esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions  
### Synthesis and Functionalization of Janus Nanoparticles (JNPs)  
Polystyrene seed PS seed-NPs with an average diameter of 305 \(\pm\) 3 nm were synthesized using surfactant-free emulsion polymerization, according to Scheme 2. Onto these PS seed NPs, a second lobe was grown through a seeded emulsion co-polymerization of 3-(trimethoxysil)propyl-methacrylate (TSPM) and 3-cyanopropylriethoxysilane (TESPN) monomers followed by phase separation, see Scheme 2, resulting in Janus nanoparticles bearing -CN groups on the second lobe (JNPs-CN) according to previously reported methods published by our group [10; 15]. The size of the second lobe relative to the PS lobe could be adjusted by varying the volume of TSPM and TESPN monomers in relation to a reference weight of 1 g of PS NPs. Thus, a homologous series of JNPs-CN was created by using 2 mL, 3 mL and 4 mL of monomers per 1 g of PS NPs. The nitrile groups on the surface of the second Janus lobe were hydrolyzed under reflux in hydrochloric acid, resulting in Janus nanoparticles bearing carboxylic acid surface functional groups (JNPs-COOH), as shown in Scheme 2. The conversion was confirmed through IR spectrometry (Figure S1) and the pH-dependent measurement of the surface zeta potential in Figure S2 and the comparative values of the homologous series of JNPs in Table S1. Through SEM imaging it was confirmed that the appearance of the particles was not altered by the acid treatment (Figure S3), a testament to the good chemical stability of polymeric JNPs.
# Thermomechanically Induced Precipitation in High-Performance Ferritic (HiperFer) Stainless Steels
## 2 Materials and Methods  
### Alloy Production and Processing  
Two material batches, named 17 Cr5_1 and 17 Cr5_2 (actual chemical compositions stated in Table 1), were vacuum-induction-melted from high-purity raw materials, soaked at 1250 \({}^{\circ}\)C for 2 h, and air-cooled. Specimens of \(15\times 15\times 65\) mm\({}^{3}\) in size were cut from the homogenized blocks to be used for simulated TMP.  
To simulate integrated thermomechanical processing at the laboratory scale, a Bahr TTS 820 thermomechanical treatment simulator was utilized at the Steel Institute (IEHK) of the Northrhine-Westfalian Technical University (RWTH), Aachen. Simulated TMP incorporated recrystallization/dissolution annealing at 1200 \({}^{\circ}\)C for 40 min [8], followed by a single-step deformation simulating the final deformation step of the integrated thermomechanical treatment  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Batch ID:** & **Cr** & **W** & **Nb** & **Si** & **Mn** & **Fe** & **C** & **N** \\ \hline
**17 Cr5\_1** & 17.43 & 3.85 & 0.9 & 0.25 & 0.25 & 77.33 & \textless{}0.01 & \textless{}0.001 \\
**17 Cr5\_2** & 17.30 & 3.97 & 0.93 & 0.27 & 0.23 & 77.40 & \textless{}0.01 & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical compositions of the trial alloys (wt. %)  
Figure 2: Processing of HiperF steel via (**a**) standard multistep processing (MSP) and (**b**) integrated thermomechanical processing (TMP). T\({}_{\text{d}}\): Dissolution temperature of the Laves phase.  
route. The preliminary thickness-reduction steps involved in real-life processing were not covered by this study, because these would have been extinguished by recrystallization (i.e., prior to the final thickness-reduction step, cf. Figure 2b). Table 2 summarizes the variations of deformation temperature (T\({}_{\varphi}\)), deformation degree (\(\varphi\)), and holding time after deformation (t\({}_{\text{H}}\)). The TMP parameters were intended to create a rise in dislocation density to, in turn, induce boosted particle strengthening via increased precipitation of Laves particles of suitable size and spacing. Laves-phase particles pin the dislocations they precipitate on, and thus make nonpermanent dislocation strengthening (at least partly) permanent [27].
## 1 Introduction  
This age is the age of connection--connecting fields of knowledge, which have been developing independently for a long time, but also connecting ideas, cultures, philosophies, and peoples. Globalization is closely linked to the digitalization of the world. More and more actions take place in the virtual reality instead of the physical sphere; the material world is enriched by the extended reality. The progress influences the society, as well as individuals, all the inhabitants of the developed parts of the world, and the whole planet.  
There is an increasing development in the field of data analysis. The new datasets are bigger and more complex than those recorded in previous periods. They are often unstructured or semi-structured, ideally enriched by metadata. These new sets are frequently measured in terabytes or even petabytes. There is an increasing demand for executing the data in real or near-real time, or for predictive maintenance, which helps to prevent failures, thus significantly decreasing the time and finances needed to fix the problems which have already occurred. The methods for operating data that were used in previous periods are ceasing to be effective. They are not flexible enough, and they do not suffice the rising demands anymore.  
The traditional statistical methods for operating data are therefore being substituted by more complex and more practical methods collectively known as Big Data, developed specifically for working with huge data volumes. The phases of data processing are described by CRISP-DM methodology, which is generally accepted for its intuitive nature, simplicity, and logicality. This methodology serves as a firm base for data analysis and data scientists.  
In the analysis, the better we know the process we are working with, the data sources we draw from, and the overall context, the simpler the investigation of error sources and searching for hidden patterns are. The data scientists cooperate with the experts from the examined field and consult the project's next steps with them. It is advantageous for the data scientists to follow the progress of data analysis, its current fields of interests, new methods, and solutions, as well.
# Design and Implementation of C-Band Large-Power Planar Butler Matrix in SRS
## 2 Principle and Design Method
### Square Coaxial Line
#### 2.2.2 Electrical Performance Simulation  
Use the 3D electromagnetic simulation software CST Studio Suite 2020 to simulate the square coaxial line. The length is 1000 mm, and the material is aluminum. The electric field distribution is entirely symmetrical. The highest electric field intensity is located on the edge of the inner conductor, and the peak electric field is about 3500 V/m, as shown in Figure 3. The insertion loss is less than 0.01 dB within a given frequency range, and the return loss is greater than 65 dB, as shown in Figure 4.  
Figure 3: E-field distribution of square coaxial line.  
Figure 2: Square coaxial line cross-section structure.
# T-Splines for Isogeometric Analysis of the Large Deformation of Elastoplastic Kirchhoff-Love Shells
###### Abstract  
In this paper, we develop a T-spline-based isogeometric method for the large deformation of Kirchhoff-Love shells considering highly nonlinear elastoplastic materials. The adaptive refinement is implemented, and some relatively complex models are considered by utilizing the superiorities of T-splines. A classical finite strain plastic model combining von Mises yield criteria and the principle of maximum plastic dissipation is carefully explored in the derivation of discrete isogeometric formulations under the total Lagrangian framework. The Bezier extraction scheme is embedded into a unified framework converting T-spline or NURBS models into Bezier meshes for isogeometric analysis. An a posteriori error estimator is established and used to guide the local refinement of T-spline models. Both standard T-splines with T-junctions and unstructured T-splines with extraordinary points are investigated in the examples. The obtained results are compared with existing solutions and those of ABAQUS. The numerical results confirm that the adaptive refinement strategy with T-splines could improve the convergence behaviors when compared with the uniform refinement strategy.  
isogeometric analysis; Kirchhoff-Love shell theory; elastoplastic; T-splines; adaptive refinement +
Footnote †: journal: Applied Sciences
## 2 Preliminaries
### Definition of the Lattice  
Let \(\mathbf{A}=\{\mathbf{a}_{1},\mathbf{a}_{2},...,\mathbf{a}_{m}|\mathbf{a}_{i}\in\mathbb{R}^{n}\}\) be a set of linearly independent column vectors; and the lattice composed of this set of vectors is defined as follows:  
\[\Lambda(\mathbf{A})=\mathcal{L}(\mathbf{a}_{1},\mathbf{a}_{2},...,\mathbf{a}_{m})=\{\mathbf{A}\mathbf{ x}|\mathbf{x}\in\mathbb{Z}^{m}\}, \tag{1}\]  
and \(\mathbf{A}\) is called the base of lattice \(\Lambda(\mathbf{A})\). Most cryptosystems are constructed using an integer lattice, i.e., \(\mathbf{a}_{i}\in\mathbb{Z}^{n}\). If \(n=m\), then \(\Lambda(\mathbf{A})\) is said to be a full-rank lattice.  
**Definition 1**.: _Given matrix \(\mathbf{A}\in\mathbb{Z}_{q}^{n\times m}\) and \(\mathbf{u}\in\mathbb{Z}_{q}^{n}\), define the following two \(q-\) module lattices:_  
\[\Lambda_{q}^{\perp}(\mathbf{A})=\{\mathbf{x}\in\mathbb{Z}^{m}:\mathbf{A}\mathbf{x}=\mathbf{0} \text{mod}q\}, \tag{2}\]  
\[\Lambda_{q}^{\mathbf{u}}(\mathbf{A})=\{\mathbf{x}\in\mathbb{Z}^{m}|\mathbf{A}\mathbf{x}=\mathbf{u} \text{mod}q\}. \tag{3}\]
## 3 Results and Discussion  
### Stability of the Proposed Algorithm  
The scheme in (20) is stable if two conditions are satisfied, one for each term (\(H_{1}^{-1}x_{n}\) and \((I-\Delta tK^{\prime})^{-1}\Delta tPu_{n+1}\)) that is on \(H_{1}^{-1}\), the one-step state-to-state transition matrix, and on \((I-\Delta tK^{\prime})^{-1}\Delta tP\), the input-to-state transition matrix:  
* \(\rho\big{[}H_{1}^{-1}\big{]}<1\).
* \(K^{\prime}\) must have eigenvalues with negative real part; this means that each subsystem must be stable, being \(K^{\prime}\) is the state transition matrix of the ISM with feedback edges deleted. If any one of the subsystems is unstable and is stabilized using an output-feedback stabilization method and deleting the corresponding feedback, the resulting feed-forward-only system becomes unstable again.  
As regards the first condition, matrix \(BM^{\prime\prime}C\) is the product of bock diagonal matrices with \(M^{\prime\prime}\), a strictly upper block triangular matrix, giving a strictly upper block triangular matrix; such matrix is nilpotent, \((BM^{\prime\prime}\,C)^{\prime}=0\), \(r\) is its rank. This property allows for quickly calculating the inverse of \(I+\Delta tBM^{\prime\prime}C\):  
\[(I+\Delta tBM^{\prime\prime}\,C)^{-1}=I+\sum_{i=1}^{r-1}(-1)^{i}(\Delta tBM^{ \prime\prime}\,C)^{i}. \tag{21}\]  
Using (21) in \(H_{1}\) gives:  
\[\begin{split} H_{1}=(I+\Delta tBM^{\prime\prime}\,C)^{-1}(I- \Delta tK^{\prime})=,\\ =I-\Delta tK^{\prime}-\Delta tBM^{\prime\prime}\,C-\Delta t^{2}BM^ {\prime\prime}\,CK^{\prime}+[\sum_{i=2}^{r-1}(-1)^{i}(\Delta tBM^{\prime\prime }\,C)^{i}](I-\Delta tK^{\prime})=,\\ =I-\Delta t(A+BM^{\prime}C+BM^{\prime\prime}\,C)-\Delta t^{2}BM^ {\prime\prime}\,CK^{\prime}+[\sum_{i=2}^{r-1}(-1)^{i}(\Delta tBM^{\prime\prime }\,C)^{i}](I-\Delta tK^{\prime})=,\\ =I-\Delta tK+O\big{(}\Delta t^{2}\big{)},\end{split} \tag{22}\]  
and finally,  
\[H_{1}=H+O(\Delta t^{2}). \tag{23}\]  
Therefore, the eigenvalues of the perturbed matrix \(H_{1}\) are a perturbed version of those of \(H\) (Classic Euler implicit, Equation (13)), maintaining greater than one of their magnitudes (therefore less than one the magnitude of the eigenvalues of \(H_{1}^{-1}\)) if the second-order perturbation is sufficiently small. In any case, [PERSON]'s implicit method, from which comes the method proposed here, is only of the first order and could be improved for accuracy reasons. The step size \(\Delta t\) should be anyway small enough to meet a certain error tolerance independently of stability conditions. For stiff systems, higher accuracy can be achieved using a higher-order methods and/or variable step size \(\Delta t_{k}\) while still maintaining stability [62].
# Study on the Influence of File Installation Disturbance on the Settlement of Composite Foundation
## 3 Methodology
### Composite Foundation Settlement Calculation  
Composite modulus method and stress modified method are commonly used in settlement calculation. When considering the former, \(E_{sp}{{}^{\prime}}\) can be represented as:  
\[E_{sp}{{}^{\prime}}=mE_{P}+(1-m)E_{s} \tag{3}\]  
where \(m=\) pile replacement ratio; \(E_{P}=\) deformation modulus of pile.  
\(E_{P}\) of CMP and PHC pile is typically tens of times and hundreds of times, respectively, higher than \(E_{s}\) of the surrounding soil between the piles. According to Equation (3), when calculating \(E_{sp}{{}^{\prime}}\), the contribution of \(E_{s}\) is minimal. Therefore, the composite modulus method cannot effectively reflect the impact of pile driving on settlement.  
Figure 4: Linear relationship between \(E_{s}{{}^{\prime}}\) and \(q_{t}\).  
Figure 3: The curve obtained by fitting the compression test results using \(\epsilon_{p}=\epsilon_{0}\cdot\exp(-bp^{m})\).  
When considering the stress modified method, it determines the load shared by the soil between the piles based on the pile-soil stress ratio. The presence of the reinforced body is ignored, and only the compression of the soil between the piles is calculated.  
The load shared by soil between piles, \(p_{s}\), is:  
\[p_{s}=\frac{1}{1+m(n-1)}p=\mu_{s}p \tag{4}\]  
where \(p\) = embankment load; \(n\) = pile-soil stress ratio, which can be obtained through static load test; \(\mu_{s}\) = disturbance coefficient.  
According to layer-wise summation method, the settlement of the reinforced area, \(S_{e}\), is:  
\[S_{e}=\mu_{s}\sum_{i=1}^{n}\frac{\Delta p_{i}}{E_{si}^{\prime}}H_{i} \tag{5}\]  
where \(\Delta p_{i}\) = additional stress increment of the \(i\)-th reinforced layer; \(H_{i}\), \({E_{si}}^{\prime}\) = thickness and compression modulus of the \(i\)-th soil layer between piles after pile driving.
## 2 Methodology Framework
### Processing of Ground Motion Sequences  
The processing flow of ground motion sequences is shown in Figure 4. Firstly, 6635 ground motion records were collected from PEER's ground motion database [28] for generating the training set. Meanwhile, 1390 ground motion records were collected from the K-Net database [29], for generating the validation and test sets. Subsequently, each ground motion sequence was scaled to 10 different amplitudes (i.e., PGA = 1-10 m/s\({}^{2}\)), resulting in 80,250 ground motions.  
The 40 building models established in Section 3.1 were analyzed using the collected ground motions through time history analysis and the damage states of all buildings were calculated. The damage states are none, slight, moderate, extensive, and complete damages, which are labeled as 0, 1, 2, 3, and 4, respectively. The labeled samples are prepared for the use of model training in the next section.  
Since ground motion records were collected from different stations with different sampling rates and durations, it is necessary to down sample and truncate the ground motion records to meet the needs of the MI-CNN model. Specifically, all ground motion records were down sampled to 50 Hz, and truncated to 20 s sequences, i.e., 10 s before and after the peak acceleration point, to ensure the same data size for all ground motion sequences. If a ground motion does not have enough data before or after the peak acceleration point, the 20 s time window will be shifted backward or forward to ensure that the peak acceleration is close to the center of the truncated sequence (Figure 5). The processing of a ground motion record results in a sequence of 1000 acceleration points.  
Figure 4: Processing flow of ground motion sequences.  
Figure 3: Building model: (**a**) MDOF shear model; (**b**) tri-linear backbone curve model; (**c**) design response spectrum of the Chinese code for the seismic design of buildings [22].  
The processed ground motion sequences, PGA, the building fundamental period (\(T_{1}\)), and the building seismic fortification level (\(FL\)) together with the calculated damage states of buildings were collected as samples for training, validating, and testing the proposed MI-CNN model. The distribution of samples in the training set is presented in Figure 6.  
It is noteworthy that the samples generated from 6635 ground motion records of PEER were used as the training set, while the samples generated from 1390 ground motion records of K-Net were used as the validation and testing sets. This makes sure that the training and testing sets are generated from different earthquakes, which means the testing of the MI-CNN model is rigorous, and the testing results can demonstrate the applicability of the model in predicting building seismic damages for future earthquakes.
## 2 Materials and Methods
### Non-Reference Quality Assessment Metrics
#### 2.3.1 Quality Metrics
The value of variance may be calculated from Equation (5):  
\[\sigma=\frac{1}{N}{\sum\limits_{i=1}^{N}{\sum\limits_{j=1}^{N}{\left|{p(i,j)-\mu} \right|}}}, \tag{10}\]  
\begin{tabular}{l} \hline \hline
**Input:** Radargram after standard pre-processing \\ \hline Compute MSCN coefficient for each pixel in the image:  
MSCN = ComputeMSCN(I)  
Divide the input radargram into non-overlapping blocks of size 32-by-32:  
Blocks = DivideIntoBlocks(I, BlockSize = 32 \(\times\) 32)  
Identify high spatially active blocks based on the variance of the MSCN coefficients. For each block in Blocks:  
Calculate variance of MSCN coefficients  
If Variance of MSCN > Threshold  
Mark block as high spatially active  
Generate activityMask using the identified high spatially active blocks:  
activityMask = GenerateActivityMask(Blocks, HighSpatialActivity)  
Evaluate distortion due to blocking artifacts and noise using the MSCN coefficients. For each block in Blocks:  
Calculate distortion using MSCN coefficients  
Assess blocking artifacts and Gaussian noise  
Classify Blocks(Blocks, ThresholdCriteria)  
Generate noticeableArtifactsMask from the distorted blocks  
Compute the PIQE score for input image  
PIQE_score = MeanScore(DistortedBlocks)  
**where  
L\({}_{i}\)--Laplacian value in the ith pixel of the image;  
\(\mu_{\rm L}\)--average Laplacian value in the image;  
N--number of pixels in the image. A high value of Laplacian operator variance is characteristic for highly sharp images, where clear edges are visible. On the other hand, a low value of variance indicates an image that is blurred, which may be caused by the presence of noise or artifacts resulting from the compression process.
# Quality Assessment of Dual-Parallel Edge Deblocking Filter Architecture for HEVC/H.265
## 5 Quality of PEDBF for HEVC/H.265
### Quality Assessment Procedure  
To measure the quality of the V-DPEDBF architecture, the following steps are executed sequentially.  
1. Original raw video is given as input to the HEVC Test Model to obtain the reconstructed video by disabling the in-loop filter
2. Reconstructed video data from the HM is given as input to the dual-parallel edge deblocking filter (PEDBF) architecture.
3. The input video to PEDBF architecture is split into luminance (Y), chrominance Cb (U) and, chrominance Cr (V) components.
4. Each video component is segregated into image frames and stored into frame buffers.
5. Each image frame of the Y, U, and V components is fetched from the frame buffers.
6. Each image frame is split into uniform pixel blocks of size 16 \(\times\) 16.
7. Each 16 \(\times\) 16 block is again split into four 8 \(\times\) 8 blocks.
8. DBF is applied at the boundary of two 8 \(\times\) 8 blocks simultaneously for vertical filtering.
9. The vertically filtered output data are transposed and moved into pixel buffers to perform horizontal edge filtering.  
Figure 11: Resource sharing architecture of strong filter—partial view.  
10. Horizontal edge filtering is performed after the vertical edge filtering is performed for the entire 16 \(\times\) 16 block.
11. The horizontally filtered output is transposed and stored into the frame buffer.
12. The filtered video of PEDBF is reconstructed from the frame buffer.
13. The _PSNR_ of the filtered video is computed.
14. Original raw video is given as input to the HEVC Test Model to obtain the reconstructed video by enabling the in-loop filter.
15. The _PSNR_ of the filtered video from the HEVC reference software is computed and the results are compared.  
The flow diagram to perform the quality assessment is shown in Figure 12.
## 3 Proposed EP-MAC Protocol Design
### Reservation of Slot and Data Transmission
The slot allocation and transmission technique of the EP-MAC protocol is explained in Figure 5. Consider a simple sensor network consisting of four sensor nodes namely A, B, C, and D and every node runs in the same schedule. Now let us consider that, the communication is possible between node A and D (link AD) and node B and C (link BC) simultaneously without any interference. We further consider a frame which consists of an active period or listen period which is represented by \(t_{L}\) and a sleep period represented by sleep part (\(t_{s}\)). Active period is further divided into two parts. The first part is used for synchronization and the second part is used for making reservation of data slots by sensor nodes. In this part sensor nodes exchange several RTS and CTS for data slot reservation. The next part of the frame is the sleep part, which will be used for data transmission between nodes. The sleep part is subdivided into a few data slots. In this example we take four data slots. The ration of listen and sleep period depends on the duty cycle of the operation of sensor nodes. So, as the SYNC period is fixed, with the change of the duty cycle the span of reservation slot and number of data slot will be changed accordingly. Figure 5: Timing diagram of sensor nodes working in EP-MAC. We consider some random transmission between nodes to explain the different scenario of operation of EP-MAC protocol. Let each of the four nodes A, B, C and D have data to send to other nodes. And let the node pair AB exchange RTS and CTS with each other as the first successful pair. The next pair is BC and then sequentially AD, BD and CD. According to our proposed algorithm the data slot 1 will be allocated for the communication between AB. Now the next pair who won the contention is BC. Since B already participated in the data slot 1 so there is no scope of parallel transmission in slot 1 for the BC link. Hence data slot 2 will be allocated for transmission between BC. The third pair is AD. For this pair data slot 1 cannot be allocated because node A is already participating in communication of data slot 1. During the data slot 2 communications will take place between node B and node C. But link BC and link AD are simultaneously collision free transmission free link. So, data slot 2 can be assigned to AD also. For the next pair BD, since B is already participating in the previous two data slot so new data slot 3 is allocated to BD. Again, for the next pair CD though during data slot 1 neither C nor D is participating in the communication but data slot 1 cannot be given to CD. Because link AB and link CD s transmission will interfere with each other. So, data slot 4 will be allocated for transmission of CD.