esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Optoacoustic (OA) Imaging Setup  
The OA imaging setup (Figure 3) employed the concept of dark-field acoustic resolution photoacoustic microscopy (AR-PAM) [20], where the scanning OA head is represented by a conical fiber-optic illumination system (CeramOptec, Germany) combined with a spherically focused acoustic detector made of a polyvinylidene difluoride (PVDF) piezo film with a 25 \(\upmu\)m thickness (IAP RAS, Russia) [21]. The ultrasound antenna with a numerical aperture of 0.6 and a receiving band of 35 MHz provided a spatial resolution of 40 \(\upmu\)m at a probing depth of up to 3 mm. For probing, a tunable 2214 PC laser (LOTIS-TII, Belarus) with a wavelength range of 410-2100 nm, pulse duration of 18 ns, and pulse repetition rate of 10 Hz was used. OA imaging was performed at the probing wavelengths of 690 nm and 785 nm, corresponding to the optical absorption peaks of BPD and IRDye800. Scanning was  
Figure 1: Schematic of the experimental setup for fluorescence imaging: 1—dark chamber, 2,3—laser sources 690 and 785 nm, 4—optical diffusor, 5—experimental object, 6—objective lens, 7—filter wheel, 8—CCD.  
Figure 2: Absorption and emission spectra of benzoporphyrin derivative (BPD) and IRDye800.  
performed by two M-664 one dimensional positioning stages (PIMicos, Germany). OA A-scans from individual laser pulses were digitized by a 16 bit ADC Razor 16 (GaGe, USA) with a 200 MHz sampling rate. Raw OA A-scans were processed using a digital 50 MHz low-pass filter, and the resulting raw OA B-scans were reconstructed based on the synthetic aperture technique [22].
# A Single-Launch Deployment Strategy for Lunar Constellations
## 1 Introduction
The single-launch deployment strategy proposed here is developed based on the characterization of low-energy trajectories which cross the phase space surrounding the libration point \(L_{1}\), named the equilibrium region [34; 35]. Operating in the dynamic framework of the elliptic restricted four-body problem (ER4 BP), which is significantly more accurate than the CR3 BP to model the Sun-Earth-Moon system, it is proved that a satellite can be transferred from a low-energy trajectory to an orbit with desired semimajor axis, eccentricity, inclination, and RAAN by providing a single \(\Delta V\) when crossing the equilibrium region. If compared to other solutions available in the literature [32; 33], this novel deployment strategy (i) does not require the station-keeping phase around any LPO, and (ii) allows the selection of the target orbital plane by a single maneuver. These concepts are applied to design the transfer of multiple satellites departing from the same trajectory into different lunar orbits with desired semimajor axis, eccentricity, inclination, and RAAN. The mission consists of two phases. During the first one, the satellites are stowed in the launch vehicle, moving along a lunar escape trajectory with parameters selected such that a powered gravity-braking at the periselenium allows the transfer of the vehicle and its payload along a low-energy trajectory directed towards the equilibrium region. The second phase starts when each satellite maneuvers independently to be injected into a low-energy trajectory approaching the Moon with the desired inclination, RAAN, and periselenium radius. A final maneuver to adjust the semimajor axis concludes the deployment. The method is evaluated using numerical tools simulating the deployment of a lunar constellation of elliptic inclined orbits providing global coverage [30]. The paper is organized as follows. Section 2 collects the theoretical background on the CR3 BP, the equilibrium region, the topological characterization of low-energy trajectories, and an extension of these solutions to the Sun-Earth-Moon system. In Section 3, the deployment strategy is developed, and in Section 4, it is verified through numerical simulations, and the results are compared to other solutions available in the literature.
## 5 Conclusions  
Our research contributes to the growing list of L8 and S2 harmonization methods developed for their synergistic use. Harmonized L8 and S2 satellite data are useful for reliable monitoring of natural resources such as vegetation and water, especially for applications that require more temporal data. In this paper, we propose a conditional generative adversarial network (CGAN) for predicting S2-like images from L8. Based on the results, we find that CGANs are effective in increasing the spatial resolution of the L8 spectral bands to match with the S2 bands. Secondly, CGANs can predict the three red edge bands  
Figure 9: S2-like RE predictions by GAN and CNN from L8 with known misregistration errors between training pairs. Scene A and Scene B are translations by GAN and CNN. (**A1**,**B1**) Predictions by GAN; (**A2**,**B2**) Predictions by CNN. All images are layer stacked as (R: RE3, G: RE2, B: RE1).  
available only in S2 from available L8 spectral bands, demonstrating its ability to increase the spectral resolution of a satellite product. Hence, CGANs can be used to translate L8 data for increasing the temporal resolution of the original S2 data product. Thirdly, CGANs generate S2-like imagery that is closer to the original S2 data compared to CNNs, making them more robust to outliers. This capability of CGANs highlights their benefit, despite the more complex training process they require. While our method was successful in translating Landsat 8 to S2, it can be used to translate satellite data products that have similar measurement principles and comparable spatial resolutions.  
Conceptualization, R.M. and D.L.; writing--original draft preparation, R.M.; writing--review and editing, D.L.; visualization, R.M.; supervision, D.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data and code are presented in this study are available publicly at [[https://github.com/Rohit18/Landsat8-Sentinel2-Fusion](https://github.com/Rohit18/Landsat8-Sentinel2-Fusion)]([https://github.com/Rohit18/Landsat8-Sentinel2-Fusion](https://github.com/Rohit18/Landsat8-Sentinel2-Fusion)) (accessed on 20 October 2023).  
The authors declare no conflict of interest.
# Study on Flow Characteristics of Venturi Accelerated Vortex Drainage Tool in Horizontal Gas Well
## 4 Results and Discussion
### Pressure Drop with Different Liquid Flow Rates  
The instantaneous pressure drops in the horizontal tube (\(P_{1}\)-\(P_{2}\)) are shown in Figure 13 when the gas flow rate is 300 m\({}^{3}\)/h and the liquid flow rate is 1, 3, 5, 10, 15 L/min. As can be seen, the pressure drop is less than zero. As the liquid flow rate increases the pressure drop increases, and it tends to become unstable. This effect is consistent with the observed phenomenon of the spiral flow. An elevated liquid flow rate results in greater flow resistance. The negative pressure drop of the venturi accelerated vortex tool is also higher than for the other structures, indicating that this structure can form a more stable spiral flow.  
The average pressure drop in the horizontal tube (\(P_{1}\)-\(P_{2}\)) is shown in Figure 14 for the tube drainage, the vortex tool, and the venturi accelerated vortex tool when the liquid flow rate was 1-15 L/min and the gas flow rate was 300 m\({}^{3}\)/h. The average total pressure drop of these (\(P_{1}\)-\(P_{3}\)) is shown in Figure 15.  
Figure 14: The average pressure drop of different tools in the horizontal tube with different liquid flow rates (1, 3, 5, 10, 15 L/min) and the gas flow rate of 300 m\({}^{3}\)/h.  
Figure 13: Instantaneous pressure drop of venturi and vortex tool in the horizontal tube with different liquid flow rates (1, 3, 5, 10, 15 L/min) and the gas flow rate of 300 m\({}^{3}\)/h. (Supplementary Figure S2).  
As can be ascertained from Figure 14, the average negative pressure drop for the venturi accelerated vortex tool is the highest, followed by the vortex tool, while it is the lowest for the tube drainage. This outcome is consistent with the analysis of spiral flow.  
As shown in Figure 15, the average total pressure drop of the horizontal and vertical tubes gradually increases with the increase in liquid flow rate. This effect can be ascribed to the increase in the liquid flow rate that requires greater kinetic energy to carry the liquid out of the tube. Moreover, with the increase in the liquid flow rate, the average total pressure drop of the vortex tool is significantly lower than that of the tube drainage. The overall pressure drop for the venturi accelerated vortex tool is the smallest.
# Multimodal Diarization Systems by Training Enrollment Models as Identity Representations +
## 8 Conclusions  
This paper presents the ViVoLAB submission to the IberSPEECH-RTVE 2020 Multimodal Diarization Challenge. In this work, we developed two monomodal subsystems to separately address face and speaker diarization. Each system is based on state-of-the-art DNN approaches. In addition, we introduced a new back-end approach for the face subsystem. This approach consists of training a learnable vector with the aDCF loss function to represent each face enrollment identity. Using these enrollment models for the identity assignment process instead of just the cosine similarity, the results have achieved a relevant improvement over the average embedding directly and the application of cosine similarity. We have demonstrated that there is still room for improvement in each of the systems because the results obtained are too high in both subsets and in both systems. Moreover, future work can be carried out on the fusion of both systems, which could improve the final results, especially by disambiguating the identification process. The high DER values for misses and false alarms in the face and speaker subsystem, respectively, should be addressed by that fusion.  
Conceptualization, V.M. and A.M.; Investigation, V.M. and A.M.; Methodology, V.M. and A.M.; Software, V.M., I.V., P.G. and A.M.; Supervision, A.M., A.O. and E.L.; Writing--original draft, V.M.; Writing--review and editing, A.M., A.O. and E.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the European Union's Horizon 2020 research and innovation programme under Marie Sklodowska-Curie Grant 101007666; in part by MCIN/AEI/10.13039/501100011033 and by the European Union \"NextGenerationEU\"/PRTR under Grant PDC2021-120846-C41, by the Spanish Ministry of Economy and Competitiveness and the European Social Fund through the grant PRE2018-083312, and by the Government of Aragon (Grant Group T36_20R).  
The authors declare no conflict of interest.
# State-of-the-Art Model for Music Object Recognition with Deep Learning
## 1 Introduction  
Today, many pieces of music are recorded and passed down by scores. Musical scores play a decisive role in the development of music. For centuries, music has been preserved in the form of pictures, whether the composer's manuscript or a published version. However, the storage of music scores in picture form has brought plenty of difficulties for Music Information Retrieval (MIR) [1]. Music scores ought to be recorded in digital format, which is unique to music, rather than pixels, which are unrelated to each other, to make it easy for people to retrieve or edit it. To edit a score stored in a picture, we have to manually enter elements one by one into music notation software before making changes and adjustments. There are many existing music-encoding formats including MEI [2] and MusicXML [3]. Through the use of these professional music encoding files, which are equivalent to the source code of the music, computers can quickly and structurally acquire all of the information held in the score. It can be said that the digitization of music scores is the basis of music information retrieval. The process of converting a score in picture form into a digitally structured format is called Optical Music Recognition (OMR). Optical music recognition is the application of Optical Character Recognition (OCR) to music to allow the recognition of musical scores in editable or playable forms such as MIDI (for playback) and MusicXML (for page layout) [4]. Compared with other symbols, the proportion of notes, which is used to record pitch and time values and has semantic information, is extremely high in a score. Therefore, note recognition is the core of score recognition. A traditional OMR system includes four steps [5]: (1) image pre-processing, (2) removal of staff lines and classification of music symbols, (3) musical notation reconstruction, and (4) musical notation encoding. In the traditional OMR process, it is necessary to detect and remove the staff lines in order to classify music symbols. Even though many people have made efforts to this end, the effects have not been satisfactory. Nowadays, following the rapid development of Deep Learning, it has been widely used in various fields and has achieved subversive effects. Therefore, we combined deep learning technology with OMR, proposing an end-to-end OMR pipeline which has achieved results in monophonic score recognition. Furthermore, this process can be extended to the recognition of polyphonic scores. The rest of the paper is organized as follows: Section 2 gives a summary ofrelated works, Section 3 provides an introduction of the dataset used, Section 4 describes our model in detail, Section 5 details the experimental method used and the results obtained, and Section 6 gives a summary of the conclusions and future works.
# Concrete with Organic Waste Materials as Aggregate Replacement
## 2 Materials and Methods
### LCA Methodology
#### 2.4.2 Life Cycle Inventories  
Since the mechanical tests were conducted in Great Britain (GB), we selected datasets representing activities specific to this country. For Portland cement, we relied on the coinvent activity \"market for cement, Portland\" considering \"Europe without Switzerland\" as the location. Concerning the aggregates, the sand and gravel datasets were considered for the fine and coarse aggregates, respectively. To model these materials, we considered the specific situation of GB. In this country, approximately 22% of the total aggregate requirements for construction are met by dredging sand and gravel from the seabed surrounding the British Isles, while the remaining part is quarried on land [40]. Both inventories for the marine and land aggregates were modeled based on the data provided by the Crown Estate in a report assessing the energy consumption of the extraction of marine aggregates [41]. We then included the water consumption for the two considered sources of aggregates [42]. For the water used in the concrete mixes, we considered the coinvent activity \"market for tap water\" with \"Europe without Switzerland\" as the location. To model the alternatives incorporating organic waste, we started with the traditional concrete activity and reduced the input of sand by 10%, replacing it with the respective organic waste. Notably, organic waste was directly used to produce the concrete mix without any preprocessing activities. In this way, as a first approximation, we modeled the organic materials as waste, without any burden associated with their production and treatment.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Rice Husk Ash** & **Wood Ash** & **Wheat Straw** & **Corncb** \\ \hline Biogenic carbon content (variation) & 5.5\% (3\%–8\%) [21] & 19\% (4\%–34\%) [16] & 46.5\% (41.9\%–51.1\%) [24] & 46.8\% (42.1\%–51.5\%) [25] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Biogenic carbon content of the considered organic waste.  
Figure 3: System boundary of traditional concrete production and of the alternative concrete with organic waste.
# Near-Nyquist-Limit Optical Communication and Ranging Method Based on Waveform Matched PPM
## 3 PPM Symbol Decision Algorithm Based on Waveform Matching  
The position of the pulse slot within the symbol reflects the information carried by the PPM symbol. With the digital signal \(r(j)\) sampled and discretized by ADC, the estimated pulse slot position of PPM symbol can be described as:  
Figure 4: Signal arrival time.  
\[\hat{j}=\arg\max(\sum_{j=i}^{i+M-1}r(j)), \tag{12}\]  
where \(M\in\mathbb{Z}\) is the number of sampling points in a slot. \(i=0,M,...,(L-1)\times M\). For integrated communication and ranging scenarios, the bit error performance of (12) is degraded due to the non-integer oversampling ratio, especially near the Nyquist sampling limit.  
We propose a \(L\)-PPM symbol decision method based on waveform matching to solve the energy leakage problem at near-Nyquist-limit. This method accumulates adjacent sampling points to reduce pulse energy loss, as in (12). The maximum point of the accumulated signal is applied to find the pulse slot sampling points and neighboring points. Then the method matches those points in the pulse waveform expression, which is obtained and modeled by the actual situation. The pulses have different shapes of different systems. The ideal shape is a rectangle expressed by (2). In high-speed conditions, the pulse behaves as an approximately quadratic function when the system bandwidth is insufficient, since the PPM signal is an ultra-wideband signal. The pulse output by SNSPD shows a rapid rise followed by an exponential decay, as shown in Figure 5. Traditional interpolation algorithms such as the Gardner method can only achieve linear interpolation when the oversampling rate is near the Nyquist sampling limit, which differs from the waveform characteristics of the pulse signal. Therefore, the pulse waveform is modeled, and the sampled data are matched to the pulse waveform model to reconstruct the best sampling point and position of the pulse. The pseudo-code of the proposed method is described in Algorithm 1.  
```
0: received signal samples \(r(j)\)
0:\(j=0\), PPM pulse slot time-domain model \(s(t)\)
1: Calculate the sum of adjacent points within a PPM symbol.
2:for\(0\leq j\leq L\times M-1\)do
3:\(r^{\prime}(j)=r(j)+...+r(j+M)\)
4:\(j=j+1\).
5:endfor
6:\(\hat{j}=\arg\max(r^{\prime}(j))\).
7: Fit \(\{r(\hat{j}-j_{1}),...,r(\hat{j}),...,r(\hat{j}+j_{2})\}\) to \(s(t)\) to get the reconstructed discrete waveform \(s(j),j_{1},j_{2}\in\mathbb{Z}\) decided by \(s(t)\). For example, when \(s(t)\) is a quadratic function, there is \(j_{1}=j_{2}=1\).
0: the re-estimated pulse slot position after waveform matching \(\hat{j}=\arg\max(s(j))\).
```  
**Algorithm 1** Waveform matched PPM symbol decision algorithm.
## 3 Results
### Association Analysis of DAMs and DEGs  
Based on Pearson's correlation coefficients (PCCs) between the DEGs and DAMs, nine quadrant diagrams were constructed to reveal the variations in metabolites and their corresponding genes. The DEGs and DAMs in quadrants 3 and 7 had positive correlations, while the DEGs and DAMs in quadrants 1 and 9 had negative correlations (Figure 9). Additionally, the DEGs and DAMs were annotated to the KEGG pathway database to determine their common biological pathways. These significantly changed pathways in all of the comparison groups mainly included metabolic pathways, biosynthesis of secondary metabolites, phenylalanine metabolism, limonene and pinene degradation, and monoterenoid biosynthesis (Figure S7). Interestingly, compared with the W vs. B group,  
Figure 8: KEGG enrichment analysis of all DEGs: (**A**) Comparison groups of W vs. B. (**B**) Comparison groups of W vs. R. (**C**) Comparison groups of B vs. R.  
more common pathways were enriched in the W vs. R group (Figure S7), suggesting that red light affects more pathways at the transcriptional and metabolic levels.  
In addition, an O2 PLS model was generated to determine the relationships between the DEGs and DAMs, which could be used to screen important variables from one set of data that influence the other. In the gene loading, 7 genes localized to chloroplasts were among the top 10 DEGs (Figure 10A), including carbonic anhydrase (Cluster-9688.94804), hydroxy-3-methylbut-2-enyl diphosphate reductase (Cluster-9688.72219), phosphoglycolate phosphatase 1A (Cluster-9688.101671), sugar partition-affecting protein (Cluster-9688.46987), WAT1-related protein (Cluster-9688.69111), PsbQ (Cluster-9688.96670) and plastid transcriptionally active 16 (Cluster-9688.82754), suggesting their importance in regulating metabolites. The top 10 DAMs displayed in Figure 10B, such as (2-bromoethyl)-oxirane (XMW0397), undecanoic acid (KMW0592), 1-dodecanol (KMW0629) and E-1-methoxy-4-hexene (XMW0279), were most closely associated with the DEGs.  
Figure 9: Quadrant diagram representing the association of transcriptomic and metabolomic variations in the W vs. B (**A**), W vs. R (**B**), and B vs. R (**C**) groups. The black dashed line indicates the difference threshold. Outside the threshold line, there are significant differences in genes/metabolites, while inside the threshold line the genes/metabolites are unchanged. Each dot represents a gene/metabolite. Black dots represent invariant genes/metabolites, green dots represent differentially accumulated metabolites with unchanged genes, red dots represent differentially expressed genes with unchanged metabolites, and blue dots represent both differentially expressed genes and differentially accumulated metabolites.