esa-sceva/satcom-chunk-collection huggingface dataset:
# A Case Study of a Large Unstable Mass Stabilization:
###### Abstract  
This case study presents the engineering approach conducted for stabilizing a landslide that occurred at \"El Portaler\" Pass in the Central Spanish Pyrenees activated due to the construction of a parking lot. Unlike common slope stabilization cases, measures projected here were aimed at slowing and controlling the landslide, and not completely stopping the movement. This decision was taken due to the slow movement of the landslide and the large unstable mass involved. The degree of success of the stabilization measures was assessed by stability analyses and data obtained from different geotechnical investigations and satellite survey techniques such as GB-SAR and DinSAR conducted by different authors in the area under study. The water table was found to be a critical factor in the landslide's stability, and the tendency of the unstable slope for null movement (total stability) was related to the water table lowering process, which needs more than 10 years to occur due to regional and climatic issues. Results showed a good performance of the stabilization measures to control the landslide, demonstrating the effectiveness of the approach followed, and which became an example of a good response to the classical engineering duality cost-safety.  
landslide; safety factor; Central Spanish Pyrenees; soft rocks; stabilization measures +
Footnote †: journal: Applied Sciences
## 9 Array Metamaterial Results  
The metamaterial unit cell does not act alone to exhibit the correct exotic electromagnetic properties; some series of array designs were simulated. The array performance was analyzed by choosing a few array types, such as 1 \(\times\) 2 array (11 \(\times\) 22 mm\({}^{2}\)), 2 \(\times\) 2 array (22 \(\times\) 22 mm\({}^{2}\)), and 4 \(\times\) 4 array (44 \(\times\) 44 mm\({}^{2}\)). This selected array was numerically simulated,  
Figure 11: E-field, H-field, and surface current distributions of the Coptic cross shape-based metamaterial unit cell. (**a**) Surface current at top plane, (**b**) magnetic Field (H-field), (**c**) electric Field (E-field), (**i**) 2.02 GHz, (**ii**) 6.985 GHz, (**iii**) 9.985 GHz, (**iv**) 14.425 GHZ, and (**v**) scale and axis.  
using CST simulator as the same method of unit cell. Figure 12 shows the results of the scattering parameters for selected types of array. From Figure 12a, it can be seen that the \(S_{11}\) result of all arrays shows the same response only for the first and second resonance frequency. The \(S_{11}\) results of the unit cell and 1 \(\times\) 2 array reveal the same response. The third and fourth resonance frequency of the 2 \(\times\) 2 array and 4 \(\times\) 4 array are shifted to the lower frequency due to the mutual coupling effect. Figure 12(b) presents the \(S_{21}\) results of changing at each resonance frequency for selected types of array. The \(S_{21}\) results of the unit cell and 1 \(\times\) 2 arrays exhibit the same resonance frequency at 2.02, 6.985, 9.985, and 14.425 GHz with the magnitude of \(-\)29.45, \(-\)25.44, \(-\)19.05, and \(-\)24.45 dB, respectively. The third and fourth resonance frequencies shift lower for the 2 \(\times\) 2 array and 4 \(\times\) 4 array compared to the unit cell. The first and second resonance frequency for all the arrays exhibits the same response. The mutual coupling effect is responsible for this discrepancy between the resonance frequencies.
## 2 Materials and Methods
### Data Preprocessing  
In order to obtain optimal classification, the central region of the image was automatically selected as the ROI. The cropped region ensures a coverage of the polyp area, as well as its surrounding texture. Successively, the dataset was normalized by subtracting the mean and by dividing the standard deviation of the pretrained ImageNet data. For the last step, each input image was resized to \(229\times 229\) pixels in the RGB color space. To increase the generalization of the network, data augmentation was used to enhance the model capabilities for our classification task. In this study, the training images are augmented by a combination of flipping, shifting and \(\pm\)90\({}^{\circ}\) rotation, contrast enhancement, blurring, and zooming.
# DRL-Based Dynamic Destroy Approaches for Agile-Satellite Mission Planning
## 4 Experimental Simulation
### Simulation Scenarios
#### 4.1.1 Task Scheduled Rate  
In scenarios involving intensive regular tasks, the primary evaluation metric for planning is the number of scheduled tasks. Given that there is no significant difference in the profit of regular tasks, the task scheduled rate serves as a more accurate reflection of the algorithms' planning efficiency. Emergency tasks, on the other hand, can be given priority and scheduled in advance to ensure their timely execution. The calculation formula of the task scheduled rate is as follows:  
\[r_{s}=\frac{n_{Scheduled}}{N_{Total}} \tag{20}\]  
where \(n_{Scheduled}\) is the number of tasks that have been scheduled, and \(N_{total}\) is the total number of tasks waiting for planning.
## 3 Results and Discussion
### Antimicrobial Activity of Essential Oils
Gram-negative bacteria, with an additional outer membrane, often present a greater barrier to hydrophobic agents. However, certain essential oils can penetrate this layer, demonstrating broad-spectrum activity. In contrast, the other essential oils, such as lemon, clove, nutmeg, and anise, contain fewer or less concentrated antimicrobial components. While they may possess some minor antibacterial properties, the concentration of active substances like citral (lemon), eugenol (clove), and anethole (anise) might not be sufficient to produce a strong inhibitory effect on the tested bacterial strains, especially when compared to the potent compounds found in oregano and laurel oils [95; 96; 97; 98; 99; 100].
## 2 Related Work  
Multiple different GAN models have been used to augment data related to various diseases across the subfields of medicine. For instance, [PERSON] et al. [6] proposed the use of GANs to improve disease localization in pulmonary pathology. [PERSON] et al. [7] suggested that GANs could be used to address the issue of data scarcity in mammography and trained for the mass detection of breast cancer. Additionally, [PERSON] and [PERSON] [8] concluded that with a small dataset related to diabetic neuropathy, GAN has a more significant effect on data augmentation than traditional augmentation.  
In neuro-oncology, several GAN models have been introduced to improve brain tumor classification. [PERSON] et al. used a GAN-based augmentation of multi-class brain tumor classification from magnetic resonance imaging (MRI) scans and synthesized data for meningioma, glioma, and pituitary tumor [9]. The tumor was classified by designing a deep CNN model. A similar GAN was introduced by [PERSON] et al. [10]. Their study used a three-dimensional (3D) U-net GAN architect to augment the images, and a random forest classifier was used for classification. [PERSON] et al. [11] introduced another GAN for the creation of MRI brain fusion images with detailed contrast information of brain tumor tissues of multi-modal MRI images. The generator model of GAN in their study was a nested U-net structure with residual U-shaped blocks. The proposed model outperformed many common fusion techniques according to qualitative metrics. Similarly, [PERSON] et al. [12] also studied GANs for brain tumor classification in multimodality MRI images.  
However, in many earlier studies (e.g., [6; 7; 12; 13]), the impact of GAN-based augmentation has been confirmed only for specific choices of the numbers of the original images and the GAN-generated images. For instance, [PERSON] et al. [12] reported that adding 198 GAN-generated images to the training data of 792 original images increased the accuracy of a CNN-based classifier from 78.46% to 81.03% but did not repeat this experiment with different choices for numbers of images. Consequently, this raises a question about how sensitive the benefit gained from the GANs is to the exact number of GAN-generated used and their ratio to the original images. Therefore, an appropriate GAN model tested with a different number of training datasets is essential to find out how the number of GAN-generated images affects the performance of the binary classifier.  
In our paper, we evaluate the impact of including synthetic images created by a GAN in the training data of a CNN on its accuracy in classifying brain MRI images based on whether the patient has a brain tumor or not. We build a deep convolutional GAN (DC-GAN) for creating synthetic images and a modified U-Net CNN for image classification. We use statistical testing to assess whether the observed differences in the accuracy of the classification CNN trained with training datasets are statistically significant or not.
## 1 Introduction
### Assembly State Determination Based on the Six-Point Positioning Principle  
According to the six-point positioning principle, the number of constrained DOFs corresponding to each mating plane is different. Thus, the main mating plane and the first and second auxiliary mating planes are defined based on the number of constrained DOFs, i.e., three DOFs are constrained by the main mating plane, and two and one DOFs are separately constrained by the first and second auxiliary mating planes, respectively.  
If parts are assembled by multiple mating planes, the case in which several DOFs are simultaneously constrained by different mating planes may occur. This can result in an uncertain correspondence between the main and auxiliary mating planes and the mating planes. In addition, the case in which a DOF is still unconstrained after assembly may occur because the second auxiliary mating plane can be absent. For instance, an assembly with two mating planes is shown in Figure 4. The DOF rotating around the X-axis may be constrained by the XOY or XOZ plane after assembly. Thus, the main and first auxiliary mating planes may correspond to the XOY and XOZ planes or the XOZ and XOY  
Figure 3: Framework of computing position and orientation deviations by successively determining contact points. DOF, degree of freedom.  
planes. The two cases correspond to the two types of assembly states as well as the PODs. Consequently, determining the possible states of the assembly with multiple planes is the foundation for computing the PODs by successively determining the contact points.  
All or some of the six DOFs should be constrained after assembly, depending on the geometric structure of the assembly. According to the six-point positioning principle, the main and auxiliary mating planes of the assembly are determined. If the assembly is completely or incompletely positioned, only one case in which the main and auxiliary mating planes correspond to each mating plane exists. The same concerns the assembly state. However, if over-constrained positioning exists, several DOFs will be simultaneously constrained by the contact points on different mating planes. Therefore, all the possible cases of the main and auxiliary mating planes should be considered to acquire multiple assembly states.  
Owing to the correspondence between the constrained DOFs and the contact points on each mating plane as well as the multiple types of positioning cases that are established according to the six-point positioning principle, all the possible assembly states can be identified. This lays a foundation for computing PODs by successively determining the contact points.
# Review on Fast Pyrolysis of Biomass for Biofuel Production from [PERSON]
## 3 Biomass Generalities
### The Generations and Different Forms of Biomass  
Biofuels are either solid (char), liquid (e.g., bio-oil from pyrolysis, Fischer-Tropsch products or methanol from synthesis gas, ethanol from sugar/starch fermentation, biodiesel from the transesterification of vegetable oils or animal fats) or gaseous (biogas from anaerobic digestion or methane and other light hydrocarbons from synthesis gas) [1].  
If biomass is subjected to an elevated temperature in the absence of oxygen (pyrolysis), it is converted into solid carbon, gaseous and liquid products, which can be used as fuels. Liquid products include pyrolysis oil or bio-oil, with a density of around 1.2 kg/L. Bio-oil has a water content of 14 to 33% by weight, which cannot be easily removed by conventional methods (e.g., wastewater treatment, distillation), but bio-oil can be phase-separated above a certain moisture level, bearing in mind that the gross calorific value of bio-oil is generally between 15 and 22 MJ/kg, which is lower than the calorific values of conventional fuel oil (43.5 MJ/kg) and conventional heating oil (43-46 MJ/kg) [19].  
There are three different generations of biofuel production in the biomass field: the first generation involves the production of biofuel from seeds, the second generation involves the production of biofuels from non-food crop residues, and the third generation involves the production of biofuel from microorganisms or from oil generated by microalgae.  
The biofuels produced from biomass come in various forms: alcohols, pure vegetable oils, esters and biogas.  
* Alcohols are produced from the fermentation of plant starch or sugar, such as corn, beets, wheat, and sugar cane. The product obtained is called bioethanol.
* Pure vegetable oils are generated through the simple cold pressing of oilseeds, notably rapeseed, sunflower and oil palm.
* Esters are derived from oil plants and are obtained via a complex chemical transformation of the oils resulting from the pressing of the seeds.  
Biogas is the gas generated by the fermentation of organic matter in an anaerobic environment. It is a combustible gas consisting mainly of methane and carbon dioxide [12].
## 2 Modeling of a Mobile Satcom Antenna  
The paper focuses on a double gimbal MSA. The innermost gimbal is an elevation gimbal on which the antenna array is mounted, while the outmost gimbal is an azimuth gimbal which is mounted on the base. According to the coordinates system definition and the conversion relationships between different coordinates, a dynamic model of the mobile satcom antenna is derived.
# Deployment Protection for Interference of 5G Base Stations with Aeronautical Radio Altimeters
## 4 Results and Discussion
### Discussion
#### 4.6.2 The Integrity and Accuracy of the Model  
This paper ensures the integrity and accuracy of the model by referencing extensive work from reputable research institutions. Regarding 5G base station parameters, the paper relies on standards from 3 GPP. For interference and antenna modeling, references are made to ITU recommendations. Additionally, interference thresholds are derived from experimental results provided by AVI and relevant requirements from ICAO. For scenario analysis, this paper draws on guidance provided by RTCA. These institutions provide parameters based on industry standards and experimental findings, ensuring the integrity and accuracy of the model.
# Solution Merging in Matheuristics for Resource Constrained Job Scheduling
## 5 Experiments and Results
### Study of CMSA  
As with MS, we can also use the constructive heuristic or ACO (labelled CMSA-ACO) within CMSA to generate solutions. The parameters of CMSA, including the time limit for solving the restricted MIPs (\(t_{\text{iter}}=120\) s), ACO iterations (5000) and the maximum age limit (\(a_{\text{max}}=5\), for instances with 8 or fewer machines and \(a_{\text{max}}=3\), for instances with 9 or more machines) were determined as a result of the parameter tuning experiments (See Appendix C). As in the case of MS, we can observe that ACO (Table 2) provides a distinct advantage across all problem instances.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Inst.** & \multicolumn{4}{c}{**CMSA-Heur**} & \multicolumn{4}{c}{**CMSA-ACO**} \\ \hline  & **Best** & **Mean** & **SD** & **Iter.** & **Best** & **Mean** & **SD** & **Iter.** \\ \hline  
88 & 9941.69 & 84.49 & 32.0 & 7800.87 & **7968.64** & 79.34 & 7.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: CMSA with ACO. For 25 runs conducted, the best (_Best_) and average (_Mean_) solution qualities with associated standard deviations (_SD_) are provided. The table also shows the average number of iterations (_Iter.) conducted for each problem instance. Statistically significant results, using a pairwise \(t\)-test, at the 95% confidence interval are highlighted in boldface.  
Overall, constructing the solutions with ACO seems to help the iterative process of CMSA to focus on very good regions of the search space. For RCJS, the results demonstrate that, irrespective of the details of the merge method, solution merging works much better as an additional step to improve results in a population based metaheuristics than as the main solution intensification method on its own.  
In contrast to the case of MS-Heur and MS-ACO, it is interesting to observe that the number of iterations performed by CMSA-Heur is of the same order of magnitude as that of CMSA-ACO. Even though CMSA-Heur usually performs more iterations than CMSA-ACO, in a small number of cases--concerning the small problem instances with up to four machines, in addition to instance 5-21--there are fewer iterations conducted by CMSA-Heur. Investigating this more closely by examining the restricted MIP models generated within the CMSA versions, we found that the constructive heuristic provides slightly more diversity as several ACO colonies converge to the same solution. In the context of CMSA, this leads to more variables in the restricted MIP and hence to a significant increase in MIP solving time. This increase in time for the merge step consumes most of the time saved in the faster time of the constructive heuristic compared to ACO.
## 3 Characteristics of SACP System with Activated-Carbon-Based Conductive Mortar
### Results and Discussions  
#### 3.2.1 CP Potential and Current Density  
After the general mortar and activated-carbon-based conductive mortar (admixture ratios of 5%, 10%, and 15%) specimens were prepared, freshwater was poured into the specimen. After that, potential and current measurements were made for 60 days. Figure 9 shows the CP potential variations. A silver/silver chloride reference electrode (SSCE) was employed for potential measurements. The initial potential before CP application was \(-61\) mV/SSCE for the general mortar specimen and \(-50\), \(-46\), and \(-37\) mV/SSCE for the activated carbon specimen with admixture ratios of 5, 10, and 15%. After applying the CP system, potentials dropped significantly at the initial stage, and fluctuations were observed for six days. The fluctuations in the initial stage were due to the generation of oxide film on the anode. Then, clear indications of a decrease in potential were observed since the oxidation film was well generated. In addition, conductive mortar specimens showed lower potential than the general mortar specimen. The higher the admixture ratio, the lower the potential. Since activated carbon contributes to lower resistivity, a better CP effect can be observed when adding more activated carbon by looking at the potential after 60 days. For example, the specimen with an admixture ratio of 15% can have about 115 mV lower CP potential than the general mortar specimen.  
Figure 10 shows the CP current density variations. CP current density was estimated by dividing the measured current by the rebar surface area. A large CP current density was detected during the initial stage. CP current density had large fluctuations during the initial six days and gradually increased for 54 days, which shows the relationship between  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Composition \% by Weight**} \\ \hline Lead & 0.005 max \\ \hline Iron & 0.010 max \\ \hline Cadmium & 0.005 max \\ \hline Copper & 0.7 to 0.9 \\ \hline Zinc & Remaining \\ \hline \multicolumn{2}{c}{**Mechanical Properties**} \\ \hline Ultimate tensile strength & 152–200 MPa \\ \hline Hardness (Rockwell 15T) & 59–69 \\ \hline Minimum ductility & 7.1 mm \\ \hline \hline \end{tabular}
\end{table}
Table 4: Composition and mechanical properties of Zn mesh anode.  
Figure 9: CP potential variations for 60 days.  
CP potential and current density. After 60 days, the results showed that the higher the admixture ratio, the higher the CP current destiny. Current density is closely related to the concrete resistivity, and this result further supports the low resistivity observed in activated-carbon-based conductive mortar. The higher CP current in conductive mortar specimens demonstrates that they can provide better CP efficiency. For example, the current density measured in the specimen with an admixture ratio of 15% is 1.4 times higher than that in the general mortar specimen.
## 2 Material  
The HPLDs addressed in this paper are broad-area semiconductor lasers with epitaxial structure similar to that reported in [5]. The epitaxial structure consists of a single 8 nm, 1.1% compressively strained \(\mathrm{In_{x}Ga_{1-x}As}\) quantum well (QW) embedded between asymmetric, graded-index, separate-confinement heterostructure \(\mathrm{Al_{x}Ga_{1-x}As}\) layers.  
The QW and the epitaxial layer thicknesses, doping, and compositions were designed and optimized through the software tool Harold(r) by Photon Design [6]. High slope efficiency (>1.1 W/A), low series resistance (<20 m\(\Omega\)), and high wall plug efficiency (>60%) were achieved with 4.5 mm-long Fabry-Perot laser diodes 95% HR/2% facet-coated with AR. Due to proper facet treatment, an output optical power in excess of 20 W was consistently achieved at 976 nm.  
Figure 1(a) shows the vertical refractive index profile of the epitaxial heterostructure and the vertical section of the optical mode. Figure 1(b) shows the calculated optical gain as a function of photon wavelength \(\lambda\) and at different carrier concentrations \(N\) for TE and TM polarization, i.e., \(G_{\mathrm{TE}}(\lambda,N)\) and \(G_{TM}(\lambda,N)\), respectively. The heavy hole (\(e_{1}-hh_{1}\)) to light hole (\(e_{1}-lh_{1}\)) energy gap displacement, enhanced by the QW compressive strain, produces a negligible TM gain compared to TE gain at laser emission wavelength, resulting in an intrinsic DoP of nearly 1.  
Figure 1: (a) PM scheme of two laser beams \(\mathrm{P_{1}}\) and \(\mathrm{P_{2}}\) with a PBC and an HWR. Thick shaded lines and arrows represent the main beam components while thin shaded lines and arrows represent the minor components. TE polarization is shown in orange while TM polarization is shown in blue. (b) 350 W multi-emitter module is based on PM of two laser beams obtained by two sets of the HPLD investigated here.  
The optical confinement of the broad area HPLD investigated in this work is defined by the epitaxial structure along the vertical direction, or fast axis (FA), and by a ridge waveguide along the horizontal direction, or slow axis (SA). A wide ridge width of about 200 \(\upmu\)m is used in order to produce a power density at the output facet sufficiently low to avoid catastrophic optical mirror damage during device operation. The optical field is single-mode along FA and multi-mode along SA, with associated optical filamentation.