esa-sceva/satcom-chunk-collection huggingface dataset:
# Heart Failure and Diabetes Mellitus: Biomarkers in Risk Stratification and Prognostication
## 5 Point-of-Care Clinical Diagnostics in HF
### Natriuretic Peptides  
As mentioned above, NPs are released into sera as a consequence of myocardial stress, such as stretching, overload, inflammation, or ischemia. Therefore, these peptides constitute important biomarkers for risk assessment in HF and are already in clinical application (2017). Originally, there were at least four rapid tests for the quantification of BNP available: (1) The Biosite Triage BNP (Biosite Diagnostics, San Diego, CA, USA) has been widely used in several clinical trials as an approved BNP rapid assay for bedside testing (2017, 2018). This assay is based on a fluorescence immunoassay signal detection which is directly dependent on the amount of BNP in plasma. Quantitative results are gained within 15 min after sampling (2018); (2) The second assay is based on an electrochemuminescent procedure that detects NT-proBNP which may be obtained within 18 min by a Roche Cobas analyzer (Roche Diagnostics, Indianapolis, IN, USA) (2018); (3) The DVIA Centaur BNP(r) assay by Bayer Healthcare LLC (Tarrytown, NY, USA) (2018); and the (4) AxSY(r) BNP assay by Abbott Diagnostics (Abbott Park, IL, USA) (2018) also have found wide application in several studies (2018). Several more rapid kits have been rolled out, mainly manufactured by Alere, Roche, Quidel and pts Diagnostics (2018).
## 3 Results and Discussion
### Identification of Phenolic Compounds by LC-QToF-MS  
A total of 21 compounds were tentatively characterized in the EVOO samples, including phenolic alcohols, phenolic acids, flavonoids, lignans and secoiridoids (Table 4). The phenolic acids characterized were vanillic acid and p-coumaric acid, while the flavonoids identified were luteolin, naringenin, apigenin, and hesperetin. Hesperetin and p-coumaric acid were only identified in the flavored olive oil, suggesting that their presence is due to the addition of _Origanum majorana_ L. These compounds have previously been identified in the leaves of the plant [59]. p-coumaric acid is a nutraceutical compound and a strong inhibitor of oxidative degradation, which enhances the stability of olive oil [60; 61]. The CH=CH-COOH group of p-coumaric acid contributes to promoting antioxidant activity and preventing the process of primary oxidation [62; 63; 64]. The high bioavailability and bioaccessibility of p-coumaric acid promotes the nutritional value of olive oil [61; 65] by preventing and treating cardiovascular diseases, inflammation, diabetes, and nervous system conditions, while also demonstrating various pharmacological effects such as anti-tumor, antibacterial, and anti-aging properties [66]. Furthermore, other functions of p-coumaric acid include anxiolytic, analgesic, anti-ulcer, antipyretic, and antiplatelet aggregation activities [65]. Hesperetin is a trihydroxyflavanone, which is considered a powerful antioxidant with high nutritional value. The addition of hesperetin to soybean oil-in-water emulsions has been found to improve the oxidation stability [67]. The supplementation of hesperetin in feed additives improves the lipid-related characteristics of eggs [68]. Hesperetin also exhibits anti-inflammatory, antiviral, and neuroprotective activities [69] and has been shown to inhibit chemically induced mammary tumorigenesis, colon carcinogenesis, reduced heart attack incidents, and blood pressure [70]. Luteolin and apigenin are major flavonoids in olive oils, which are derived from the glucosides found in the drupe [71]. Flavonoids are important for human health as they present anti-inflammatory and antioxidative properties [72]. Hydroxyrosol and tyrosol are important phenolic compounds of olive oil with antiatherogenic, cardioprotec
## 4 Results
### Evaluation of Required Dataset Size for Updates  
For the minimum size of a database, there are hardly any reference values in the literature. In an empirical study, [PERSON] et al. (2004) defined a value of 9% of the total project data, or 50 data points in absolute numbers for one example project, above which an update is reasonable [29]. These values serve as initial indicators; however, due to the uniqueness of each construction project and the different project-specific behavior of the individual input parameters, an overall definition of a sufficient dataset size poses significant challenges. Here, an estimation of the earliest possible time for an update is investigated too to generate further evidence by examining the behavior of the ringbuild and advance processes. Figure 15 shows the development of the deviation in the process durations for the ring construction and advance processes as average values of the MCS over the project's progress.  
The percentage deviation in the predicted advance duration compared to the actual duration improves from the initial prediction to the first update (10% project progress) from 22% to 11%. For the prediction of the process duration of ring building, the prediction has an accuracy of \(\pm\)10% already after the second update at 25% (500 m) of the project progress. A prediction accuracy of less than 5% is achieved after 75% (1500 m) of the project progress.  
The evaluation indicates that improvements to the original prediction are already achieved after 10% project progress and thus supports the benchmark given by [PERSON] et al. (2004) [29].  
Figure 14: Comparison of the actual remaining project duration of a tunneling project with 2000 m length to the predicted remaining project duration with integration of actual construction progress into simulation (“shifted prediction”) and with additionally updated input parameters (“updated simulation results”) [34].  
However, as the determination of the required amount of data is highly dependent on the behavior and influencing parameters of the respective parameters as well as on the project-specific conditions, the benchmark for a minimum dataset size should be viewed with caution. This is particularly true for the tunneling speed parameter, which is highly dependent on uncertain geological conditions.
## 4 Proposed Scheme
### Pseudonym ID and Domain Key Updating Phase
#### 4.6.2 Batch Join  
Once some vehicles want to join the domain \(D_{y}\), the TA executes additional operations to update the pseudonym ID and domain key. For example, when the vehicles \(v_{2}\), \(v_{4}\), \(v_{6}\), and \(v_{8}\) are ready to enter the domain \(D_{y}\), the TA executes the following steps:  
* Rather than calculating \(x_{i}\) and \(y_{i}\) for these vehicles, the TA takes the multiplied numbers of \(x_{i}\) and \(y_{i}\) from \(vbs_{2}\), \(vbs_{4}\), \(vbs_{6}\), and \(vbs_{8}\), which has been pre-calculated in the system setup phase.  
\[\mu^{-}=\mu+(vbs_{2}+vbs_{4}+vbs_{6}+vbs_{8}) \tag{9}\]  
TA should select an updated domain key \(s_{d}^{-}\) and multiply it by \(\mu^{-}\) to form a rekeying message, as per Equation (8).  
TA randomly picks a value \(r_{i}^{-}\in Z_{q}^{*}\) and computes the corresponding \(R_{i}^{-}=r_{i}^{-}P\) vehicle \(V_{i}\) the domain. It then computes a pseudonym ID \(AID_{i}^{-}=ID_{i}\oplus H_{1}(s||R_{i}^{-})\) for each vehicle \(V_{i}^{-}\) in the domain, where \(i=2,4,6,8\).  
TA deliveries the pseudonym ID and updated domain key as a broadcast message. Vehicle \(V_{i}\) obtains newly updated domain key \(s_{d}^{-}\) because \(vbs_{i}\) (i.e., \(vbs_{2}\), \(vbs_{4}\), \(vbs_{6}\), and \(vbs_{8}\)) are contained in \(\mu\).  
Therefore, if \"\(n\)\" vehicles want to join the vehicle's multicast domain, the TA executes \"\(n\)\" addition operations to update the domain key, which translates to big-oh of 1 (O(1)) calculation complexity. Furthermore, TA only sends one message to the vehicles in the multicast domain.
## 1 Introduction
VOD responds to vegetation seasonal dynamics similar to those recorded by the VNIR MODIS vegetation indices (VIs: NDVI, EVI, and LAI) and an independent bioclimatic growing season index (GSI; [1; 6; 18]). VOD has been shown to track seasonal changes in croplands in spring wheat producing regions of North America and winter wheat producing areas of Volga River Basin in Russia, and heatwaves that devastated crop production in Northern Eurasiaa [19; 20] similar to that of NDVI and EVI [21; 22]. VOD SOS corresponds well toMODIS NDVI and EVI green-up dates, and SOS estimates from flux tower gross primary production (GPP) and ecosystem respiration, but with temporal offsets [1]. Phenometrics derived from the downward Convex Quadratic (CxQ) model include Peak Height (PH) and Thermal Time to Peak (TTP), which are more consistent than SOS and EOS, because they are minimally affected by snow and frost and other non-vegetation related issues [23; 24]. PH, estimated as a quadratic function of accumulated growing degree-days (AGDD) [21; 22] or accumulated relative humidity [25], has been found to have significant positive correlation with agricultural production statistics for rainfed agriculture. In the application of the CxQ model, a piece of a parabola is fit to the response variable as a quadratic function of an accumulated seasonal forcing, such as AGDD. The advantage of the CxQ model is the phenometrics PH and TTP can be calculated directly from the fitted parameter coefficients. The disadvantage is that some LSP shapes are not well approximated by a parabolic curve. We observed in earlier work [21] that only at the very center of the growing season were VOD cropland time series fitted well by the CxQ model. In this study, we hypothesize that VOD phenometrics characterize cropland dynamics in a manner complementary to MODIS NDVI phenometrics, but with a temporal lag. The aims of this study are: (1) to compare the VOD phenometrics TTP and PH estimated from passive microwave time series using a maximum value approach with the NDVI phenometrics TTP and PH estimated from MODIS data using the CxQ model; (2) to quantify temporal offsets between the two sets of phenometrics; (3) to interpret the origin of these offsets; and, importantly; and (4) to demonstrate the validity of the VOD approach so that these microwave phenometrics can be used in cloud-obscured areas.
# UAV-Assisted Dynamic Clustering of Wireless Sensor Networks for Crop Health Monitoring
## 8 Simulation
### Simulation Model  
As per our designed protocol, a wireless sensor node is equipped with two different frequencies: 433 MHz for localization and identification of nodes and 2.4 GHz for data transmission. We developed a sensor node simulation model using two NICs CC2420 [19] and CC1021 [20] as shown in Figure 9a. Both NICs are composed of MAC and physical layer and the sensor node consists of three layers (Application, Network and NIC). CC2420 is a 802.15.4 compliant Network Interface Card (NIC) operating at 2.4 GHz frequency and having built-in CSMA/CA in MAC layer, while CC1021 is a low power RF transceiver for narrowband systems operating at 433 MHz. The sensor node is considering as smart enough that it can negotiate a communication height with the UAV by considering the amount of data that must be transmitted and the remaining energy power level. It utilizes only 0.825 \(\mu\)W of energy while operating with the CC1021 for localization. The largest energy depletion factor is data transmission between the CH and UAV using a higher frequency, e.g., WiFi. If the considered node is selected as CH then the energy consumption of the CC2420 is optimized by limiting the agreed CH-UAV height. The UAV receives data at a lower height suitable for the CH. The UAV model is created with same NICs as in the sensor nodes. The only difference is the addition of mobility components in the main module as shown in Figure 9b.  
The UAV is simulated to fly with a constant speed of 20 m/h and a height of 200 m. Initially, it switches on its CC1021 module and starts sending the beacon message. Once the UAV identifies the location of the selected CH, it starts moving towards it and reduces its height up to an agreed level. When the UAV approaches the CH, it will change its communication module from CC1021 to CC2420. All these simulation are available on YouTube links [21; 22; 23; 24; 25].
# Infrared and Visible Image Fusion via Feature-Oriented Dual-Module Complementary
## 3 The Proposed Method  
This section describes the algorithm framework depicted in Figure 1. First, the input images are fed into the SGCM to obtain abundant spatial feature information. In this module, three operators are proposed for specific roles: PCA is introduced to estimate the overall contour instead of the usual dimensionality reduction, saliency is used to highlight the region of interest, and the contrast estimation operator is proposed by constructing coefficient equations between the source images to adaptively preserve the gradient texture, followed by obtaining the capture maps. Then, a Gaussian-Laplacian pyramid algorithm is used to obtain the predicted image. By comparing the image features between the predicted and raw images, we find that infrared thermal information is lost, and thus propose the IBSM. In the IBSM, two operators applied to the exposure modality are improved by focusing on the image intensity. Then, the supplementary maps are obtained by multiplying the corresponding gradient map and intensity maps estimated via the Sobel gradient operator and Gaussian curve with the given weights. Finally, the fused image is obtained by adding the predicted image to the refined image, calculated by weighting the supplementary maps and source images. The specific steps are as follows.
## 3 Nonthermal Methods
### Sonodynamic Technology (SDT)  
Sonodynamic technology (SDT), originally developed in the medical field for the treatment of cancer, is now being explored for its applicability in food safety. SDT uses ultrasound to activate sonosensitizers, generating reactive oxygen species (ROS) that destroy the cellular structures of microorganisms. Sonosensitizers can be used for food sterilization, being effective in combating bacteria and viruses without affecting the quality of food products [6].
## 4 Joint Space Trajectory Planning Based on Improved Quantum Particle Swarm Optimization  
### Example 2: Comparison between Improved QPSO and MATP  
Reference [1] established the existence of the optimal solution within the feasible region for the mathematical model of the joint (driving limb) space trajectory planning presented in Equations (36)-(41). Therefore, the use of the improved QPSO algorithm in heuristic search within the feasible region is a viable optimization technique.  
Table 1 specifies the target parameters required for planning the trajectory of a particular joint (driving limb) of the robot at a given time. The improved QPSO algorithm can be employed to optimize the trajectory under these conditions. Joint space trajectory planning is applicable to both series robots (joint type) and parallel robots (driving limb type). Thus,  
Figure 5: Acceleration curve.  
Figure 6: Speed curve.  
in Table 1, the unit of measurement used for each data field is not significant as long as unity is maintained.  
Using the parameters specified in Table 1, trajectory planning is performed using the improved QPSO algorithm, and the iterative convergence curve is depicted in Figure 7. It is evident from the figure that the algorithm converges rapidly during the iterative process, with the optimal value being attained after 20 iterations. The efficiency and accuracy of the improved QPSO algorithm have satisfied the demands of the engineering applications.  
Reference [1] proposes a joint space trajectory planning method based on graph planning to achieve minimum acceleration trajectory planning (MATP) in joint space. By using the MATP method, a trajectory that satisfies the initial and final state conditions can be obtained, and the ultimate acceleration of the joint actuator can be minimized.  
Using the same parameters, trajectory planning is performed using the MATP meth-od and compared with the results of the improved QPSO algorithm. The acceleration, speed, and position curves are compared in Figure 8. The results show that the acceleration, speed, and position obtained by both planning methods satisfy the initial and final state requirements, and the maximum acceleration limits of the two trajectories are identical. However, the forward maximum acceleration of the improved QPSO algorithm is smaller than that of MATP. From Figure 8a, it can be observed that the acceleration of the trajectory obtained by the improved QPSO planning is also smaller than that of the MATP method, indicating that the acceleration changes more smoothly.
## 3 Results  
### Characterization of Bare Silica Nanoparticles and Pluronic F-127  
Before studying the association between Pluronic F-127 and silica nanoparticles in an aqueous medium, it is important to evaluate the behavior of the unmodified silica nanoparticles and Pluronic F-127 in an aqueous medium. The silica nanoparticles used had an average hydrodynamic diameter of about 15 \(\pm\) 4 nm, as determined by DLS, and an average \(\zeta\) potential of (\(-\)35.7 \(\pm\) 0.8 mV), which is consistent with the results previously reported by [PERSON] et al. [26], indicating a negative charge due to the presence of dissociated silanol groups on the nanoparticles' surface. This is of paramount importance to ensure the stability of the nanoparticles in an aqueous medium. Despite the effective negative charge of the particles, this does not imply complete dissociation of all of the silanol groups on the surface of the nanoparticles; therefore, it is possible that hydrogen bonding interactions may occur through non-dissociated silanol groups on the surface of the nanoparticles.  
The characterization of Pluronic F-127 aqueous solutions has been extensively discussed in our previous publication [17], and only the most fundamental aspects will be highlighted here. As the weight fraction of Pluronic F-127 in the solution increases, the viscosity of the solution increases progressively. This increase in viscosity is particularly noticeable when the copolymer weight fraction reaches about 120 mg/mL, with a sol-gel transition occurring at a copolymer weight fraction of about 200 mg/mL \(w/w\). This sol-gel behavior is consistent with findings from previous studies on various polymers within the Pluronic family [27; 28; 29; 30]. This transition limits the range of Pluronic F-127 concentrations that can be effectively used, and we therefore limited our study to a maximum concentration of 10 mg/mL, where the viscosity of Pluronic F-127 solutions and their mixtures with silica remains close to that of Milli-Q water.
## 2 Methodologies  
### YOLOv5s  
YOLO is a series of one-stage networks for object detection in which the categories and coordinates of the target can be obtained directly by solving a regression problem. Its advantage is reflected in the use of an end-to-end network to shorten the detection time considerably. As the smallest model size among the fifth version of the YOLO series, YOLOv5s is widely accepted for deployment and real-time inference. As shown in Figure 1, it is commonly divided into three parts: backbone, neck, and head [35]. The backbone refers to the network that typically employs CSPDarknet to extract features. CSPDarknet is mainly composed of five units, including FOCUS, CBS, RES, CSP, and SPP. The neck is a network used to further enhance the effect of feature extraction, while the head is a classifier or regressor for extracted features. By using these three parts, YOLOv5s is competent in extracting, enhancing, and predicting target features. It uses Generalized Intersection over Union (GIoU) instead of Intersection over Union (IoU) as the model loss function, leading to an increase in the measurement of the intersection scale. Such an increase contributes to solving the problem that IoU cannot be optimized when two boxes do not intersect. The GIoU loss can be formulated as follows:  
\[\left\{\begin{array}{c}IoU=\frac{A\cap B}{A\cup B}\\ GIoU=IoU-\frac{|C-(A\cup B)|}{|C|}\end{array}\right., \tag{1}\]  
where \(A\) denotes the ground truth, \(B\) indicates the prediction box, and \(C\) represents the smallest closed convex object between \(A\) and \(B\).
# Study of the Failure Mechanism of Soft Rock Mining Roadways Based on Limit Analysis Theory
## 1 Introduction
proposed bolt-mesh-cable coupled support technology [19; 20], the active pressure relief method [21; 22], subregional and phased dynamic control technology [23; 24], and prestressed truss anchor cable control technology from multiple perspectives [25] and realized the effective control of the deformation of the rock surrounding soft rock roadways. For the study of soft rock roadway support, we focus on soft rock, clarify the mechanical deformation mechanism and nonlinear deformation behavior of soft rock, identify the key aspects of support technology [26; 27], and implement effective roadway support design to fundamentally solve the deformation problem of soft rock roadways. From the above analysis, it can be seen that there are still many key problems to be solved urgently in the stability analysis of roadway-adjacent rock. It is necessary to carry out systematic and in-depth research on the failure model, scope and evolution law of roadways to establish a roadway stability analysis model more in line with engineering practice and put forward a scientific and reasonable solution method which provides a theoretical basis for the stability analysis of and control measures for roadways. The deformation and failure of roadway-adjacent rock is mainly due to the generation and expansion of cracks [28; 29; 30; 31; 32; 33]. The energy principle is one of the most reliable analysis methods in geotechnical engineering and can well explain the failure process of roadway-adjacent rock [34]. The failure of the rock surrounding a roadway is essentially due to the energy flow and conversion within the rock unit. The energy dissipation is closely related to the damage state and failure mode of the rock [35; 36]. Soft rock is inevitably accompanied by energy conversion in the process of load failure and crack propagation [37]. The limit analysis method studies the problem from the perspective of energy dissipation. Its theoretical model is simple and clear, and it is widely used in the analysis of practical engineering problems. The rigid slider model based on the assumption of the limit analysis method can conveniently analyze and consider the influence of various complex factors on the stability of roadway-adjacent rock. Therefore, this paper constructsa failure model of soft-rock-roadway-adjacent rock through the limit analysis theory in plastic mechanics and derives calculation formulas for the self-weight power of the slider in the velocity discontinuity line of the roadway-adjacent rock, the work power of the surrounding rock pressure and the energy dissipation rate of the velocity discontinuity line to obtain the upper limit objective function of the height of the velocity discontinuity line of the surrounding rock. Based on a soft rock mining roadway in the Anyang Coal Mine, the failure model, range and evolution pattern of the roadway are analyzed, and its evolution characteristics are verified by physical simulation tests to reveal the instability mechanism of the soft rock mining roadway.