esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of Recording Energy  
A high recording energy might be effective at forming deep magnetic fringes. Figure 6a illustrates the recording energy dependence of the diffraction efficiency obtained with numerical simulation. As shown in this figure, the diffraction efficiency (black circle symbols) increases with recording energy up to 60 mJ/cm\({}^{2}\). However, it reaches saturation, even if the recording energy increases higher than that. To investigate the mechanism in detail, the formation procedure of the magnetic fringe was observed through numerical simulation. The calculated shapes of the magnetic fringe at several recording energies are shown in Figure 6b. As shown in this figure, the total depth of the magnetic fringe increases with the recording energy density; however, the fringe merges near the surface at 81 mJ/cm\({}^{2}\) and 102 mJ/cm\({}^{2}\). Thus, the effective depth of the fringe is almost the same at approximately 1.6 \(\upmu\)m for every case, and this limited effective fringe depth results in the saturation of the diffraction frequency at high recording energies. To investigate the procedure in detail for this merging of adjacent fringes, the time evolution of the temperature distribution in the medium was investigated for the spatial frequency of 1500 line-pairs/mm after laser irradiation, as shown in Figure 6c. The high-temperature region over the Curie temperature surrounded by green lines is separated immediately after laser irradiation; the maximum temperature immediately after laser irradiation reaches approximately 400 \({}^{\circ}\)C, and the minimum temperature between the high-temperature region near the surface is still at a room temperature of 25 \({}^{\circ}\)C. As the excess heat in the high-temperature region mainly diffuses laterally to the low-temperature region because of the large temperature gradient, the temperature of the low-temperature region rises to a temperature higher than the Curie temperature within 10 ns, as shown in Figure 6c. However, if the shape of the high-temperature region immediately after laser irradiation can be maintained, a high diffraction efficiency can be obtained, as shown in Figure 6a (red square symbols), in which the diffraction efficiency was calculated using a virtual fringe shape identical to the shape of the region heated higher than the Curie temperature immediately after laser irradiation. This strongly suggests that deep and clear magnetic fringes may be recorded if we can suppress the effect of heat diffusion from the regions with excessively high temperatures.  
Figure 5: XMCD mapping images of magnetic interference fringes recorded at spatial frequencies of (**a**) 500 and (**b**) 1500 line-pairs/mm, and (**c**) Fe intensity at the same location as that for 500 line-pairs/mm. In the case of 500 line-pairs/mm, contrast changes at 2-\(\upmu\)m intervals are observed in the XMCD image, while the stripes of the XMCD tend to be disturbed in the dark regions of the Fe intensity distribution shown in (**c**). On the other hand, the contrast changes at 0.7-\(\upmu\)m intervals are less clear in the case of 1500 line-pairs/mm.
# Virtual Machine Migration Strategy Based on Multi-Agent Deep Reinforcement Learning
## 4 VM Migration Strategy Based on MADRL
### Simulation Experiment Results and Analysis  
#### 4.3.1 Experimental Environment and Data Set  
In order to evaluate the performance of the VM migration strategy based on MADRL proposed in this paper, a simulation environment is developed based on Python language, and the tasks and servers in the environment are modeled. In the simulation process, time is divided into periods of equal length, and the interval is set as 1 s. Server running status is generated based on the machine_usage.csv file in the open source dataset cluster-trace-v2018 [27]. The dataset contains information about 4000 servers and corresponding online application containers as well as the operation of offline computing tasks in 8 days. A task model is generated based on the batch_task.csv file in the dataset. When considering the task mobility, this experiment uses real vehicle driving datasets to validate the proposed VM migration strategy. The dataset consists of 4316 cabs driving for 24 h in Shanghai, China on 20 February 2007 [28]. Each vehicle trajectory consists of a set of time-stamped GPS points, and each GPS point has seven fields, which are cab ID, time stamp, longitude and latitude of the cab's current location, instantaneous speed at this time, clockwise angle to north (unit: 2 degrees), and cab status (status: empty or with passengers). After pre-processing the raw data, five VMs were set to wait for migration in each time period, and 50 MEC servers were randomly deployed.
## 2 Materials and Methods
### Methodology
#### 2.2.2 Detecting Vegetation Change
The BFAST01 algorithm is a simplified variant of BFAST and seeks the major change in the time series. These two models are analogs to each other; however, two important differences exist between them. (1) unlike BFAST, BFAST01 fits a season-trend model to the data to detect only major changes while ignoring small structural changes. (2) BFAST separately and iteratively applies first a seasonal and then a trend model while BFAST01 takes both together into account simultaneously [38]. BFAST and BFAST01 need to be run in the R environment, and the related packages can be downloaded from [[https://mirrors.tuna.tsinghua.edu.cn/CRAN/](https://mirrors.tuna.tsinghua.edu.cn/CRAN/)]([https://mirrors.tuna.tsinghua.edu.cn/CRAN/](https://mirrors.tuna.tsinghua.edu.cn/CRAN/)) (accessed on 14 February 2022). The trend types of the whole time series is divided according to the trend before and after a significant mutation point in the time series (Table 1). There are three major types of change patterns (Figure 3), including monotonic changes (increase or decrease), interrupted changes (increase or decrease with interruption), and trend reversals (decrease to increase or increase to decrease). \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline
**Type of Shift** & **Meaning** \\ \hline (1) monotonic increase & No obvious mutation was detected, and the overall trend showed a monotonic increase \\ (2) monotonic decrease & No obvious mutation was detected, and the overall trend was monotonic decrease \\ (3) monotonic increase (with positive break) & 1 obvious mutation was detected, and the overall trend showed a monotonic increase \\ \hline \end{tabular}
\end{table} 
Table 1: Descriptions of trend Types. Figure 3: Examples of trend types: type 1, monotonic increase; type 2, monotonic decrease; type 3, monotonic increase (with positive break); type 4, monotonic decrease (with negative break); type 5, interrupted increase (with negative break); type 6, interrupted decrease (with positive break); type 7, reversal: increase to decrease; type 8, reversal: decrease to increase.
## 2 Materials and Methods
### Cell Culture, Cell Viability, and Anti-Inflammatory Efficacy  
We assessed the anti-inflammatory potential of JNUCC 0626 by determining its ability to inhibit nitric oxide (NO) production in RAW 264.7 murine macrophage cells. RAW 264.7 cells are widely utilized in in vitro models for inflammation research, specifically for studying immune responses and testing anti-inflammatory treatments. NO is recognized as a key mediator in the inflammatory process, being produced in macrophages by inducible nitric oxide synthase (iNOS) in response to stimuli such as lipopolysaccharide (LPS). Inhibiting NO production helps assess whether a treatment or compound can reduce the inflammatory activation of these immune cells.  
The RAW 264.7 murine macrophage cells were purchased from the Korean Cell Line Bank (KCLB, Seoul, Republic of Korea) and cultured in Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% fetal bovine serum (FBS) and 1% penicillin/streptomycin. The cells were maintained in a humidified incubator with 5% CO\({}_{2}\) at 37 \({}^{\circ}\)C. To assess cell cytotoxicity, an MTT assay was performed. The RAW 264.7 cells were seeded at a density of 1.5 \(\times\) 10\({}^{5}\) cells/well in 24-well plates and allowed to adhere overnight. The cells were then treated with various concentrations of JNUCC 0626 extracts and lipopolysaccharideaccharide (LPS, 1 mg/mL) for 24 h. After treatment, 0.2 mg/mL of MTT reagent was added to each well, and the cells were incubated for an additional 4 h. The medium was then removed, and the resulting purple formazan crystals were dissolved in 800 \(\upmu\)L of DMSO per well. Absorbance was measured at 570 nm using a microplate spectrophotometer ([PERSON], BioTek, Santa Clara, CA, USA).  
NO production was assessed using the Griess assay. The cells were seeded at a density of 1.5 \(\times\) 10\({}^{5}\) cells/well in 24-well plates and allowed to adhere overnight. They were then treated with various concentrations of JNUCC 0626 extracts and LPS (1 mg/mL) for 24 h. Cell culture supernatants were mixed with an equal volume of Griess reagent (100 \(\upmu\)L) in a 96-well plate and incubated at room temperature for 10 min. Absorbance was measured at 540 nm using a microplate spectrophotometer (Epoch, BioTek, CA, USA). The nitrite levels measured in the supernatant served as an indicator of NO production.
# Evaluating the Physicochemical Properties of Some Kosovo's and Imported Honey Samples
## 3 Results and Discussion
### Electrical Conductivity  
The electrical conductivity in Kosovo's honey samples ranged between 0.12 and 0.75 mS cm\({}^{-1}\) (mean value \(\pm\) SD = 0.39 \(\pm\) 0.18), while the electrical conductivity of imported honey samples ranged between 0.07 and 1.02 mS cm\({}^{-1}\) (mean value \(\pm\) SD = 0.26 \(\pm\) 0.28). All samples had conductivity values below 0.8 mS cm\({}^{-1}\), which is the maximum value allowed by Council Directive 2001/110/EC [10], apart from sample IH8, which showed an electrical conductivity value of 1.02 mS cm\({}^{-1}\). The sample IH8 was chestnut honey and had the highest conductivity, ash, and pH. Other studies have also reported that the conductivity of honey depends on the acid as well as the ash contents, where higher ash and acid contents show higher conductivity [37; 38; 39]. When comparing the electrical conductivity, a significant difference (\(p<0.05\)) was shown between the values of local and imported honey samples. The conductivity values in this study are close to values reported by other authors [40; 41]. The mean electrical conductivity values in Kosovo's honey samples (0.39 mS cm\({}^{-1}\)) are lower when compared to some mean values from other studies conducted in Italy (0.67 mS cm\({}^{-1}\)) and Bulgaria (0.69 mS cm\({}^{-1}\)) [42; 43].
## 1 Introduction
proposed the PhaseLift algorithm [36], which involves lifting the phase retrieval problem that satisfies the non-convex constraint, and directly transforming this problem into solving the Hermitian matrix with rank one, achieving the transformation from non-convex constraints to convex constraints. However, because of the matrix lifting procedure, these SDP algorithms are unsuitable for large-scale signal recovery problems. These algorithms are simple, and it is easy to obtain a local minimum solution. In recent years, many researchers have begun to explore the relationship between phase retrieval and structural information processing, such as applying the sparse representation of image structures to phase retrieval problems [37; 38]. Sparse representation is one of the core research issues of compression sensing. It utilizes a linear combination of a small number of elements to describe the signal in some bases or dictionaries, simplifying the representation of complex signals [39; 40]. Hence it is widely used in multiple image processing fields [41; 42; 43]. More recently, several robust phase recovery algorithms combating Gaussian noise have been proposed for targeting the noise in the measured values. For example, a fast local search method called greedy sparse phase retrieval (GESPAR) [44] is used. This is based on a fast 2-opt local search method with the known sparsity priors, and by iteratively updating the support of the underlying signal, the damped Gauss-Newton method is used to estimate the local minimum of the objective function. However, this algorithm cannot be used efficiently due to the defect of the Gauss-Newton method in the local convergence problem, especially when the residual is large. In order to improve the calculation efficiency, this paper proposes an improved efficient local alternative search algorithm for sparse phase retrieval. First, the phase retrieval problem is formulated as an optimization, in terms of minimizing the sum of squared errors with support information. We then use proprietary techniques to estimate the signal, using a continuous optimization algorithm to ensure convergence to the critical point. Compared with the existing methods, our method--referred to as greedy autocorrelation retrieval Levenberg-Marquardt (GARLM)--is easy to implement and can recover signals more effectively. The rest of the paper is organized as follows: the formulation of the phase retrieval and the support information is discussed in Section 2. In Section 3, after a brief overview of the general Levenberg-Marquardt (LM) method and 2-opt method, we describe our proposed algorithm in detail. Simulation results are presented in Section 4, and conclusions are drawn in Section 5.
## 4 Discussion
The measurement data clearly show that hydrogenation causes the formation of the \"hydrogen embrittlement\". In each analyzed case after hydrogenation, the force necessary to break the specimen was lower than in case of non-hydrogenated alloys. The alloy 1050A (Al 99.5) showed the greatest difference in measurements (11%--for 10 min of hydrogenation time, and 37%--for 25 min of hydrogenation time) before and after hydrogenation. This state is due to the fact that alloy 1050A (Al 99.5) is the alloy containing the lowest amount of elements [38], and therefore has high plasticity. Therefore, the absorbing hydrogen has a significant effect on the hydrogen embrittlement formation in the 1050A alloy [47; 48]. The lowest difference (5%--for 10 min of hydrogenation time, and 26%--for 25 min of hydrogenation time) in the results between the non-hydrogenated and the hydrogenated alloys can be seen in the case of alloy 5754 (Mg3). However, this alloy is characterized by the lowest plasticity of all the analyzed alloys. While the difference in measurements, before and after hydrogenation, for alloy 6060 (MgSi) was 8%--for 10 min of hydrogenation time, and 30%--for 25 min of hydrogenation time. Based on the results of measurements, it can be clearly stated that the hydrogenation of the analyzed alloys causes a reduction in their mechanical properties. Therefore, one of the basic goals should be not only to study the mechanism of the development of stress corrosion cracks in aluminum alloys, but also to find effective methods of inhibiting the development of cracks, and thus extending the operational reliability time of the aluminum structure. There are three effective methods of inhibiting the development of corrosion and stress corrosion cracking: application of protective layers, cathodic protection, and application of corrosion inhibitors. However, the protective coatings are subject to aging, and in the place of their local destruction, points of stress corrosion development are formed, from which the crack propagation begins [9]. While the cathodic protection by the cathodic polarization of metal causes its hydrogenation, it may accelerate the development of stress corrosion cracking. Therefore, it seems that the most effective method of inhibiting the development of stress corrosion cracking is the use of hydrogenation inhibitors. Heavy metal compounds are the most frequently used inhibitors. The advantage of the inhibitor method is its high efficiency. Moreover, the inhibitors can be used with other corrosion protection methods. In conclusion, along with the study of the mechanism of the development of stress corrosion cracks, one should therefore focus on conducting research aimed at selecting appropriate inhibitors for aluminum alloys.
# A Cost-Aware Framework for QoS-Based and Energy-Efficient Scheduling in Cloud-Fog Computing
## 7 Evaluation
### Workload Characterizations and Design of the Cloud-Fog Computing Environment  
The IoT layer consists of devices distributed throughout the fog environment, that are with various platforms and architectures. Such devices are modeled in this paper by sensors that detect, collect, and transmit data for processing in fog nodes and the cloud computing environment. Jobs delivered by such device sensors are thus heterogenous in their service demands, costs, and QoS requirements. The cloud layer consists of computing servers resided in data centers that provide on-demand services with high processing performance. A one-tier cloud layer is adopted with three servers \([R_{1},R_{2},R_{3}]\), each of which respectively entails a queue \([Q_{1},Q_{2},Q_{3}]\) to buffer fog jobs \(\mathbf{J}\) for execution and each server follows the \(M\)/\(M\)/1 queuing system.  
Fog nodes \(\mathbb{F}\) deliver a set of \(\ell\) atomic, independent fog jobs \(\mathbf{J}\) to the dispatcher \(cd\) of the cloud layer. The dispatcher \(cd\) utilizes a scheduling strategy to allocates each fog job \(J_{i}\) on a particular cloud queue \([Q_{1},Q_{2},Q_{3}]\) for execution. Since the service demand \(\mathcal{E}_{i}\) for each fog job \(J_{i}\) can be estimated beforehand using workload prediction models, the \(\mathcal{E}_{i}\) is thus assumed to be known in advance and modeled by an exponential distribution with a service mean \(\mu_{\mathcal{E}}=1\) as follows:  
\[\mathcal{E}_{i}=\exp(\mu_{\mathcal{E}}=1) \tag{36}\]
# A Deep Learning Review of ResNet Architecture for Lung Disease Identification in CXR Image
## 3 Materials and Methods
### Experimental Setting  
The modeling procedure is carried out at this point in order to obtain weights that can be used to properly categorize CXR. Several hyperparameters, including the learning rate and optimizer used, are chosen for model training in this training. The batch size is 32 and the number of epochs used is 50. The optimizer used in this study is Adam, since it produces good results quickly [43]. The initial learning rate is \(1\times 10^{-6}\); however, Adam calculates the learning rate using an adaptive approach based on various parameters. Adam can be used as an optimization technique as a substitute for the classical stochastic gradient descent procedure to update the weights iteratively based on training data.  
A loss function plays a crucial role in assessing the performance of an algorithm in modeling data. It quantifies the disparity between the algorithm's current output and the desired or expected output. Focal loss is a specific type of loss function utilized in multi-class classification tasks, particularly when dealing with imbalanced data. This function is designed to focus on examples that challenge the model's accuracy, preventing it from making unreliable predictions. Consequently, focal loss helps the model improve its performance on challenging examples, making it effective in overcoming these obstacles [44].  
Existing data are divided into training data and test data, with a comparison ratio of 80% (16,933 images) for training data, which include 2893 COVID-19 images, 1076 pneumonia images, 4810 lungs opacity images, and 8730 normal lung images. The remaining 20% (4232 images) are for test data, which include 723 COVID-19 images, 269 pneumonia images, 1202 lungs opacity images, and 2183 normal lung images. Additionally, images in the preprocessing stage are classified using the ResNet-50, ResNet-101, and ResNet-152 architectures. The general classification process is depicted in Figure 2 using the ResNet-50, ResNet-101, and ResNet-152 architectures.
## 2 Materials and Methods
### Photocatalytic Activity Experiment  
The photocatalytic experiments were performed for the removal of Nile blue (NB) dye, bentazon herbicide (BZ), and their mixture (BZ + NB) under UV and sunlight irradiations. First, 20 mg of TiO\({}_{2}\) was mixed in 60 mL of model water (solution of NB, BZ, and solution of NB and BZ) with 5 ppm pollutant concentration. To attain the absorption/desorption equilibrium, polluted solutions in the presence of TiO\({}_{2}\) photocatalyst were placed in the dark and stirred for 1 h. Subsequently, the mixture solutions were exposed to irradiation of a UV lamp (300 W, Oriel Instruments, Newport, CA, USA) and natural sunlight. The intensity of the light was recorded during all the experiments by a power meter (Thorlabs, Newton, NJ, USA, model PM100D), finding a constant intensity of 126.5 mW/cm\({}^{2}\) and 2.58 mW/cm\({}^{2}\) for UV light and for sunlight, respectively. During the experiments, 3 mL of solution was taken after a consecutive time interval of 20 min and evaluated by using a double-beam UV-Vis spectrophotometer (Lambda 750, Perkin Elmer, Waltham, MA, USA). The degradation efficiency of the TiO\({}_{2}\) catalyst was calculated through Equation (1) [20].  
\[Degradation\ efficiency=(C_{0}-C_{t})/C_{0}\times 10 \tag{1}\]
# Numerical Analysis of the Beam-Column Resistance Compared to Methods by European Standards
###### Abstract  
The optimisation of the design method for verification of slender steel beam-columns is still a current issue not only from scientific point of view, but also for design practice. Therefore, the main objective of this paper is comparison of the suitability of established design approaches, according to the European standards for steel and aluminium structures, on the basis of numerical simulations. Thus, a finite element model was validated on the basis of experimental analysis available in the scientific literature. To perform the comparison of accuracy of design approaches according to European standards, a commercial software program ANSYS was used for observation of the resistances of beam-columns. The resistance of european I beams with parallel flanges (IPE) and a rectangular hollow cross-section (RHS) were investigated for four load cases on a simply supported member and also on a pinned-fixed beam column with linear bending moment distribution, where the resistance of the cross-section governs. Finally, the conclusions for suitability of the respective design approaches are discussed, together with some findings that arose from this work.  
beam-columns; steel structures; imperfections; numerical analysis; second-order theory +
Footnote †: journal: Applied Sciences