esa-sceva/satcom-chunk-collection huggingface dataset:
# Broadcasting in Stars of Cliques and Path-Connected Cliques +
## 2 Broadcasting in Windmill Graphs  
We define \(Wd_{k,l}\) to be a windmill graph containing \(n\) vertices distributed across \(k\) cliques \(C_{1},C_{2},\ldots,C_{k}\), such that each clique has \(l\) vertices. Furthermore, \(Wd_{k_{l}}\) includes a universal vertex \(u\), which is directly linked to all other vertices in the graph.  
It can be observed that the broadcast time remains the same, regardless of the choice of the originator vertex, whether it is the universal vertex or a clique vertex. In both cases, after the first time unit, the only informed vertices will be the universal vertex and one clique vertex. Moreover, the broadcast time is unaffected by which specific clique vertex is informed first, as all cliques contain the same number of vertices. Therefore, for simplicity, we assume the universal vertex as the originator throughout this section.  
Lemma 1: _Let \(Wd_{k,l}\) be a windmill graph on \(n\) vertices containing \(k\) cliques \(C_{1},C_{2},\ldots,C_{k}\) each of size \(l\), and a universal vertex \(u\). There exists an optimal broadcast scheme, wherein after informing one vertex in each of the \(k-1\) cliques, the universal vertex will broadcast only to the last clique starting at time unit \(k\)._  
Algorithm 1 illustrates the procedure of broadcasting in a windmill graph from universal vertex \(u\). Based on Lemma 1, broadcasting can be summarized as the universal vertex informing one vertex in each clique \(C_{1}\ldots C_{k-1}\). This phase lasts for \(k-1\) rounds, following which the universal vertex broadcasts exclusively to clique \(C_{k}\) and the previously informed vertices contribute to the broadcasting process within their respective cliques. The latter phase requires another \(\lceil\log\left(l+1\right)\rceil\) time units, leading to the following result for the overall broadcast time of the windmill graph:  
\[b(G)=k-1+\lceil\log\left(l+1\right)\rceil\]  
```
0: Windmill graph \(Wd_{k,l}\), originator vertex \(u\).
0: A broadcast scheme for \(Wd_{k,l}\).
1:for\(i=1\to k\)do
2:\(u\) informs a vertex \(v_{i}\) in clique \(C_{i}\)
3:\(v_{i}\) broadcasts in \(C_{i}\)
4:endfor
5:while\(C_{k}\) has an uninformed vertex do
6:\(u\) broadcasts in \(C_{k}\)
7:endwhile
```  
**Algorithm 1** Broadcast algorithm for windmill graph from universal vertex \(u\)
## 3 Results and Discussion
### Thermogravimetric Analysis  
Figure 2 indicates the thermogravimetric analysis of all cellulose preparations and CMSC. All samples exhibit a small weight loss in the low-temperature range (<100 \({}^{\circ}\)C), corresponding to the evaporation of absorbed water [43]. The weight loss rate was within 12% to 13% of the total weight. Cellulose B (Figure 2D) shows an earlier weight loss that begins at 200 \({}^{\circ}\)C and reached a dominant peak at 370 \({}^{\circ}\)C. This is due to the presence of hemicellulose that decomposes easily, and has weight loss between 200 and 300 \({}^{\circ}\)C [44] which were not seen for cellulose A, C, D, and E. The maximum weight loss rate (0.8 wt %/\({}^{\circ}\)C) occurred at 350-400 \({}^{\circ}\)C, which took place for all samples where cellulose was seen to decompose, providing mainly volatile products. Due to the complex composition and structure of lignin, the decomposition process is slower and covers a wide range of temperature than cellulose and hemicellulose. The decomposition shows broad and flat peaks with a gently sloping baseline, which heightens the difficulty in defining the activation energy for the reaction. This is where cellulose stands out from hemicellulose as it shows a sharper thermal analysis peak, inducing a definite tailing section at a higher temperature. When the temperature was greater than 400 \({}^{\circ}\)C, almost all cellulose was pyrolyzed with a very low solid residue (0.1 wt %/\({}^{\circ}\)C) left. Lignin decomposition happened slowly over the whole temperature range [45] and the solid residue left from lignin are about ~46 wt %. The conversion of cellulose to CMSC through chemical reaction affects both the molecular structure and bonding energy of the material, resulting in the different thermal behavior of CMSC [46]. Figure 2H shows the derivatograms of CMSC where the highest weight loss occurs at 310 \({}^{\circ}\)C, and this is due to the loss of CO\({}_{2}\) from carboxymethyl groups of CMSC [47]. The degradation temperature of CMSC decreases as compared to cellulose due to the presence of sodium. This is due to the interference with crystallinity as the presence of random irregularities produced by the relatively bulky side groups of carboxymethyl substituted on the cellulose backbone [48]. Replacing the hydroxyl  
Figure 1: FTIR (Infrared Spectroscopy) spectra of commercial cellulose (**A**); sago biomass (**B**); commercial lignin (**C**); cellulose A (preparation 1) (**D**); cellulose B (preparation 2) (**E**); cellulose C (preparation 3) (**F**); cellulose D (preparation 4) (**G**); cellulose E (preparation 5) (**H**) and carboxymethyl sago cellulose (CMSC) (**I**).  
group of cellulose by -O-Na+ leads to the swelling of the molecule, thereby increasing its reactivity towards carboxymethylating agent to form CMSC. Hence, the migration of Na+ into the cellulose lattice planes disrupts the hydrogen bonded crystalline region and, therefore, weakens the energy bonding of cellulose, which results in reduced stability of CMSC to heat.
# A High-Density Crowd Counting Method Based on Convolutional Feature Fusion
## 4 Conclusions  
In this paper, a deep convolutional neural network method based on feature fusion (FF-CNN) was proposed to solve the problem of crowd counting and density distribution estimation in crowded scenes. The proposed FF-CNN mapped the crowd image to its corresponding crowd density map and got the crowd count by integrating the crowd density map. For obtaining a higher-quality crowd density map, the geometry adaptive kernels were adopted to generate high-quality ground truth density maps during training and the deconvolution technique was used to achieve the fusion of high-level and low-level features to get richer features. The absolute count loss with weight 0.00004 was used to get a more accurate crowd count. For enhancing the generalization ability of FF-CNN, the original images were cropped with a random cropping method for each iteration to increase the sample diversity. The experimental results on the ShanghaiTech dataset showed that the fusion of low-level features and high-level features can extract richer features and can get comparable results with state-of-the-art methods. Also, it is adaptive to different scenes and different crowd density levels, and is robust to scale and perspective changes.  
Conceptualization, H.L. and J.S.; Data curation, W.W. and Z.W.; Funding acquisition, H.X.; Investigation, Z.X.; Methodology, H.L. and W.W.; Project administration, J.S.; Software, Z.X. and Q.Z.; Supervision, J.S.; Validation, H.L., H.X. and Z.W.; Visualization, W.W.; Writing--original draft, H.L. and W.W.; Writing--review & editing, J.S., H.X. and Q.Z.  
This work was funded by National Key R&D Program of China (Grant No. 2017 YFB0802400).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# A New String Edit Distance and Applications
## 2 Restricted Forensic Levenshtein Distance
### Details of the RFL Algorithm
#### 2.3.5 Multiple Motifs and Time Complexity  
Some forensic DNA loci have multiple motifs (e.g., [ACT]5 [AGGT]12). A pair of stutter dictionaries is computed for every motif, and at each step in the RFL algorithm the dictionaries of each motif are checked. This allows for any number and combination of preset motifs to be included in the calculation, each with fast dictionary lookups.  
The proposed algorithm allows any prespecified set of motifs. For every additional motif, two inner for-loops are added at each step in the dynamic programming matrix to check the cost of inserting and deleting anywhere from \(1\) to \(2k_{i}-1\) characters from the dictionary associated with that motif of length \(k_{i}\). Because there are two for-loops running through the list of all motifs, and within each of them there is another for-loop running through \(1\) to \(2k_{i}-1\) characters for motif \(i\), the algorithm grows in complexity by those factors. Where the classical Levenshtein distance is \(O(n_{a}n_{b})\) for strings of length \(n_{a}\) and \(n_{b}\)the RFL distance theoretically requires \(O(n_{a}n_{b}mk)\) time, where \(m\) is the number of motifs and \(k\) is the length of the largest motif \(\max(k_{i})\), assuming \(O(1)\) dictionary lookup speed. However, in applications with standard autosomal STRs, motifs are all of length at most 5 (\(2k_{i}-1\leq 9\)), so this algorithm can be considered as \(O(n_{a}n_{b}m)\), growing with the number of motifs considered as well as the lengths of the strings. Additionally, standard autosomal STRs have no more than a handful of motifs to be considered--a maximum of three, in this work--so the asymptotic growth becomes \(O(n_{a}n_{b})\) once more.
## 8 Recent Experiments of SDM Transmission Using FMFs
Figure 34: (**a**) Set-up of a 12 \(\times\) 12 MIMO-based transmission; (**b**) impulse response after 65-, 195-, and 650-km transmission [48]. Figure 35: (**a**) Q-factors vs. power for 15 WDM channels; (**b**) Q-factors vs. WDM channels; (**c**) singular values after 650-km transmission; (**d**) average MDL vs. frequency per WDM channel [48]. Figure 33: (**a**) Setup of a 90 \(\times\) 90 MIMO-based transmission; (**b**) Q-factors for QPSK and 16 QAM; (**c**) intensity-averaged impulse response attained; (**d**) intensity transfer matrix; (**e**) average Q-factor for QPSK and 16 QAM [47]. Last but not least, the set-up of another 6 \(\times\) 6 MIMO-based SDM/WDM transmission system using a 70-km DGD-compensated three-mode fiber is shown in Figure 36, whereas the received signals are off-line processed by firstly re-sampling the signals to two samples per signal, along with CD and frequency-offset compensation, which is then followed by a 6 \(\times\) 6 MIMO FDE with 600 symbol-spaced taps using a data-aided LMS algorithm along with carrier-phase recovery and BER counting for Q-factor calculations [49]. The Q-factors of the center channel at 1550 nm for 64 QAM, 16 QAM, and QPSK signals as a function of distance for full space-division multiplexed, quasi-single-mode, and single-mode fiber transmission, respectively, are presented in Figure 37, with the markers indicating the measured distances [50]. A summary of these recent experimental validations using MIMO DSP is presented in Table 2 while an updated graph of mode numbers versus the transmission distance is presented in Figure 38, which summarizes the state-of-the-art SDM-WDM experiments [51; 52; 53]. Further improvement in the near future will most likely come from the soft-output maximum-likelihood algorithms, including repeated tree search and single tree search, to further improve the efficiency of the MIMO DSP [54].
## 3 Results and Discussion
### Statistical Results  
Pearson's correlation coefficients (r) were calculated and are shown in Figure 5. A positive correlation was observed between thorium and radium with correlation coefficients of r = 0.933, but not with 40K. The role of potassium is completely independent of the other natural radioactive chains in the samples analyzed. 226 Ra and 232 Th showed a positively significant correlation with Mn, Ti, and S, while they showed a negative correlation with organic C at the significance level of r = -0.892 and r = -0.988, respectively. 40K exhibited a positive correlation with Si, Al, Na, P, and Mn (and, of course, with oxide K2O), suggesting that they have a common origin. Furthermore, a negative correlation was observed between the radionuclide 40K and Ca (r = -0.962). Since this element is highly abundant in seashells and eggshells, it confirms that materials containing it will have a low radioactivity concentration of 40K.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Specimens** & **I** & **Raq (Bq/kg)** & \
\begin{tabular}{c} **D** \\ **(nGy/h by Bq/kg)** \\ \end{tabular} &
\begin{tabular}{c} **E** \\ **(mSv/y)** \\ \end{tabular} \\ \hline CP-100 & 0.29 \(\pm\) 0.02 & 79.5 & 36.5 & 0.179 \\ \hline CP-20-E-80 & 0.07 \(\pm\) 0.01 & 19.4 & 8.9 & 0.044 \\ \hline CP-20-SC-80 & 0.07 \(\pm\) 0.01 & 18.9 & 8.8 & 0.043 \\ \hline CP-20-OR-80 & 1.39 \(\pm\) 0.06 & 331.8 & 174.7 & 0.857 \\ \hline CP-92.5-E-7.5 & 0.25 \(\pm\) 0.03 & 68.8 & 31.8 & 0.156 \\ \hline CP-92.5-SC-7.5 & 0.26 \(\pm\) 0.03 & 72.5 & 33.4 & 0.164 \\ \hline CP-92.5-OR-7.5 & 0.34 \(\pm\) 0.04 & 91.6 & 43.9 & 0.215 \\ \hline \end{tabular}
\end{table}
Table 6: Activity concentration index (I), radium equivalent activity (Raq), absorbed (D), and effective dose rate (E). Note: CP: Portland cement; M: scallop shells; E: eggshell; OR: biomass fly ash.  
Concentrations of elements were also tested for any correlation with the physicochemical properties of the materials. Most elements were significantly correlated with each other. Iron was significantly correlated with Si and Al (r = 0.959 and r = 0.984, respectively). From the results, it can be concluded that Ca concentration has a strong negative correlation with Mg, Na, K, and P content. In the case of CS, it had a strong positive correlation with Mn (r = 0.927) and S (r = 0.945). The CS content also showed a strong correlation with \({}^{238}\)U content.
## 4 Experimental Results and Analysis
### Analysis of Ablation Experiments  
To evaluate the effectiveness of CBAM, ResNet-NAM and BiLSTM for feature extraction, ablation experiments are conducted, and networks are designed to remove each module separately. The evaluation metrics of each model are measured on the previous test set, and the comparison results are shown in Table 5. Incorporating the NAM attention mechanism into the ResNet18 BasicBlock structure reduces the RMSE and RTE values of the ResNet-NAM model by 0.2 and 0.15, respectively. Adding CBAM after the maximum pooling layer reduces the RMSE and RTE values by 0.27 and 0.2, respectively. The above results confirm the effectiveness of the interactive assignment of feature weights by CBAM and NAM. In the proposed algorithm, the CBAM outperforms the NAM in terms of improving navigation accuracy. The addition of the BiLSTM layer to ResNet18 reduces the RMSE value by 0.93 and the RTE value by 0.68. This indicates that the BiLSTM is more important for the learning ability of time-related features than the attention mechanism. The addition of  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network Model** & **Trajectory Length/m** & **RMSE/m** & **RTE** & **End Coordinates** \\ \hline CNN & 149.69 & 7.5 & 6.01\% & (2.92,\(-\)58.26) \\ LSTM & 172.68 & 3.295 & 2.86\% & (3.25,\(-\)72.83) \\ ResNet18 & 174.54 & 3.228 & 2.77\% & (3.35,\(-\)74.32) \\ ResNet50 & 170.69 & 2.313 & 1.54\% & (\(-\)1.55,\(-\)74.53) \\ RBCN-Net & 180.54 & 0.761 & 0.56\% & (\(-\)7.34,\(-\)80.27) \\ Ground truth & 176.42 & & & (\(-\)6.12,\(-\)80.17) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Specific evaluation indicators.  
Figure 9: Comparison of five competing networks trajectory and the real trajectory.  
NAM and CBAM attention mechanisms to ResNet-LSTM reduces the RMSE value of 0.24, RTE value of 0.2, RMSE value of 0.39 and RTE value of 0.35, respectively. The RBCN-Net augments the ResNet-LSTM with both attention mechanisms, and the RMSE value is as low as 2.079 and the RTE value is as low as 1.481%. RBCN-Net reduces the RMSE value of 0.6 and the RTE value of 0.44 compared to ResNet-LSTM. RBCN-Net is more accurate for inertial data mapping compared with other models.
# Design and Diagnostics of High-Precision Accelerator Neutrino Beams
## 10 Mitigation Techniques of Beam Systematics at the Neutrino Detectors
### High Precision NDs  
Even if the near/far cancellation worked perfectly for a monochromatic neutrino beam, it would fail for a real beam where the spectrum extends in a broad band. The main reason is that the ND records the entire flux because its dimension is comparable with the dimension of the beam at \(L_{ND}\), while the FD traces only the forward part of the beam since it covers a tiny solid angle. At the location of the FD, the transverse size of the neutrino beam is much larger than the size of the detector. The actual region seen by the FD thus depends on both \(L_{ND}\) and the off-axis angle of the FD. The net result is that the spectrum at ND is different from the spectrum at FD and we cannot rely anymore on Equation (5).  
This challenge is being faced in two ways by modern long-baseline experiments. In the last ten years, the ND complex has significantly increased in complexity. A quite radical view was pursued by T2K building the ND280 and INGRID detectors [149; 150]. INGRIDis a simple scintillator-iron detector that monitors the relative changes of the beam on-axis and provides an overall normalization. The ND280 is located off-axis along the path to the FD, i.e., Super-Kamiokande [151] (SuperK). It is very different from SuperK because the neutrino rate is too high to be properly reconstructed with a water Cherenkov detector but, on the other hand, has a much better granularity and PID capability. More recently, the detector is being upgraded to increase the angular acceptance up to >90\({}^{\circ}\). In this way, ND280 can reconstruct in detail the beam composition and spectrum to reproduce the phase space visible at the FD with a precision that would be inconceivable for Super-K. It identifies the potential background directly measuring the particles that escape detection in water. Notable examples are NC events where the pions are below the threshold for Cherenkov light production, NC + \(\pi^{0}\) events and events where the final state hadrons re-scatter or are re-absorbed inside the nucleus. All this information corrects the neutrino interaction simulation in a data-driven model, which is then employed to estimate the actual efficiency and purity of the SuperK \(\
u_{e}\) charged-current (CC) sample.  
The price to pay is a substantial increase of complexity because the Monte Carlo systematics are cured by ND beam data and we give up the \"two-identical detector\" principle. Ten years ago there was some skepticism on the outcome of this approach and ND280 was supposed to be integrated by an _intermediate detector_ identical to SuperK and a high-precision tracker based on Liquid Argon. These doubts were superseded during the run of ND280, which achieved a flux systematic <8%. The task has been further eased by the unexpectedly large size of \(\theta_{13}\) that makes the signal rate ten times larger than the estimate of the T2K Proposal.
# Quasi-All-Passive Thermal Control System Design and On-Orbit Validation of Luojia 1-01 Satellite
## 7 Conclusions  
By adopting the presented quasi-all-passive TCS for the Luojia 1-01 satellite we have successfully addressed the problem of low heat inertia and high temperature fluctuation with very limited resources allocated for thermal control. As a result, the key instruments like the camera and battery can function well in a preferable temperature environment. Moreover, the successful application of flexible heat conducting material addressed the poor heat conduction between instruments and mounting panels because of the use of shock absorber devices.  
In-orbit temperature data indicates that the camera is stable at 12 \({}^{\circ}\)C, the battery is stable at 19 \({}^{\circ}\)C, and the instruments outside the satellite cabin range from 10 \({}^{\circ}\)C to 25 \({}^{\circ}\)C. The inside temperature fluctuation of the satellite when not facing the Sun is stable at 4 \({}^{\circ}\)C when the camera is not working. Taking advantage of the in-orbit temperature data, the thermal model is further improved and the result after amendment shows that temperature difference is controlled within 3 \({}^{\circ}\)C. Finally, the in-orbit data is close to the data obtained from ground thermal balance test and thermal analysis. The quasi-all-passive TCS proves to be effective and has reference value for small and micro satellite TCS design.  
Data curation, Q.L.; Formal analysis, L.K.; Funding acquisition, L.Z.; Resources, S.G.; Writing--original draft, L.Y.  
This research was funded by National Natural Science Foundation of China (NSFC), grant number No. 41501383.  
This paper is finished with the help of my colleagues in Chang Guang Satellite company and Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences. So, I would like to thank [PERSON], [PERSON], [PERSON], [PERSON] who are also co-authors of this paper.  
The authors declare no conflict of interest. Together We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled.
## 2 Mathematical Modelling of Propagating Signal
### Numerical Results  
In Figure 2, we present a visualisation of the acoustic path bundles and reflections when they are travelling through water. We simulate three different scenarios where the transmitter and the receiver are located at different depths, heights, and distances from  
Figure 1: Evolution of the acoustic signal amplitude (energy) over time while travelling through water for different oscillator damping factors. The evolution of the acoustic amplitude is visualised through the representation using the concept of damped harmonic oscillators, Milne energy, and Milne oscillator.  
each other. We selected three different scenarios, which are representative of scenarios with the transmitter at a height lower than the receiver (Scenario 1), with the transmitter and receiver at the same height (Scenario 2), and with the transmitter height higher than the receiver (Scenario 3). Scenario 1 involves the receiver being positioned 50 m above the waterbed and being higher than the transmitter, which is located 10 m from the bottom. They are situated 1 km apart from each other. In Scenario 2, both the transmitter and the receiver are positioned at the same height of 50 m from the bottom, and the total water depth is 500 m. The distance between the two communicating nodes is now 2 km. Scenario 3 involves the separation of the transmitter and the receiver by 5 km, with the transmitter positioned at a higher height than the receiver. However, if the transmitter is at a lower height than the receiver, numerous signal paths and reflections will be experienced. With an increase in depth, the echoes tend to disappear, rendering only a few weak direct signal paths. With an increase in the distance between the transmitter and the receiver, weak signal paths and a few reflections appear.