esa-sceva/satcom-chunk-collection huggingface dataset:
# Hydraulic Analysis of a Passive Wedge Wire Water Intake Screen for Ichthyofauna Protection
## 3 Results
### Comparison of Laboratory Experiments with Numerical Test Results  
The performed numerical simulations allowed for a graphical presentation of the obtained velocity distributions in the hydraulic trough near the wedge wire screen that is shown in Figure 22a-d. The obtained normal velocity values were read at the points where laboratory measurements were made. The summary of values obtained during laboratory measurements and numerical simulations is shown in Figure 22e-g. As can be seen, the results of numerical simulations do not differ significantly from experimental results. In the case of tests without the use of a deflector inside the analyzed screen, the difference between laboratory tests and numerical simulations averaged about 19%, in the case of tests with a deflector with non-uniform and uniform holes 20% and 13%, respectively.  
Figure 21: Maximum velocities in cross-sections along the screen at 0\({}^{\circ}\) (above the screen), 45\({}^{\circ}\), 90\({}^{\circ}\), and 135\({}^{\circ}\) without deflector, with deflector with uniform openings, and with deflector with non-uniform openings at 0.5 cm, 2.5 cm, 5.1 cm, and 7.6 cm from the screen surface obtained in laboratory tests in the absence of flow in the hydraulic channel.  
Figure 22: Distribution of (**a**) velocities and (**b**) normal velocities without a deflector installed inside the screen (**c**) normal velocities with a deflector with non-uniform openings (**d**) normal velocities with a deflector with uniform openings installed inside the screen obtained in CFD simulations in the absence of flow in the hydraulic channel; distribution of normal velocities in cross-sections at an angle of 0\({}^{\circ}\) (above the screen), along the length of the cylindrical wedge wire screen (from 0 to 15 cm) at different distances from the surface of the screen (0.5; 2.5; 5.1; 7.6 cm) obtained in numerical simulations and laboratory measurements in the tests in the absence of flow in the hydraulic channel (**e**) without deflector, (**f**) with deflector with non-uniform openings (**g**) with deflector with uniform openings.  
In conclusion, the results of the simulations carried out largely coincide with the experimental results. This confirms the correctness of their conduct as well as the correctness of the subsequent analysis of the obtained results. A detailed analysis of the numerical simulations makes it possible to draw conclusions analogous to those of the experimental studies in terms of the distributions of the normal components of the velocity, the critical locations (with the highest velocity values) as well as the use of the deflector and its effect on reducing local velocity values.
# Seamless Weather Data Integration in Trajectory-Based Operations Utilizing Geospatial Information
## 2 Materials and Methods
### Data Sources and Study Area
#### 2.1.2 Numerical Weather Prediction Models  
The weather data used in this study correspond to the LDAPS and GDAPS (Global Data Assimilation and Prediction System) grid datasets within the ADS-B observation area on 19 March 2024 (Figure 1). LDAPS is a high-resolution weather prediction system for local scales, while GDAPS is a weather prediction system for global scales. LDAPS provides detailed weather information for the Korean Peninsula and its surrounding areas, whereas GDAPS offers global weather information. These systems play crucial roles in providing comprehensive weather data for different flight phases in our 4 DT-Wx prototype system. These datasets conform to the GRIB2 format, a standard specification proposed by the WMO. The GRIB2 format is a binary file structure that compresses large amounts of data using modularization and object-oriented approaches.  
These NWP (Numerical Weather Prediction) models use a 3D grid system, including two spatial dimensions \(i\) and \(j\) (longitude and latitude) and altitude \(k\), to characterize the atmosphere. This system represents various weather variables (temperature, pressure, wind components, humidity, etc.) across different altitudes. In this study, as the main objective was to extract trajectory-based data, the research was conducted using an isobaric model.  
* The GDAPS dataset has the following spatial and temporal characteristics:
* A horizontal resolution of 10 km;
* A total of 70 layers;
* A 288 h forecast (00, 12 UTC);
* A 87 h forecast (06, 18 UTC).  
The LDAPS dataset has the following spatial and temporal characteristics:  
* A horizontal resolution of 1.5 km;
* A total of 70 layers;
* A 36 h forecast (00, 06, 12, 18 UTC).  
Figure 1: Study area and data description (white lines—ADS-B data; blue boundary—Incheon FIR).
## 2 Molecular Mechanisms of Antimicrobial Agents Recalcitrance in Clinically Important Biofilms
### Nutritional Limitation and Stress Responses
#### 2.2.2 Oxidative Stress Responses  
There are several pieces of evidence showing that a mechanism contributing to the bactericidal effect of antibiotics relies on an antibiotic-induced rise in reactive oxygen species (ROS), which might reach the lethal threshold [45; 46; 47; 48; 49; 50]. Briefly, bactericidal antibiotics accelerate cellular respiration [45; 51], leading to the overproduction of deleterious oxygen species (ROS) such as superoxide radical anion and hydrogen peroxide, which further disrupts iron homeostasis. Iron from the iron-sulphur clusters in redox enzymes of the respiratory chain is oxidised from Fe\({}^{2+}\) to Fe\({}^{3+}\) by hydrogen peroxide in a Fenton reaction, which generates increased amounts of hydroxyl radicals [52; 53]. The latter contribute to cell death by producing oxidative damages to critical biomacromolecules such as DNA [51; 52; 54; 55]. Bacteria counteract to reduce such damages using detoxifying enzymes, namely catalase, which decompose hydrogen peroxide. For instance, ciprofloxacin-induced overproduction of ROS in _P. aeruginosa_ biofilms is diminished by KatA catalase, since a catalase-deficient \(\Delta\)katA mutant biofilm treated with ciprofloxacin produced higher amounts of hydroxyl radicals and ROS than the wild-type biofilm [47]. In addition, the viability of the mutant biofilm was decreased. As a concluding remark, the upregulation of catalase would contribute to the increased tolerance of _P. aeruginosa_ biofilms towards ciprofloxacin. The stringent response triggered by nutrient starvation upregulates catalase activity [56; 57] and decreases the production of pro-oxidant molecules such as 4-hydroxy-2-alkylquinolines (HAQs) [56; 58; 59], as discussed in the next section.
# A Fundamental Study on an SAP Mixed Asphalt Mixture for Reducing the Urban Heat Island Effect
## 3 Results and Discussion
### Thermal Properties Results  
#### 3.2.1 Results of Indoor SAP Asphalt Thermal Characteristics  
Table 3 shows the surface temperature recovery delay time of SAP-500 and SAP-3500 asphalt compared to Non-SAP asphalt. It took about 20 min for the Non-SAP asphalt to recover to its original temperature after the first spray, and about 24 min after the second spray. The SAP content of 3% for both SAP types showed a similar delay, as shown in Table 2. However, as the content of the SAP increased, a significant delay time difference occurred. As per previous results, it was expected that the absorption performance of SAP-500 would be excellent, so the temperature recovery time delay effect would be better than that of SAP-3500 even when asphalt was mixed, but SAP-3500 was found to be more effective. It seems that SAP-3500, which has a faster initial absorption rate, absorbs a lot of moisture due to having an insufficient moisture supply, resulting in a relative temperature delay effect.  
After the observations, it was confirmed that the higher the SAP content, the better the temperature recovery time delay effect. Generally, porous asphalt moisture is stored in a large air void and then evaporated, thereby delaying the recovery temperature of the pavement. The thermal behavior of porous asphalt with large porosity curves upward when it is sprayed [27; 28; 29]. In this way, it was determined that the SAP can be mixed with asphalt to exhibit a temperature recovery delay effect in a similar tendency to porous asphalt, thereby causing a cooling effect on the road pavement surface [29].  
Figures 14 and 15 show changes in the asphalt surface temperature based on the SAP type and content. The results obtained showed that the temperature recovery delay time increased as the SAP content increased. Furthermore, the thermal behavior of the  
Figure 13: Comparison of weight loss results after dynamic immersion.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Content** & **1-2 Times Spraying** & **2-3 Times Spraying** \\ \hline SAP-500 3\% & +5 min & +12 min \\ \hline SAP-500 5\% & +9 min & +24 min \\ \hline SAP-500 7\% & +16 min & +49 min \\ \hline SAP-3500 3\% & +6 min & +14 min \\ \hline SAP-3500 5\% & +10 min & +26 min \\ \hline SAP-3500 7\% & +33 min & +60 min \\ \hline \hline \end{tabular}
\end{table}
Table 3: Temperature recovery delay effect of SAP asphalt.  
asphalt mixture with an SAP content of 7% was similar to the thermal behavior of porous asphalt with a large porosity. For both SAP types, the SAP 7% specimens had much better temperature recovery delay times than the other SAP specimens. This was because the SAP 7% specimens had a large amount of SAP that was not fully covered by the asphalt binder, which made a large amount of SAP readily available for water absorption, resulting in a longer time delay. For the 3% and 5% SAP specimens, almost all the SAP was covered and well mixed during manufacturing.
## 4 MISAO-ICEEMDAN-WLSSVM Model  
### Intrinsic Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN)  
EMD is suitable for decomposing non-stationary and highly random data. It can decompose the original signal into a series of intrinsic mode functions (IMF) with each variable frequency and reflect the local characteristics of nonstationary signals. EEMD and CEEMDAN are further improvements to the EMD algorithm that can solve the mixing problem during EMD. ICEEMDAN is a signal decomposition method proposed by [PERSON] et al. [20] that extracts IMF components by adding white noise with a mean and variance of 0 and 1, respectively. It can effectively reduce noise interference, mode aliasing, and reconstructed signal distortion in the intrinsic mode functions of EEMD and CEEMDAN algorithms. The specific decomposition process of ICEEMDAN is as follows:  
1. Add white noise \(E_{1}\left[\omega^{i}\right]\) to the original signal \(X\): \[X^{(i)}=X+\beta_{0}\ E_{1}\left[\omega^{i}\right]\] (18) where \(\omega^{i}\) is the \(i-th\) white noise that was added;
2. Decompose the original signal using the ICEEMAN method to obtain the first IMF component: \[\left\{\begin{array}{c}R_{1}=\frac{1}{T}\sum\limits_{i=1}^{I}M[X^{i}]\\ \widetilde{C}_{1}=X-R_{1}\end{array}\right.\] (19) where \(R_{1}\) is the first order residual and \(\widetilde{C}_{1}\) is the first IMF component.
3. Solve the second IMF component: \[\left\{\begin{array}{c}R_{2}=\frac{1}{T}\sum\limits_{i=1}^{I}M\{R_{1}+\beta _{1}\cdot E_{2}\left[\omega^{i}\right]\\ \widetilde{C}_{2}=R_{1}-R_{2}\end{array}\right.\] (20) where \(R_{2}\) is the second order residual and \(\widetilde{C}_{2}\) is the second IMF component.  
Figure 5: [PERSON] test results of MISAO and peer algorithms in CEC2017.  
(4) And so on, the \(h-th\) IMF component can be obtained as follows:  
\[\left\{\begin{array}{l}R_{h}=\frac{1}{l}\sum\limits_{i=1}^{l}M\{R_{h-1}+\beta_{ h-1}\cdot E_{h}\big{[}\omega^{i}\big{]}\\ \widetilde{C}_{h}=R_{h-1}-R_{h}\end{array}\right. \tag{21}\]  
where \(R_{h}\) is the \(h-th\) order residual; \(h=1,2,3,\cdots,N\); and \(\widetilde{C}_{h}\) is the \(h-th\) IMF component.
## 1 Introduction  
Most individuals infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are protected from progression to severe disease and death by vaccination. However, some patient populations with underlying conditions do not qualify for [1] or do not have access to the vaccine [2]. Moreover, protection by vaccination is relatively short-lived due to waning antibody levels at 6-9 months post vaccination [3], and because of reduced neutralization of variants by vaccination-elicited antibodies [4; 5; 6; 7; 8; 9; 10]. In addition, the current vaccines against SARS-CoV-2 will most likely not protect against future coronavirus outbreaks. Therefore, there is a need for safe therapeutic agents. To this end, several drugs have been investigated, including remdesivir [11], molnupiravir [12; 13], favipiravir [14] (nucleoside analogues), tociliaumab and sarilumab (interleukin-6 receptor modulators) [15; 16], and ritonavir-boosted nirmatrelvir (main protease inhibitor (M\({}^{\text{pro}}\)) [17], some of which are also being investigated as combination therapies [18; 19].  
SARS-CoV-2 makes use of human angiotensin-converting enzyme II (hACE2) expressed in the airway to infect its host [20]. The viral spike (S) protein recognizes and binds to hACE2, whereafter the spike protein is primed either at the cell surface by transmembrane protease serine subtype 2 (TMPRSS2) or within the endosomal pathway by cathepsin L [21; 22; 23]. This priming event facilitates cell fusion, which in turn results in release of the viral content into the host cell [24]. The same two proteases are involved in S protein priming and entry of SARS-CoV-1 [23]. This makes hACE2, TMPRSS2, and cathepsin L key targets for blocking SARS-CoV-1 and SARS-CoV-2 entry into cells [23]. As such, efforts have been made to find drugs that target these proteins [25; 26; 27; 28; 29; 30; 31], such as the small molecules K777 and camostat mesylate, which respectively target cathepsin L [29] and TMPRSS2 [32].  
One approach to discover new coronavirus antivirals is phenotypic screening, which is a cell-based, untargeted, and unbiased screening approach to discover primary hit compounds that lend themselves to further optimization [33]. Phenotypic screening can identify compounds that act on different stages of the virus replication cycle (entry-stage blockers, host-targeting antivirals, protease inhibitors, and replication blockers) [34]. Typically, after hit identification in phenotypic screening runs, the target of the hit must be identified by more specialized assays.  
Here, we report the discovery of Z-Tyr-Ala-CHN\({}_{2}\) from a phenotypic screen. The molecule acts early in the viral replication cycle by targeting cathepsin L. Due to its cell-specific activity, the compound is a valuable research tool for studying the entry and replication mechanisms of SARS-CoV-2 and other coronaviruses.
## 3 Numerical Tests  
In this section, we intend to test the efficiency of Algorithm 1 by numerical experiments. We use Algorithm 1 to solve eleven examples, some of them are proposed the first time, some of them are modified by the examples of the references (such as [26, 27]).  
The smoothing function of \(F\) as \(\tilde{F}_{i}(x,\mu)=\sqrt{F_{i}(x)^{2}+\mu}\) is used in solving Examples 1-4. From Example 5 to Example 11, the smoothing function of \(F\) is defined by (see [26]). \(\tilde{F}(x,\mu)=\mu\ln\sum_{i=1}^{m}\exp(\frac{f_{i}(x)}{\mu}),\) where \(F(x)=\max\{f_{1}(x),\cdots,f_{m}(x)\}\), \(i=1,\cdots m\).  
Throughout the experiments, we set \(\sigma=10^{-2},m=1.5,m_{1}=0.5.\) In Examples 1-3 and Examples 5-8, we set \(\varepsilon=10^{-4},\delta=10^{-3},\eta=0.4,\mu_{0}=0.2\). Example 4, in which we set parameters \(\varepsilon=10^{-3},\delta=10^{-2},\eta=0.1,\mu_{0}=0.02\). In the case of Examples 9-11, we set \(\varepsilon=10^{-2},\delta=10^{-3},\eta=0.4,\mu_{0}=0.2\). We choose \(\Psi(x)\leq\varepsilon\) as the termination criterion. Our numerical results are summaried in Tables 1-11, where all components of \(x_{0}\) are randomly selected from \(0\) to \(10\). We randomly generate 10 initial points, then implement Algorithm 1 to solve the test problem for each initial point. By the numerical of results of Examples 10-11, we can see that Algorithm 1 is suitably to solve the large scale problems.  
**Example 1.** We consider Equation (1), where \(F\) is defined by \(F(x)=|2x-1|\).  
The exact solutions of this problem are \(0\) and \(0.5\).  
**Example 2.** We consider Equation (1), where \(F=\left(\begin{array}{c}|2x_{1}-1|\\ |4x_{2}+x_{1}-\frac{1}{2}|\end{array}\right).\) There are three exact solutions as \((\frac{1}{2},0)^{T},(0,\frac{1}{8})^{T}\) and \((0,0)^{T}\).  
**Example 3.** We consider Equation (1), where \(F=\left(\begin{array}{c}|5x_{1}+x_{2}-x_{3}|\\ x_{1}^{2}+4x_{2}-x_{3}-2\\ 5x_{2}^{2}-6x_{1}-2x_{3}\end{array}\right).\)  
\((0,\frac{1}{2},0)^{T}\) is one of the exact solutions of this problem.
# Coordinated Control of Virtual Power Plants to Improve Power System Short-Term Dynamics
## 1 Introduction
### Literature Review
#### 1.2.1 Short-Term Dynamics  
The common techniques for improving the VPP's short-term dynamics are achieved by the frequency control of the devices installed in the VPP itself. Reference [8] presents a equivalence between Virtual Synchronous Machines (VSMs) and frequency-droop control for converter-based microgrids. In [9], a study of DERs in distribution systems to the frequency control of TG is provided. The coordinated VPP frequency control method for fast frequency regulation proposed in this paper is based on the frequency control technologies discussed in [10] that combines with the active power injection signal from VPP to TG. It is relevant to note that the fast frequency regulation provided by non-synchronous DERs and ESSs can be also viewed as the provision of synthetic inertia [11].
# Traffic Aware Scheduler for Time-Slotted Channel-Hopping-Based IPv6 Wireless Sensor Networks
## 3 Related Work
### Adaptive Approaches
#### 3.2.2. Tesla  
The traffic-aware elastic slotframe adjustment scheduler (TESLA) [10] follows the same allocation of slotframes as Orchestra, except for the unicast slotframe. Instead of one slotframe, where both receive and transmit slots are allocated, TESLA proposes two slotframes, one for receiving unicast traffic and another for transmitting unicast traffic. Each node adjusts the size of the receiving slotframe to the traffic load. The traffic load information is piggybacked on the packets received from neighbors. If the size of the receiving slotframe changes, the node informs its neighbors about the change so they can adapt their transmitting slotframe size. The TESLA scheduler is compared on testbeds with TSCH minimal scheduling, RBS and SBS Orchestra. TESLA performs better with respect to reliability and energy efficiency but introduces overhead whenever the receiving slotframe size changes.
# Characteristics and Causes of Coastal Water Chemistry in Qionghai City, China
## 3 Results  
### Basic Physical and Chemical Properties of Water Bodies  
The minimum, maximum, mean, and standard deviation of each indicator of the analyzed water samples in terms of the results are shown in Table 1. The content of the dominant cations in the water in the study area showed Na\({}^{+}\) > Ca\({}^{2+}\) > Mg\({}^{2+}\) > K\({}^{+}\) and the average concentration of the dominant cation Na\({}^{+}\) was 34.432 mg\(\cdot\)L\({}^{-1}\). The dominantanion content showed Cl\({}^{-}\) > SO\({}_{4}\)\({}^{2-}\) > HCO\({}_{3}\)\({}^{-}\) > CO\({}_{3}\)\({}^{2-}\), and the average concentrations of the dominant anions Cl\({}^{-}\), SO\({}_{4}\)\({}^{2-}\), and HCO\({}_{3}\)\({}^{-}\) were 30.69 mg\(\cdot\)L\({}^{-1}\), 18.49 mg\(\cdot\)L\({}^{-1}\), and 15.40 mg\(\cdot\)L\({}^{-1}\), respectively. The mean equivalent concentration of Cl\({}^{-}\) accounted for 46% of the total anions, and the mean equivalent concentration of SO\({}_{4}\)\({}^{2-}\) accounted for 28% of the total anions. The coefficient of variation could characterize the stability of ionic components, and the larger the coefficient of variation, the more susceptible the ionic component was to the influence of the external environment.
# An End-to-End Video Steganography Network Based on a Coding Unit Mask
## 2 Proposed End-to-End Video Steganography System
### Architecture  
In this section, we present PyraGAN, a generative adversarial network combined with a CU mask to hide random n-bit binary messages in a host video frame. The proposed architecture is shown in Figure 1 and consists of three components: (1) An encoder, which takes cover video frames and secret messages as the input, and outputs steganographic (abbreviated as stego) video frames (Section 2.1.2), which combine the CU mask of the cover video frames and the attention mechanism to facilitate the visual quality of the stego video frames. (2) A decoder, which takes the stego video frame as the input and tries to recover the secret messages (Section 2.1.3). (3) A discriminator, which scores the quality of the cover video frames and stego video frames (Section 2.1.4).
## 3 Materials and Methods
### Baselines
#### 3.3.2 Autoencoder  
A semi-supervised learning approach was also investigated in this paper. Precisely, we employed the spatio-temporal residual autoencoder (R-STAE), recently proposed in [22]. As described in Section 2.2, this architecture is composed of ConvAE and Conv.LSTM layers, with the addition of residual blocks that result in lower reconstruction errors.  
To employ this model using our datasets, the input size was modified to 224 \(\times\) 224. In addition, Adam with a learning rate of \(10^{-4}\) was the optimizer employed, as was performed for the customized 3D-CNN. The mean squared error (MSE) was used to calculate the average squared difference between the reconstructed and real frames. This method assigns a label to the input instance based on the normality score calculated using the equation below, also employed in [22].  
\[\text{normscore}=1-(\text{MSE}-\text{min}(\text{MSE})/\text{max}(\text{MSE})) \tag{1}\]  
The normality score values are expected to be higher for normal instances and lower for the anomalous ones. Hence, the score taken from Equation (1) is compared against an empirically defined threshold.