esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Driving Mechanisms and Relative Contributions to NPP Variability
#### 3.5.2 Relative Contributions of Different Influential Factors to NPP Variations  
In this section, the relative contributions of human activities and climate change to NPP changes in the YRB are calculated based on the slopes of NPP, HNPP, and PNPP variations (Figure 11). Overall, human-induced interventions contribute 59.28% to the total, while climate change contributes 40.01%. Further analysis of the NPP improvement areas shows that the relative contributions of human-induced interventions and climate variability are 59.95% and 39.70%, respectively. In the NPP degradation areas, the relative contributions of human-induced interventions and climate variability account for 56.12% and 41.46%, respectively. In the entire study area, human intervention predominates, accounting for 4.69% of the NPP improvement areas. Climate change, on the other hand, dominates, constituting 2.83% of the NPP improvement areas. Regions where human activities contribute more than 50% to NPP account for 53.76% and are mainly located in the northwest of the Jinsha River Basin, the western part of the Hanjiang River Basin, and the eastern part of the Jialing River Basin. Regions where climate change contributes more than 50% to NPP account for 25.27% and are scattered across various river basins.
# A Comparative Analysis of Feature Detectors and Descriptors for Image Stitching
## 2 Overview of Feature Detectors and Descriptors
### [PERSON] et al. [51] presented the Binary Robust Independent Elementary Features (BRIEF) descriptor, which is based on binary strings. First, binary strings are computed from image patches, then the individual bits are generated by comparing the brightness of pairs of points along the same lines. BRIEF is fast compared to other descriptors such as SIFT and SURF because SIFT and SURF have a high-dimensional descriptor vector, which requires more time and memory to compute. It can be used with the detectors which do not have their own descriptors.
# Modeling of Downlink Interference in Massive MIMO 5G Macro-Cell +
###### Abstract  
Multi-beam antenna systems are the basic technology used in developing fifth-generation (5G) mobile communication systems. In practical implementations of 5G networks, different approaches are used to enable a massive multiple-input-multiple-output (mMIMO) technique, including a grid of beams, zero-forcing, or eigen-based beamforming. All of these methods aim to ensure sufficient angular separation between multiple beams that serve different users. Therefore, ensuring the accurate performance evaluation of a realistic 5G network is essential. It is particularly crucial from the perspective of mMIMO implementation feasibility in given radio channel conditions at the stage of network planning and optimization before commercial deployment begins. This paper presents a novel approach to assessing the impact of a multi-beam antenna system on an intra-cell interference level in a downlink, which is important for the accurate modeling and efficient usage of mMIMO in 5G cells. The presented analysis is based on geometric channel models that allow the trajectories of propagation paths to be mapped and, as a result, the angular power distribution of received signals. A multi-elliptical propagation model (MFM) is used and compared with simulation results obtained for a statistical channel model developed by the 3 rd Generation Partnership Project (3 GPP). Transmission characteristics of propagation environments such as power delay profile and antenna beam patterns define the geometric structure of the MPM. These characteristics were adopted based on the 3 GPP standard. The obtained results show the possibility of using the presented novel MPM-based approach to model the required minimum separation angle between co-channel beams under line-of-sight (LOS) and non-LOS conditions, which allows mMIMO performance in 5G cells to be assessed. This statement is justified because for 80% of simulated samples of intra-cell signal-to-interference ratio (SIR), the difference between results obtained by the MPM and commonly used 3 GPP channel model was within 2 dB or less for LOS conditions. Additionally, the MPM only needs a single instance of simulation, whereas the 3 GPP channel model requires a time-consuming and computational power-consuming Monte Carlo simulation method. Simulation results of intra-cell SIR obtained this way by the MPM approach can be the basis for spectral efficiency maximization in mMIMO cells in 5G systems.  
5G; downlink; interference; signal-to-interference ratio (SIR); massive MIMO; multi-beam antenna system; multi-elliptical propagation model; 3 GPP standard +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 3 Results and Discussion--Geometric Conditions  
### Effect of the Enlarged Part  
Figure 6 shows the load-horizontal displacement curve based on the enlarged height. The yield load was calculated by the load (P)-displacement (S) method, and the failure load was selected as the load at the maximum allowable displacement (195.5 mm) of the cable. The maximum allowable displacement of the Ulsan Grand Bridge-with its span length of 1150 m-was calculated as follows: maximum allowable displacement = 0.017 \(\times\) span length = 0.017 \(\times\) 1,150,000 mm = 195.5 mm. As shown in Figure 6, the failure load rapidly increased due to the installation of the enlargement H. When the enlarged height was 4.0 m or higher, the rate of increase of the failure load slowed down.  
Figure 7 shows the surrounding soil failure mode and the angle based on the enlarged height of the tunnel-type anchorage. The failure angle refers to the angle made between the boundary surface of the surrounding rock (which was in parallel to the load-applied direction) and the failure surface in the enlargement of the tunnel-type anchorage. When the enlarged height of the anchorage was 0 m, the failure angle was 0\({}^{\circ}\), exhibiting a pull-out failure shape. The results reveal that enlarged heights of 2.0 m, 4.0 m, 6.0 m, and 8.0 m had failure angles of 14\({}^{\circ}\), 20\({}^{\circ}\), 22\({}^{\circ}\), and 23\({}^{\circ}\), respectively, and their failure shape was a wedge. When the wedge failure angle increases, the pull-out resistance of the anchorage due to the pull-out load of the cable also increases, thereby ensuring the stability of the anchorage. The ratio increase values (failure angle for each enlarged height/failure angle at 2.0 m of the enlargement height) of the failure angle based on the enlarged height were 1.0, 1.43, 1.57, and 1.62. When the enlarged height was 4.0 m, the ratio of the failure angle increased by 43%; however, the ratio increase of the failure angle above the enlargement height of 4.0 m decreased to 57% and 62%.  
Figure 5: Enlarged height and spacing of the tunnel-type anchorage: (**a**) enlarged height and (**b**) spacing.  
Figure 6: Load–horizontal displacement curve with enlarged height.
## 6 Results and Discussions
### Results of Installing a Portable Energy System  
An extra portable energy system installed in the demand side is normally used as the backup generators to improve system security. However, in this part, simulation results are mainly focused on demonstrating that the added portable energy system has great potential in participating in a demand-response program, which can enhance network peak load regulating capacity and improve system economy.  
Table 4 shows the optimal output power of different energy carriers in the traditional microgrid which does not have any portable energy system installed. To prove the added portable energy system has a great potential in participating in demand-response and enhancing network peak load regulating capacity, Table 5 shows the optimal output power of different energy carriers in the traditional  
Figure 5: The optimal output power of different energy generators at \(\beta=0.9\).  
Meanwhile, the microgrid starts to sell electrical power to the main grid. It is worth noting that by adding a portable renewable energy system to participate in a demand-response program, the output of CHP can be reduced at this time, and this allows reduction of fossil fuel cost. In the fourth period, with the slight decrease of network electrical loads, the fuel cell decreases its output, and meanwhile, the battery is charged in this period. Next, in the fifth period--the second peak demand time of day--with installation of a portable energy system, the output of the CHP system can be reduced about 14%, which reduces fossil fuel cost. Additionally, by installing the portable energy system, instead of importing high-price power from the main grid, the microgrid can sell electrical power back to the main grid at a relatively high price. In this way, system economy can be improved. Finally, for the last period, the demands are relatively low, and therefore the outputs of all units are reduced.  
In conclusions, the output power of CHP is significantly reduced by installing portable renewable energy resources. Moreover, the added renewable energy resources can cooperate with other local generators to supply power at peak times and deliver redundant power back to the main grid at a higher price. By installing the portable energy system, network peak load regulating capacity has been significantly improved and system daily operational cost is reduced by 28.29%.
###### Abstract  
This study explores the efficiency and applicability of a HONO collection system that incorporates an ultrasonic nozzle and spray chamber for the measurement of ambient air. The system demonstrates (1) a remarkable efficiency of 97.7% across two serial stages, (2) lower detection limits of 0.15 ppb for HONO, and (3) an absence of interference from NO\({}_{2}\) or OH radicals. Practical ambient monitoring with the HONO collection system revealed typical diurnal variations in HONO, O\({}_{3}\), and HNO\({}_{3}\) concentrations, aligning with photolysis dynamics. Notably, HONO concentrations peaked at 0.37 ppb during nighttime and decreased to 0.27 ppb by midday. O\({}_{3}\) demonstrated an inverse relationship with HONO, especially during ozone depletion phases, with \(r^{2}\) values of 0.94, 0.81, and 0.52 across various intervals. The HONO/NO\({}_{x}\) ratio during periods of enhanced HONO suggested the presence of additional formation mechanisms beyond heterogeneous NO\({}_{x}\) reactions. Moreover, ozone levels often fell below 20 ppb, indicating a consistent inverse correlation with HONO, thereby reaffirming further mechanisms of HONO formation beyond heterogeneous NO\({}_{x}\) reactions. The real-time atmospheric chemical reactions involving HONO, monitored concurrently with O\({}_{3}\) and NO\({}_{x}\), were effectively validated by the HONO collection system employed in this investigation.  
HONO; ultrasonic nozzle; spray chamber +
Footnote †: journal:Previous research has explored various strategies for measuring atmospheric HONO. [PERSON] et al. [16] reported HONO levels using a high-efficiency denuder scrubber paired with ion chromatography (IC), noting an average concentration of 0.36 ppb. Moreover, [PERSON] et al. [17] utilized a quantum cascade-tunable infrared laser differential absorption spectrometer, recording an average concentration of 0.93 ppb. [PERSON] et al. [11] implemented the MARGA to concurrently measure HNO3 and HONO at hourly intervals, finding concentrations that spanned roughly 1 to 7 \(\mathrm{\SIUnitSymbolMicro g}\)/m3.  
In this research, a spray chamber integrated with an ultrasonic nozzle system was developed to collect HONO while simultaneously measuring PM2.5 and other gaseous components. The ultrasonic nozzle system, applied in diverse fields such as food, pharmaceuticals, and coatings, is noted for its ability to generate droplets of consistent size [18; 19; 20]. This system offers economic benefits by facilitating the application of IC under similar conditions employed in a conventional PM2.5 ion component analysis.
## 7 Experiments
### Analysis of Text Representation and Pre-Processing Influence on Deep Learning Models
It is possible that the Twitter embeddings are built using the data closer to the domain (as tweets may be similar to the comments). We used F1 metric to select the best performing model, Twitter GloVe embeddings without pre-processing was the best method from that point of view. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Accuracy** & **AUC Score** & & **Precision** & **Recall** & **F1 Score** \\ \hline
**TF-IDF** & 0.8971 & 0.8437 & micro avg.: & 0.60 & 0.05 & 0.09 \\  & & & macro avg.: & 0.33 & 0.02 & 0.03 \\ \hline
**TF-IDF + PP** & 0.9038 & 0.8533 & micro avg.: & 0.70 & 0.23 & 0.35 \\  & & & macro avg.: & 0.35 & 0.13 & 0.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance of the composed BiLSTM + CNN model with TF-IDF text documents representation. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Accuracy** & **AUC Score** & & **Precision** & **Recall** & **F1 Score** \\ \hline
**word2 vec** & 0.8835 & 0.9791 & micro avg.: & 0.62 & 0.74 & **0.67** \\  & & & macro avg.: & 0.58 & 0.53 & **0.51** \\ \hline
**word2 vec + PP** & 0.8787 & 0.9769 & micro avg.: & 0.61 & 0.71 & 0.65 \\  & & & macro avg.: & 0.59 & 0.45 & 0.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance of the composed BiLSTM + CNN model with gensim word2 vec embeddings. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Accuracy** & **AUC Score** & & **Precision** & **Recall** & **F1 Score** \\ \hline
**Common Crawl GloVe** & 0.8904 & 0.9796 & micro avg.: & 0.63 & 0.72 & 0.67 \\  & & & macro avg.: & 0.60 & 0.52 & 0.53 \\ \hline
**Common Crawl GloVe + PP** & 0.8621 & 0.9766 & micro avg.: & 0.54 & 0.79 & 0.64 \\  & & & macro avg.: & 0.47 & 0.55 & 0.46 \\ \hline
**Twitter GloVe** & 0.8787 & 0.9798 & micro avg.: & 0.59 & 0.77 & **0.67** \\  & & & macro avg.: & 0.57 & 0.59 & **0.56** \\ \hline
**Twitter GloVe + PP** & 0.8903 & 0.9771 & micro avg.: & 0.65 & 0.67 & 0.66 \\  & & & macro avg.: & 0.63 & 0.51 & 0.54 \\ \hline fastText & 0.8839 & 0.9787 & micro avg.: & 0.61 & 0.73 & 0.66 \\  & & & macro avg.: & 0.58 & 0.52 & 0.51 \\ \hline fastText + PP & 0.8947 & 0.9749 & micro avg.: & 0.63 & 0.70 & 0.67 \\  & & & macro avg.: & 0.49 & 0.44 & 0.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance of the composed BiLSTM + CNN model with pre-trained GloVe and fastText embeddings.
## 3 Optically Pumped Lasers
### Vertical Cavity Surface Emitting Lasers (VCSELs)
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Active Material** & **Cavity Geometry** & **Threshold** & **Peak wav. (nm)** & **Pump Pulse Length** & **Pump wav. (nm)** & **Ref.** \\ \hline FAPBr\({}_{3}\) & DBR-DBR VCSEL & 18.3 \(\mu\)cm\({}^{-2}\) & 552.4 & 340 ps & 355 & [81] \\ CsPb\({}_{3}\)Sr\({}_{0.8}\)SrPb\({}_{3}\) & DBR-DBR VCSEL & 13.5 \(\mu\)cm\({}^{-2}\) & 552.5 & 340 ps & 355 & [79] \\ CsPbPb\({}_{3}\) ZrO & random (ASE) & 207 \(\mu\)cm\({}^{-2}\) & \(\approx\)565 & 50 fs & 400 & [43] \\ CsPbBr\({}_{3}\) microsphere & WGM & 0.42 \(\mu\)cm\({}^{-2}\) & 545 & 40 fs & 400 & [63] \\ CsPbBr\({}_{3}\) microspasure & WGM & 2.2 \(\mu\)cm\({}^{-2}\) & \(\approx\)536 & 50 fs & 400 & [49] \\ CsPbBr\({}_{3}\) microcube & WGM & 16.9 \(\mu\)cm\({}^{-2}\) & \(\approx\)535 & 100 fs & 400 & [54] \\ CsPbBr\({}_{3}\) microcwire & WGM & 5.0 \(\mu\)cm\({}^{-2}\) & \(\approx\)546 & N/A fs & 355 & [61] \\ CsPbBr\({}_{3}\) microcube & WGM & 214 \(\mu\)cm\({}^{-2}\) & 536.8 & 1.1 ns & 355 & [56] \\ CsPbBr\({}_{3}\) microspureure & WGM & 16.7 \(\mu\)cm\({}^{-2}\) & \(\approx\)534 & N/A & 355 & [50] \\ CsPbBr\({}_{3}\) & DFB & 7.2 \(\mu\)cm\({}^{-2}\) & 539.1 & 300 ps & 355 & [24] \\ CsPbBr\({}_{3}\) & DBR-DBR VCSEL & 2.2 \(\mu\)cm\({}^{-2}\) & 538 & 300 ps & 355 & [24] \\ CsPbCl\({}_{3}\) microspasure & WGM & 5 \(\mu\)cm\({}^{-2}\) & \(\approx\)427 & 150 fs & 400 & [59] \\ CsPbCl\({}_{3}\) microspasure & WGM & 32 \(\mu\)cm\({}^{-2}\) & \(\approx\)425 & N/A & 355 & [50] \\ CsPbBr\({}_{3}\) microspure & WGM & 28.4 \(\mu\)cm\({}^{-2}\) & \(\approx\)709 & N/A & 355 & [50] \\ CsPbBr\({}_{3}\) NCs & DBR-DBR VCSEL & 0.39 \(\mu\)cm\({}^{-2}\) & 522 & 50 fs & 400 & [79] \\ CsPbBr\({}_{3}\) NCs & DBR-DBR VCSEL & 98 \(\mu\)cm\({}^{-2}\) & 522 & 5 ns & 355 & [79] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._
## 4 PAH Occurrence and Formation in Processed Meat, Edible Oils, and Cereal-Derived Products
### Features Underlying PAH Formation in Meat and Meat-Derived Products
Nutmeg oil and ginger oil nanoemulsions were investigated for their protective effects in grilled beef patties after a 90-day storage period (after cooking), resulting in a significant improvement in antioxidant activity, a decrease in the lipid peroxidation rate, and a corresponding PAH reduction [172]. By contrast, should the meat products be subjected to storage prior to cooking, the present antioxidants might undergo autooxidation and shift to pro-oxidant action. In turn, undesirable changes in the product (e.g., lipid peroxidation) may be promoted. In this setting, pork sausages grilled after 8 days of storage showed elevated PAH levels, increased lipid peroxidation (TBARS), and decreased antioxidant levels (DPPH, ABTS) when compared to sausages grilled after 4 days of storage [164]. Notably, the effect of storage may be regulated by the casing type in conjunction with the surface area. This is attributed to the smooth morphology and low porosity of edible coatings/synthetic casings, limiting PAH permeability into the product [15; 136; 139; 169; 173].
## 3 Proposed System
### Multiview Features Extraction and Representation
#### 3.2.1. Region-Based Geometric Features  
The shape of a region can be described by two types of features. One is the region-based and the other is boundary-based features. The region-based features are less affected by noise and occlusion than boundary-based features. Therefore, region-based features are better choice to describe the shape of a region [32]. Generally, a shape is described by a set of numbers known as descriptors. A good descriptor is one which has the ability to reconstruct the shape from the feature points. The resultant shape should be an approximation of the original shape and should yield similar feature values. The proposed feature descriptor has been designed around these parameters. These features have been computed as follows:  
1. First of all, we calculate the centroid of a human silhouette using Equation (2), as shown in Figure 3. \[C_{m}=(x_{c},\;y_{c})\] (1) where \[x_{c}=\frac{\sum_{i=1}^{n}x_{i}}{n}\;\text{and}\;y_{c}=\frac{\sum_{i=1}^{n}y_{ i}}{n}\] (2)
2. After computing the centroid by Equation (2), a radius of the silhouette is computed.
3. The silhouette is divided into 12 radial bins with respect to its centroid using a mask; this division has been made with intervals of 30\({}^{\circ}\). These bins are shown in Figure 4. \[\text{(4)}\] The following region-based features are computed for each bin of the silhouette. \[\text{(a)}\] _Critical points:_ As we move the mask on the silhouette in counter-clockwise direction, a triangle or quadrangle shape is formed in each bin. We compute the critical points (corner points) of each shape and their distances. There can be different numbers of critical points for each shape; therefore, the mean and variance of these points have been computed as features. It gives us 2 \(\times\) 12 = 24 features for each silhouette. \[\text{(b)}\] _Area:_ The simple and natural property of a region is its area. In the case of a binary image, it is a measure of size of its foreground. We have computed the area of each bin, which provides 12 important features for each human silhouette. \[\text{(c)}\] _Eccentricity:_ The ratio of the major and minor axes of an object is known as eccentricity [33]. In our case, it is ratio of distance between the major axis and foci of the ellipse. Its value is between 0 and 1, depending upon the shape of the ellipse. If its value is 0, then actually it is a circle; if its value is 1, then it is a line segment. We have computed eccentricity for each of the 12 bins. \[\text{(d)}\] _Perimeter:_ This is also an important property of a region. The distance around the boundary of a region can be measured by computing the distance between each pair of pixels. We have computed perimeter of each bin forming a triangle or quadrangle. \[\text{(e)}\] _Orientation:_ This is an important property of a region, which specifies the angle between x-axis and major axis of the ellipse. Its value can be between \(-90^{\circ}\) and \(90^{\circ}\).  
Figure 3. Example image of the human silhouette with centroid.
## 3 Results and Discussion
### System Heat Exchange Performance Analysis
#### 3.5.2 Model Validation  
Based on the previous set of optimal filling rate conditions, the experimental data of different inlet air volume, inlet air temperature, and inlet water temperature were substituted into the model (Equation (16)) for calculation. The relative error between the calculated value and the experimental value is shown in Figure 11 below. As shown, the error of the prediction formula for the overall heat transfer of the system is about \(\pm 12\%\), which can basically meet the requirements for quickly predicting the heat transfer of the system in practical applications. When the backplane heat pipe is in the optimal refrigerant filling rate condition, it is considered that the system is in the optimal operation state, and its heat exchange can be calculated by the \(\varepsilon\)-NTU method. In actual operation, we only need to measure the inlet air temperature, inlet air flow, inlet water temperature, and inlet water flow, which are four physical quantities convenient for measurement. Then the corresponding heat transfer value of the system can be obtained by Equation (16). It is of great significance for real-time prediction and optimization control of heat transfer in a micro-channel backplane heat pipe system.