esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Measuring the intensity and distribution of atmospheric optical turbulence at large-aperture astronomical telescope sites is crucial to optimizing turbulence correction for different layers. A real-time measurement of turbulence distribution in large-aperture telescopes would be valuable for the parameter optimization of adaptive optics (AO) systems, especially for large field-of-view AO systems such as multi-conjugate adaptive optics (MCAO) and ground-layer adaptive optics (GLAO). Based on the GLAO system of NVST at FSO, a real-time measurement system was deployed to assess the site's atmospheric turbulence intensity and distribution. This system is, to our knowledge, the first real-time turbulence parameter measurement system in the world with an AO system. We adopt pseudo-open loop methods to restore the turbulence information from the close-loop data of GLAO and measure the turbulence strength and distribution. Multiple subaperture pairs are used instead of a pair of subapertures for fitting calculation to increase the measurement accuracy. Two conventional measurement algorithms, SLODAR and S-DIMM+, are compared with the data from the open-source simulator SOAPY, to cross-verify the correctness of our calculation based on the data process of pseudo-open loop data and multiple subaperture pairs. The simulation results show that for two layers' turbulence input, approximately 93% of the turbulence is correctly detected with the SLODAR method and the given parameters of wavefront sensors and correctors, while the S-DIMM+ is 87%. Real-time measurements of atmospheric turbulence at the NVST site were carried out on 28 May 2023. The observation results indicated that approximately 80% of the turbulence was located below an altitude of 2000 m; only a few appear in the upper height.  
atmospheric optical turbulence; pseudo-open-loop reconstruction; real-time measurement 2023
## 3 Results  
### Model Calibration against Experimental Results  
The model described by Equation (12) has two free parameters as defined in Equation (14), namely \(\gamma\) and \(\beta\). Both terms describe the fluid action exerted directly on the beam, the first one being proportional to the velocity of the beam, the second to its position. The rationale behind such modelling choice lies in allowing for both a bidirectional (\(\beta-\)term) and a unidirectional (\(\gamma-\)term) net power transfer between beam and fluid, in a single cycle, in the limit of harmonic swaying.  
The values of the two free parameters have been determined through a minimization procedure of a loss function between the experimental and the numerical spectra. The best set of parameters yields the results in Figure 5, where the leading frequency of the oscillation can be reproduced with an error of 0.1%. The comparison of time histories of tip motion, showing a marked match of the amplitude as well, confirms the correctness of the calibration procedure. The best fit values of \(\gamma\) and \(\beta\) are 0.0626 and 3.11, respectively. As will be demonstrated in the next section, this implies that in whole oscillation, a net power transfer takes place directly from the fluid to the beam.
## 3 Results and Discussion
Also, the syngas with a ratio of 1:1 is produced at a rate of 18.6 mol/s-cm\({}^{2}\) at \(-0.6\) V vs. RHE, which can be used for the Fischer-Tropsch synthesis [47]. Furthermore, the stability test was conducted at \(-0.4\) V vs. RHE for the R-Ag/TiO\({}_{2}\)/_p_-Si photocathode. Supplementary Figure S6 shows the excellent stability for the R-Ag/TiO\({}_{2}\)/_p_-Si photocathode indicating no significant decreases in both current density and faradaic efficiency for CO.  
Figure 4: (**a**) Production rates for syngas and CO/H\({}_{2}\) ratio for photoelectrochemical CO\({}_{2}\) reduction on R-Ag catalysts on SiO\({}_{2}\) patterned TiO\({}_{2}\)/_p_-Si photocathodes at each potential. (**b**) Schematic of photoelectrochemical reduction of CO\({}_{2}\) on R-Ag catalysts on SiO\({}_{2}\) patterned _n_-TiO\({}_{2}\)/_p_-Si photocathode. Energy band diagrams of (**c**) R-Ag/_n_-TiO\({}_{2}\)/_p_-Si and (**d**) SiO\({}_{2}\)/_n_-TiO\({}_{2}\)/_p_-Si photocathodes. This PEC performance for syngas production on Ag catalysts is realized by using the model proposed here. Figure 4b shows the schematic of our proposed model of photocathode for CO\({}_{2}\) reduction. This model, patterning SiO\({}_{2}\) layer and filling the remaining region with Ag catalysts using the electrodeposition method, can allow the light absorption through the SiO\({}_{2}\) layer and promote the CO\({}_{2}\) reduction reaction on Ag catalysts. This is attributed to the energy band structure of the photocathode, as shown in Figure 4c,d. The band diagrams were drawn based on the previously reported flat band diagrams [44, 48, 49, 50] (Figure S7). SiO\({}_{2}\) is an insulating material, and thus has a high band gap. Due to the intrinsic energy band property of SiO\({}_{2}\), the high electronic potential barrier is formed at the Ag/SiO\({}_{2}\) interface, which blocked transportation of the electrons to electrolyte for reaction. On the other hand, since Ag particles have metallic properties, the electrons are easily transferred through Ag particles to electrolyte. Therefore, the electrochemical reaction only occurs at the R-Ag catalysts, which improves the efficiency of CO\({}_{2}\) reduction reaction. Furthermore, this model can be adapted to other catalysts, so it can be seen as a promising model for photocathode on PEC CO\({}_{2}\) reduction study.
## 3 Results
### Semantic Segmentation of an Oblique Photography 3D Model
#### 3.1.4 Semantic Segmentation  
Three scenes were selected for the segmentation test, as shown in Figures 15 and 16.  
Figure 14: Variation in classification accuracy and mean F1 scores with changing \(\gamma\) values: (**A**) training classification accuracy; (**B**) mean F1 scores.  
Figure 15: Test results of 3D semantic segmentation for two small local scenes: (**A**) original oblique photographic data; (**B**) mesh point cloud data; (**C**) semantic classification results.  
The precision, recall and interchange ratio (IoU) were used to evaluate the classification results of multiple categories: a binary confusion matrix was adopted, the positive case was assumed to belong to the current class, and the negative case was assumed to belong to the non-current class. This study first used the training set data to train the model and then applied the test set data to the trained model. The results are summarized in Table 4. In the test dataset, the classification accuracy was high, particularly when the building accuracy reached 0.87. For 323,081 points in the test scenario data, the running time of semantic segmentation was 98 s.
# Effects of Introducing Rest Intervals in Functional Fitness Training
###### Abstract  
Background: Functional Fitness Training (FFT) is a new exercise modality prioritizing functional multi-joint movements executed at high intensity as a circuit. Objective: To examine the impacts of introducing rest intervals in a FFT workout compared to \"rounds for time\" (RFT) FFT. Materials and Methods: Participants were 25 resistance-trained adults who completed two FFT workouts 1 week apart. The study design was crossover such that in a given session half the participants completed the standard and the other half the adapted FFT (FFIadapted). The workouts consisted of the same exercises (circuit of four rounds of exercises), but one (FFIadapted) included preset rest intervals (three sets of 1 min after each completed round). Before and after the workouts, countermovement jump ability and blood lactate were measured. Heart rate (HR) and ratings of perceived exertion (RPE) were measured post-exercise. Results: For both the standard and adapted protocols, mean HR was 90% age-predicted maximum. Final RPE was also similar for both workouts (~15-15.5) and indicated a \"hard\" work intensity. Both FFTs took the same time to complete (~13 min). Furthermore, no significant differences were observed in jump ability between FFTs. In contrast, lactate (15.11 \(\pm\) 3.64 vs. 13.48 \(\pm\) 3.64 mmol:L\({}^{-1}\), \(p\) < 0.05), measured 3 min post-exercise, was significantly lower in FFTadapted. Conclusions: In FFTadapted, there was a significant reduction in RPE and blood lactate concentrations after exercise, while there were no significant differences in either HR or jumping ability, compared to a FFT workout in RFT methodology.  
Keywords:countermovement jump; high-intensity training; lactate; muscular fatigue; rate of perceived exertion +
Footnote †: journal: _Sci._
# A Review of PCI Express Protocol-Based Systems in Response to 5G Application Demand
## 4 Technical Strategy Using PCI Express, CXL, USB, NVMe, and Security
### Technical Strategy Using CXL  
In 2020, CXL rev.2.0 was released, based on PCIe 5.0 and the native PCIe 5.0 protocol. CXL modifies PCIe layer protocols and behavior to extend the capability of the high-speed serial bus. The market for PCIe 4.0 devices in 2020 and 2021 took advantage of this demand. For the server market, PCIe 5.0 and CXL were introduced in 2021 and 2022. CXL replaces part of the CPU memory and cache behavior to achieve a root complex increase to cover memory resources. These changes address the root complex bottleneck in the PCIe topology with a high-speed serial bus such as PCIe 5.0. These changes in the PC topology for high-speed serial buses allow the release of the memory resource control method from the CPU root complex to the CXL root complex. System resources are managed through the CXL bus, which contains cache behavior, in order to realize the system as a component base through the CXL bus for usage. A virtual channel with multiple access methods allows CXL to work more efficiently.
# Dynamic Load Redistribution of Power CPS Based on Comprehensive Index of Coupling Node Pairs
## 4 Simulation Analysis
### Establishment of Comprehensive Indexes  
As described in Section 2, the active-business importance index is established according to (1) to (5), and then the coupled node pair utilization index is established according to (6) to (11). The horizontal coordinate indicates the information side node number in the coupled node pair. Index weighting coefficients \(\beta_{cp}\) and \(\gamma_{cp}\) are taken as 0.5, dual network capacity margin coefficients \(\alpha_{p}\) and \(\alpha_{c}\) are taken as 0.5, inter-network failure probability \(\rho\) is 0.5, load adjustable parameters are selected according to information security partition, I and II are taken as 1.1, III and IV are taken as 1.0.  
From Figures 9 and 10, it is seen that there is no linear relationship between the two indexes of vulnerability and importance. From Figure 9, it can be seen that the 6 th pair of  
Figure 8: Cyber-side topology.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Node of Cyber-Side** & **Node of Power-Side** & **Node of Cyber-Side** & **Node of Power-Side** \\ \hline
1 & 19 & 16 & 11 \\
2 & 28 & 17 & 18 \\
3 & 27 & 18 & 17 \\
4 & 10 & 19 & 9 \\
5 & 6 & 20 & 13 \\
6 & 12 & 21 & 14 \\
7 & 22 & 22 & 21 \\
8 & 2 & 23 & 26 \\
9 & 20 & 24 & 29 \\
10 & 4 & 25 & 30 \\
11 & 15 & 26 & 1 \\
12 & 23 & 27 & 5 \\
13 & 8 & 28 & 3 \\
14 & 24 & 29 & 7 \\
15 & 25 & 30 & 16 \\ \hline \hline \end{tabular} The numbers listed in Table 3 represent the name or number of the node in their respective subnetworks, such as node 4.  
\end{table}
Table 3: Cyber-physical node coupling relationship.  
nodes has the highest active-business degree index, i.e., this pair of nodes is relatively the most important, and from Figure 10, it can be seen that the vulnerability of this pair of nodes is also the highest, so when some nodes on the cyber side are attacked for load reallocation, the load of this pair of nodes should be allocated first, and due to its highest combined vulnerability, it should receive the smallest proportion of the load when accepting the load allocated by the failed neighbor nodes. Then, look at the 1 th node pair, which has a low vulnerability and low importance relative to the other nodes, this means even if it is attacked, it should be allocated later, and when its neighbor node fails and needs to allocate load to it, it can take most of the load and thus reduce the burden of other nodes.
# Linewidth Measurement of a Narrow-Linewidth Laser: Principles, Methods, and Systems
## 3 Research Progress
### Delayed Self-Heterodyne Method
#### 3.3.3 Gain Compensation Loop Delayed Self-Heterodyne Method  
In 1990, [PERSON] et al. proposed a gain-compensated cyclic delayed self-heterodyne (R-DSH) method [38]. Multiple transmission fibers and AOM form a loop, increasing the delay time between two laser beams and reducing the fiber length. A PD can obtain infinite orders of photocurrent spectral lines, and their current spectral density can be analyzed through a spectrum analyzer to obtain the linewidth. The technology reduces the cost of fibers and allows for easy measurement of the laser linewidth.  
In 2012, [PERSON] et al. demonstrated an R-DSH method for high-resolution laser line measurements [45], as shown in Figure 7a. R-DSH increases the delay time through the optical cycle of a heterodyne ring interferometer (HRI). Optical filtering is applied at the HRI output to eliminate and reduce distortion, and the maximum delay of using optical filtering is limited to approximately 180 km. High-order frequency shift components introduce distortion in the beat frequency spectrum, limiting the number of cycles.  
In 2021, [PERSON] et al. proposed a polarization-insensitive R-DSH method (PI-RDSH) for measuring sub-kHz linewidth [100], as shown in Figure 7b. Introducing FRM effectively reduces polarization-induced attenuation without active polarization control. The combination of the three FRMs ensures that the output polarization state (SOP) of the interferometer is independent of the fiber birefringence, achieving passive polarization-insensitive linewidth characterization.  
In 2022, [PERSON] et al. demonstrated an improved scheme for a passive R-DSH interferometer for linewidth measurement [101], as shown in Figure 7c. An additional pair of fiber couplers is introduced in traditional R-DSH interferometers to independently balance the power of the reference beam and recursive fiber loop to achieve optimal system performance. The scheme can achieve a greater number of detectable beat frequencies and finer linewidth resolution under the same delay, as shown in Figure 7d.  
The beat frequency power spectrum obtained by the cyclic self-heterodyne method is a series of beat frequency signals composed of double-sided bands of laser noise and recirculation oscillation. The equivalent delay time increases with increasing frequency order, and the linewidth of the beat frequency peak tends toward the laser linewidth. Gain  
Figure 7: (**a**) Schematic of the laser lineshape measurement apparatus based on the R\(-\)DSH method [45], (**b**) schematic of the PI-RDSH laser linewidth measurement setup [100], (**c**) experimental setup of the improved recirculating delayed self-heterodyne interferometer [101], and (**d**) beat signals detected by the proposed RDSHI [101].  
compensation reduces the length of the delay fiber and eliminates the influence of \(1/f\) noise. The compensation of the gain and the length of the delay fiber need to be repeatedly adjusted to avoid loop oscillation.
## 3 Results and Discussion
### Effect of Contact Time  
The effect of contact time on the adsorption of phenols (40 mg L\({}^{-1}\)) on GO and RGO were investigated at pH 7 and 298 K (Figure 4). It was shown that the adsorption capacities of phenols increased quickly at the initial 20 min, and the adsorption/desorption equilibrium was reached at about 60 min. The fast adsorption was related to the structure of graphene with a single atomic layer. It was found the reduction of GO increased the values of \(q_{\rm e}\) on the adsorbents (RGO > GO). The values of \(q_{\rm e}\) for PE, ME, IPE, BPA and PPE on a given adsorbent were remarkably distinguished from each other, which increased from 0.271, 0.356, 0.454, 0.4 and 0.70 mmol g\({}^{-1}\) on GO to 0.483, 0.841, 1.117, 1.56 and 2.054 mmol g\({}^{-1}\) on RGO, respectively. Table 1 presents the existent adsorption capacity of PE and BPA in the presence of different adsorbents [31; 32; 33; 34; 35; 36]. From Table 1, it was observed that RGO for the adsorption of PE and BPA had a relatively large adsorption capacity among the adsorbents.  
Figure 4: Effect of contact time for the adsorption of six phenols on (**a**) GO and (**b**) RGO.  
Figure 3: UV absorption spectra of the adsorbent (1), naphthalene (2) and their mixture (3). The adsorbents were (**a**) GO and (**b**) RGO. Curve 4 is the sum of (1) and (2) by calculation.  
To investigate the mechanism of the adsorption process, two conventional kinetic models (pseudo-first-order and pseudo-second-order) were applied to analyze the experimental data (see Supplementary Materials for more details). The kinetic parameters and correlation coefficients are summarized in Table S1. It was found that the kinetic data were fitted well by the pseudo-second-order model for all the five tested phenolics. Furthermore, the experimental adsorption capacity (\(q_{\rm e,exp}\)) was also in accordance with the calculated adsorption capacity (\(q_{\rm e,cal}\)) obtained from the pseudo-second-order model. These results indicated that the pseudo-second-order kinetic model fit the adsorption of BPA on graphene better than the pseudo-first-order model.
# Spintronic RF-Direct on-off Keying Modulation Using a Frequency Division Multiplex
## 3 Design of the Spin Modulator
### Matching the Circuit on Each STNO  
The channel interval is decided between the two signals of spintronic oscillators to ensure there is no interference, considering that an STNO needs a wide separation distance between frequencies due to its poor signal purity. The signal level at the offset point corresponding with the channel separation between two channels from each carrier frequency should be selected to be below the minimum sensitivity, \(-79\) dBm, considering the linewidth and signal level. We thus selected frequencies of 3.5 GHz and 4.2 GHz with 700 MHz channel separation for two STNOs for the channel separation to be at least three times the linewidth.  
A T-junction structure is applied to the PCB for assigning the frequencies generated on two STNOs to transmit a signal in the air through a single antenna.  
We designed matching networks with a low pass filter (LPF) circuit for a 3.5 GHz STNO, and a high pass filter (HPF) circuit for a 4.2 GHz STNO, to provide operation without interference between the STNOs. Each output port of the matching circuit was then connected with the other ports of the T-junction, taking into consideration an antenna that was situated at the center port of the T-junction. The STNO has a resistance of 80 \(\Omega\) at an AP state, where the STNO provides good signal oscillation [14].  
An HPF circuit is applied to obtain higher isolation at 3.5 GHz as well as low insertion loss at 4.2 GHz, and vice versa in the LPF circuit. Higher isolation and low insertion loss cause all of the signals to flow into the antenna port of the T-junction. Measured results of the T-junction are a 3 dB insertion loss and 7 dBc isolation at the 3.5 GHz, and 3.5 dB insertion loss and 6.5 dBc isolation at the 4.2 GHz.  
Figure 5 shows the matching results for each frequency of 3.5 GHz and 4.2 GHz. The 3.5 GHz matching results yield a matching result of a return loss of \(-10\) dB or less in the band range of \(+/-50\) MHz, while the frequency band of 4.2 GHz or higher has a matching result of a return loss of \(-5\) dB or higher. Conversely, 4.2 GHz matching results show that a matching result of less than a return loss of -10 dB in the \(+/-50\) MHz frequency band range is obtained, while a frequency band of less than 3.5 GHz has a matching result of more than a return loss of \(-5\) dB.  
Figure 4: Block diagram of wireless spintronic communication based on an STNO array.  
The antenna is located immediately after the T-junction and it has a band from 3.1 GHz to 5.2 GHz, and 2 dBi gain.  
The number of channels is decided with broadband oscillation and linewidth characteristics. The operation bandwidth of STNO is already sufficiently broad, and the linewidth will be improved soon. It helps a communication circuit to be designed even without any filter for isolation between channels.
# An Implementation of the HDBSCAN* Clustering Algorithm
## 4 Results and Discussion
### Cluster Assignments and Outlier Scores  
Tribuo Hdbscan and the reference Java implementation produce identical cluster assignments and outlier scores for each of the three datasets. A JUnit assertion in the notebook is used to validate these results. This confirms that the new HDBSCAN* implementation, Tribuo Hdbscan, produces correct results.  
When comparing Tribuo Hdbscan with the Python module hdbscan, there are some differences in the cluster assignments. Adjusted mutual information scores are an effective way to compare the assignments when the labeling technique is different. For example, Tribuo Hdbscan uses the label \"0\" to indicate an outlier where hdbscan uses \"\(-\)1\". Interestingly, for the first dataset of Gaussians with four centroids and 2000 points, both models obtained an adjusted mutual information score close to 0.80 when comparing the computed cluster assignments to the actual cluster assignments. Recall that an adjusted mutual information value of one indicates perfect correlation between results. Computing the adjusted mutual information between the Tribuo Hdbscan and the hdbscan cluster assignments gives a score of 0.98. For the second dataset of Gaussians with three centroids and 5000 points, both models achieve an adjusted mutual information score of 1.0 when comparing the computed cluster assignments to the actual cluster assignments. The third dataset of credit card usage data produces the least similar cluster assignments. There are no \"ground truth\" cluster assignments to use for comparison for this dataset. Computing the adjusted mutual information between the Tribuo Hdbscan and the hdbscan cluster assignments gives a score of 0.82. This dataset effectively demonstrates that subtle differences between the way these two HDBSCAN* implementations have been developed can produce different clustering results. Table 3 shows the adjusted mutual information scores from the cluster assignments for trained models of each clustering implementation for the described datasets. Scores of N/A are shown for the Credit Card dataset since there are no \"ground truth\" cluster labels that can be used to calculate an adjusted mutual information score.  
When comparing the outlier scores between Tribuo Hdbscan with the Python module hdbscan, the results were quite different for every dataset. Because these are decimal values between zero and one, some investigation was made to determine if the differences were very small fraction values. Even with difference tolerances of 0.01, the results are still significantly different.  
\begin{table}
\begin{tabular}{l c c c} \hline
**Dataset** & **Ref Impl** & **Tribuo Hdbscan** & **Python hdbscan** \\ \hline
4 Centroids, 3 Features, 2000 points & 0.80 & 0.80 & 0.79 \\
3 Centroids, 7 Features, 4000 points & 1.0 & 1.0 & 1.0 \\ Credit Card Data, 8949 points & N/A & N/A & N/A \\ \hline \end{tabular}
\end{table}
Table 3: The adjusted mutual information scores of the cluster assignments for trained models.
# A New LoRa-like Transceiver Suited for LEO Satellite Communications +
## 4 Results and Discussion
### Channel Coding and Interleaving in LoRa  
In order to increase the robustness of the LoRa modulation against interfering bursts and off-by-one demodulation errors, bits were encoded before the chirp generation. The encoding stages are as follows: