esa-sceva/satcom-chunk-collection huggingface dataset:
# Management of Acute Life-Threatening Asthma Exacerbations in the Intensive Care Unit
## 3 Management of Acute Asthma Exacerbation
### Magnesium Sulphate  
Intravenous (IV) and nebulised magnesium sulphate (MgSO4) have long been proposed as a treatment for severe acute asthma. IV MgSO4 is commonly used in the initial stages of refractory severe asthma in the ED, but there is little literature on its use or effectiveness beyond this stage. The mechanism of action of MgSO4 is only partially understood but is thought to comprise the following: inhibition of the cellular uptake of calcium via activation of Na+Ca2+ pumps, muscle relaxation via inhibition of myosin and calcium interaction and calcium-dependent acetylcholine release at motor neuron terminals, and inhibition of prostacyclin and nitric oxide synthesis. Furthermore, there appears to be a degree of anti-inflammatory response via inhibition of mast cell degranulation, T-cell stabilisation, and the attenuation of an individual's neutrophil respiratory burst rate, provided by the inhibition of calcium uptake. The overall result of these effects is to increase bronchodilation and reduce the severity of acute asthma [33; 34; 35; 36].  
The 3 Mg Trial randomised 1109 adult patients presenting to EDs with acute severe asthma (as defined by BTS guidelines), with those suffering life-threatening features excluded [37]. Participants were randomised to IV MgSO4 therapy, nebulised MgSO4 therapy, or standard therapy alone. The study did not demonstrate any clinical benefits from either form of magnesium therapy. Even though there was evidence of some beneficial effect on subsequent rehospitalisation, there did not appear to be any effect on dyspnoea (via the visual analogue scale) or on peak expiratory flow rate (PEFR) when compared to a placebo. There was no evidence of improvement from a placebo with nebulised MgSO4 [37]. A Cochrane review and meta-analysis of 13 studies including 2313 patients concluded that \"a single infusion of 1.2 g or 2 g IV MgSO4 over 15 to 30 min reduces hospital admissions and improves lung function in adults with acute asthma who have not responded sufficiently to oxygen, nebulised short-acting \(\beta\)2 agonists and IV corticosteroids [38]. However, the effect of MgSO4 on patients with life-threatening or near-fatal asthma in preventing the need for mechanical ventilation and mortality of mechanically ventilated ICU patients is not known. Currently, magnesium sulphate is recommended for those who have refractory symptoms despite standard initial management [10].
## 3 Results and Discussion
### Keywords and Trending Topics of the Research on Pavement Management
#### Development of New Pavement Management Systems and Sustainability
Hence, some new models were proposed, such as OPTIVAV, which calculates all the costs and the value of the road after its service-life [159], and is a method for selecting the best chip seal treatment option based on LCCA analysis [160], and a hybrid technique that considers thresholds in management [161]. A discount rate must be applied in LCCA, and it can significantly influence the decision. Hence, the impact of the discount rate in a new LCCA system was analyzed [162]. Furthermore, the right moment for conducting M&R activities is a vital factor of the LCCA, and, therefore, consequences of postponing treatments have been quantified [163], and a tool that indicates the correct moment and extension of the road to be repaired is presented [164]. Nonetheless, the transportation sector has an extraordinary impact on climate change policy [165]. Nevertheless, most of the efforts for greenhouse gas (GHG) emissions reduction were focused on vehicle technologies and alternative fuel options [166], and PMSs have not taken into account environmental parameters [167; 168]. However, this trend was shifted during the first years of the 21 st century, and sustainability and environmental factors emerged as key factors in PMS strategies [169], as the amount of papers, in that period, about this topic, certifies [170; 171; 172; 173; 174; 175; 176; 177]. Additionally, the reduction of the energy consumed and resulting GHG emissions from M&R activities are specifically being incorporated to PMSs, introducing it as a new goal in multi-objective optimization [178; 179], or as a third step in new three-stage optimization methodology [180]. Lastly, new technologies are introduced in the field of PMSs, like application of iPad [181] or using the Google public cloud [182].
# Quantum-Key-Distribution (QKD) Networks Enabled by Software-Defined Networks (SDN)
## 2 Progresses in QKD Networks
### Experimental Verification of the Networks  
In 2017, [PERSON] et al. used a subcarrier quantum system to propose a dynamic quantum routing and secure communication method based on OpenFlow protocol in SDN, further demonstrating the feasibility of applying SDN techniques to QKD networks [22]. In 2018, to effectively alleviate the problem of in-band noise in the QKD network, [PERSON] et al. used machine learning-based approach to estimate physical performances of quantum channel for the successful key generation and transmission [23]. In the same year, [PERSON] et al. demonstrated that QKD resources can be successfully allocated through SDN control under DDoS attacks [24]. The results indicate that the application of SDN technology is conducive to alleviate the impact of DDoS attack on QKD networks.
## 4 HUVEC Identification
### Cellular Markers
#### 4.2.1 Protein Markers
P-selectin mediates the formation of activated platelet rosettes on leukocytes [98]. Factor VIII antigen or von Willebrand Factor (vWF). Factor VIII antigen is perinuclear and has been associated with WPB. The vWF is synthesized by vascular EC and is considered as a specific endothelial marker. It is normally expressed in EC, although expression is dramatically reduced in damaged vascular cells [49; 93]. vWF expression tends to decrease with each passage, reaching a staining percentage of 93%, 77% and 63% in passages 3, 9, and 11, respectively. vWF detected by immunohistochemistry can be used for EC identification, and to check junctional maturity and HUVEC homogeneity [57; 84]. Claudin-5. EC express VE-cadherin at adherent junctions and claudin-5 at tight junctions. Claudin-5 mediated adhesion is independent of calcium. It is a marker for other EC, such as endothelial dermal cells of the microvasculature [100]. Claudin-5 can be detected by immunofluorescence microscopy [101]. Integrin \(\alpha_{v}\beta_{3}\). Integrin \(\alpha\
u\beta 3\) is a receptor for extracellular matrix proteins, such as vitronectin, fibronectin and vWF, expressed in EC. It is considered as a marker of angiogenesis [88], and its main action is to maintain the integrity of the blood vessel wall. This protein can be identified by flow cytometry using CD51/61 monoclonal antibodies [84].
# Real-Time Water Level Monitoring Based on GNSS Dual-Antenna Attitude Measurement
## 5 Discussion
In future studies, we plan to upgrade the receiver to enable the recording of data received by both antennas. This will allow us to analyze the effect of different elevation cutoff angles on the monitoring accuracy and verify the stability of our method under different GNSS systems. Conceptualization, Z.P., W.J., J.L. and P.Z. ; methodology, Z.P., J.L. and P.Z. ; software, Z.P., W.J., J.L. and P.Z. ; validation, Z.P., W.J. and P.Z. ; formal analysis, P.Z. ; investigation, Z.P. and P.Z. ; resources, Z.P., P.Z. and M.S. ; data curation, M.S. and P.Z. ; writing--original draft preparation, P.Z. ; writing--review and editing, Z.P., W.J., J.L. and P.Z. ; visualization, P.Z. ; supervision, Z.P. ; project administration, Z.P. All authors have read and agreed to the published version of the manuscript. This research was funded by the Beidou Water Conservancy Comprehensive Application Demonstration Project (GFZX030303020127) and China Institute of Water Resources and Hydropower Research Fundamental Research Operation Fund Special Project (JZ110145B0012023, 90022306). The data set analyzed in this study is managed by the China Institute of Water Resources and Hydropower Research. Corresponding authors can be made available upon request. The authors are grateful for the technical support and assistance provided by Shanghai Sinan Satellite Navigation Technology Company Limited during the experimental period. The authors declare no conflict of interest. Figure 14: Number of satellites tracked during 10–14 July.
###### Abstract  
Medical ultrasound is an imaging technique that utilizes ultrasonic signals as information carriers, and has wide applications such as seeing internal body structures, finding a source of a disease, and examining pregnant women. The most commonly used ultrasonic transducer today is based on piezoelectricity. The piezoelectric transducer, however, may have a limited bandwidth and insufficient sensitivity for reduced element size. Laser-generated ultrasound (LGUS) technique is an effective way to resolve these issues. The LGUS approach based on photoacoustic effect is able to greatly enhance the bandwidth of ultrasound signals and has the potential for high-resolution imaging. High-amplitude LGUS could also be used for therapy to accomplish high precision surgery without an incision. Furthermore, LGUS in conjunction with optical detection of ultrasound allows all-optical ultrasound imaging (i.e., ultrasound is generated and received optically). The all-optical platform offers unique advantages in providing high-resolution information and in facilitating the construction of miniature probes for endoscopic ultrasound. In this article, a detailed review of the recent development of various LGUS transmitters is presented. In addition, a recent research interest in all-optical ultrasound imaging, as well as its applications, is also discussed.  
Keywords:ultrasound imaging; laser ultrasonics; laser-ultrasound transducer; laser-generated focused ultrasound; all-optical ultrasound imaging +
Footnote †: journal: Applied Physics Letters
# Investigating Feature Selection and Random Forests for Inter-Patient Heartbeat Classification
## 2 Materials and Methods
### Heartbeat Classification  
The Random Forest (RF) algorithm is the method used in this work for the classification of heartbeats. Briefly, an RF is an ensemble of \(C\) trees \(T1(X),T2(X),...,TC(X)\), where \(X=x1,x2,...,xm\) is an m-dimension vector of inputs. The resulting ensemble produces \(C\) outputs \(Y1=T1(X),Y2=T2(X),...,Yc=Tc(X)\). \(Yc\) is the prediction value by decision tree number \(c\). The output of all these randomly generated trees is aggregated to obtain one final prediction \(Y\), which is the average value of all the trees in the forest. An RF generates \(C\) number of decision trees from \(N\) number of training samples. For each tree in the forest, bootstrap sampling (i.e., randomly selecting the number of samples with replacement) is performed to create a new training set which is then used to fully grow a decision tree without pruning [33]. In each split of a node of a decision tree, only a small number of m features (input variables) are randomly selected instead of all of them (this is known as random feature selection). This process is repeated to create \(M\) decision trees in order to form a randomly generated forest.  
The training procedure for a randomly generated forest can be summarized as follows:  
1. Select a random sample from the training dataset.
2. Grow a tree for each random sample with the following modification: at each node, select the best split among a randomly selected subset of input variables, which is the tuning parameter of the random forest algorithm. The tree is fully grown until no further splits are possible and not pruned.
3. Repeat steps 1 and 2 until \(C\) such trees are grown.  
Here, the RF implementation given by the class _RandomForestClassifier_ of the Scikit-learn package for Python [30] is used. The number of trees is the only parameter explored and all other parameters are left with their default values. In order to find an optimum number of trees, classifiers are trained with an increasing number of trees using the top ten features found in the previous section and the performances in terms of _Acc_ are observed. The results obtained from \"leave-one-patient-out\" cross-validation on the training set DS1 are shown in Figure 2. It can be observed that after 40 trees the performance of classifiers does not increase and may actually diminish.
## 5 Experiments and Results  
In order to verify the practical application effect on the ionospheric oblique backscatter sounding of the system with the miniaturized L-shaped antenna array proposed in this paper, a verification experiment was carried out on 14 January 2019 at Xiantao, in Hubei province, China. As shown in Figure 8, the experiment adopts the mode of off-site to avoid the near-field interference caused by continuous wave transmitting. The transmitting subsystem was set at Wuhan (114.48\({}^{\circ}\)E, 30.50\({}^{\circ}\)N), and the multi-channel receiver and antenna array are placed at Xiantao (113.50\({}^{\circ}\)E, 30.17\({}^{\circ}\)N). The distance between the two places is about 89.32 km. The transmitting antenna points to 113\({}^{\circ}\) in azimuth. As the oblique backscatter sounding has a large coverage, on the far-field condition, the transmitting and receiving subsystem can be approximated as the same address. The sounding adopts the continuous wave modulated by a 511-bit pseudo-random two-phase m-sequence, with a transmitting power of 500 W. The bit-width is 25.6 us and the corresponding distance resolution is 3.84 km. The operating frequency band is selected in 5-15 MHz, with a sweeping step of 0.2 MHz. The number of coherent accumulations is 256.
## 2 Channel Reliability Modeling for 5G Communications
### Minimum Road-Based User Reliability Solution Method
#### 4.2.2 Reliability Calculation of Users Within the Isolated Island  
The successful operation of the planned islanding is related to factors such as the operational status of the islanding and the operation strategy of distributed power sources within the islanding. Therefore, the probability that a planned island can form and operate stably can be expressed as  
\[p_{q,i}=p_{ES}p_{DG}p_{q} \tag{21}\]  
where \(p_{q}\) is the probability that the planned island \(q\) can provide power to the planned island users in island operation mode. \(p_{ES}\) is the probability of the effective operation of the energy storage battery. \(p_{DG}\) is the probability of the effective operation of DG.  
The effective operation of a micro power supply relies on the monitoring, control, and effective probability of feedback information from the information system in the CPDS.  
\[\left\{\begin{array}{l}p_{DG}=A_{DGM}A_{DGC}A_{DGF}\\ p_{ES}=A_{ESM}A_{ESC}A_{ESF}\end{array}\right. \tag{22}\]  
where \(A_{DGM}\), \(A_{DGC}\), and \(A_{DGF}\) represent the effective probabilities of DG monitoring, control, and control feedback information in the island, respectively. \(A_{ESM}\), \(A_{ESC}\), and \(A_{ESF}\) are the effective probabilities of energy storage battery monitoring, control, and control feedback information, respectively.  
For user n within the planned island, when there is an external fault, there is a probability that user \(p_{q}\) can restore the power supply through the planned island. Therefore, the outage rate of user n within the planned island can be expressed as  
\[\lambda_{L,\text{n}}=(1-p_{q})\sum\limits_{i=1}^{M_{up}}\lambda_{i}+\lambda_{ eq,\text{n}} \tag{23}\]  
where \(M_{ulq}\) represents the number of components or equivalent branches on the minimum path between island \(q\) and the main power supply.  
When a failure occurs outside the planned island, if the planned island \(q\) can successfully enter the island operation, the average power outage time inside the planned island is the island switching time \(t_{is}\); otherwise, the power outage time is the equivalent of thepower outage time outside the planned island. Therefore, the average power outage time for user n is  
\[r_{L,n}=\frac{1}{\lambda_{L,n}}\cdot\left[(1-p_{q})\sum_{i=1}^{M_{up}}\lambda_{i}r _{i}+\sum_{i=1}^{M_{up}}\lambda_{i}p_{q}t_{is}+\lambda_{eq,n}r_{eq,n}\right] \tag{24}\]  
where \(\lambda_{eq,n}\) and \(r_{eq,n}\) are the equivalent failure rate and outage time from user n to PCC, respectively.
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 2 RS Applications in Ocean
### Sea Surface Temperature (SST)
#### 2.3.3 Summary and Future Direction  
The availability of more RS observations and advanced SST retrieval algorithms in recent years has facilitated generating high-quality SST products. However, there are still several challenges and opportunities in SST estimation that need to be addressed in the future. For example, although DL algorithms have exhibited better performance for deriving accurate SST products, they are data-hunpy models and not robust against variation in data distributions, resulting in a reduction in their generality in SST estimation [156]. To address this issue, several rule-based information can be added to the learning process of DL algorithms to improve their robustness. Transfer learning or semi-supervised learning could also improve the efficiency of DL algorithms in SST estimation [157; 158]. Moreover, since many DL algorithms have been developed in recent years, comparing their performance in SST prediction can show their potential benefits and extend novel research ideas. Of course, such studies require different local and global datasets, which must be prepared in collaboration with space agencies, oceanographic institutes, universities, and research institutes.  
Figure 7: SST maps from **(a)** GEOS-16, **(b)** Meteosat-11, and **(c)** Meteosat-18 [151].  
Extensive advancement has been made in large-scale SST mapping with TIR and microwave radiometers. However, the corresponding products still need to have better spatial and temporal resolutions. Estimating precise SST from satellite observation in the Polar regions (high latitude regions) has remained a challenge due to different factors, such as atmospheric conditions. More advanced SST algorithms are required for producing accurate SST products in these regions. Moreover, Unmanned Aerial Vehicles (UAVs), which are equipped with thermal sensors have emerged as a feasible and low-cost option for retrieving SST and temperature profiles from Polar regions [159].  
High-resolution SST products are one of the most critical factors for generating accurate and stable climate models. As mentioned before, such products can be retrieved from UAV observations, but typically on a local scale. In this case, advanced image/signal processing algorithms are required to produce global high-resolution SST products from low-resolution RS observations, which are nothing but super-resolution algorithms. Although several DL-based super-resolution methods have recently been developed to generate high-resolution SST maps [158], more research work is required for this purpose.
## 3 Triaxial Photogrammetry and Numerical Simulation
### Numerical Simulation  
ABAQUS finite element software 2022_09_29-02.11.55 183150 was used to simulate a cylindrical soil specimen with a diameter of D = 0.0391 m and H = 0.080 m in three dimensions. The upper end of the specimen is open and permeable. The bottom is fixed, the pore pressure is set to zero to simulate drained conditions, confining pressure is applied to the sides, and downward displacement is applied to the top. The mesh uses a hexahedral element type, neutral axis method, and sweeping technique. The loading plate is assumed to be completely smooth, and the selected cell is an eight-node hexahedral cell with three-way linear displacement and three-way linear pore pressure (C3D8P). There are 2000 elements in the mesh of ABAQUS. The model's calculation parameters are shown in Table 7. The models are crucial for accurately simulating the soil specimen's behavior. The analysis process should consider the soil layer's initial ground stress, initial pore water pressure, and initial void ratio.  
Figure 12: Deformation of soft clay in triaxial test. (**a**) Conventional triaxial deformation diagram and (**b**) photogrammetric deformation figures.  
The CU test is divided into consolidation and undrained shear phases, so two analysis steps are set. The first step is the consolidation and ground stress balance analysis, and the second is the undrained shear analysis. Each step of the analysis process was meticulously executed, ensuring the validity of the results. During the shear process, undrained shear was modeled by sealing the pore pressure and rendering the top surface impermeable. In the first step of the numerical simulation, the testing protocol applied a confining pressure to create a self-equilibrating stress state. Subsequently, a vertical load was used in a displacement-controlled manner at the top of the specimen until the test reached its conclusion, with the soil sample experiencing a displacement of 12 mm and an axial strain of 15%.  
Figure 13 shows the deformation of the model after the test. As can be seen from the figure, after the model deformed, the most significant expansion phenomenon occurred in the middle area. At the same time, the two ends remained unchanged and still maintained a cylindrical appearance. Comparing the results obtained by the ABAQUS simulation (Figure 13) with the deformation state of the specimen after the traditional triaxial test (Figure 12), it can be observed that in both cases, the specimen showed the characteristics of the largest deformation in the middle and the smallest deformation in the area affected by the end constraints. This deformation law is highly consistent with the situation observed during the actual test, which further verifies the reliability and rationality of the relevant research results and provides a strong basis for the subsequent in-depth study of the mechanical properties of soil.