits_{j-1}^{n}x_{ j}}(j\
eq i) \tag{3}\]  
where \(w_{ij}(d)\) represents the spatial weight matrix of the topological relationship between elements \(i\) and \(j\); \(\sum\limits_{j-1}^{n}w_{ij}(d)x_{j}\) represents the sum of the attribute values of all \(x_{j}\) elements within the distance, \(d\), of the point (excluding \(x_{i}\)); and \(\sum\limits_{j-1}^{n}x_{j}\) is the comprehensive attribute value of all \(x_{j}\) elements (excluding \(x_{i}\)). For the selection of the \(d\) value, we determined it using the Euclidean distance method. Subsequently, points without statistical significance were removed, and the remaining points were used for kernel density analysis.  
The functional equation for kernel density analysis is  
\[f(x)=\frac{1}{n\hbar}\sum K\left(\frac{x-x_{i}}{h}\right) \tag{4}\]  
Figure 3: Spatiotemporal baseline diagram.  
where \(f(x)\) is the probability density function; \(h\) is the window size; \(n\) is the number of deformation points; and \(K(x)\) is the kernel function [32; 33].
# Detection of Welding Defects Tracked by YOLOv4 Algorithm
###### Abstract  
The recall rate of the original YOLOv4 model for detecting internal defects in aluminum alloy welds is relatively low. To address this issue, this paper introduces an enhanced model, YOLOv4-cs1. The improvements include optimizing the stacking method of residual blocks, modifying the activation functions for different convolutional layers, and eliminating the downsampling layer in the PANet (Pyramid Attention Network) to preserve edge information. Building on these enhancements, the YOLOv4-cs2 model further incorporates an improved Spatial Pyramid Pooling (SPP) module after the third and fourth residual blocks. The experimental results demonstrate that the recall rates for pore and slag inclusion detection using the YOLOv4-cs1 and YOLOv4-cs2 models increased by 28.9% and 16.6%, and 45% and 25.2%, respectively, compared to the original YOLOv4 model. Additionally, the mAP values for the two models are 85.79% and 87.5%, representing increases of 0.98% and 2.69%, respectively, over the original YOLOv4 model.  
weld defects; deep learning; target detection; YOLOv4 2025 15 2026
## 3 Treatment of Phases of Coherence Functions
### Phase Formulation  
Based on the decomposition result \(\beta_{jk}(\omega)\) of complex coherence matrix, its phase angle \(\theta_{jk}(\omega)\) can be obtained by the phase formula of complex number and can be represented as  
\[\theta_{jk}(\omega)=\left\{\begin{array}{ll}\tan^{-1}\left\{\frac{\mathrm{Im }\left[\beta_{jk}(\omega)\right]}{\mathrm{Re}\left[\beta_{jk}(\omega)\right]} \right\},&\text{when }\mathrm{Re}\left[\beta_{jk}(\omega)\right]>0\\ \pm\frac{\pi}{2},&\text{when }\mathrm{Re}\left[\beta_{jk}(\omega)\right]=0\text{ and }\mathrm{Im}\left[\beta_{jk}(\omega)\right]\
eq 0\\ \tan^{-1}\left\{\frac{\mathrm{Im}\left[\beta_{jk}(\omega)\right]}{\mathrm{Re} \left[\beta_{jk}(\omega)\right]}\right\}\pm\pi,&\text{when }\mathrm{Re}\left[\beta_{jk}(\omega)\right]<0\text{ and }\mathrm{Im}\left[\beta_{jk}(\omega)\right]\
eq 0\\ \pi,&\text{when }\mathrm{Re}\left[\beta_{jk}(\omega)\right]<0\text{ and }\mathrm{Im}\left[\beta_{jk}(\omega)\right]=0\end{array}\right. \tag{14}\]  
where \"\(\mathrm{Im}\)\" and \"\(\mathrm{Re}\)\" denote imaginary and real parts, respectively. Based on the definition of complex phase, its value should fall into the range of \(\left[-\pi,\pi\right]\). However, in almost all previous studies [6], the phase is generally expressed as  
\[\theta_{jk}(\omega)=\tan^{-1}\left\{\frac{\mathrm{Im}\left[\beta_{jk}(\omega) \right]}{\mathrm{Re}\left[\beta_{jk}(\omega)\right]}\right\} \tag{15}\]  
In this study, this is termed as \"classical phase formula\". Obviously, Equation (15) can only represent the phase in the range of \(\left(-\pi/2,\pi/2\right)\). Thus, the complex phase calculated by Equation (15) may introduce errors for the simulation results. In this study, the influence of Equation (15) on the four wind field simulations will be studied in detail, in terms of time history samples and correlation functions.
## 2 Materials and Methods
### Testing of Optimization Results
#### 2.3.1 Geodetic Networks Used for Testing  
Four testing geodetic networks were designed, simulating practical types of geodetic networks that can be employed in practice:  
* The first network was designed as a simple regular square-like network, with observations possible from all four points of the network, using the forced centering for both instrument and target.
* The second network is triangular and also allows observations from all points of the network. It simulates a network for staking out a line construction, also using forced centering.
* The third network (\"bridge\") simulates an accurate network for staking out, e.g., a highway bridge. In the vicinity of the construction, there are four points with forced centering, while at greater distances, there are additional reference points from where no observations are possible.
* The last network (\"building\") represents a network for staking out a building. Reference points are stabilized on the perimeter of the construction site and determined from free standpoints.  
The coordinates of all networks are detailed in Table 1. Figure 2 provides visualization of all four networks and Table 1 shows the basic parameters of the networks.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Network**} & **Points to Be** & **Points Eligible** & **Max Distance** & **Number of** \\  & **Determined** & **to Serve as** & **between Points** & **Possible** \\  & **Standpoints** & **[m]** & **Measurements** \\ \hline Square-like & 4 & 4 & 158.7 & 36 \\ Bridge & 8 & 4 & 175.3 & 60 \\ Triangular & 8 & 8 & 595.5 & 78 \\ Building & 13 & 3 & 220.2 & 78 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic characteristics of individual geodetic networks used for testing.
## 4 Evaluating the Performance of WSN Routing Strategies with Respect to DSA
### Simulation Setup  
The simulation was performed using RMASE in Prowler [28]. RMASE [29] is an application implemented in the probabilistic wireless network simulator Prowler, which provides realistic and simple radio/MAC models. RMASE supports a layered routing architecture for plug-and-play common routing components. This application consists of a network topology model, an application model and a performance model. RMASE was developed for dealing with the challenge of comparing different routing algorithms for sensor networks. Both the physical layer and the medium access layers were modified to include licensed user activity, which is modeled to be independent. This simulation uses a simple signal interference noise ratio (SINR) radio propagation model based on [28] and is represented by:  
\[P_{rec\;,ideal}\;\left(d\right)\leftarrow\;P_{transmit}\;\left/(1+d\;^{\gamma}\right) \tag{4}\]  
\[\mathrm{P}_{rec\;}(i,j)\leftarrow\mathrm{P}_{rec\;,ideal}(d_{i,j})\cdot(1+ \alpha\;(i,j)) \tag{5}\]  
\[\mathrm{P}_{rec\;,pu}(d)\leftarrow\mathrm{P}_{rec\;,ideal}(d)\cdot(1+\alpha\; (d)) \tag{6}\]  
In Equation (4), \(P_{transmit}\) represents the sender's signal strength, and \(P_{rec\;,ideal}(d)\) is the ideal received signal strength at a distance d between two nodes \(i\) and \(j\). The fading model parameter \(\alpha\) is a random variable with a normal distribution _N(0,\(\sigma_{\omega}\))_. According to this model, a signal can only be received if the SINR limit as perceived from both neighbors and the primary users is above a threshold \(\Delta_{n}\)_and_\(\Delta_{p}\), respectively, along with a value of receiver noise variance (\(RNV\)). By default, the only transceiver is tuned to the data channel. The presence of interference from the neighboring nodes or the primary user is determined by comparing the received power in Equation (5) with \(\Delta_{n}\) and in Equation (6) with the sensing thresholds \(\Delta_{p}\). Thus, the inter-arrival of the licensed user is modeled based on an exponential distribution. As a two-state, ON-OFF process with ON rate \(\beta\) and OFF rate \(\alpha\), the state transition follows a Poisson arrival process. Based on this assumption and using renewal theory, posteriori probabilities of both states are calculated as follows:  
\[P_{ON}=\frac{\beta}{\beta+\alpha};P_{OFF}=\frac{\alpha}{\alpha+\beta} \tag{7}\]  
Each sensor node is attached to a single transceiver, and the medium access control layer used has carrier-sense multiple access with collision avoidance (CSMA/CA). Table 1 shows the relevant RMASE simulation parameters. After setup, each scenario was run five times, and an average was taken.
# Radiation Qualification by Means of the System-Level Testing: Opportunities and Limitations
## 4 Limiting Factors of Qualification
### Number of SUT Samples to Test
#### 4.2.2 Considerations on Number of SUT Samples in SEE Tests  
Considering system-level SEE testing, a recommendation from ESCC 22500 to test 3 samples (i.e., system units/instantiations in this case) seem to be appropriate for systems where parts are taken from the same production lots. The effect of each SEE on the system should be considered as independent from other SEEs, therefore such a system-level test might be considered as a simultaneous test of many integrated circuits inside of the system, incorporating part-to-part variability. Therefore, there is no need to perform tests on a higher number of SUT units, even if there is a higher number of integrated circuits in the system.