esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In order to investigate the effects of drying temperature on calcium soil nitrogen detection and its characteristic bands by NIR spectroscopy, calcium soil was detected using NIR spectroscopy after five different temperatures. The spectra were pretreated by five preprocessing methods, the characteristic bands and intervals were selected by CARS and BIPLS, and PLS was used to model and analyze the spectral data.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Group** & **Number** & \multicolumn{3}{c}{**Characteristic Bands**} & \(R_{e}^{2}\) & \
\begin{tabular}{c} **RMSEC** \\ **(mg/kg)** \\ \end{tabular} & \(R_{p}^{2}\) &
\begin{tabular}{c} **RMSEP** \\ **(mg/kg)** \\ \end{tabular} & **RPD** \\ \hline
25 \({}^{\circ}\)C & 9 & 1614 1626 1647 1649 1651 1664 1666 1668 1669 & 0.738 & 0.428 & 0.728 & 0.408 & 2.01 \\
40 \({}^{\circ}\)C & 10 & 1638 1640 1642 1641 1669 1671 1673 1675 1680 & 0.974 & 0.132 & 0.966 & 0.128 & 5.03 \\
60 \({}^{\circ}\)C & 10 & 928 933 1596 1656 1661 1669 1671 1673 1675 & 0.951 & 0.181 & 0.946 & 0.172 & 4.53 \\
80 \({}^{\circ}\)C & 10 & 940 942 1644 1669 1671 & 0.966 & 0.152 & 0.961 & 0.143 & 4.98 \\
105 \({}^{\circ}\)C & 5 & 958 1141 942 1644 & 0.917 & 0.242 & 0.903 & 0.227 & 3.37 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The characteristic variables and prediction effects of CARS-BIPLS-PLS.  
Figure 5: The comparison of different algorithms.  
The main conclusions are as follows: (1) The drying temperature did have an obvious influence on soil nitrogen detection using NIR spectroscopy, and it was necessary to find the suitable drying temperature and spectral pretreatments to enhance the detection accuracy; (2) the drying temperatures ranging from 40 \({}^{\circ}\)C to 80 \({}^{\circ}\)C of calcium soil nitrogen detection accuracy were the best. The calcium soil nitrogen detection had the worst results when soil was placed at 25 \({}^{\circ}\)C. The O-H bond in soil might be the main factor that influenced the prediction accuracy; (3) the prediction effect of soil nitrogen was at its optimum using the method of CARS-BIPLS-PLS. In general, the drying temperature had an obvious influence on the detection of soil nitrogen by NIR, and the suitable drying temperature was of great significance to enhance the detection accuracy.  
This work is supported in part by the National keypoint research and invention program of the thirteenth (2016 YFD0700304) and Natural Science Foundations of China (Grant No. 61405175).  
This work presented here was carried out by collaborations among all authors. [PERSON] and [PERSON] conceived the idea. [PERSON], [PERSON], [PERSON] and [PERSON] worked together on associated data and carried out the experimental work. [PERSON] and [PERSON] drafted the manuscript. [PERSON] and [PERSON] provided their experience and co-wrote the paper with [PERSON]. All authors contributed to, reviewed, and improved the manuscript.  
The authors declare no conflict of interest.
## 2 Results  
Approximately 300 mass spectra were assessed for each analytical replicate and MS operating condition in this comprehensive approach for a complete screening of phytochemicals (Figure 3).  
This procedure allowed a detailed evaluation of the rose MeOH extract fraction and the tentative identification of up to 146 phytochemicals (Table A1 (Appendix A)). The most represented classes of polyphenic compounds were flavonoids (flavonols, flavones, flavan-3-ols, flavanones) with a total of 68 polyphenols identified for the first time. Some polyphenols were identified for the first time in the genus _Rosa_ (family _Rosaceae_).  
These are the flavones: Chrysoeriol, Hispidulin, 5,7-Dimethoxyluteolin, Cirsimarittin, Crissiliol, Tricin, Jaceosidin, Nevadains, Syringetin, Isovitexin, Genistein \(C\)-glucose malonylated, Chrysin 6-C-glucose-6\({}^{\prime\prime}\)-O-deoxyhexoside; flavanols: Dihydrokaemperfelol, Rhamnetin II, Kaempferol-3-O-\(\alpha\)-L-rhammoside, Taxifolin-O-pentoside, Taxifolin-3-O-hexoside, Isornhametin triacetyl hexoside; flavan-3-ols: Epiafzelechin and Gallocatedchin; flavanone: Naringenin, Fustin; phenolic acids: Caffeic acid, Citric acid, Hydroxy methoxy dimethylbenzoic acid, Hydroxyferulic acid, Ellagic acid, \(p\)-Coumaroylquinic acid, Ginkgoic acid, Salvianolic acid D, Salvianolic acid B; stilbenes: Pinosylvin, Resveratrol, 3-Hydroxyresveratrol;  
Figure 3: Representative chemical profiles of the _R. rugosa_ (Primorye, Russia) total ion chromatogram from the MeOH extract.  
lignans: Pinoresinol, Arctigenin; coumaris: 3,4,5-Trimethoxycoumarin, Frazin; anthocyanins: Cyanidin 3-O-glucoside, Delphinidin \(O\)-pentoside, Pelargonidin 3-O-(6-O-malonyl-\(\beta\)-d-glucose), Cyanidin 3-(6\({}^{\prime\prime}\)-Succinyl-Glucoside), Delphinidin malonyl hexoside, Cyanidin 3-O-dioxxyl-glucose, Delphinidin 3,5-dihexoside, etc.
## 5 Future Development  
### Integration of L-Band SAR Data and the Tomography Algorithm  
[PERSON] et al. used TomoSAR technology to process L-band forest data obtained by monitoring at different times, extracted the distribution and spatial patterns of forests by a compressed sensing approach, and further utilized two complementary search methods to find the local maximum value and reconstruct the spatial patterns, so as to realize the inference of forest structure information and the assessment of the effect of delineating forests [64]. [PERSON] et al. found that in the forests with a height of above 30 m and biomass up to 500 t/hm\({}^{2}\), the strong ground return of P-band can be seen in the tomography images [65]. [PERSON] et al. used L-band SAR data in Trauenstein, Germany, to provide forest vertical-structure information, coupled with high spatial- and temporal-resolution images; they developed TomoSAR technology and reconstructed 3D models, and the results were verified based on airborne LiDAR data [66]. This experiment opened the door for 3D-based forest monitoring. [PERSON] et al. compared P-band and L-band TomoSAR profiles, using the Land Vegetation Ice Sensor (LVIS), and discrete-return LiDAR to monitor and estimate tropical forest-structure parameters [67]. The extracted radar reflectance yielded RMSE  
\begin{table}
\begin{tabular}{l l l l} \hline
**Observed Object** & **L-Band** & **C-Band** & **X-Band** \\ \hline Sea Ice & \({}^{*}\) & \(\surd\) & \(\surd\surd\) \\ \hline Snow (type and thickened layer) & \(\surd\surd\) & \(\surd\surd\) & \(\surd\surd\) \\ \hline Soil moisture & \(\surd\surd\) & \(\surd\surd\) & \(\surd\surd\) \\ \hline Soil roughness & \(\surd\) & \(\surd\surd\) & \(\surd\surd\) \\ \hline Soils & \(\surd\surd\) & \(\surd\) & \({}^{*}\) \\ \hline Water-land boundaries & \(\surd\surd\) & \(\surd\) & \(\surd\surd\) \\ \hline Vegetation & \(\surd\surd\) & \(\surd\surd\) & \(\surd\surd\) \\ \hline Vegetation moisture & \(\surd\surd\) & \(\surd\surd\) & \(\surd\surd\) \\ \hline Ocean & \(\surd\surd\) & \(\surd\surd\) & \({}^{*}\) \\ \hline Geological structure, structure & \({}^{*}\) & \(\surd\) & \(\surd\) \\ \hline Desert underground & \(\surd\surd\) & \(\surd\) & \({}^{*}\) \\ \hline \end{tabular}
\end{table}
Table 2: Penetration characteristics of L-band in various conditions and its comparison with C-band and X-band (Note: \(\surd\)—very good; \(\surd\)—good; \({}^{*}\)—not good).  
values of 3.02 m and 3.68 m for P-band and L-band, respectively, and the corresponding determination coefficients were 0.95 and 0.93. [PERSON] et al. reviewed the features of L-band TomoSAR reconstruction and discussed the unique ability of reconstructing radar reflectance using TomoSAR to reveal the 3D structure and temporal changes of forests [68]. The authors emphasized the importance of penetration sensitivity to vegetation elements.
## 6 Limitations and Use Cases
### Use Cases  
Based on the results of the case study and experiment, our method is expected to guide practitioners and researchers in the following possible use cases UC1-UC3.  
* When new members are assigned to an existing project, our categorization method can help them roughly understand the types and contents of tickets addressed in the target software system development by categorizing tickets automatically with an estimation of the appropriate number of categories. This use case is particularly supported by the answers to research questions RQ1-RQ2.
* When new members are assigned to an existing project, our visualization method can help them roughly understand the relationships among the ticket categories, the lifetime and feature of each category addressed in the target development, and support prioritization of the features for the next development. This use case is particularly supported by the answers to research questions RQ2-RQ4.
* The results of our case study and experiment can serve as a reference for the software requirements engineering and related community, including practitioners and researchers, for practicing and researching further project comprehension methods and tools. Since the ticket lifetime and feature visualization procedures are somewhat independent from the ticket categorization procedure in our method, researchers may independently extend or replace the categorization method and the visualization method.
## 4 Where Next?
The first challenge is due to the images from regular 3D DIC systems being seriously degraded by intense thermal radiation, emanating from both the test specimen and the heating device. The authors overcame this challenge by using an active UV illumination and bandpass filter imaging. This combination offered extraordinary performance in acquiring high-quality surface images with almost constant image contrast in extreme environments. The speckling challenge was overcome by using the sample surface as the speckle pattern. As the natural texture on the surface changes during the ablation, the authors used an incremental calculation strategy using updated reference image pairs for each 3D DIC analysis. The authors validated the viability and efficacy of the new method, by conducting ablation tests in an arc-heated wind tunnel. The experimental results clearly show the ablation at the front of the specimen, with a sufficient and almost constant image contrast. These results also confirm the capability of the UV imaging system in suppressing the adverse influence of thermal radiation [66]. This section shows that of the potential paths that future 3D DIC can be extended into for high energy deformation events, the most important one is the use of non-visible light. The use of non-visible light allows for high-speed 3D DIC to be performed on internal structures, for example the internal structures of foams, with high spatial resolution, by using X-rays, or extreme thermal environments, with the use of UV light. Figure 4: Reproduction of Figure 1 of [PERSON] et al. [64], showing simplified schematics of X-ray imaging techniques. (**a**) MFPS frame rate, propagation based XPCI; (**b**) kFPS frame rate diffraction topography; (**c**) single-bunch, single grating interferometry and spec based XPCI at ID19. © IOP Publishing Ltd and Sissa Medialab. Reproduced with permission of IOP Publishing. All rights reserved.
# Spatial and Momentum Mapping Modes for Velocity Map Imaging Spectrometer
## 1 Introduction  
The velocity map imaging (VMI) technique aims to acquire the momentum distribution of charged particles. In 1987, [PERSON] and [PERSON] introduced the ion imaging technique to project the 3D momentum distribution of CH\({}_{3}^{+}\) onto a 2D image detector [1]. A 2D image detector typically consists of a microchannel plate (MCP) pair followed by either a phosphor screen with a camera or alternatively a delay line anode with electronic system. Ten years later, [PERSON] and [PERSON] improved momentum resolution with a focusing lens [2]. With this lens, the charged particles with the same initial radial momentum will arrive at the detector at almost the same radius, insensitive to their initial positions. So, the blur on the image due to the source volume is significantly reduced and the momentum resolution is greatly improved. The maximum detectable energy is set by the voltages applied to the spectrometer electrodes. The VMI technique provides a 4\(\pi\) solid collection angle, and the high efficiency is typically only limited by the detector. Based on the kinetic energy spectrum and angular distribution, the ionization and dissociation processes of atoms, molecules, and clusters can be explored. As a result, the VMI spectrometers have been widely used in the photoionization and photodissociation experiments over the past several decades [3; 4; 5; 6; 7; 8]. Since achieving a higher imaging resolution and magnification capabilities is always desired, many lenses have been developed to improve the relative energy resolution at low kinetic energy [9] and extend the electron energy limit up to keV [10; 11].  
In the experiment, it is important to put the laser focus as close as possible to the VMI reaction center to improve the resolution. Moreover, the pump-probe technique has beenemployed for the real-time monitoring of reactions [12; 13; 14; 15; 16; 17; 18]. The difficulty of alignment is further increased because one needs to find the spatial-temporal overlap between the pump and probe pulse. Thus, it would be advantageous to view the focus points of two laser pulses directly from the detector as precisely as possible, especially during the very limited beamtime at a large research facility [19]. Even when a single laser pulse is used, the reaction rate can be sensitive to the intensity variation in the source region. Thus, a source-position resolved measurement will be more precise than a source-position integrated one [20; 21]. In such cases, spatial map imaging mode is developed and detailed [22]. It would be beneficial to establish both velocity and spatial mapping of the ionization within the same spectrometer [23].  
In the previous paper [11], we achieved a 1.5% relative electron energy resolution at 500 eV. To explore its performance at a lower but much more widely used kinetic energy, i.e., 100 eV, we develop two additional working modes for the velocity map imaging (VMI) spectrometer: the spatial mapping mode and high-resolution momentum mapping mode.
## 4 Case Study of Data Collaboration
### Visual Traffic Flow Detection Based on DFA
#### 4.3.4 Traffic Flow Mining  
Based on contour detection, our above-deduplicated image collection is mined and extended to real intersections.  
In this paper, we selected seven intersections in Wuxi commercial centre to mine the travel traffic flow from 8:00 to 9:00 in the morning and 17:30-18:30 in the evening. As shown in the box plot in Figure 9, the traffic flow variance between intersections A and B during the morning rush hour and intersections A, B, and D during the evening rush hour is large, so they are marked as key mining intersections.  
Figure 8: UAV video process. (**a**) Frame difference processing. (**b**) Thresholding. (**c**) Image dilation. (**d**) Select the detection area and draw the outline.  
Based on the above analysis, the traffic flow data mining based on DFA is further carried out at key intersections. As can be seen in Figure 10a, intersection A and intersection B show obvious morning peak characteristics, and the traffic flow continues to show a congestion trend from 8:00 to 8:44, and then drops sharply from 8:45. As can be seen from Figure 10b, intersections A, B and D show an obvious evening peak trend, with a sharp increase in traffic flow from 17:45 and continued congestion thereafter.
# A Review of the Water Desalination Technologies
## 2 The State of the Art
### Hydration (HY)  
Desalination by Hydration (HY) is based on the production of gas hydrates, which are crystalline solids composed of water (host) and gas (guest) molecules like nitrogen, carbon dioxide and methane [58; 59]. The dissociation of 1 m\({}^{3}\) of hydrates can produce 0.8 m\({}^{3}\) of water and 164 m\({}^{3}\) of gas in standard conditions [18].  
As the generation of hydrates requires less severe thermodynamic conditions (T < 20 \({}^{\circ}\)C and P > 30 bar) than other phase transition desalination techniques, the idea behind HY desalination is the production of hydrates and then the separation into the components (gas and water). A schema of a Hydration plant is reported in Figure 26 [18].  
In detail, after a preliminary refrigeration seawater is mixed inside a reactor with a mixture of propane and carbon dioxide. This mixture is transferred into the crystallizer, where hydrates are formed thanks to the low temperature and high pressure. The hydrate slurry is separated from the brine and transferred into a decompos  
Figure 25: The working principles of a Capacitive Deionization unit.  
supply, hydrates are converted into freshwater and gas. The last one is recovered to be reused to produce hydrates [18].  
This technology should require a lower energy expenditure in comparison with MSF and RO, however no commercial plants are available, as capital costs are high [18].
## 4 Results and Discussion
### Comparison in All Classes with the State-of-the-Art  
We performed a comparative assessment to evaluate whether any improvements were obtained by making use of the extended, quality aware generated datasets. The comparison was made between our best performing model, based on RetinaNet, and the best model in [25], which used MobileNet. The results of this analytical exercise are summarized in Table 7. The RetinaNet-based model performed consistently better than the MobileNet implementation, showing better accuracies and higher recall for all of the classes (D11 and D40 in particular, as expected).  
This fact is important as they are the classes that we are more interested in identifying for the purpose of road damage monitoring applications, where these classes must be detected with high reliability. These results can be correlated with the better representation of these training instances in our dataset, which was an issue in previous research [25]. The researchers have cited the class D43 (lane marking degradations) as an exception, given that the samples for this class are limited, but we believe that such simpler samples are by definition easier to classify, which might explain the results.  
For our implementation, we made use of VGG19 for feature extraction as in [21; 22], but we report the full metrics for all eight classes in Table 2, whereas the authors in [21] use a combined single metric (a mAP of 0.8279), instead of the usual individual per-class comparison. From all of the previous results (summarized in Tables 5-7), we can confidently assert that the quality aware augmentation strategy implemented in this study was indeed effective.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Metric**} & \multicolumn{8}{c}{**Road Damage Instance**} \\ \hline
**[PERSON] et al. [25]** & **D00** & **D01** & **D10** & **D11** & **D20** & **D40** & **D43** & **D44** \\ \hline Recall & 0.40 & 0.89 & 0.20 & 0.05 & 0.68 & 0.02 & 0.71 & 0.85 \\ Precision & 0.73 & 0.64 & 0.99 & 0.95 & 0.68 & 0.99 & 0.85 & 0.66 \\ Accuracy & 0.81 & 0.77 & 0.92 & 0.94 & 0.83 & 0.95 & 0.95 & 0.81 \\ \hline
**Best Model (Ours)** & & & & & & & & \\ \hline Recall & 0.60 & 0.90 & 0.40 & 0.40 & 0.76 & 0.70 & 0.80 & 0.70 \\ Precision & 0.87 & 0.70 & 0.89 & 0.92 & 0.92 & 0.88 & 0.87 & 0.82 \\ Accuracy & **0.91** & 0.81 & 0.92 & **0.95** & **0.95** & **0.98** & 0.95 & **0.84** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison with the best method previously published in the state-of-the-art [24] for all of the classes in Table 2; we show the results for recall precision and accuracy for both methods.  
Figure 10: Training and validation loss curves for the augmented and quality aware datasets.
# Improving Multi-Label Learning by Correlation Embedding
## 4 Experiment
### Data Sets  
In this paper, a total of 15 multi-label benchmark data sets are used to verify the effectiveness of our method. Detailed information about these data sets are summarized in Table 1. For each data set \(\mathsf{S}\), \(|\mathcal{S}|\) denotes the number of instances, \(dim(\mathcal{S})\) denotes the number of features, and \(L(\mathcal{S})\) denotes the number of labels. In addition, \(LCard(\mathcal{S})\) is cardinality, which indicates the average number of labels belonging to instances, and \(rDep(\mathcal{S})\) denotes the ratio of unconditionally dependent label pairs.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**ID** & **Data Set** & \(|\mathcal{S}|\) & \(dim(\mathcal{S})\) & \(L(\mathcal{S})\) & \(LCard(\mathcal{S})\) & \(rDep(\mathcal{S})\) \\ \hline
1 & rcv1v2(subset1) & 6000 & 944 & 101 & 2.88 & 0.202 \\
2 & rcv1v2(subset2) & 6000 & 944 & 101 & 2.63 & 0.179 \\
3 & delicious & 16,105 & 500 & 983 & 19.02 & 0.143 \\
4 & enron & 1702 & 1001 & 53 & 3.38 & 0.141 \\
5 & recreation & 5000 & 606 & 22 & 1.42 & 0.455 \\
6 & Stackex-coffee & 225 & 1763 & 123 & 1.99 & 0.017 \\
7 & Stackex-ches & 1675 & 585 & 227 & 2.41 & 0.030 \\
8 & Stackex-chemistry & 6961 & 540 & 175 & 2.11 & 0.056 \\
9 & Stackex-philosophy & 3971 & 842 & 233 & 2.27 & 0.040 \\
10 & Stackex-cs & 9270 & 635 & 274 & 2.56 & 0.049 \\
11 & Stackex-cocking & 10,491 & 577 & 400 & 2.23 & 0.034 \\
12 & Corell6k001 & 13,766 & 500 & 153 & 2.86 & 0.142 \\
13 & Corell6k002 & 13,761 & 500 & 164 & 2.88 & 0.128 \\
14 & Water-quality & 1060 & 16 & 14 & 5.073 & 0.473 \\
15 & flags & 194 & 19 & 7 & 3.392 & 0.381 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of datasets.
# Multimode Tree-Coding of Speech with Pre-/Post-Weighting +
## 4 G.727 DPCM Encoder  
Differential pulse code modulation (DPCM) with an adaptive pole-zero short-term linear predictor is the basic structure of the ITU-T G.726 standard [24] and its embedded coding version G.727 [25; 26]. Both codecs have selectable transmitted bit rates of 40, 32, 24, and 16 kbps from best to worst reconstruction quality. G.727 is an embedded codec so it has enhancement and core bits, and the coding rate is often specified in terms of enhancement and core bits/sample as (x,y), where x represents the total of the enhancement and core bits, and y refers to the number of core bits. Therefore, G.727 coding rates are (5,4), (5,3), and (5,2) for 40 kbps, (4,4), (4,3), and (4,2) for 32 kbps, (3,3) and (3,2) for 24 kbps, and (2,2) for 16 kbps. Figure 2 is a block diagram of the encoder and Figure 3 is a block diagram of the decoder. The specific structures and adaptation rules for \(A(z)\) and \(B(z)\) are developed in Section 4.1.  
The quantizer is represented by \(Q\) in the figures and the quantizers and adaptation rules are described briefly in Section 4.2.  
Figure 1: Block diagram of the Multimode Tree Coder with backward pitch predictor and perceptual pre- and post-weighting filters, the zero-coefficient of short-term predictor is updated by the input of the pitch predictor.  
Figure 2: DPCM Encoder with Pole-Zero Predictor.