esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Fibers on Mixture Design of Stone Matrix Asphalt
## 1 Introduction
### Test Methods
#### 2.2.2 SMA Sample Preparation  
In this study, SMA specimens were prepared using the compaction method. The dimensions were 101.6 mm \(\times\) 63.5 mm. According to the standard requirement and field construction experience, the fiber content (by the weight of the total mix) used in the SMA mixture was selected, which was 0.1%, 0.2%, 0.3%, 0.4%, 0.5%, and 0.6%, respectively. The fiber types and contents are shown in Table 8.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Composite**} & \multirow{2}{*}{**Percentage**} & \multicolumn{8}{c}{**Mesh Size (mm/\%)**} \\ \cline{3-11}  & & **16.0** & **13.2** & **9.5** & **4.75** & **2.36** & **1.18** & **0.6** & **0.3** & **0.15** & **0.075** \\ \hline
9.5–16 mm & 44\% & 100 & 85.1 & 12.3 & 0.40 & 0.40 & 0.40 & 0.40 & 0.40 & 0.40 & 0.40 \\
4.75–9.5 mm & 33\% & 100 & 100 & 97.9 & 8.6 & 0.5 & 0.50 & 0.50 & 0.50 & 0.50 \\
0–2.36 mm & 13\% & 100 & 100 & 100 & 100 & 92.6 & 62.0 & 36.1 & 19.9 & 13.6 & 9.9 \\ Mineral filler & 10\% & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 99.3 & 95.4 & 85.3 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Gradation of SMA-13 asphalt mixture.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Different Fibers** & **Floculent Lignin Fiber** & **Mineral Fiber** & **Polyester Fiber** & **Blended Fiber** \\ \hline Asphalt aggregate ratio (\%) & 5.9 & 5.5 & 5.7 & 5.9 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Asphalt aggregate ratio of SMA mixtures with different fibers.  
Figure 1: Gradation of the SMA-13 mixture.  
The density was determined with the surface-dry condition method (T0705-2011/JTG E20-2011), which was very similar to ASTM D2726-14. There were mainly two differences. First, ASTM D2726-14 explained if the temperature of the specimen differs from the temperature of the water bath by more than 2 \({}^{\circ}\)C (3.6 \({}^{\circ}\)F), the specimen should be immersed in the water bath for 10 to 15 min; instead of 3 to 5 min. The immersed time in the water bath was only 3 to 5 min in the T0705-2011. Secondly, ASTM D2726-14 required, after determining the mass in water and in a saturated-surface dry condition, thoroughly drying the specimen to a constant mass at 110 \(\pm\) 5 \({}^{\circ}\)C (230 \(\pm\) 9\({}^{\circ}\)F). While it only required making the specimens thoroughly dry, it did not illustrate the temperature in the T0705-2011.  
The loose fibers (by the weight of total mix) were first blended with the hot aggregates to prevent the asphalt binder from draining during the mixing procedure. The heated asphalt binder was added after mixing 1 to 1.5 min. Then the heated filler was added until all of the aggregate was completely covered. The total mixing time was 3 min. During the mixing process, the temperature was kept between 170 \({}^{\circ}\)C and 180 \({}^{\circ}\)C. Finally, the Marshall specimens were made with dimensions of 101.6 mm \(\times\) 63.5 mm using the compaction method. For note, the final temperature was greater than 145 \({}^{\circ}\)C.
## 4 The Proposed Method
### Adaptive Multi-Channel Spatio-Temporal Hypergraph Neural Network
#### 4.3.2 Multi-Feature Fusion Module Based on Cross-Channel Attention Mechanism
Then, the attention score of each channel feature is calculated separately according to the attention matrix, and the calculation process is represented as follows:  
\[\alpha_{PCJ} =\sigma(\mathbf{A}_{PCJ,N-PCJ}\mathbf{W}_{v_{1}}\mathbf{f}_{N-PCJ}+ \mathbf{A}_{PCJ,N-PCB}\mathbf{W}_{v_{1}}\mathbf{f}_{N-PCB}) \tag{26}\] Figure 7.0 (27) Figure 7.1  
where \(\mathbf{W}_{v}\) is the trainable parameter matrix and \(\sigma\) is the activation function. It is worth noting that the attention score is used to adjust the contribution of different channel features to action recognition, thereby highlighting the features that have a greater impact on human action recognition. The attention score is then used to weight and sum the features of the three channels, and the adaptive fusion of the multi-channel features is represented as  
Figure 7.2  
Finally, the fused features are globally pooled and passed through the fully connected layer before \(Softmax\) is used to calculate the action prediction probability values, with the predicted category being the action category \(\mathbf{z}\) with the highest probability value:  
Figure 7.3  
where \(GAP\) denotes the global average pooling operation and \(FCL\) denotes full connection layer. The multi-feature fusion module based on cross-channel attention mechanism achieves the effective fusion of information features of each channel by learning the features of the three channels, reasonably using the diversity and complementarity of physically connected joints, non-physically connected joint, and non-physically connected bone in human action, weakening the interference of noise and effectively improving the accuracy of action recognition.
# Application of Angle Related Cost Function Optimization for Dynamic Path Planning Algorithm
## 3 Problem Analysis
In heuristic searches, it is very important to evaluate the location. The evaluation method can control the size of search space and search time which directly affects the efficiency and accuracy of the algorithm. Different evaluation methods will achieve different results. For the A* algorithm, according to the control of its evaluation function, the search space is as shown in the blue part of Figure 3, and the search space is much smaller than the space of Dijkstra algorithm, as shown in Figure 1.  
According to our analysis, we summarize the three algorithms in Table 1. It can be concluded from Table 1 that the A* algorithm can obtain the optimal solution, and the search space for A* is the smallest. Also, A* has the lowest time complexity, with better robustness and convergence, and can better meet the needs of real-time systems [19]. Figure 3: Search space of the A* algorithm. Figure 2: Search space of the ant colony algorithm. So, in this paper, we present the Spatial Optimized Dynamic Path Planning algorithm based on the optimization of the A* algorithm.
## 3 Results and Discussion
### Evaluation of Potentially Robotic Traits
#### 3.2.5 Antioxidant Activity  
The antioxidant activity assay showed that all the tested strains had high antioxidant abilities. In comparison to the control sample of Sacch_boulardi (65.10%), eight strains presented with a higher antioxidant activity. The strains included two _P. kudriavzevii_ (099_Pich_kudriavzevii, 67.05% and 101_Pich_kudriavzevii, 81.36%) and six _M. pulcherrima_ (105_Metsch_pulcherrima, 74.01%; 107_[PERSON]_pulcherrima, 76.47%; 110_[PERSON]_pulcherrima, 81.90%; 111_[PERSON]_pulcherrima, 82.05%; 112_[PERSON]_pulcherrima, 80.92%; and 113_[PERSON]_pulcherrima, 81.23%). The results of the assay are presented in Figure 5. Our results are similar to the results obtained by [PERSON] and [PERSON] (2016). [PERSON] and [PERSON] (2019) obtained high results (above 50%) only for 31.6% of their isolated yeast strains but not for P. _kudriavzevii_, which showed about 35% antioxidant activity. [PERSON] et al. (2017) reported antioxidant activity for _M. pulcherrima_ strains in the range of 11.65-60.12% and for _S. cerevisiae_ between 46.12 and 66.49%, with the reference CODEX strain showing 65.58%. This was below the results obtained by our strains. [PERSON] et al. (2019) obtained very good results from yeast isolates in reaction with DPPH, which exceeded 80%, and in _P. kudriavzevii_ and _S. cerevisiae_ which exceeded 90%. In the study by [PERSON] et al. (2014), the highest percentage in the reduction of DPPH was for two _Saccharomyces_ strains (3 and 6) and one _P. kudriavzevii_ (1200) with values of 33.71%, 32.66%, and 33.42%, respectively, which was 2-2.5 times less than in this analysis report.  
Figure 5: Results of the antioxidant activity of tested strains (One-Way ANOVA: strain: F (27,56) = 72,221, \(p\) = 0.0000, se \(\pm\) 0.011803).  
In general, it can be stated that such differences in this activity can arise from various factors--from genetic variations, differences in the strains' metabolic activity, or even other environmental conditions, which may influence the metabolic activity of individual strains [45; 46; 47].
## 2 Materials and Methods  
### Patient Data and Surgical Procedure  
This study obtained approval from the Ethics Committee of IRCCS Istituto Ortopedico Rizzoli (Prot. gen. n. 26146 del. 31.10.2006). Eleven male patients with clinical and radiological signs of ON of the femoral head gave their informed written consent to this study. We selected patients according to specific inclusion and exclusion criteria. Inclusion criteria foresaw the enrolment of male patients with a mean age between 18-50 years and clear signs of hip osteonecrosis by MRI. Exclusion criteria foresaw the exclusion of patients showing metabolic diseases, rheumatoid arthritis, autoimmune and neurological disorders. In this study, five ON patients showed NTFH (mean age 37 \(\pm\) 4; range: 30-43), whereas six ON patients had PTFH (mean age/SD: 30 \(\pm\) 3; range: 25-37). The aetiology of the NTFH group included glucocorticoid treatment. ON patients included in the NTFH group did not report aetiology for alcohol abuse and autoimmune diseases; only one patient had a chronic bowel disorder. NTFH and PTFH patients underwent preoperative MRI and X-ray using the Association Research Circulation Osseous (ARCO) evaluation system [41]. This system considers the size of the necrotic lesion, its femoral head extent, and the joint involvement (Stage I: normal on X-ray and CT; Stage IV: the destruction of joint with secondary arthritic changes; A, B and C describe the extent of ON involvement: A: non-articular, B: medial; C: central). We reported a scientific diagram of the weight-bearing area of the femoral epiphysis where we harvested biopsies (see Supplementary Figure S1). Patients' femoral heads underwent surgical repair with a synthetic resorbable osteochondral scaffold plug. During the procedure, surgeons collected osteochondral biopsies of the lesions from the weight-bearing area of the femoral head with a 10-mm diameter through a 12-14-mm deep trocar. All samples were processed for histological and immunohistochemical analyses. We used the femoral head from three male multi-organ donors (mean age/SD: 35 \(\pm\) 5; range: 30-40) as healthy controls. Multi-organ donors did not suffer from any musculoskeletal disease. We selected the donors through the bone bank program for tissue donation after the family' s donor consent. Femur harvesting was performed within six hours from asystole, and involved its excision and placement in Dulbecco-modified Eagle medium with L-glutamine, NaHCO\({}_{3}\), and antibiotics, and storage at 4 \({}^{\circ}\)C.
## 6 Simulation Results
### Performance Results  
#### 6.2.1 Effect of Power Randomization  
As an evolution of the E-SSA solution, the new demodulation strategy was also based on the key features of the E-SSA protocol, e.g., SIC with power spreading. Indeed, as recalled in Section 4, E-SSA benefits from a power imbalance across the received bursts compared to simple SSA. Even for the ME-SSA-based receiver, it has been confirmed that the power randomization on the uplink improves the efficiency compared to the case of no power spreading.  
This has been proven for all the ModCods and spreading factors defined, as reported below. In particular, the measured PLR was compared in the case of no power randomization--where all the bursts were received with the same C/N values equal to the target value listed in Table 3--and in the case of a burst power randomized in a 5 dB range, i.e., for every burst a random number was drawn from a uniform distribution between 0 dB and 5 dB varying the reference power, and where the target C/N was obtained with power spreading equal to 0. It is to be noted that the test was carried out also with the transmission of two different waveforms (TFI#9 and TFI#5) on-air simultaneously.
## 2 Materials and Methods
### Partial Least Squares Regression (PLS)  
Regression models for total carotenoid were developed by partial least squares (PLS) combining spectral information (X) with the dependent information (Y), i.e., TCC determined by UV-Vis. The spectra were reduced in spectral range (1300-3000 cm\({}^{-1}\) or 920-3000 cm\({}^{-1}\)), and divided into two datasets by the Kennard-Stone algorithm; one dataset, containing around 70% of the spectra, was used for model calibration, and the remaining 30% of the data was used for testing the model predictiveness.  
The following spectral pre-processing techniques (besides water and CO\({}_{2}\) elimination), alone or in combination, were tested: no spectral pre-processing, standard normal variation (SNV), smoothing (Savitzky-Golay with filter width 15), first derivative (Savitzky-Golay with filter width 15), second derivative (Savitzky-Golay with filter width 15), always followed by mean centering.  
The model dimensionality, a.k.a. the number of latent variables was individuated by the venetian blind cross-validation strategy. Statistical parameters such as root mean error of calibration (_RMSEC_) and prediction (_RMSEP_) and coefficient of determination (\(R^{2}\)) of both calibration (\(R^{2}\)\({}_{CAL}\)) and prediction (\(R^{2}\)\({}_{PRED}\)) were used to identify the most appropriate model. Furthermore, the prediction ability of the obtained models was evaluated by comparing the _RMSEP_ with the Standard Error of Laboratory (_RMSEL_) according to the procedure proposed by [PERSON] and [PERSON] [16]. The models were developed with PLS Toolbox (v. 8.5, Eigenvector Research, Inc., Seattle, WA, USA) working under MATLAB environment (v. 2016a, Mathworks, Inc., Natick, MA, USA).
## 3 Experiments  
### Experimental Rig  
To automate the acquisition and processing of images belonging to the arc magnet surface, we designed an experimental rig, as shown in Figure 3. It is a machine vision system containing a computer, a visible-light camera, a ring light source, a conveyor belt, two pairs of photoelectric switches, and a sorting cylinder. In this system, the arc magnets to be identified are transported from the inlet to the image acquisition area via a conveyor belt. The first pair of photoelectric switches are mounted below the light source. The camera above the light source is triggered to take an image when the arc magnet in transit blocks the optical path of this pair of photoelectric switches, leading to the availability of the data associated with the surface of such an arc magnet. After that, the computer stores the corresponding data and runs our proposed model to analyze the image to derive the results of the surface defect identification. Finally, the identified arc magnet continues to be transported to the second pair of photoelectric switches. At this point, the arc magnet identified as defect-free is transferred to the qualified product outlet, while the sorting cylinder driven by the trigger signal of the second pair of photoelectric switches pushes the one identified as defective to the unqualified product outlet. Since most surface defects occur on the outer curved surface, we only used the experimental rig to collect images of that surface in this study.
# Management of Acute Life-Threatening Asthma Exacerbations in the Intensive Care Unit
## 3 Management of Acute Asthma Exacerbation
### Oxygen Therapy and Targets  
Although asthma is primarily a large airways disease, some patients with acute asthma exacerbation may present with hypoxemia of varying severity, and targeted oxygen therapy is recommended. The BTS guidelines currently recommend an oxygen saturation of 94-98% [13]. However, there is contention in the literature surrounding the level at which oxygen targets should be set for critically ill patients, with concern regarding the effects of hyperoxemia on deleterious outcomes [14]. A randomised controlled trial (RCT) of 106 patients presenting to the ED with acute severe asthma concluded that high-concentration oxygen therapy (8 L/min via a medium-concentration Hudson facemask) significantly increases PaCO\({}_{2}\) and recommended a titrated regime where oxygen is only administered to patients suffering from hypoxemia with an oxygen saturation of \(\leq\)92% [15].  
The 2018 Improving Oxygen Therapy in Acute-illness (IOTA) systematic review and meta-analysis reviewed 16,037 hospitalised patients with various acute conditions and highlighted that hyperoxemia may be associated with adverse outcomes [16]. In this study, liberal oxygen therapy was associated with an increased in-hospital mortality. Several other studies have provided conflicting evidence [17; 18] regarding ideal oxygen targets, and as such, guidelines for its use in acute illnesses, including asthma, still need to be updated [19; 20]. To draw definitive conclusions, additional high-quality RCTs are required to understand the physiological effects of oxygen, especially during acute asthma exacerbations. The UK-ROX and MEGA-ROX trials are multi-centre trials aiming to recruit a large number of ICU patients (16,500 and 40,000, respectively) to identify the ideal oxygen saturation target for critically ill mechanically ventilated patients [21; 22]. These trials should provide important insight into an intervention previously regarded as a cornerstone of management for a wide range of conditions. Sub-group analysis of these large trials may give additional guidance specific to asthmatic patients in the ICU. While waiting for further evidence, it is essential to avoid both hypoxemia and hyperoxemia, and supplementing inspired oxygen to the current recommended oxygen saturation target of 94-98% appears safe and appropriate [10; 13].
## 4 Experiments
### Application and Datasets
#### 4.2.6 University of Southern California Human Activity Dataset  
This USCHAD dataset was collected from 14 subjects from accelerometers and gyroscopes recording 12 activities. Some of the activities included walking forward, walking left, walking right, walking upstairs, walking downstairs, running forward, and so on. It is a 12-category and unbalanced dataset.
# Game Algorithm for Resource Allocation Based on Intelligent Gradient in HetNet
## 1 Introduction
Therefore, an effective and intelligent Nash equilibrium solution algorithm is needed. In this paper, the Stackelberg game model is adopted to cope with modeling resources allocation. First, we build a spectrum allocation scheme with the concept similar to community expansion, where macro base station pushes partial users under severe cross-layer interference to femtocells nearby and contributes partial spectrum as return. Then considering the interest of both the macro base station and femtocells, one kind of utility function without using pricing strategy is built to improve the overall system throughput capacity. Finally, given that optimal strategy value range is continuous, which renders the existing algorithms impossible to work out Nash equilibrium, a gradient algorithm is introduced for the first time to solve Nash equilibrium, based on which an effective and intelligent gradient algorithm is proposed. Simulation results show that this scheme has improved the performance of the system to a certain extent. The rest of the paper is organized as follows. Section 2 presents the system model. Section 3 explains the utility functions and optimization objects analysis. Section 4 introduces the iterative algorithm by adopting the gradient descent algorithm. In Section 5, the simulation is given to evaluate the improved performance of the proposed algorithm. Finally, we show the conclusion of the paper.
## 2 Network Structure and Fault/Event Model
### Sensor Network Structure  
The sensor field is assumed to be divided into \(M\times N\) square-shaped grids as illustrated in Figure 1, where there are nine grids, A through I, and \(l\) is the side of a square grid. Immediately after deployment, the sensor network is assumed to carry out grid construction process, and each sensor node figures out the grid it belongs to. Sensor nodes in each grid form a cluster, where a cluster head is selected dynamically. All other nodes in the cluster communicate directly with the cluster head, although multi-hop communication can be used without modification of the proposed event detection scheme. Two types of communication are defined here for event detection: one for communication between the cluster head and cluster members and the other for communication between neighboring cluster heads.  
Figure 1: Sensor network structure for fault-tolerant event detection.  
Although each grid can make a decision on an event based on the sensor readings of its member nodes, the accuracy might not be high especially for a relatively small event region. When such an event region lies across four neighboring grids, for example, each grid might have insufficient number of event-nodes to apply the well-known majority voting. In that case, high detection accuracy can only be obtained by lowering the threshold, resulting in a considerably high false alarm rate, except for low fault probability.  
In order to cope with poor performance in the case of a small event region, we further divide each grid (in solid lines) into four sub-grids (in dotted lines) as shown in Figure 1, where each grid, except for the corners and sides, overlaps with eight square regions (SRs from here on) of 2 \(\times\) 2 sub-grids, in eight different directions. In Figure 1, the grid E in the center, for example, has 8 overlapping SRs. In the NW direction, for example, there is an SR, in thick dotted lines. We name it SR\({}_{ABDE}\), to indicate the four grids involved. In the N direction, the SR can be denoted by SR\({}_{BE}\) (_i.e._, two sub-grids from grid B and two sub-grids from grid E).  
An improved detection accuracy can be obtained if event detection is performed at each SR, along with the original grid. This extension requires inter-grid communications between neighboring cluster heads to send the information regarding the sensor readings at each sub-grid. As an illustration, the event region, in dotted circle in Figure 1, lies across the four grids D, E, G, and H. The event is most likely to be detected by a threshold test at SR\({}_{DEGH}\).  
The reason for using only 2 \(\times\) 2 sub-grids is two-fold. First, any further divisions require additional memory and computation, and inter-grid communication overhead. Second, the resulting performance gains would be marginal unless a threshold test needs to be applied at a smaller sub-grid level.
## 2 Materials and Methods
### Data and Preprocessing  
#### 2.2.1 SIF Data  
The Tropospheric Observatory Programmed Observatory (TROPOMI) is carried on the Sentinel-5P (Sentinel-5P) satellite, which has a spatial resolution of 3.5 km \(\times\) 7 km and a revisit period of 17 days, enabling it to acquire global daily images [34]. [PERSON] et al. reanalyzed the TROPOMI SIF products in the 743-758 nm band range [35], synthesizing 0.05\({}^{\circ}\) resolution enhanced SIF products (eSIF) with improved signal-to-noise ratios compared to the original TROPOMI SIF data. This was achieved by performing an 8-day back-performing without altering the underlying SIF mechanism, thus preserving the physiological characteristics and data structure of original SIF product. The main genesis of SIF is shown in Equation (1) [4; 36; 37]. To improve the reliability of the downscaled SIF product, the experiments selected the eSIF dataset of 2018-2020 and chose the vegetation  
Figure 1: Overview map of the Shandong study area.  
index data related to the chlorophyll content of plants, along with meteorological data representing vegetation growth environments, as the driving variables to carry out the downscaling study [38; 39; 40].  
\[SIF=PAR\times fpar\times\Phi_{SIF} \tag{1}\]  
In Equation (1), _PAR_ stands for light and effective radiation, _fpar_ is the proportion of photosynthetically active radiation absorbed by the vegetation, and \(\Phi_{SIF}\) is the fluorescence quantum yield at the photosystem level.