esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Latitude Impact Analyses  
The 258 gauges over the TP were reorganized with increasing latitudes (northward), and their daily rainfall intensity was plotted according to time and latitude. Figure 9 presents a series of continuous rainfall events from the gauge, IMERG and 3B42V7 datasets in the warm season of 2014 when the Indian monsoon prevailed over the plateau. Before June, sporadic rainfall activities occurred from satellite products (i.e., IMERG and 3B42V7) and gauge measurements. Additionally, there was intermittent rainfall activity captured by both satellite products at the beginning of the Indian monsoon in early May 2014. It gradually weakened from latitudes of 26.5\({}^{\circ}\)-32.5\({}^{\circ}\) in the plateau.  
Moisture surge events dominated the plateau from early June to mid-September in 2014, which were characterized by abundant rainfall activities from north to south for both satellite products and gauge observations. For instance, there were several latitudinal gradients of rainfall occurring from mid-June to the end of August, which were regarded as typical moisture surge events over the TP. Within the gauge-based domain, an insignificant latitudinal gradient existed during the mature phase of the Indian monsoon when rainfall should be relatively widespread across all latitudes. Over the plateau, rain gauges are often installed at low altitudes or sites with residents. It is worth noting that [37] proposed that moisture surge events tend to influence low-elevation stations more than middle or high elevations, and the gauge-based rainfall presented in Figure 9 might ignore such an elevation-specific response.  
In addition, ground-based gauges failed to capture rainfall events during the end of the Indian monsoon period in September. We deduce that sparse gauges are the primary reason; however, the specific reason needs to be further investigated in the future. Overall, both satellite products could  
Figure 8: Comparison of IMERG and gauge-based daily rainfall in the warm season of 2014 over the TP: (**a–d**) the probability of raining days, light rainfall (0–10 mm/d), moderate rainfall (10–25 mm/d) and heavy rainfall (\(\gtrsim\)25 mm/d) in the warm season as the elevation increases. Note that the grey square stands for 258 gauges recorded according to elevation; the black line denotes gauge observations; the red one stands for the IMERG estimates; and the blue one is the 3B42V7 products.  
detect rainfall activities synchronously under the domination of Indian monsoon periods, and IMERG does not show obvious superiority to 3B42V7 during the comparison.
# Compact 15 mJ Fiber-Solid Hybrid Hundred-Picosecond Laser Source for Laser Ablation on Copper
## 3 Experimental Results and Discussion
### Compensation for Self-Focusing and Thermally Induced Brefringence Effect
Furthermore, a negative lens L1 with a focal length of \(-300\) mm is inserted to compensate for the thermal lens effect of Nd:YAG 2. The Nd:YAG double-pass amplifier with this structure can obtain ideal thermal birefringence compensation. We simulate the change of the mode field diameter of the Nd:YAG double-pass amplifier, as shown in the inset in Figure 6. This illustrates that the principle of laser beam transmission is to maintain the propagation of parallel light as much as possible, and the laser spot diameter in the center of the Nd:YAG 2 is the largest. After being amplified from the regenerative amplifier, we accurately control the incident single pulse energy, and SA introduces additional energy loss. Finally, the 0.8 mJ hundred-picosecond pluses are injected to QCW laser diode side-pumped Nd:YAG double-pass amplifier. The Nd:YAG 2 is operated at a pump power 420 W, and a total pulse energy of 14.8 mJ is obtained. The output power is detected behind the HWP5 and PBS, which is reduced to 14.58 mJ. Therefore, the depolarization rate is 1.5%. The main reason for not achieving complete compensation of thermally induced depolarization is the deviation of the focal length of the L1 and the misalignment of the optical path.
## 4 Healthcare Knowledge Management Systems
### Brain Hemorrhage Diagnosis Support
#### 4.2.2 Testing Phase  
After the training process, we evaluate the proposed model for brain hemorrhage detection and classification on the test dataset. The preprocessing and feature extraction are also performed on the testing set before evaluating the model. The trained Faster R-CNN Inception ResNet v2 is then used to detect and classify four common types of brain hemorrhage. It can correctly detect the contours of entire hemorrhage regions with an accuracy of 100%. An example of multiple hemorrhages detection on an image is presented in Figure 15. It can predict bleeding time from 2 to 3 days, recognize hemorrhage type as ICH and SAH, and accurately segment bleeding regions.  
The average precisions (AP) of the proposed model for four types of brain hemorrhage (EDH, SDH, SAH, and ICH) are 0.7, 0.59, 0.72, and 0.71, respectively (Figure 16). This model gives the mAP value of 0.68 for the detection and classification of four classes of brain hemorrhage. The results show that the system can support doctors in accurately diagnosing cerebral hemorrhage and providing appropriate treatment regimens.  
Figure 14: Loss values over training steps.  
Figure 15: Multi- brain hemorrhages segmentation.
## 5 Results
### Flows over the ONERA M6 Wing  
Finally, flows over an aerodynamic ONERA M6 wing [41, 42, 43] are considered to validate the developed DG code in relatively more practical simulations. Nearly incompressible flow at Mach number \(Ma=0.01\) and typical transonic shocked flow at \(Ma=0.84\) with the same angle of attack (\(\alpha=3.06^{\circ}\)) are selected and tested to show the abilities of coping with both nearly incompressible and compressible flows for the methods developed. A relatively coarse mesh, having only 62,897 tetrahedron elements, is generated and used in the present third-order (\(p=2\)) accurate flow simulations mentioned. The corresponding meshes on the surface of the wing and in the symmetric plane are presented in Figure 14.  
Figure 12: Contours of the Mach number over hemispherical headform. (**a**)With curved boundary treatment; (**b**)without curved boundary treatment.  
Figure 13: Surface pressure coefficients over hemispherical headform (Cao [35], Kazem [39], Experiment [40]).  
In Figure 15, the obtained contours of the pressure coefficient on the surface of the wing are first presented for the mentioned transonic case, together with the isolines of the pressure coefficient in the symmetric plane, in order to have a global view of captured flow fields. The corresponding distributions of the surface pressure coefficient at six typical sections along the wingspan are also presented in Figure 16. It can be observed that the obtained results at the sections are all in a reasonable agreement with the numerical results [41] and experimental data [42] in view of the strength or location of the captured shock.  
Figure 14: Meshes (wing surface, symmetric plane).  
Figure 15: Contours of pressure coefficient (\(Ma=0.84\), \(\alpha=3.06^{\circ}\)).  
Following the Reference [43], the obtained distributions of the surface pressure coefficient at the specific wingspan section \(\eta=0.80\) are then presented (see Figure 17) for the case of the nearly incompressible flow mentioned. It can be noted that the results captured are in good agreement with other numerical results [43]. The corresponding contours of the surface pressure coefficient of the wing, together with the field isolines of the pressure coefficient in the symmetric plane, are also presented in Figure 18 to have a view of flow fields. As shown in Figure 19, a significant improvement is again achieved in the convergence rate when preconditioning is adopted. The successful flow simulations of the surface pressure coefficient of the wingspan section \(\eta=0.84\) are presented in Figure 19.  
Figure 16: Distributions of surface pressure coefficients at six wingspan sections (Mani [41], Experiment [42]). (**a**) \(\eta=0.20\); (**b**) \(\eta=0.44\); (**c**) \(\eta=0.65\); (**d**) \(\eta=0.80\); (**e**) \(\eta=0.90\); and (**f**) \(\eta=0.95\).
## 4 Discussion  
### Correlation Analysis of Impact Sound Reduction Performance According to Bare Slab Thickness  
A correlation analysis was conducted on the reduction performance of the same floor mat with the same floor structure. To examine the reliability of the measurement and evaluation methods specified in KS F 2865, a correlation analysis was conducted on the impact sound reduction performance and single-number quantity evaluation. The Korea standard code of KS F 2865 specifies a requirement for bare slab thicknesses to be in the range of 120 mm to 210 mm for measuring and evaluating floor covering materials [36]. An analysis was conducted to determine the correlation between impact sound reduction performance and slab thickness. The evaluation was based on the single-number quantities derived at slab thicknesses of 150 mm, 180 mm, and 210 mm. The impact sources analyzed were a tapping machine, bang machine, and rubber ball.
# Topological Interference Management for K-User Downlink Massive MIMO Relay Network Channel
## 6 The \(K\)-User Relay-Aided MIMO Interference Channel
The relay transmits the signal vector over the time slot n in the form of \(\mathbf{\hat{X}}^{[R]}[n]\!\!=\!\!\beta(\mathbf{V}_{k}^{[k]}(n)\hat{\mathbf{u} }_{M}^{[k]})\)  
\[\mathbf{y}_{j}^{k}(n)=\beta\mathbf{h}_{j,i}^{[R,k]^{T}}(n)\sum_{j=1}^{K} \mathbf{V}_{k}^{[k]}(n)\hat{\mathbf{u}}_{M}^{[k]}+Z_{j}^{[R]}(n). \tag{38}\]  
It is important to design the beamforming matrices for aligning the interference signals from the transmitter and relay. To accomplish this goal, the beamforming matrices should satisfy the consecutive R-STIA conditions:  
\[\underbrace{ \begin{bmatrix}\mathbf{h}_{[c_{1}]}^{[K]^{T}}(k)\\ \mathbf{h}_{[c_{2}]}^{[(K-1),k]^{T}}(k)\\ \mathbf{h}_{[c_{3}]}^{[(K+1),k]^{T}}(k)\\ \cdot\\ \cdot\\ \cdot\\ \cdot\\ \mathbf{h}_{[c_{k}]}^{[K,k]^{T}}(k)\\ \end{bmatrix} }_{K-1\times M}=\beta\underbrace{ \begin{bmatrix}\mathbf{h}_{[c_{ 1}]}^{[1,R]^{T}}(n)\\ \mathbf{h}_{[R_{2}]}^{[(K-1),R]^{T}}(n)\\ \mathbf{h}_{[R_{k}]}^{[(K+1),R]^{T}}(n)\\ \cdot\\ \cdot\\ \cdot\\ \cdot\\ \mathbf{h}_{[R_{k}]}^{[K,R]^{T}}(n)\\ \end{bmatrix} }_{K-1\times M}\underbrace{\mathbf{V}_{k}^{[k]}(n)}_{M\times M}.\]  
It is necessary to design the beamforming matrix, \(\mathbf{V}_{k}^{[k]}(n)\), such that the intended receivers receive the same information during the time slots, until the data symbols are recovered. Where \(n\) represents time slots, \(n\in(1,2,...,k)\),  
\[\mathbf{V}_{k}^{[k]}(n)=\frac{1}{\beta}(\mathbf{\hat{H}}(n)^{\dagger}(\mathbf{ \hat{H}}(n)\mathbf{\hat{H}}(n)^{\dagger})\mathbf{\hat{H}}(n)). \tag{39}\]\(K\)-users transmit \(M\) data symbols, where the beamforming matrices are \(\mathbf{V}_{k}^{[k]}(n)=\mathbb{C}^{M\times M}\). To attain this objective, the beamforming matrices should satisfy the R-STIA condition \(\mathbf{\hat{H}}(n)=\beta\mathbf{\hat{H}}(n)\mathbf{V}_{k}^{[k]}(n)\), where \(\mathbf{\hat{H}}(n)\) represents the channel matrices containing the desired channel coefficients, after applying \(\mathbf{V}_{k}^{[k]}(n)\) to the relay beamforming matrix. \(\mathbf{\hat{H}}(n)\), is the channel matrix from the relay to all the receivers in the \(n\)th time slot. The complementary matrix, \(\mathbf{H}_{[\alpha]}^{[K,k]^{T}}(n)\), is denoted as follows:  
\[\mathbf{H}_{[\alpha]}^{[K]^{T}}(n)=[\mathbf{h}_{[\alpha_{1}]}^{[K ]^{T}}(n),\mathbf{h}_{[\alpha_{2}]}^{[K]^{T}}(n),...,\mathbf{h}_{[\alpha_{k}]} ^{[K]^{T}}(n)]. \tag{40}\]  
Therefore, we can calculate the relationship between the input and output channel coefficients:  
\[\Longrightarrow\mathbf{\hat{y}}_{c_{1}}^{[1]}(1)=\mathbf{h}_{[c_{1}]}^{[1,1]^{T}}(1)\{\mathbf{u}_{[1]}^{1}+\mathbf{u}_{[2]}^{1}\}+\mathbf{\hat{z}}_{[ c_{1}]}^{[1]}(1), \tag{41}\]  
where \(n=\{1,2,3,...,K\}\). We can conclude that interference-free communication is achievable without CSIT knowledge at the base station, provided the Alamouti space-time transmission technique and the MMSE precoder method are used for managing and aligning the interference signals using the delayed CSIR knowledge between the base stations and the mobile users.
# Initial Assessment of the LEO Based Navigation Signal Augmentation System from Luojia-1A Satellite
###### Abstract  
A low Earth orbiter (LEO)-based navigation signal augmentation system is considered as a complementary of current global navigation satellite systems (GNSS), which can accelerate precise positioning convergence, strengthen the signal power, and improve signal quality. Wuhan University is dedicated to LEO-based navigation signal augmentation research and launched one scientific experimental satellite named Luojia-1A. The satellite is capable of broadcasting dual-frequency band ranging signals over China. The initial performance of the Luojia-1A satellite navigation augmentation system is assessed in this study. The ground tests indicate that the phase noise of the oscillator is sufficiently low to support the intended applications. The field ranging tests achieve 2.6 m and 0.013 m ranging precision for the pseudorange and carrier phase measurements, respectively. The in-orbit test shows that the internal precision of the ephemeris is approximate 0.1 m and the clock stability is \(3\times 10^{-10}\). The pseudorange and carrier phase measurement noise evaluated from the geometry-free combination is about 3.3 m and 1.8 cm. Overall, the Luojia-1A navigation augmentation system is capable of providing useable LEO navigation augmentation signals with the empirical user equivalent ranging error (UERE) no worse than 3.6 m, which can be integrated with existing GNSS to improve the real-time navigation performance.  
Luojia-1A satellite; satellite-based augmentation system; GNSS signal augmentation; LEO navigation +
Footnote †: journal: Physics Letters B
# In Vitro Antibacterialential Activity of Marine Microalgae Extract against _Vibrio harveyi_
## 2 Materials and Methods
### Disc Diffusion Antibacterial Assay  
The various microalgae extractions were tested for their antibacterial activity against the pathogenic bacteria strains of _V. harveyi_. A modified agar disc diffusion assay method was used to investigate the antibacterial activity of algal extracts [18]. Petri dishes containing TSA and 2% (_w/v_) NaCl were seeded with _V. harveyi_ inoculum at three different concentrations: 10\({}^{5}\) CFU mL\({}^{-1}\), 10\({}^{6}\) CFU mL\({}^{-1}\), and 10\({}^{7}\) CFU mL\({}^{-1}\). To test the extracts' activity, sterile filter paper discs (6 mm) were impregnated with 20 \(\upmu\)L of the various algal extracts. The discs were allowed to dry at room temperature before being placed on test plates inoculated with _V. harveyi_. As a control, discs with the same volume of extractants (20 \(\upmu\)L) were made. The plates were incubated for 48 h at 35 \({}^{\circ}\)C. Extracts containing antibacterial components produced distinct, clear, and circular zones of inhibition around the filter discs, and this positive activity was quantified by measuring the growth inhibition zone (mm) surrounding the discs after 24 h and scoring them based on the diameter of the inhibition zone [19].
## 4 Effect of the Injection Rate Shape
### Microscopic Spray Characteristics
The same spray cone angle measurement results are shown in Figure 15 (right graph) for the same peak injection rate (same peak rail pressure) with different injection rate shapes; the results agree with those in the previous section. The shape with a peak rail pressure of 600 MPa and a spray cone angle of 43\({}^{\circ}\) will have spray cone angle larger than approximately twice that of the peak rail pressure of 150 MPa (21\({}^{\circ}\)). Figure 15 (left graph) shows a difference in the spray angle event under the same peak rail pressure. The spray angle increases when the acceleration of the injection rate increases. It can be observed that the QIGD injection rate is significantly small but features a continuous decrease in the spray angle compared to the other injection rates. On the other hand, the GIQD injection rate recorded the largest spray angle at the beginning, followed by a slight decrease, and then remained constant (compared to the other injection rates). This phenomenon demonstrates that the increased injection acceleration rate has a significant effect on the fuel distribution ability, which directly affects the spray angle size. The spray angle is higher when the injection rate is higher because the injection rate can increase the rail pressure, thereby resulting in a higher particle force that can cause a higher penetration force leading to better distribution. The spray angle is an important parameter that helps us understand the global characteristics of the spray. The spray angle and quantity evaluation provide useful information about the airflow in the spray [29; 30; 31], where the spray angle is an indicator of gas. In general, the greater the spray angle, the higher the increase in gas entrainment, resulting in improved mixing [32]. Another interesting observation is the effect of the injection rate shapes on spray penetration. The simulation results reveal longer vapor penetration at a higher peak injection rate due to the efficient spray distribution, while the lower peak injection rate yielded poor vapor penetration because the low injection rate resulted in poor spray distribution and spray penetration performance. A spray tip that penetrates too long will result in wet combustion chamber walls, causing excessive soot formation and a waste of fuel. On the other hand, if the penetration time is too short, the mixing efficiency and optimum combustion will be compromised. In addition, the simulation results show that the QIGD injection rate with a high initial fuel injection rate quickly causes the initial penetration. This suggests that the injected fuel is very well atomized and has a significant effect on the onset speed of the combustion phenomenon. In the case of the GIQD injection rate, a very low initial injection rate may result in poor fuel atomization and cause an increase in the ignition delay. The evaporation process and mixing behavior will be discussed further in the next section. Figure 15: Comparison of the spray distribution angle for different injection rate shapes. (**a**) Spray angle and (**b**) spray cone angle.
## 4 Experiments  
### Datasets and Settings  
Our model was tested using the publicly available Voice Bank + DEMAND dataset, which consists of speech recordings from the CSTR VCTK Corpus and background noise from the DEMAND database. The training set includes 11,572 sentences provided by 28 speakers, and the test set includes 824 sentences provided by 2 unseen speakers. To generate the training set, we used eight natural and two artificial background noise processes under different SNR levels (ranging from 0 to 15 dB with an interval of 5 dB), while the test set was generated using five unseen background noise processes under different SNR levels (ranging from 2.5 to 17.5 dB with an interval of 5 dB).  
The sampling rate of all sentences was 48 kHz and, in the training set, they were sliced into 3 s units, while no slicing was done in the test set. A Hanning window of length 20 ms and a hop length of 10 ms were applied. After the STFT process, a power law compression with a compression coefficient of 0.5 was applied to the spectrum, and then reversed on the final estimated complex spectrum. Finally, the inverse STFT was applied to recover the time-domain signal. The AdamW optimizer was used to train both the generator and discriminator for 100 epochs. In the first stage, the learning rate of the LB-Net was set to \(1\times 10^{-3}\). In the second stage, the learning rate of the LB-Net was fine-tuned to \(1\times 10^{-4}\), while the learning rate of the MB-Net and the HB-Net was set to \(1\times 10^{-3}\).
# An Intelligent Multimodal Biometric Authentication Model for Personalised Healthcare Services
## 2 Background and Related Works
### Machine Learning Algorithms
#### 2.1.3 KNN  
KNN is a simple supervised algorithm that relies on the assumption that data with similar attributes will most likely have similar outcomes [41]. In KNN, the decision is taken based on the similarity between a given training and test sets. The training examples are asserted using a number \(n\) of attributes, and each of these attributes denotes a point in n-dimensional space using distinct classes. To predict the unknown dataset, it calculates the closest distance between the K training sets. A given dataset is \(DS\), where \(D\) is a matrix of features from a data point, and \(L\) is a class label. KNN then will estimate the conditional distribution of \(L\) given \(D\) and classify a data point to the class with the highest probability. Given a positive integer \(k\), KNN looks at the \(k\) observations closest to a test data point \(d_{0}\) and estimates the conditional probability that it belongs to class \(c\) using the following formula:  
\[P(L=c|D=d_{0})=\frac{1}{k}\sum_{i\in\mathcal{S}_{0}}I(l_{i}=c), \tag{6}\]  
where \(S_{0}\) is the set of k-nearest observations and \(I(l_{i}=c)\) is an indicator variable that is equal to 1 if a given data point \(d_{i_{t}},l_{i}\) in \(S_{0}\) is a member of class \(c\), and 0 if otherwise. After estimating these probabilities, KNN classifies the data point \(d_{0}\) under the class in which the previous possibility is the greatest.
# Code Edit Recommendation Using a Recurrent Neural Network
## 7 Discussion  
In this section, we discuss why CERNN works, and then explain the additional experiments to evaluate the time performance of CERNN.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Project**} & \multicolumn{5}{c}{**CERNN (W = 3)**} & \multicolumn{5}{c}{**MI-EA**} \\ \cline{2-9}  & **P** & **R** & **F1** & **\#R** & **P** & **R** & **F1** & **\#R** \\ \hline Mylyn & 0.77 & 0.52 & 0.62 & 3728 & 0.79 & 0.62 & 0.69 & 1096 \\ Platform & 0.86 & 0.59 & 0.70 & 995 & 0.85 & 0.34 & 0.48 & 977 \\ PDE & 0.65 & 0.49 & 0.56 & 114 & 0.52 & 0.59 & 0.55 & 144 \\ ECF & 0.73 & 0.58 & 0.65 & 47 & 0.39 & 0.58 & 0.46 & 45 \\ MDT & 1.00 & 0.82 & 0.90 & 103 & 1.00 & 0.41 & 0.58 & 19 \\ Avg./Total & 0.80 & 0.60 & **0.69** & 4987 & 0.71 & 0.51 & **0.55** & 2281 \\ Weighted & 0.79 & 0.54 & **0.64** & 4987 & 0.79 & 0.50 & **0.59** & 2281 \\ Avg./Total & & & & & & & & \\ \hline \end{tabular}  
* P denotes Precision, R Recall, F1 F-score, and #R the number of recommendations.  
\end{table}
Table 4: Comparison of the recommendation accuracies of CERNN (W = 3) and MI-EA when stopping recommendations if the first edit is found to be false.