esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Numerical Results
### Simply Supported Cantilevered Beam
#### 6.1.3 Stability of the Results with Respect to Mesh Resolution: A Trade-Off with Computational Cost  
In the context of topology optimization, the stability of results with respect to mesh resolution presents a twofold challenge:  
* On one hand, the numerical method may be sensitive to the level of discretization, implying that accurate and reliable solutions can only be achieved with sufficiently fine meshes.
* On the other hand, finer meshes enable a richer representation of the design space, allowing for a broader range of topologies to be captured as the continuous, infinite-dimensional domain is projected onto its discrete, finite-dimensional counterpart. The two aspects above are investigated next solving three times the very same SISO problem of Section 6.1.2. The meshes of the three runs are as follows 1. Number of elements in \(Y\) direction = 16; number of elements in \(X\) direction = 48; 2. Number of elements in \(Y\) direction = 32; number of elements in \(X\) direction = 96; 3. Number of elements in \(Y\) direction = 64; number of elements in \(X\) direction = 192; The optimal topologies along with the convergence curves for the three cases are respectively shown in Figures 5-7. The CPU time needed to achieve convergence for the three runs were respectively as follows: \[t_{1}=539\ \text{s}\,t_{2}=4382\ \text{s}\,t_{3}=23453\ \text{s}\] (34)
## 5 Numerical Results
### Performance Comparison of Load Balancing  
Finally, we have tested the effect of \(\beta\) on the load-balancing mechanism. The performance of slice load balancing is tested in terms of the average variance of the slice load, where QGJRA-SUL with the variance of the slice load (\(-\beta\cdot\varsigma\)) as a penalty term and QGJRA-SUL without a penalty term are compared in 200, 400, 600, 800, and 1000 episodes, respectively. The number of episodes where the DDPG-Agent is trained is represented by \(N_{episode}\). As shown in Figure 8, all curves show first rise and then fall, corresponding to the BS load from light loads to medium loads, and then to full loads. The loads of slices are more balanced at low and full loads because resources are not being used much, or are all being used. The main factors affecting the QoS guarantee of slices are resource allocation policy near the full load. The performance in the load balancing of QGJRA-SUL with the penalty term outperforms that of QGJRA-SUL without the penalty term. obviously. The average variance of slice load \(\varsigma\) is 0.0465 for \(\beta=0\) and 0.0125 for \(\beta=3\) at step 4, respectively. The DDPG-Agent with \(\beta=3\) achieved a 73% increase in the performance of load balancing than that with \(\beta=0\) near the full load. The reward function with the penalty term can lead the DDPG-Agent to realize load balancing well. In addition, in the training process, QGJRA-SUL with the penalty term can make DDPG-Agent learn the QoS satisfaction and load balancing of slices simultaneously, which can avoid the QoS satisfaction degradation caused by some slices being overloaded and some slices being underloaded.  
Figure 6: The adaptation of QoS for different weights. The weights of each slice in turn become the maximum value, and the others remain the same. (**a**) The slice-level QoS satisfaction rate of each slice. (**b**) The weights of each slice.
## 2 Model Construction and Simulation Experiment  
Since the influence of the length of PE chain, the radius and chirality of the CNT on molecular chains orientation, and the aspect ratio of the CNT on the reinforcement effect of composite materials, had been investigated [18, 19, 23], in the constructed PE/CNT model, each PE chain contained 120 repeating units, and the single-walled carbon nanotube was (10,10) armchair type terminated by hydrogen atoms at both ends, with a length of 241.03 A. In the real system of PE/CNT nanocomposites, the content of the CNT was low and most of them were PE molecular chains, so twenty amorphous PE chains were randomly distributed on the surface of the CNT, as shown in Figure 1a, which depicts the initial model of the composite system. After structural optimization, this initial model ran for 100 ps at 550 K to simulate the melting process. Figure 1b shows the initial conformation after melting under static conditions. Then, the initial conformation after melting was shear loaded along the \(z\)-axis and ran for 700 ps at a shear rate of 0.01 ps\({}^{-1}\) to simulate the shear flow of melt during injection molding. The conformation after shear is shown in Figure 1c. Both PE molecular chains and the CNT tended to be parallel to the shear flow direction, which is consistent with the simulation results reported by [PERSON] and [PERSON] et al. [24; 25]. To compare the change in the morphology of the molecular chains between the sheared pure PE system and sheared composite system after the cessation of shear, the oriented PE chains induced by shear flow in the composite system were selected as the initial conformation of the pure PE system after shear, as shown in Figure 1d.  
Next, the static and sheared composite systems and the sheared pure PE system were cooled in stages, from 500 to 380 K, respectively, and cooling at each stage was 30 K. [PERSON] et al. showed that whether the CNT was fixed had little effect on the simulation results [26], so in order to save simulation time, the position of carbon atoms of the CNT in the system was fixed. The simulated ensemble was an NVT ensemble (constant number, constant volume, and constant temperature) with a total simulation time of 500 ps. The final conformation of each stage served as the initial conformation of the next stage. Material Studio software was used for calculation. The Nose-Hoover temperature control method was adopted to keep the system temperature constant. The COMPASS force field [27], suitable for the calculation of organic and inorganic materials, and the Velocity Verlet integral algorithm, with excellent performance and wide application, were adopted, and the calculation time step was 1 fs. The cutoff distance was 12.5 A.
# Microbial and Enzymatic Biodegradation of Plastic Waste for a Circular Economy
## 2 Microbial and Enzyme-Based Degradation of Plastic
### Enzymatic Degradation of Polymers
#### 2.3.3. Esterase-Catalysed Depolymerisation of Plastics  
Esterases released by both bacteria and fungi play a role in plastic degradation by acting on the polymer surface, creating micro-fractures in the polymer structure which leads to further degradation [93; 94]. This mechanism has been observed in various microorganisms, showing the diverse sources and strategies for plastic degradation. For instance, some thermophilic bacteria secrete an esterase-like enzyme which targets the amorphous regions of PET and PLA polymers [122]. Similarly, the _Comomonas acidovorans_ bacterium produces an esterase capable of degrading low molecular weight PLA [123]. The fungal species _Aspergillus flavus_ and _Aspergillus tubingensis_ also secrete esterases which can degrade plastic [124; 125].  
Esterase activity is typically limited to short-chain acyl esters [94], but recombinant technology allows fine-tuning of enzymes through site-directed mutagenesis, which has shown promising enhancement in their degradation capabilities [126]. A notable approach can be observed in the case of _Tenebrio molitor_, where five genes were cloned into an _E. coli_ expression system, with the feruloyl esterase-like enzyme (TmFae) exhibiting the most PET degradation at 50 \({}^{\circ}\)C. Through site-directed mutagenesis of TmFae, two amino acids, Leucine at position 95 (L95) and Proline at position 122 (P122), which were mutated along with the hydrophobic amino acid Tryptophan (W), resulting in increased interaction with PET [127]. Another esterase which was originally found in _Ideonella sakaiensis_ was expressed in _E. coli_, and a double mutation, S238F and W159H, produced a narrow active site, like _Thermobifida fusca_ cutinase (TfCut). This mutant showed enhanced catalytic activity against PET [128]. Similarly, an engineered recombinant esterase from _Pseudomonas aeruginosa_ demonstrated 60% enhanced depolymerisation activity [129]. These mutations are examples of how site-directed mutagenesis can optimise enzyme activity for applications in plastic degradation.
# Formulation Strategies to Improve Oral Bioavailability of Ellagic Acid
## 4 Formulation Strategies for Improving EA Oral Bioavailability
### EA Encapsulated in Polymeric Carriers
#### 4.4.1 Eudragit\({}^{\circledR}\) Microspheres
Release studies, carried out at pH 7.4, showed an initial faster release of about 35% during the first 10 h, followed by a more controlled release phase, with further 55% of drug released in 24 h. In vitro anticancer activity against HepG2 hepatocellular carcinoma cells and HCT 116 colorectal cancer cells revealed a significant cytotoxic effect of EA loaded in CS-PEG-PLGA NP vs. free EA at every tested dosage (up to 100 \(\upmu\)M), suggesting an efficient NPs uptake by the cells. Also [PERSON] et al. used PCL for successful entrapment of EA through the emulsion-evaporation method [75]. In this case, EA, dissolved in PEG 200, was mixed with PCL, solubilized in ethyl acetate, and the mixture was emulsified by homogenization with an aqueous solution containing a stabilizer (PVA, DMAB or poloxamer 407 ranging from 0.1% to 1% _w/v_). The resulting _o/w_ emulsion was sonicated for 1 min, then diluted in water and kept under stirring for 6 h up to complete evaporation of the organic solvent. The obtained NPs were centrifuged, washed with distilled water to remove unentrapped EA, and freeze-dried. As previously observed, 1% DMAB yielded the smallest mean NP size (193 nm). In fact, by creating a positive charge layer around the internal phase, DMAB provided electrostatic repulsion, while PVA and poloxamer, being uncharged, induced only a steric stabilizing effect. The type of stabilizer affected also EE and DL. DMAB gave the lowest EE (66.4%-73.5%) and DL (56.8%-63.9%), whereas PVA provided the highest EE (78.3%-90.3%) and DL (68.1%-78.6%). These differences in EA entrapment were probably linked to the unequal EA aqueous dissolution granted by the stabilizer. Release studies at pH 7.4 illustrated the typical two-step profile, with an initial fast release driven by diffusion for the first 12 h, and a second slow release due to the retarded diffusion of EA from the NPs inner layers. Finally, the release of EA from DMAB-PCL NPs reached up to 48% of the loaded amount in 8 days. The anticancer efficacy of EA-PCL NPs tested on HCT-116 colon adenocarcinoma cells was 6.9-fold enhanced in comparison to free EA, meaning that encapsulated EA was significantly superior in reducing HCT-116 cell survival. Moreover, a pharmacokinetic study by oral administration in rabbits showed that the nano-sized formulation improved the relative EA bioavailability by 3.6 times.
## 2 Overview of Current Methods for Connecting Modular Units
### Interlocking Method  
When stacking modular units, protruding pins on the bottom of the upper module columns, are used for accurate positioning of the upper and lower modules, these also resist the lateral force through interlocking. In the interlocking system used by the Kullman Building Corporation [10; 11], pins welded to the bottom of the module columns can be used to adjust the location and direction, as well as reduce construction error. The upper and lower modules are coordinated using the interlocking device, and the left and right modules are connected by adding steel plates and using blind bolts. In addition, welding of the left and right modules is conducted using the bolt connection of each module in the beam section in the lower part of the concrete floor, and the steel plates in the ceiling beam section.  
Another example of the interlocking method is the connection system used in the Unitised Building [12]. With this system, the upper and lower modules and left and right modules are connected using exposed columns to the sides of the modules and steel blocks welded to the top and bottom so that the modules interlock horizontally with bolts and rods. There is also a modified method that replaces the steel blocks welded to the top and bottom of the columns with thick plates; a certain amount of space is needed between the left and right modules for connection, as the columns to the sides of the modules are exposed.  
While connecting modules from the exterior using interlocking devices is advantageous with regard to the completeness of the interior and exterior finish, space for aerial work is necessary when connecting units at the front and rear surfaces of modules [10; 11]. There is also the disadvantage of not being able to connect the fourth stacked module at a single nodal point when units are connected via the side of the module [12].
# A Lightweight AI-Based Approach for Drone Jamming Detection
## 2 Related Works
This work builds upon the research done in [12], where the authors analyze a Machine Learning (ML) based approach for the detection of jamming attacks. The authors propose the use of Convolutional Neural Networks (CNNs) to detect jamming attacks in an IoT drone scenario. The authors of [12] show that their CNN model is able to achieve a high detection rate against constant jamming, but struggles in the classification of periodic jamming and normal traffic. In [13] a lightweight reinforcement learning algorithm for anti-jamming in UAV communications is presented. This solution leverages spectrum state abstraction to efficiently adapt to dynamic jamming environments, rather than focusing directly on jamming detection. We propose a lightweight approach that integrates a specifically tuned Isolation Forest with a Majority Rule module to reduce the number of false positives and improve resistance against environmental noise. Our solution leverages the IF algorithm for jamming detection in the scenario of a 6G drone network, testing the model against the dataset created by [12]. Isolation Forest has been successfully applied in a variety of scenarios for the detection of anomalies. The authors of [14] propose an Isolation Forest-based approach for the detection of GPS spoofing attacks, while the authors of [15] successfully applied a combination of Decision Trees and Isolation Forest to classify and detect jamming attacks in a mobile scenario. Table 1 shows a summary of the detailed approaches for jamming detection in scientific literature. The Isolation Forest algorithm was chosen for its low computational and memory requirements and good performance in the detection of anomalies. Our approach reaches a high detection rate against both constant and periodic jamming attacks, while also being able to classify normal traffic with a high degree of accuracy. We also detail the tuning of the model's hyperparameters to fit our specific scenario and compare it against the default model. During the tuning phase, the resource-constrained nature of the scenario was a key deciding factor in the selection of the more appropriate hyperparameters.
# Enhancing 3D Lung Infection Segmentation with 2D U-Shaped Deep Learning Variants
## 3 Material and Methods
### Data Preprocessing
#### 3.2.2 Image Resizing and Pixel Scaling  
To ensure uniformity in the CT-scan size and facilitate effective model training, a two-step process involving image resizing and pixel scaling is implemented. Initially, all data samples are resized to a consistent size of 128 \(\times\) 128 pixels, ensuring standardization  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Patient ID** & **Source** & **Size** \\  & & **(w \(\times\) h \(\times\) d)** \\ \hline radiopoaedia\_4\_85506\_1 & Radiopaedia & 630 \(\times\) 630 \(\times\) 39 \\ radiopoaedia\_7\_85703\_0 & Radiopaedia & 630 \(\times\) 630 \(\times\) 45 \\ radiopoaedia\_10\_85902\_1 & Radiopaedia & 630 \(\times\) 630 \(\times\) 39 \\ radiopoaedia\_10\_85902\_3 & Radiopaedia & 630 \(\times\) 630 \(\times\) 418 \\ radiopoaedia\_14\_85914\_0 & Radiopaedia & 630 \(\times\) 401 \(\times\) 110 \\ radiopoaedia\_27\_86410\_0 & Radiopaedia & 630 \(\times\) 630 \(\times\) 66 \\ radiopoaedia\_29\_86490\_1 & Radiopaedia & 630 \(\times\) 630 \(\times\) 42 \\ radiopoaedia\_29\_86491\_1 & Radiopaedia & 630 \(\times\) 630 \(\times\) 42 \\ radiopoaedia\_36\_86526\_0 & Radiopaedia & 630 \(\times\) 630 \(\times\) 45 \\ radiopoaedia\_40\_86625\_0 & Radiopaedia & 630 \(\times\) 630 \(\times\) 93 \\ coronacases\_001 & RAIOSS & 512 \(\times\) 512 \(\times\) 301 \\ coronacases\_002 & RAIOSS & 512 \(\times\) 512 \(\times\) 200 \\ coronacases\_003 & RAIOSS & 512 \(\times\) 512 \(\times\) 200 \\ coronacases\_004 & RAIOSS & 512 \(\times\) 512 \(\times\) 270 \\ coronacases\_005 & RAIOSS & 512 \(\times\) 512 \(\times\) 290 \\ coronacases\_006 & RAIOSS & 512 \(\times\) 512 \(\times\) 213 \\ coronacases\_007 & RAIOSS & 512 \(\times\) 512 \(\times\) 249 \\ coronacases\_008 & RAIOSS & 512 \(\times\) 512 \(\times\) 301 \\ coronacases\_009 & RAIOSS & 512 \(\times\) 512 \(\times\) 256 \\ coronacases\_010 & RAIOSS & 512 \(\times\) 512 \(\times\) 301 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Patient ID, source, and size information for each patient’s CT-scan.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Before Mask Preprocessing** & **After Mask Preprocessing** \\ \hline
0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison table of lung and infection masks before and after mask preprocessing.  
across the dataset. Following the resizing step, the pixel values of the CT-scans undergo scaling using the MinMax scaler. This transformation maps the original pixel values to a normalized range of 0 to 1. By applying this scaling process, the data values are homogenized, promoting consistency and facilitating a more efficient model training process. The MinMax scaler method can be represented by Equation (1), as shown below:  
\[x_{scaled}=\frac{x-x_{min}}{x_{max}-x_{min}} \tag{1}\]  
where \(x_{scaled}\) represents the scaled pixel value, \(x\) denotes the original pixel value, \(x_{min}\) represents the minimum pixel value in the CT-scan, and \(x_{max}\) denotes the maximum pixel value in the CT-scan.
## 4 Principle Laws
### Temporal Dependence  
Temporal dependence means the spectral value of a pixel at t\({}_{2}\) is correlated to that at \(t_{1}\). In other words, pixel value at t\({}_{2}\) can be estimated as a function of pixel value at \(t_{1}\). Figure 6 shows a temporal profile of NDVI of a pixel. Although the temporal profile is not a linear function, it can be divided into many short linear sections. The whole profile can be considered as a combination of all these linear sections. With this simplification, the temporal relationship can be expressed as:  
\[F_{2}(x_{i},y_{i},b)=\alpha(x_{i},y_{i},b)F_{1}(x_{i},y_{i},b)+\beta(x_{i},y_{ i},b) \tag{6}\]  
or  
\[F_{2}(x_{i},y_{i},b)=F_{1}(x_{i},y_{i},b)+\beta(x_{i},y_{i},b),\quad\quad if \quad\alpha(x_{i},y_{i},b)=1 \tag{7}\]  
where \(\alpha(x_{i},y_{i},b)\) and \(\beta(x_{i},y_{i},b)\) are two coefficients for the pixel \((x_{i},x_{j})\) of band b. Both Equations (6) and (7) are used in existing spatiotemporal data fusion methods. Here, the key is to estimate two coefficients. Different spatiotemporal methods employ different strategies. STARFM employs Equation (7) and estimates \(\beta(x_{i},y_{i},b)\) directly from coarse pixels using \(C_{2}(x_{i},y_{i},b)-C_{1}(x_{i},y_{i},b)\) assuming this coefficient is same between coarse images and fine images if the coarse pixels are pure pixels, i.e., only including one land cover type [8]. This assumption is not valid for many cases in reality, so ESTARFM added a conversion coefficient V to adjust the estimation from coarse pixels using \(V[C_{2}(x_{i},y_{i},b)-C_{1}(x_{i},y_{i},b)]\)[11]. In a method developed recently for fusing Sentinel-2 and Sentinel-3 images, Equation (6) is used and the two coefficients are estimated from coarse pixels in a moving window using least square regression [16]. This method assumes the coefficients in Equation (6) are scale invariant and uses a residual compensation step to reduce the errors induced by this assumption.  
Figure 6: Schematic diagram of a NDVI curve and its linear segments (derived from paper [43]).
# Wideband Lorenz Resonance Reconfigurable Metasurface for 5G+ Communications
## 2 Unit Cell Modeling and Design
### DC-Bias Network  
A physical RIS with a phase-controlled reflection coefficient is strongly affected by its insertion loss. The losses of the RIS depend on ohmic losses, dielectric losses, and load losses. The last one is the main source of loss fluctuations.  
In our proposed system scheme, the DC-bias circuit is properly designed to minimize loss and simplify the structure. The DC-bias point is set at the geometric center of the unit cell, which is along the non-radiating edge of the patch. Thus, the geometry of the metal structure is the same regardless of whether it is seen from an incident or a reflected wave view.  
The DC-bias circuit is made up of high-impedance lines, which can greatly reduce RF current loss. A series circuit comprising a resistor and inductor is also designed to choke the RF current. Compared with the RF choke circuit of an open-ended radial line stub with a quarter-wavelength microstrip line, the series circuit of resistor and inductor has a much wider operating frequency range. To avoid the influence due to the self-resonant frequency of the inductor, an inductor whose self-resonant frequency is higher than our operating frequency band should be selected in our design of the DC-bias circuit. So, a thin-film RF/microwave inductor from KYOCERA AVX (L0201068 BHSTR) with 0.68 nH inductance and a 31 GHz self-resonant frequency is used in this MSRRA unit cell DC-bias network. To complement the use of the inductor, a high-frequency resistor from VISHAY (FC0603E1001 BST1) with 10 K ohm resistance and low parasitic capacitance is also used in the Series DC-bias circuit. These device selections ensure a good isolation in DC and RF fields and reduce additional losses resulting from the biasing network, which usually occur in the reconfigurable RIS elements with an extra DC-bias or controlling network. Moreover, the Series DC-bias circuit composed of resistor and inductor are placed on the bottom metal layer of the unit cell structure below the ground plate layer; hence, it minimizes the effect of the bias circuit on the scatterer.