esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Nitrogen and Phosphorus Removal
## 3 Results
### Nutrient Mass Loads
#### 3.4.2 Nutrient Mass Loads for Storms with No Tank Bypass  
There were five storm events in 2019 for which tank bypass did not occur and the full runoff volume from each storm event was treated in the treatment tanks with MBBR prior to entering the woodchip bioreactors (Figure 6). For all storms, TN mass load decreased between System Inflow and  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Entire Treatment System** & \\ \hline  & **Cumulative Mass Removal (kg)** & **RE (\%)** \\ \hline
**TN** & 32.5 & 75.8\% \\
**NH4+-N** & -1.97 & -56.4\% \\
**NO2–+NO3-N** & 0.04 & 70.4\% \\
**TP** & 2.37 & 25.8\% \\
**SRP** & 1.46 & 19.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cumulative mass removal of nitrogen (N) and phosphorus (P) mass loads and overall removal efficiency (RE %) in 2019 for 15 storm events where loads were calculated, between System Inflow and Combined Bioreactor Outflow (i.e., from East and West Bioreactor Outflows). Negative values indicate an export of nutrient. TN = total N, NH4+-N = ammonium-N, NO2+NO3–N = nitrite + nitrate-N, TP = total P, SRP = soluble reactive P.  
Figure 5: Box and whisker plots of phosphorus (P) species concentrations from each sampling location. Bottom and top boxes indicate 25 th and 75 th percentiles, respectively, the middle line shows the median, whiskers show the min and max, and points denote any outliers. TP = total P, SRP = soluble reactive P.  
Combined bioreactor Outflow (i.e., sum of WB Outflow and EB Outflow), however there was generally an increase in TN mass load within the treatment Tanks (Figure 6).  
These storm events were analyzed separately (Table 3). Cumulative nutrient mass loads and average removal efficiencies were calculated between the System Inflow and Tank Outflow, Tank Outflow and WB, and Tank Outflow and EB. Nutrient mass loads from individual storm events are shown for these locations. The treatment tanks with MBBR on average for each storm event increased nutrient mass loads, and overall most mass removal occurred within the WB (Table 3).
# Imaging Flow Velocimetry with Laser Mie Scattering
## 4 Fundamental Measurement Limits
### Particle Motion
#### 4.2.3 Illumination Effects  
The light illumination influences the measurement due to the photon momentum (particle motion) and due to the light absorption (particle heating). Both effects are described by [PERSON] et al. in [157; 158]. Concerning the radiation pressure, the induced force on a spherical particle due to the photon momentum results in the maximal measurement error:  
\[\Delta v=\frac{I\cdot d_{\text{p}}}{12c\eta} \tag{17}\]  
with \(c\) as light velocity, \(d_{\text{p}}\) as particle diameter, \(\eta\) as the dynamic viscosity of the flow fluid and \(I\) as the incident light intensity. For the seeding particles listed in Table 1 in air flows at room temperature (\(\eta=15\times 10^{-6}\) Pas) and \(I=100\,\text{W}/\text{mm}^{2}\), i.e., assuming a light power of 1 W distributed over a cross-sectional area of 100 um times 100 um, the measurement error is maximal 2 mm/s and 1 mm/s, respectively. For imaging techniques, the light intensity is typically smaller and the resulting error negligible.  
Furthermore, multiple scattering on particles occurs, which can be crucial for Doppler measurement techniques. An introduction to this phenomenon regarding DGV measurements is given in [139].
## 2 Materials and Methods
### Quantitative Real-Time RT-PCR Analyses  
To validate the microarray results, qRT-PCR was performed with a Mic Real-Time Cycler (BioMolecular Systems, Upper Coomera, QLD, Australia) with fluorescent DNA-binding dye using a QUANTUM EvaGreen(r) PCR Kit (Syngen Biotech, Warsaw, Poland) according to the manufacturer's instructions. The primers used to quantify the expression of target genes (Table S1) were chosen based on data from qPrimerDB (version 1.2). Primer specificity was verified by melting curve analysis. qRT-PCRs were performed in triplicate. Each 20-\(\mu\)L reaction mixture contained 4 \(\mu\)L of QUANTUM EvaGreen(r) PCR Kit mix (Syngen Biotech, Warsaw, Poland), two primers (4 pmol of each) and 1 \(\mu\)L of template cDNA, which was synthesized from 240 ng of total RNA treated with TURBO(tm) DNase (Thermo Fisher Scientific, Warsaw, Poland) using a smART First Strand cDNA Synthesis kit (EurX, Gdansk, Poland) according to the supplier's protocol. qRT-PCRs were carried out under the following conditions: 95 \({}^{\circ}\)C for 15 min, followed by 40 cycles of 95 \({}^{\circ}\)C for 15 s, 51.5 \({}^{\circ}\)C for 20 s, and 72 \({}^{\circ}\)C for 20 s. The crossing threshold cycles (Cq) were determined using micPCR software v2.8.10. Then, the fold changes of the gene expression levels, corrected by efficiency, were analyzed. The _ACT1_ gene was used as the normalization reference (internal control) for target gene expression ratios. The Pfaffl method [21] was applied to calculate relative expression with respect to that of _ACT1_.
# Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation
## 3 Carbon Satellite Remote Sensing Inversion Techniques  
Atmospheric components have different sensitive bands for spectral absorption, scattering, and refraction. Based on the inversion theory, the relationship between the concentration of the target gas and the satellite-observed atmospheric absorption spectra can be expressed as a complex nonlinear function, and its mapping relationship includes the effects of emission surface characteristics, temperature, clouds, and the radiative branching of the aerosol contribution. It is also affected by the spectral response function, spectral resolution, and radiative noise associated with the effects related to instrumental characteristics. Obtaining information on atmospheric parameters from these measurements for meteorological applications requires \"inversion\" of the observational process or \"direct assimilation\" of the radiative intensity data into numerical atmospheric models. The first step is to understand the basic theory of atmospheric radiative transfer and the methods of its calculation, and on this basis to establish an accurate model describing the process of radiative transfer in the atmosphere, to provide the basis and conditions for the development of inversion algorithms.
# Structural Identification and Coagulation Effect of _Flammulina velutipes_ Polysaccharides
## 1 Introduction  
_Flammulina velutipes_, one of the edible mushrooms of Flammulina, is popular in Asia, North America, and Europe, and its production and consumption have increased rapidly due to its rich nutritional values and industrialized cultivation [1]. According to the finding of many researchers, polysaccharides, well-known key functional compounds in _F. velutipes_ fruit bodies, have a variety of bioactivities, such as immunomodulatory [2], antitumor [3], antioxidant [4], anti-proliferation [5], anti-Hepatitis B Virus(HBV) activity [6], hepatocyte protection [7], reenprotective effects [8], and so on.  
In our previous study, we found that several polysaccharides have coagulation or anticoagulation activities [9; 10; 11; 12]. Up to date, there is no research on the coagulation effect of the _F. velutipes_ polysaccharides. In this study, the crude polysaccharides, CHFVP-1 and CHFVP-2, were extracted, isolated, and purified from _F. velutipes_. Then, the relative molecular weight, physical and chemical properties, monosaccharide composition, residue segments, linkages, and the possible primary structure of the two _F. velutipes_ polysaccharides were analyzed with corresponding methods. Meanwhile, the coagulation activity of the purified _F. velutipes_ polysaccharides was studied in vitro.
# A Pedestrian Detection Network Based on an Attention Mechanism and Pose Information
## 4 Experiments  
### Datasets  
We conducted experiments on two widely used public datasets: Caltech [17] and CityPersons [12]. We performed ablation studies and compared the results of our method with those of related methods.  
Caltech [17] is a dataset widely used for pedestrian detection. Caltech includes 137 segments of approximately one-minute-long videos, totaling 250,000 frames of annotated images with a resolution of 640 \(\times\) 480. For each image containing a pedestrian, the dataset provides a compact bounding box describing the entire pedestrian. An additional bounding box is marked to outline the visible area of the occluded pedestrian. We only used data from the \"person\" category for the model training and testing. The entire dataset was divided into 11 subsets, with the first 6 subsets (set00-set05) containing 42,782 images used as the training set and the last 5 subsets (set06-set10) containing 4024 images used as the test set.  
CityPersons [12] is a diversified pedestrian detection dataset that evolved from the Cityscapes dataset. CityPersons contains a total of 5000 images (2048 \(\times\) 1024 pixels), including 2975 images in the training set, 500 images in the validation set, and 1525 imagesin the test set. In this study, only the data from the \"pedestrian\" category, which represents walking, running, or standing human targets, were used for the model training and testing. Additionally, as illustrated in Figure 4, the dataset was further divided based on different levels of occlusion: reasonably, barely, partially, and heavily occluded.
# Data Compression with a Time Limit
## 1 Introduction  
Digital technologies have revolutionized our daily lives in a short period. Thanks to mobile devices, modern personal computers, and high-speed mobile networks, we are always connected and can share photos or videos with a click.  
These important changes, along with the new applications emerging from recent innovations, have been readily accepted by end-users. However, these users remain largely unaware of the underlying mechanisms and functions: for example, how the process of playing a video or sending an image over the internet works.  
Whether you surf the internet, listen to a piece of music, watch a video or scroll through images, few people know how fundamental the data compression process is for these activities.  
Communication today is largely based on the exchange of digital information on data networks, such as the internet. In fact, one might say that these infrastructures are now fundamentally based on data compression, which enables easier and faster exchange of digital data and information. Moreover, compression is often combined with encryption to guarantee the privacy of communication.  
Typically, the choice of compression algorithms to use and the technical details to rely on depend on the application that will use the digital file and/or the destination of the message sent.  
The goal of data compression algorithms is to maximize the level of compression given the limitations of processing resources.  
Deciding how to allocate processing resources for compression tasks is not a trivial problem. In this paper, we focus on a framework that begins with multimedia data compression and studies the optimal allocation of compression resources. Our goal is to maximize the efficiency of data compression given limited computational resources, particularly with respect to a time limit (see [1, 2]).  
The paper is structured as follows: Section 2 discusses past related work and the optimization algorithm. Section 3 presents our experimental results for the lossless compression of one-dimensional and two-dimensional data. Section 4 refines the techniqueused for lossless compression to perform efficient lossy compression of images and presents experimental results on lossy compression. Finally, Section 5 provides conclusions and outlines possible new research directions.
# A Small-Sample Target Detection Method of Side-Scan Sonar Based on CycleGAN and Improved YOLOv8
## 2 Theory and Method  
The procedure of this study is shown in Figure 1. First, side-scan sonar target images were collected from the web to construct a small sample dataset. Second, the CycleGAN model was used to transfer optical remote-sensing images to side-scan sonar images for data augmentation. Third, the YOLOv8 model was revised by improving the C2f module, adding an attention mechanism, and updating the loss function.
# Application of Virtual Reality in Learning Quantum Mechanics
## 2 Virtual Teaching Modules
### Gold Foil Experiment  
The nuclear atomic model is an important theoretical model of atomic structure, and it was proposed in 1911 by [PERSON] [30], a New Zealand-born physicist. The formation of this model had a profound impact on our understanding of atomic structure, and it led to the later development of quantum mechanics. In this model, an atom is depicted as an extremely small, dense core in which positively charged protons are concentrated in the center of the core, surrounded by a light cloud of electrons.  
The model was proposed based on the famous gold foil experiment performed by [PERSON] in 1909. In this experiment, [PERSON] and his colleagues emitted a beam of \(\alpha\) particles, helium nuclei containing two protons and two neutrons, at an extremely thin gold foil. They observed that most \(\alpha\) particles passed through the gold foil unimpeded, but some were scattered when impacting the gold foil. This experimental result violated the common atomic model at that time, i.e., the \"plum pudding model\" composed of positrons and negative electrons with uniform mass distribution.  
The virtual gold foil experiment designed in this study allows learners to emit \(\alpha\) particles from the lead block by clicking the eject button (Figure 5) and observe that the electrons passing through the gold foil will produce flash points on the surrounding fluorescent screen. In addition, the learner can also switch the viewing angle to observe the scattering of \(\alpha\) particles as they approach gold atoms.  
Figure 4: Virtual photoelectric effect experiment.  
Figure 5: Virtual gold foil experiment.
## 5 Results and Discussion.
### Cross-Validation of the Estimation Methods  
In order to validate the three interpolation methods for LCV modeling, cross-validation results were compared by employing the correlation coefficients between estimated and measured values. Cross-validation results, which are reported in Figures 17 and 18, can be summarized in the following points:  
1. [label=(0)]
2. Cokriging, which accounts for both spatial correlation and two auxiliary variables, produces a more accurate LCV model than kriging;
3. PCA, which takes into account all the available attributes, showed the highest correlation between true and estimated values of LCV.  
Figure 16: 3D model of LCV (kcal/kg) spatial distribution produced by the application of PCA.  
Figure 17: Scatter plot of measured vs. estimated Lower Calorific Value (LCV) with (**a**) kriging and (**b**) cokriging.  
Moreover, in order to further compare the performance of the previous methods, we plot the statistical distributions of the LCV from samples and the LCV estimated with these methods. The results are shown in Figure 19 where it is apparent that the estimation with PCA is closer to reality.  
Figure 19: Statistical distributions of LCV from (**a**) samples, (**b**) kriging, (**c**) cokriging, and (**d**) PCA.  
Figure 18: Scatter plot of measured vs. estimated LCV with PCA.
# Improving Spatial-Temporal Data Fusion by Choosing Optimal Input Image Pairs
## 1 Introduction
In recent years, many applications have been using one image pair in the data fusion ([PERSON] et al., 2016). [PERSON] and [PERSON] (2017) obtained accurate estimates through the growing season in a semi-arid rangeland based on one base pair image and found that the prediction accuracy declined as the lag between base and prediction dates increased. Therefore, they suggested it was necessary to evaluate the performance of STARFM and the influence of base pair selection and the time lag. Third, the timing of the selected pair images with respect to vegetation growth stage can affect the prediction. For example, pairs from different seasons or phenological stages may produce different results. [PERSON] et al. (2016) analyzed the effects of pair and prediction dates using three cases by choosing data pairs from (1) the same season and the same year; (2) the same season but different years; and (3) different seasons and different years. They found that pairs from different seasons gave the poorest predictions due to inconsistencies between the coarse- and fine-resolution satellite images. [PERSON] et al. (2017) found that a base pair from autumn through winter (October and January) performed better than one from spring and summer (March, June, and July) in a Mediterranean landscape. The main objective of this study is to determine a methodology for choosing optimal input image pairs for the purpose of improving data fusion results. Specifically, we aim to: (1) evaluate the impact of image pairs from different sensor combinations (e.g., MODIS from Terra and Aqua, Landsat 7 ETM+ and Landsat 8 OLI); (2) assess the accuracy of data fusion results when different dates of image pairs are used; (3) assess the data fusion accuracy for different land cover types; and (4) recommend a strategy in data pair selection for achieving better data fusion results. In Section 2, the study areas and the data sources used are described; the STARFM data fusion algorithm and pair selection methods are explained in Section 3. The prediction accuracy based on specific date pairs and different data source combinations are evaluated and analyzed in Section 4. Factors impacting prediction accuracy are discussed in Section 5.
# Data Independent Acquisition Based Bi-Directional Deep Networks for Biometric ECG Authentication
## 3 Methodology
### Extended Data Independent Acquisition  
Unlike the data acquisition technique used in the previous chapter, we utilize random segmentation without applying QRS peak detection, which is available for any type of ECG signal or the signal dataset with no QRS complex. The original signal is blindly  
Figure 4: A framework of data preprocessing phase to carry out the proposed models according to respective datasets.  
Figure 5: (**a**) A raw ECG signal with non-linear trend. (**b**) Detrended ECG signal. (**c**) Filtered ECG signal by applying 6 th order Butterworth filter.  
segmented into segments with an equal length, which is a 2-s window (720 samples) to include at least one heartbeat, since the normal range for heart rate in a signal is from 40 to 280 beats per minute [25]. For each recording, 500 random windows were chosen, half of which were used to train and the other half for testing. The auto-correlation operation is introduced to remove the phase difference due to blind segmentation. The auto-correlation operation is applied to the segmented windows to remove the phase difference that occurs due to random segmentation and thus provides a shift-invariant multi-resolution data representation used in [5], and it is defined as:  
\[Z_{j}^{i}[t]=\sum_{m=0}^{T-t-1}Y_{j}^{i}[m]Y_{j}^{i}[m+t],\forall t\in[0,T-1], \forall i\in[1,W]. \tag{15}\]  
where \(Z_{j}^{i}[t]\) is the \(t\)th sample in the \(j\)th wavelet component of the \(i\)th ECG window after auto-correlation, \(Y_{j}^{i}[m+t]\) corresponds to \(Y_{t}^{i}[t]\) with a time lag of \(m\), \(m\) is chosen from 0 to \(T-t-1\). \(T\) and \(W\) correspond to the number of samples in an ECG window, which is 720, and the number of ECG window which is 500, respectively.  
The autocorrelation function calculates the correlation of a series with its delayed copy, i.e., the similarity between series as a function of the time lag between them [38]. Therefore, it can effectively discover repeating patterns in the quasi-periodic ECG signals even with different numbers and occurrence times of the heartbeats. After removing the phase difference, the multi-resolution data can now be fed to the networks for automatic feature learning and user identification. Figure 6 shows similar outputs when auto-correlation is applied to two different wavelet domain signal segments.
# Hamming Code Strategy for Medical Image Sharing
## 3 Hamming Codes
### Comparisons  
To further demonstrate the advantages of our proposed scheme, we compared the various features, including execution time, visual perception, non-expanded pixel, and lossless recovery, between our schemes and other related works [31; 32; 34; 37]. As can be seen in Table 5, due to the efficiency of computing Hamming code, the execution time of our approach is relatively short, which means that it can be used in most real-time systems. At the same time, the image quality of shadows provided by ours is lower than other schemes [31; 32; 37], indicating that the visual perception of shadows is unable to support the basic conditions of the doctor's diagnosis. And if the diagnosis is really required, the original images also can be recovered losslessly. In a sense, it protects the patient's health information well shown.
## 5 The Challenge of the Bolo Rendezvous and Catching
An approximate correctable course deviation as a function of the rendezvous window duration can be approximated as Equation (4):  
\[\Delta r=\frac{(a_{\max}-a_{\min})(t_{\mathit{rendezvous}}-N_{\mathit{ corrections}}\ t_{\mathit{tag}})^{2}}{4} \tag{4}\]  
If \(N_{\mathit{corrections}}=\)4, \(t_{\mathit{tag}}=\)0.2 s, \(a_{\max}-a_{\min}=\)12 m/s\({}^{2}\), \(t_{\mathit{rendezvous}}=\)3 s, the correctable trajectory error is 15 m, which is within the error margin provided by the commercial GPS. But the same trajectory correction maneuver will cost the upper stage a \(\Delta\
u\) proportional to the rendezvous window duration, which is expressed as Equation (5). \[\Delta\upsilon_{\mathit{loss}}=\frac{a_{\max}-a_{\min}}{2}\left(t_{\mathit{ rendezvous}}+t_{\mathit{engine\_startup}}\right) \tag{5}\]  
Such a \(\Delta\
u\) loss for terminal guidance would be in the order of 100 m/s for the typical upper-stages and a rendezvous window duration of 3 s.  
Payloads with attachment hardware must be designed to disconnect from last stage immediately on successful attachment with the tether tip, and at the same moment the last-stage engine must start the shutdown to prevent re-contact with the payload. Engine shutdown speed requirements are expected to be less severe compared to normal stage separation due to the payload acceleration provided by the tether. In Figure 8, a tentative concept of payload capture-associated hardware for LEO bolo is shown. The concept is derived from the Corona satellite recovery scheme and explained in detail below. **Figure 8.** A tentative hook-and-loop catch mechanism for LEO bolos. (1) tether; (2) stiffening rods; (3) load redistribution ring; (4) shock absorbing ropes; (5) saddle for hook; (6) hook release (pyrotechnic-actuated hinge); (7) payload hook; (8) payload; (9) payload adapter to rocket last stage. up-scaling normal rock climbing gear to the loads in Table 3. The pyrotechnic bolt (6) or other actuator is necessary to provide a rapid payload release, and must have a weight below 0.5 kg. The 3-m-long payload hook (7) is the most weight-critical component because it must be launched with each payload, and also requires increased payload fairing size. Although the hook itself will be about 2 kg (less than 1% of payload mass as calculated in Section 6), the added weight and size of fairing may increase gravity loss and air drag \(\Delta\)v requirements. Therefore, such a hook system strongly favors booster design without the need of fairing. Such designs were proposed in 2012 by [PERSON] [42]. The payload (8) must be strengthened to accommodate additional stress of tether catch, but because in the proposed configuration, the main shock is aligned with normal rocket booster thrust vector, payload weight penalty is likely going to be negligible. The payload adapter (9) of the Ariane-5 rocket, as described in [43], is completely adequate for the tether catch operation. Therefore, no further development is necessary on payload adapter.