esa-sceva/satcom-chunk-collection huggingface dataset:
# Distributed Resources Allocation Method for Space-Ground Integrated Mobile Communication System
## 3 System Model
### Problem Formulation
In other words, it is to make \(R\) as large as possible, but not more than \(R^{\prime}\), which is the minimum transmission rate to meet the requirements of k service. \[=\lambda_{1}\mathbf{T}+\lambda_{2}\mathbf{R}^{\prime}+\lambda_{3}\mathbf{A}(t) +\lambda_{4}\mathbf{P}_{kb}(t) \tag{2}\]1 \[=\lambda_{1}\mathbf{T}+\lambda_{2}\mathbf{R}^{\prime}+\lambda_{3}\mathbf{A}(t) +\lambda_{4}\mathbf{P}_{kb}(t) \tag{2}\]2 (12) \[=\lambda_{1}\mathbf{T}+\lambda_{2}\mathbf{R}^{\prime}+\lambda_{3}\mathbf{A}(t) +\lambda_{4}\mathbf{P}_{kb}(t) \tag{2}\]3 (13) \[=\lambda_{1}\mathbf{T}+\lambda_{2}\mathbf{R}^{\prime}+\lambda_{3}\mathbf{A}(t) +\lambda_{4}\mathbf{P}_{kb}(t) \tag{2}\]4 (14) \[=\lambda_{1}\mathbf{T}+\lambda_{2}\mathbf{R}^{\prime}+\lambda_{3}\mathbf{A}(t) +\lambda_{4}\mathbf{P}_{kb}(t) \tag{2}\]5 (15) \[=\lambda_{1}\mathbf{T}+\lambda_{2}\mathbf{R}^{\prime}+\lambda_{3}\mathbf{A}(t) +\lambda_{4}\mathbf{P}_{kb}(t) \tag{2}\]6 (16) \[=\lambda_{1}\mathbf{T}+\lambda_{2}\mathbf{R}^{\prime}+\lambda_{3}\mathbf{A}(t) +\lambda_{4}\mathbf{P}_{kb}(t) \tag{2}\]7  
In the above optimization problem, the symbol \(\chi\) represents the proportion of resource panes that can actually be used in an allocated resource block. \[=\lambda_{1}\mathbf{T}+\lambda_{2}\mathbf{R}^{\prime}+\lambda_{3}\mathbf{A}(t) +\lambda_{4}\mathbf{P}_{kb}(t) \tag{2}\]8  
The parameter \(b_{kb}(t)\) describe the state of each slice (i.e., whether it will be allocated to current service T or not) which is a crucial factor in the allocation process. \[b_{kb}(t)= \begin{cases}1,&\text{if {the slice has been allocated}}\\ 0,&\text{else}\end{cases}  \tag{19}\]The second optimization problem is to select the most appropriate allocated physical channel according to the characteristics of the users, so as to minimize the total overhead when the transmission power and the number of allocated resource blocks are determined. \[\textbf{P3:}\underset{\textbf{A}(t)}{\textbf{minimize}}\left(\textbf{C} \right)=\underset{\textbf{A}(t)}{\textbf{minimize}}\left(G\left(\textbf{T}, \textbf{R}^{\prime},\textbf{A}(t),\textbf{P}_{kb}(t)\right)\right) \tag{20}\]  
\[\textit{s.t.}\ A_{ij}(t)\in[0,1],1\leq i\leq 3,1\leq j\leq N \tag{21}\]  
\[\sum_{i=1}^{3}\sum_{j=1}^{N}A_{ij}\leq C_{ch} \tag{22}\]  
The sub problems **P2** and **P3** will be discussed in the following sections. The whole solving process has been illustrated in Figure 4. All the variables defined in this article are summarized in Table 2.
## 1 Introduction
In 2021, [PERSON] and [PERSON] in [28] presented the results of a 3-month monitoring period of a 300 MVA transformer, as a result of which it was possible to locate a defect near phase 1U, which was the PD source. In 2022, [PERSON] et al. in [29] discussed the simultaneous application of conventional and alternative PD detection methods in power equipment diagnostics. The authors compared the sensitivity of each method in the context of detecting critical insulation defects. In 2024, [PERSON] and [PERSON] in [30] presented the idea of a combined method for partial discharge detection and localization in power transformers using only one acoustic emission and UHF sensor. Simulated in COMSOL Multiphysics 5.2a and experimentally validated, this approach uses pattern recognition in MATLAB to compare real-time sensor data with a predefined database, ensuring accurate PD detection and localization. The significant contribution of this work is, to the best of the authors' knowledge, the first detailed analysis of several cases of online PD monitoring in large power transformers using both ultra-high-frequency and acoustic emission methods. Another contribution is the presentation of fault location results obtained using the TDoA technique and based on the analysis of data collected by the acoustic emission system, which allowed the search for the PD source to be narrowed to a small area of the transformer tank and significantly reduced the measurement time.
# COSMIC-2 RO Profile Ending at PBL Top with Strong Vertical Gradient of Refractivity
## 4 Results
### Vertical Profiles of Spherically Symmetric Refractivity  
In order to better compare the local variations of refractivity at different altitudes, we subtract the spherically symmetric refractivity profile (\(N(r)\)) from the total refractivity (\(N\)). Figure 6a shows the geolocation and the lowest altitudes from the earth's surface of all COSMIC-2 RO profiles on 1 August 2020. As expected, there are some Ros whose observed lowest altitudes are high over high terrains, such as the Qinghai-Tibet Plateau, the Rocky Mountains, and the Andes Mountains, due probably to terrain occlusion. There are also some RO profiles with high lowest altitudes over oceans in the tropics. The reasons for the latter case are probably associated with the complicated small-scale structures of water vapor distributions that makes the vertical gradients of refractivity strong, thus truncating the RO signal propagation [40]. This will be explained below.  
The spherically symmetric refractivity profile, which will be denoted as \(\overline{N}^{obs}(r)\), can be calculated by averaging all RO observations with the same distance from the perigee points to the earth's center. The vertical profile of spherically symmetric refractivity calculated by averaging the ERA5 reanalysis that is interpolated to RO locations is denoted as \(\overline{N}^{ERA5}(r)\). Results for all COSMIC-2 RO profiles between 45\({}^{\circ}\)S and 45\({}^{\circ}\)N on 1 August 2020 are shown in Figure 6b. As expected, both \(\overline{N}^{obs}(r)\) and \(\overline{N}^{ERAS5}(r)\) decrease with altitude. The spherically symmetric refractivity calculated from COSMIC-2 RO observations is slightly smaller than that calculated from the ERA5 reanalysis above and below 12 km where the two have the same values.  
Figure 5: (**a**) Horizontal distribution of the vertical gradient of wet refractivity (shaded) and geopotential height (contour, unit: gpm) at 900 hPa, and (**b**) Vertical cross-section of the vertical gradient of wet refractivity (shaded) and the temperature (contour, unit: K). The perigee point of ROI is shown by a black open circle and the ray tangent direction is shown in (**a**) by the black arrow. The refractivity gradient is calculated from ERA5 reanalysis at 0700 UTC, 11 August 2020.  
In the next subsection, we investigate the relationship of the vertical gradients of \(N-\overline{N}^{\text{obs}}(r)\) to the lowest altitudes of COSMIC-2 observed RO profiles. Subtracting \(\overline{N}^{\text{obs}}(r)\) from the total refractivity reduces the influence of a strong dependence of the atmospheric refractivity on altitude.
# Raman Spectroscopy to Monitor the Delivery of a Nano-Formulation of Vismodegib in the Skin
## Materials and Methods
### Transfersomes Characterization  
The Z-average was determined by dynamic light scattering (DLS) with a Malvern Zetasizer Nano (Malvern Instruments; Malvern, UK) at 25 \({}^{\circ}\)C, after appropriate dilutions. Each sample was analyzed three times. Zeta potential was determined with the same equipment in triplicate. The interaction of VDG with the transfersomes' membrane, and the changes in the membrane packing and fluidity after incorporation of the drug, were assessed with the MC540 and Laurdan probes in triplicate. For the measurement with MC540, samples were diluted with Tris-HCl buffer until a concentration of 172 uM of lipids, and the probe was added in a 1:200 molar ratio. The samples were incubated for 2 min, and then scans between 400 and 600 nm were obtained with a UV-Vis spectrophotometer (Jasco V-550, Tokyo, Japan) at 25 \({}^{\circ}\)C. The partition coefficient (PC) was calculated as the ratio of the maximum absorbance of the MC540 monomer in a non-polar phase (570 nm) with the maximum absorbance of the monomer in an aqueous phase (530 nm) [13]. A baseline correction was performed to avoid scattering measuring samples without the probe. PC was calculated by:  
\[\text{PC}=\text{A570}/\text{A530} \tag{1}\]  
Laurdan was incorporated into T and T + VDG during their preparation by adding the probe to the lipophilic phase in a 0.33 mol% concentration. Samples were diluted with Tris-HCl buffer until forming a concentration of phospholipids of 80 \(\mu\)g/mL, and the fluorescence was measured with a FluoroMate FS2 (SCINCO, Seoul, Republic of Korea) at 25 \({}^{\circ}\)C. Generalized polarization (GP) spectra of Laurdan excitation (GPex) and emission (GPem) were calculated and graphed [14]. GPex spectra were calculated by:  
\[\text{GPex}=(\text{I}_{440}-\text{I}_{490})/(\text{I}_{440}+\text{I}_{490}) \tag{2}\]  
using excitation wavelengths from 320 to 400 nm. The I\({}_{440}\) and I\({}_{490}\) are the emission intensities at 440 and 490 nm, which correspond to the emission maxima of the probe in the gel and the liquid-crystalline phase, respectively. GPem spectra were calculated by:  
\[\text{GPem}=(\text{I}_{390}-\text{I}_{340})/(\text{I}_{390}+\text{I}_{340}) \tag{3}\]  
using emission wavelengths from 420 to 510 nm. The I\({}_{390}\) and I\({}_{340}\) are the excitation intensities at 390 and 340 nm, which correspond to the excitation maxima of the probe in the gel and the liquid-crystalline phase, respectively.  
On the other hand, the molar ratio between the phospholipids and VDG was determined in triplicate. Phospholipids were quantified by the colorimetric method adapted from [PERSON] [15].  
Statistical analyses were conducted with GraphPad Prism 8.0.1 (GraphPad Software Inc., San Diego, CA, USA). Data were analyzed using Student's t-test after the normal data distribution was confirmed with the Shapiro-Wilk test. The homoscedasticity was also corroborated using the F test to compare variances. Values with \(p<0.05\) were considered significant.
## 4 Results and Discussion
The recall metric is relevant in our context because of the adverse consequences of false negatives in clinical practice. We improved the quality of comparisons between ML models using the Friedman and Nemenyi tests, which relied on the recall performance across the six datasets. The tree-based classification models examined in this study demonstrated superior performance and were grouped based on their classification metric outcomes and statistical test results. This observation is of particular importance because tree-based models are highly interpretable, which can positively influence the decision-making process of healthcare professionals. In clinical practice, the acceptance of ML-based systems increases when healthcare practitioners can easily comprehend and interpret the outputs of classification models to understand the decision-making logic, as referenced in [30]. Additionally, it is important to acknowledge that in a real-world scenario, the presence of asymptomatic patients may be seen as a limitation in the applicability of ML models [31]. However, in the context of this study, its relevance persists due to the presence of symptomatic cases that demand the attention of healthcare professionals and government authorities. Evaluating symptomatic patients remains crucial to avoid the inadvertent overuse of testing resources, especially in the face of concurrent disease outbreaks in Brazil caused by other viral infections (e.g., COVID-19 and influenza). Certain viral infections can present with similar symptoms, making it challenging for healthcare professionals to decide the appropriate type of testing needed. A symptomatic patient with limited symptoms can pose challenges for ML models. However, attribute ranking and additional information, such as whether the patient has had contact with infected individuals, are valuable factors to supplement ML models. They provide additional context and data that can aid healthcare professionals and policymakers in making informed decisions. Another limitation of this study is the number of ML models experimented with. However, we addressed this limitation by considering a set of well-established algorithms that cover various approaches, including tree-based models, linear regression, statistical learning, distance-based methods, and neural concepts.
## 3 Materials and Methods
### Traffic Direction
#### 3.4.1 Trajectory Data Refinement  
Data cleansing is a crucial process in data mining and machine learning, which involves identifying and correcting incomplete or inaccurate data, including missing values, errors, duplicate entries, and outliers. This process is essential for ensuring the consistency and accuracy of the data, enhancing the reliability of analysis results. In vessel traffic pattern recognition, AIS data often contain inaccuracies such as sensor errors, missing information, and outliers, making thorough data cleaning essential. In this study, owing to the presence of berths for fishing vessels and coastal passenger vessels located near the study area, we excluded vessel-specific voyage data, including data with vessel speeds below 1 knot, to filter out berth-specific voyage data. Removing duplicate location points is crucial for optimizing the similarity measures between vessel voyages. Consequently, we utilized the Douglas-Peucker (DP) algorithm to simplify voyages by reducing position points while preserving their essential shape. Finally, the method used in this study, dynamic time warping (DTW), has the advantage of calculating the similarity between tracks even with different numbers of position points. Nevertheless, to reduce the impact of extreme values and improve DTW accuracy, we removed voyages with the lowest 5% of location points.
# Entity-Alignment Interaction Model Based on Chinese RoBERTa
## 3 Methodology
### The Model Framework
#### 3.2.1 Basic RoBERTa Unit  
We used entity alignment as a downstream task to fine-tune RoBERTa. First, we constructed the training data \(D=\left\{\left(e,e^{\prime}_{0^{\prime}},e^{\prime}_{1}\right)\right\}\), where each triple \(\left(e,e^{\prime}_{0^{\prime}},e^{\prime}_{1}\right)\in D\) contains a query entity \(e\in E\), correctly aligned samples \(e^{\prime}_{0}\in E^{\prime}\) and negative samples \(e^{\prime}_{1}\) randomly sampled from \(E^{\prime}\). For example, for a constructed training data ([PERSON], [PERSON], [PERSON]), \"[PERSON]\" is the query entity from \(E\); \"[PERSON]\" is its correctly aligned entity in \(E^{\prime}\), which means that they point to the same person or thing in the real world; \"[PERSON]\" is a negative sample entity randomly sampled from \(E^{\prime}\). For each entity \(e\) in the datasets, we applied a pre-trained Chinese RoBERTa to accept its name as input, take the corresponding value of the CLS label, and then map it using MLP, and the result obtained was used as the vector representation of that entity.  
\[T(e)=MLP(CLS(e)) \tag{1}\]  
the RoBERTa model parameters are fine-tuned based on this vector representation and margin loss with the following equation:  
\[L=\sum_{\left(e,e^{\prime}_{0^{\prime}},e^{\prime}_{1}\right)\in D}max\left\{ 0,d\left(e,e^{\prime}_{0}\right)-d\left(e,e^{\prime}_{1}\right)+\alpha\right\} \tag{2}\]  
where \(d(.)\) denotes the Manhattan distance function of vectors \(T(e)\) and \(T(e^{\prime})\) and \(\alpha\) denotes the marginal loss. The negative sample-sampling method was derived from the BootEA model proposed by [PERSON] et al. [27]. The following interaction model uses these embeddings as the basic units. Due to the lack of computational resources, the RoBERTa units was fine-tuned using the above method, and the model parameters were fixed during the subsequent interactions.
## 4 Results
### Load-Carrying-Capacity Analysis; Comparison of Experiment and FEM Analysis Results  
The extensive analysis of failure mechanisms of all individual beams was performed based on the character of their failure and crack layouts. Both beams with no composite strip (type Br) were destroyed by over-yielding of the reinforcing bar, which had occurred before the concrete was crushed in the compression zone in the middle of the beam span. All the strengthened beams were damaged by the delamination of the composite material from the concrete surface, which was always brittle.  
In Table 1, the values of ultimate forces and maximum deflections of all laboratory-tested beams at the moment of their failure are put together, whereas their relative values are given in Table 2. Examples of the deflections of the B-4 point in the middle of the beam span (according to Figure 3) received in experiments are shown in Figure 5a. In Figure 5b, the positive comparison of the FEA and experiment results is also presented--deflection of point B-4 for the RC beam (Br) and for the beam with a CFRP strip (Bz). The high compatibility of the results visible in the chart confirms the correctness of the preparation of the FEM model and the entire procedure of numerical calculations.  
Figure 5: (a) Deflections of the point in the middle of beam span (point B-4 according to Figure 3) obtained in experiments. (b) Comparison of experiment and FEA results—point B-4 deflection for RC beam (Br) and beam with CFRP strip (Bz).  
The comparison of the ultimate loads for all beams received in the numerical analyses and recorded during laboratory studies is summarized in Table 3. It is worth mentioning that the ultimate load of the RC beam without a CFRP strip, calculated on the basis of the results of the material tests, was 94.00 kN.
## 1 Introduction  
Many microorganisms use nitrogen from urease for growth (urea amidohydrolase, EC: 3.5.1.5). Urease is an enzyme that catalyzes the hydrolysis of urea into ammonia and carbon dioxide. There is a link between health complications and ammonia production by urease [1]. In animals and humans, low pH of the stomach allows microbial strains to survive, multiply, and grow, resulting in pyclonephritis, hepatic coma, gastric carcinoma, gastric lymphoma, kidney stones, and peptic ulcer complications [2,3]. The treatment of bacterial infection with therapeutics has often proven ineffective due to drug resistance. Thus, there is a clear need for alternatives or novel treatments. Given the involvement of ureases in various diseases, pharmaceutical research has channeled considerable efforts into discovering potent and safe urease inhibitors [4; 5; 6; 7].  
Molecules with a binding site that can chelate metals are an interesting challenge and could be a promising line of action to prevent the adverse effects of ureolytic bacterial infections in humans [8]. In this regard, many types of potent urease inhibitors have been designed [9], such as dihydropyrimidines [10], urea derivatives [11], semicarbazones [12], Schiff bases [13], hydroxamic acid derivatives [14], piperazines [15], bisocumarines [16], benzimidazoles [17], and sulfonamides [18].  
Enaminone molecular scaffolds have been found in many synthetic drugs and natural products [19; 20]. Specially, pyrimidine-based enamines have demonstrated an array of biological activities owing to the presence of the alkenylamine moiety (R\({}_{2}\)NCH = CH\({}_{-}\)) in their structures, and are capable of strong bonding with metal ion chelates in biological systems [21; 22].  
The barbituric and thiobarbituric acids [(thio)pyrimidine trione analog derivatives] have been reported urease inhibitors [12]. These can be considered privileged structures because they have antifungal [23], antimicrobial [24; 25], anti-adiponectin [26], anti-sclerosis [27], anti-convulsing [28; 29], antiglycation [30], and \(\alpha\)-glucosidase inhibitory [30] properties. Several compounds **A, B,** and **C** have been reported, and showed anti-urease activity comparable to acetohydroaxamic acid (Figure 1) [31; 32].  
In this context, and as a continuation of our extended medicinal chemistry program based on the barbituric and thiobarbituric acid moieties [30; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44], here we examined the in vitro anti-urease activity of a collection of 1,3-dimethylbarbiturate-enamine compounds and their analogs, thiobarbiturate derivatives. In addition, molecular docking studies were performed to evaluate the molecular interactions of the newly synthesized compounds with selected drug targets (PDB ID 4 GY7).
## 3 Results and Discussions
### Exergy Analysis of Corn Drying with the IRCC Corn Dryer
#### 3.3.2 Exergetic Performance  
Figure 13 shows the variation of \(\eta_{\text{ex}}\) and SI with drying process for the purpose of comprehensively determining the energy performance of DC and the entire drying system during the complete drying process. The result indicates that \(\eta_{\text{ex}}\) and SI of the whole drying system decrease with the advancing of drying process and vary from 5.16% to 38.21% and 1.05 to 1.61, respectively. These results are close to those in similar agricultural product industrial dryers, such as an industrial tray dryer for cassava-starch drying (16.04% \(\leq\eta_{\text{ex}}\leq\) 30.65%; 1.19 \(\leq\) SI \(\leq\) 1.44) [1] and a convective dryer for rough rice drying (5.1% \(\leq\eta_{\text{ex}}\leq\) 29.4%; 1.05 \(\leq\) SI \(\leq\) 1.42) [4]. Compared with the entire drying system at a time, the values of \(\eta_{\text{ex}}\) and SI of DC are correspondingly higher. In terms of the heat loss characteristics investigated in Section 3.2.3, in the initial stages of the drying process, the heat loss of DC is relatively low; hence, \(\eta_{\text{ex}}\) reaches a maximum in this period, due to the fact that the decrease of heat loss means an increase of both energy and exergy efficiency. After 9 h, the relatively low \(\eta_{\text{ex}}\) (less than 20%) of DC indicates that measures should be applied to strengthen the drying kinetic. Low-temperature drying may be a feasible method to solve the problem of low-exergy efficiency in the latter stage of drying [51].
## 2 Edge Computing and Related Computing Paradigms
### Edge Computing
#### 2.4.3 Fog Computing
Compared with cloudlets, however, cloudlet computing fits well with the mobile cloudlet-cloud framework, but fog computing can support large amounts of traffic; in addition, resources in fog can be located anywhere throughout the things-to-cloud path [69]. Fog and edge computing are not identical, even though both perform the computation and storage in the edge close to end users. The OpenFog Consortium states this distinction as the hierarchical nature of fog computing, allowing computing, networking, storage, control, and acceleration anywhere along the cloud-to-things continuum; however, in edge computing, computing is performed only at the edge [37]. The most significant drawback of the above-mentioned edge computing concepts (cloudlet, ad hoc clouds, and fog computing) is that the computing is not integrated into the architecture of a mobile network; therefore, QoS and quality of experience (QoE) for users are not completely guaranteed [43]. One concept that can integrate cloud capabilities into a mobile network is the cloud radio access network (C-RAN) [70]. The C-RAN uses the idea of distributed protocol stack [71], in which some layers of the protocol stack have changed their place from distributed radio remote heads (RRHs) to centralized base band units (BBUs) (see [72]). A fog radio access network (Fog RAN/F-RAN) is a form of fog computing that is also able to be combined with mobile technologies; more details can be found in [73]. F-RAN and C-RAN can be both used for mobile networks with base stations; they can be implemented for 5G-related mobile technology deployments [73] and are more energy efficient for network operations. For more information about F-RAN, we encourage readers to refer to the reference [74].