esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed STPAP Beamforming Algorithm
### Adaptive Algorithm for Calculating the Weight Vector
\begin{array}{ll}\text{W}_{\text{all}}(i+1)&=\text{W}_{\text{all}}(i)-u \widetilde{\text{R}}(i)\text{W}_{\text{all}}(i)-\overline{\text{C}}[\text{W}_ {\text{all}}^{\text{H}}(i)\overline{\text{C}}(\overline{\text{C}}^{\text{H}} \overline{\text{C}})^{-1}-\\ &\quad u\text{W}_{\text{all}}^{\text{H}}(i)\widetilde{\text{R}}(i)\overline{ \text{C}}(\overline{\text{C}}^{\text{H}}\overline{\text{C}})^{-1}-\overset{ \text{\tiny{H}}}{1}_{J}(\overline{\text{C}}^{\text{H}}\overline{\text{C}})^{- 1}|\text{H}\\ &=\text{W}_{\text{all}}(i)-u\widetilde{\text{R}}(i)\text{W}_{\text{all}}(i)- \overline{\text{C}}(\overline{\text{C}}^{\text{H}}\overline{\text{C}})^{-1} \overset{\text{\tiny{H}}}{\text{C}}^{\text{H}}\text{W}_{\text{all}}(i)+\\ &\quad u\overline{\text{C}}(\overline{\text{C}}^{\text{H}}\overline{\text{C}})^ {-1}\overset{\text{\tiny{H}}}{\overline{\text{C}}}^{\text{H}}\widetilde{ \text{R}}(i)\text{W}_{\text{all}}(i)+\overline{\text{C}}(\overline{\text{C}}^ {\text{H}}\overline{\text{C}})^{-1}\overset{\text{\tiny{H}}}{1}_{J}\\ &=\text{T}[\text{W}_{\text{all}}(i)-u\overline{\text{R}}(i)\text{W}_{\text{all }}(i)]+\text{V}\end{array}  \tag{24}\]  
with  
\[ \begin{array}{ll}\text{T}=\text{I}-\overline{\text{C}}(\overline{\text{C}}^ {\text{H}}\overline{\text{C}})^{-1}\overline{\text{C}}^{\text{H}},\\ \text{V}=\overline{\text{C}}(\overline{\text{C}}^{\text{H}}\overline{\text{C}})^ {-1}\overset{\text{\tiny{H}}}{1}_{J}\end{array}  \tag{25}\]  
where \(\text{I}\in\mathbb{C}^{2 MK\times 2 MK}\) denotes the identity matrix. Note that the matrix \(\text{T}\) and \(\text{V}\) do not change with iteration. Furthermore, the error signal can be given by  
\[e(i)=d(i)-y(i) \tag{26}\]  
with  
\[y(i)=\text{W}_{\text{all}}^{\text{H}}(i)\text{X}(i), \tag{27}\]  
where \(e(i)\) represents the \(i\)-th iterative error, \(y(i)\) denotes the \(i\)-th iterative output data, \(\text{X}(i)\in\mathbb{C}^{2 MK\times 1}\) represents the \(i\)-th input time domain sample block, and \(d(i)\) is the reference signal. \(d(i)\) denotes the reference signal, which is the one corresponding to the signals after the first ADC in Figure 2.  
According to the above description, the flow chart of the proposed algorithm can be depicted as Figure 3. Note that it is necessary to regularly check whether ephemeris is updated. When the ephemeris is updated, the GNSS satellites in the FOV should be regrouped and meanwhile \(\mathbf{T}\) and \(\mathbf{V}\) should also be recalculated.
## 3 Generation of Cross-Correlated Random Fields
### Methodology  
#### 3.1.1 Random Fields  
Random fields \(Z(\omega,\mathbf{x})\) represent spatially distributed random variables \(Z(\omega)\). To describe a random field completely, the function of the mean value \(\mu(\mathbf{x})\), variance \(\sigma^{2}(\mathbf{x})\), and correlation coefficient \(\rho(\mathbf{x},\mathbf{x}^{\prime})\) are required.  
Utilizing random fields in the context of material modeling, it is important to note that the material parameters are positive in nature. This does not necessarily apply to the coefficients of strain-energy density functions. However, it is indispensable for the yield strength,  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**E**} & \multicolumn{2}{c}{\(\sigma_{\text{failure}}\)} & \multicolumn{2}{c}{\(\epsilon_{\text{failure}}\)} \\ \hline \multirow{2}{*}{**Specimens**} & \multirow{2}{*}{**Number of Specimens**} & **mean** & **std** & **mean** & **std** & **mean** & **std** \\  & & **Gpa** & **Gpa** & **MPa** & **MPa** & **\%** & **\%** \\ \hline Experiments & 8 & 7.95 & 0.87 & 96.1 & 6.22 & 1.97 & 0.14 \\ Data sheet & 1 & 9.69 & - & 135 & - & 2.5 & - \\ \hline \end{tabular}
\end{table}
Table 1: Experimental results obtained by tensile tests in the elastic and plastic domain.  
Figure 2: Experimental results and comparison with the data sheet.  
which contradicts the found normal distribution of the yield strength [24]. Therefore, when using Gaussian random fields and, hence, assuming normally distributed underlying random variables, negative realizations are possible [31; 32]. In conclusion, the use of Gaussian random fields for the representation of the material properties in the context of multi-scale modeling of heterogeneous material is controversial [6]. With non-Gaussian random fields, negative realizations can be avoided, and hence, a stochastic solution of second-order and the positive nature of the elasticity coefficient are guaranteed [10; 33; 34; 35; 36].  
Non-Gaussian random fields \(M(\omega,\mathbf{x})\) are defined by  
\[M(\omega,\mathbf{x})=\mathcal{G}\{Z(\omega,\mathbf{x})\}, \tag{1}\]  
where \(\mathcal{G}\) is a nonlinear mapping operator and \(Z(\omega,\mathbf{x})\) a centered, homogeneous Gaussian random field [11]. With respect to the following investigations, where the concept of random fields was used within the finite element method framework, only discretized random fields were used, requiring a finite sampling. Consequently, as long as negative values are excluded, the difference between the results based on non-Gaussian fields and Gaussian fields may not differ significantly. Therefore, the approach presented here was based on homogeneous Gaussian random fields, which also allowed using the well-known techniques presented below.
# Experimental and Parametric Analysis of Pull-Out Resistance of Notched T-Perfobond Connectors
###### Abstract  
To enhance the uplift capacity and facilitate the installation of multi-row perfobond connectors at shallow burial depths, this study puts forward a novel notched T-perfobond connector. The design incorporates an integrated flange at the bottom of the connector and a notch at the edge of the hole. Through pull-out model tests on four notched T-perfobond connectors, this research investigates their failure mechanisms and pull-out capacities. Utilizing the explicit dynamics method in ABAQUS, a finite-element model of the pull-out resistance test for notched T-perfobond connectors is established and verified against experimental data. Furthermore, a detailed parametric analysis involving 54 models is conducted, examining crucial parameters such as rib dimensions, hole geometry, flange size, notch width, bar diameter, and material properties. Based on the combined experimental and numerical results, this paper assesses the suitability of current formulas for calculating the pull-out capacity of perfobond connectors and proposes a refined calculation method specifically for notched T-perfobond connectors. All the findings reported in this paper can serve as a reference in the design and construction of composite structures.  
composite structures; perfobond connectors; flange; notch; pull-out performance; finite-element analysis +
Footnote †: journal: Applied sciences
## 3 Software Solutions
### Custom SW Tool  
#### 3.3.1 Custom Architecture  
In offshore wind turbines, failures of structural components, caused by corrosion at tower including the \"tower-platform\" junction and the entire splash-zone can cause significant and critical down-times and subsequent loss of electricity production. As no holistic commercially available tool was identified, to the best of the authors' knowledge, in the framework of the ongoing WATEREYE research project [10], a custom SW demonstrator tool was developed. The initial concept and architecture of the custom SW tool was developed in our previous work [26]. In this paper, the concept and architecture are further refined and finally implemented into a demonstrable and deployable SW tool.  
In the WATEREYE project [10], corrosion monitoring and O&M planning tools for offshore wind farms and turbine structures are developed and integrated in (a subsequent version of) the custom SW-tool. Further, in the scope of the WATEREYE project, technologies for monitoring, data analytics, modelling, and diagnosis and for wind turbine (WT) and wind farm (WF) O&M advanced control strategies have been investigated and implemented.  
The analysis listed in Table 1, resulted in the design and development of an SW-tool that attempts to cover all criteria identified in Section 2.3. For the framework of this SW tool, Qt [27] was chosen. Because of its highly scaleable characteristics, it allows developers to build components that can run on embedded, desktop and mobile computers, and it supports graphical development (inclusion via widgets). Qt supports all of today's user interface paradigms, controls and behaviors, making it easy to design really attractive HMIs that users intuitively understand. Furthermore, Qt uses the latest SSL and TLS implementations to safely encrypt data communications for cyber-secure remote-access.  
In order to leverage both web-based and SCADA-capabilities, pvbrowser [28] was chosen, which is an open source C++ GUI framework, built on top of Qt. This application framework provides a specialised browser for the client computer and an integrated development environment for creating servers that interact with data acquisition programs (daemons) for many SCADA protocols [28].  
The stock architecture of pvbrowser is shown in Figure 2a. Pvbrowser is available under the GPL v2 (program) and GNU LGPLv3 (library) licence frameworks.  
Figure 2: (**a**) Original pvbrowser architecture [28] and (**b**) modified architecture and data flow, to support the WATEREYE project [10] needs.  
For 3D-visualisation purposes, the visualisation toolkit (VTK) [29] was used. This C++ based open source toolbox is capable of filtering, processing and visualising large datasets, with numerous user and SCADA interaction capabilities. Pvbrowser provides some support for VTK, but the default browser does not support it out of the box. To setup the VTK-based Qt-widget and to streamline the interaction between the user and the widget, TCL-TK scripts (Graphical toolkit for TCL) were used, available under a BSD-type licence [30].
# Blockchain Based Secure Routing and Trust Management in Wireless Sensor Networks +
## 4 System Model
### Authentication  
When SNs communicate with ANs, the ANs authenticate the identities of SNs by utilizing a private blockchain. Furthermore, when ANs communicate with BSs, the BSs authenticate them by exploiting a public blockchain. When two ANs communicate with each other, they send requests to the BSs, therefore, mutual authentication between ANs is performed.  
The procedures of nodes' registration, mutual authentication and trust evaluation are described in Algorithms 1-3, respectively.  
```
1 begin
2/*PUB is public blockchain*//
3/*PRB is private blockchain*/
4/*SPIUB is the smart contract of public blockchain*/
5/*SPIRB is the smart contract of private blockchain*/
6 ifNodeExists(AN1D,PUB)=true||VerifyID(BS1D)=errorthen
7 return error message();AN is revoked from joiningPUB;
8 else
9 return true(AN1 Dregistered withSPUB);
10 AN1D is permanently stored inPUB;
11 end if
12 ifNodeExists(SN1D,PRB)=true||VerifyID(AN1D)=errorthen
13 return error message();
14 SN is revoked from joiningPRB;
15 else
16 return true(SN1 Dregistered withSPRB);
17 SN1D is permanently stored inPRB;
18 end if
```  
**Algorithm 1**Nodes' registration
## 4 Analysis and Discussion  
### Analysis Objectives  
The model pre-trained by MASS adopts the Transformer as a backbone architecture consisting of an encoder and a decoder. The encoder and decoder have six layers each, and the pre-trained model has cross-attention between the encoder and decoder. Therefore, we split the weights of the model into three groups. The first is the weights of word embeddings, the second is those of self-attention layers, and the third is the weights of encoder-decoder cross-attention layers. We aim to find the answers to the following questions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameters** & **Values** \\ \hline architecture & Transformer \\  & (encoder 6 layers, decoder 6 layers) \\ embedding dim. & 1024 \\ hidden layer dim. & 1024 \\ feed-forward dim. & 4096 \\ attention head & 8 \\ activation function & [PERSON] \\ optimizer & [PERSON] \\ scheduler & inverse sqrt \\ initial learning rate & \(10^{-4}\) \\ beta1, beta2 & 0.9, 0.98 \\ dropout & 0.1 \\ mini-batch size & 3000 tokens (pre-trained model) \\  & 2000 tokens (supervised/unsupervised NMT) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The hyperparameters to build the pre-trained model, the unsupervised NMT, and the supervised NMT models.  
\begin{table}
\begin{tabular}{c l c c c} \hline \hline  & & \multicolumn{4}{c}{**BLEU Scores**} \\ \cline{3-5}
**Language Pairs** & **Models** & \multicolumn{2}{c}{**Based on Pre-Trained Models**} & \\ \cline{3-5}  & & **Unsupervised NMT** & **Supervised NMT** & **Supervised NMT** \\ \hline \multirow{2}{*}{Korean–Japanese (KO-JA)} & JA \(\rightarrow\) KO & 32.76 & 51.69 & 48.12 \\  & KO \(\rightarrow\) JA & 29.07 & 41.96 & 41.02 \\ \hline \multirow{2}{*}{Korean–English (KO-EN)} & EN \(\rightarrow\) KO & 3.62 & 30.14 & 21.83 \\  & KO \(\rightarrow\) EN & 1.10 & 27.13 & 19.49 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The performances of the neural machine translation systems.  
Are word embeddings of fine-tuned NMT models located appropriately in a vector space according to their meanings?  
Can word embeddings shift in a vector space appropriately when they are recalculated through self-attention layers of an encoder?  
We assume that cross-attention layers between an encoder and a decoder reflect syntactical differences between the two languages in a certain way. Can we measure the language differences by analyzing cross-attentions between an encoder and a decoder?
## Materials and Methods
### Preparation of MIPs and NIPs
#### 2.2.2 Synthesis of MIP2 and NIP2 by Surface Layer Imprinting  
Activation of silica gel: 8.0 g of silica was mixed with 60 mL of 33% (_w/w_) methanesulfonic acid and refluxed for 8 h at 110 \({}^{\circ}\)C with stirring. Following this, the activated silica gel was obtained by filtration, washed with pure water to neutralize, and then dried under vacuum at 70 \({}^{\circ}\)C for 10 h.  
Synthesis of the MIP2: Gossypol (0.083 mmol, 300 mg) was dissolved in 3.0 mL of acetone under stirring, and 0.29 mL (8.5 mmol) of APTES was added into the mixture under stirring for 30 min to obtain the completely self-assembled complex with gossypol. Then, 58 mg of activated silica gel was added by stirring for 30 min to provide silica particles fully dispersed in solution. Then, 0.58 mL (2.6 mmol) of TEOS was added. The mixture was stirred for 5 min, and then 0.15 mL of 1.0 mol\(\cdot\)L\({}^{-1}\) acetic acid was added. The polymerization reaction was carried out at room temperature under stirring for 24 h to obtain particles with a high cross-linking structure. Finally, the particles were isolated by centrifugation at 8000 rpm for 5 min. As the control, a non-imprinted polymer (NIP2) was prepared and treated in an identical manner but in the absence of gossypol.  
Removal of template molecule: For MIP2, to remove the gossypol, the obtained polymers were repeatedly washed with 50 mL of a mixture of methanol and 6 mol\(\cdot\)L\({}^{-1}\) HCl (1:1, _v/v_) until no template molecules were detected by UV-Vis spectroscopy. Then, it was neutralized with 0.1 mol\(\cdot\)L\({}^{-1}\) NaOH solution and washed to neutral pH by pure water and methanol. For NIPs, the particles were washed to neutral pH with pure water and methanol to remove any un-reacted substances. Finally, both MIP and NIP were dried at 80 \({}^{\circ}\)C for 12 h under vacuum.
# Settlement Prediction of Footings Using \(V_{s}\)
## 1 Introduction
The maximum shear modulus (\(G_{max}\)) below the elastic threshold strain, which is the fundamental stiffness, is usually inferred from the \(V_{S}\) obtained by seismic wave propagation tests. The variation in shear modulus with strain can be determined at small to intermediate strains by laboratory tests such as the resonant column test, torsional shear test and small-strain triaxial test. Finally, reliable nonlinear stiffness variation could be determined by combining the \(G_{max}\) and \(G/G_{max}\) curve, and hence the \(V_{S}\), which is related to \(G_{max}\), can be used as a key soil property for the deformation analysis of soil-foundation systems [12]. Besides, the \(V_{S}\) can be measured non-intrusively in the gravel and cobble layers, which are difficult to characterize using penetration testing methods. Indeed, the \(V_{S}\) has great potential to replace the role of CPT-\(q_{c}\) or SPT-\(N\) values in the settlement prediction methods for determining the deformation modulus. This study aims to develop a new approach for predicting the load-settlement behavior of shallow footings based on the \(V_{S}\) and nonlinearity of soil. The development of a new method was based on the conceptual framework of [PERSON]'s method [6], which is one of the commonly used methods in engineering practice. The procedures for obtaining confinement and strain dependent modulus values from the \(V_{S}\) profile considering pressure distribution under the footing were developed. To verify the developed method, load-settlement curves for footings having a different length to breadth ratio, together with \(V_{S}\) profiles, were obtained by centrifuge modeling, and then compared with the predictions from the developed method.
# Research on Mechanical Behavior of Geogrid-Soil Interface Under Rainfall Infiltration
## 3 Results and Discussion
### Effect of Normal Stresses on Geogrid-Soil Interface Characteristics
When the infiltration time is 30 min, the final moisture content measured by moisture content sensor 2 in Figure 6c is 20.3%, 20.6%, and 20.7%, respectively, and that measured by moisture content sensor 2 in Figure 6d is 21.4%, 21.6%, and 21.1%, respectively. Upon comparison, it is found that the final moisture content at a shear rate of 1.0 mm/min is slightly higher than that at a shear rate of 1.5 mm/min. This is because a slower shear rate leads to a longer overall shearing process, allowing more rainwater to infiltrate under the same rainfall conditions, which is macroscopically manifested as higher moisture content values measured by the moisture content sensor. Based on the analysis of the above test results, it can be concluded that the moisture gradient, where the moisture content of the lower soil layer is higher than that of the upper soil layer, has multiple implications for the geotechnical behavior. In terms of shear strength, a higher moisture content reduces the shear strength. Due to the high moisture content in the lower soil layer, the friction and interlocking forces between particles are diminished. When subjected to shear forces, the lower soil layer may be more prone to relative displacement compared to the upper soil layer. For instance, in the context of slope stability, this moisture gradient may cause the soil at the lower part of the slope to exhibit a tendency to slide first, thereby increasing the risk of slope instability. In terms of compressibility, as water occupies the voids between soil particles, the lower soil layer with a high moisture content is more susceptible to compression under pressure. When an external load is applied, the compressive deformation of the lower soil layer may be greater than that of the upper soil layer, resulting in uneven settlement of the ground. From the perspective of permeability, the presence of the moisture gradient causes water  
Figure 6: Dynamic moisture content—shear displacement curves under different normal stresses. (a) J-T10-1.5; (b) J-T20-1.5; (c) J-T30-1.5; (d) J-T30-1.0. to tend to migrate from the lower layer with a high moisture content to the upper layer with a low moisture content. This water migration can alter the pore structure and particle arrangement of the soil, subsequently affecting the overall permeability of the soil.
## 2 Materials and Methods
### Biochemical Analysis  
#### 2.6.1 Lipid and Fatty Acid Profiles  
The lipid content was assessed using a chloroform-methanol-water combination, following a protocol previously described [41], modified from ref. [42]. A total of 30 mg of biomass was transferred into a 15 mL centrifuge polypropylene tube. Next, a solution containing water (0.5 mL), chloroform (2 mL), and methanol (1 mL) was introduced into the tube and stirred for 2 min. Afterward, another solution of water (2 mL) and chloroform (2 mL) was added to the mixture, which was mixed for an additional 2 min.  
The mixture was centrifuged for 10 min at 8000\(\times\)\(g\), and the organic phase (lipids) was transferred to a pre-weighed tube. The remaining liquid phase and solid biomass were then extracted two more times. The extracts were combined before evaporation using a Speed-Vac centrifugal evaporator ([PERSON](tm) SPD111 SpeedVac(tm), Thermo Fisher Scientific, Waltham, MA, USA) to eliminate the solvent at 40 \({}^{\circ}\)C under a vacuum pressure of 0.95 bar. The residue was weighed to calculate the lipid content and suspended in 200 \(\upmu\)L of chloroform for fatty acid composition analysis [41].  
Equation (4) was used to calculate the lipid content %:  
\[\text{Lipid content}(\%)=\frac{\text{Weight of lipid}(\text{g})}{\text{Weight of dried microalgae biomass}(\text{g})}\times 100 \tag{4}\]  
The fatty acid profile of _C. zofingiensis_ was analyzed using gas chromatography after converting the lipid extract into methyl esters, as previously described [41], based on [PERSON]'s method [43].  
In a screw-capped tube, 800 \(\upmu\)L of 14% boron trifluoride-methanol solution was mixed with 100 \(\upmu\)L of lipid extract. The mixture was heated at 100 \({}^{\circ}\)C for 15 min in a dry bath and then cooled to room temperature. Subsequently, 1500 \(\upmu\)L of MilliQ water and 750 \(\upmu\)L of hexane containing 1% (\(v/v\)) heptadecane as an internal standard were added. After vortexing for 2 min, the solution was allowed to settle, and the methylated fatty acids were collected from the upper phase.  
The analysis was performed using an Agilent 6850 Series II gas chromatographic system (Agilent Technologies, Santa Clara, CA, USA) equipped with a flame ionization detector (FID), a G4513A autosampler, and a polar HP 88 column (112-88A7E) with dimensions of 100 m \(\times\) 0.25 mm \(\times\) 0.2 \(\upmu\)m. One microliter of the product was injected into the system. The oven was preheated to 140 \({}^{\circ}\)C for 5 min, and the temperature was increased by 4 \({}^{\circ}\)C per minute until reaching 240 \({}^{\circ}\)C, where it was maintained for 5 min. The FID and injector temperatures were set at 260 \({}^{\circ}\)C, and nitrogen was used as the carrier gas.  
Data collection and analysis were conducted using ChemStation B.04.03 software. Fatty acid methyl esters were identified and quantified using calibration curves of standards methylated following the same protocol.
## 4 Illustrative Example of Proposed Method
### Determination of the Optimal Wavelet Decomposition Layers  
The comprehensive scores of wavelet denoising with different decomposition layers are listed in Table 4. The smaller the comprehensive score is, the better the denoising effect is. Table 4 indicates that for concrete strain data, the optimal number of decomposition layers  
Figure 6: Decomposition results using best and worst wavelet basis: (**a**) Decomposition results using sym12; (**b**) Decomposition results using bior3.1.  
is five. In terms of subjective selection, it is appropriate to choose four to six decomposition layers.
## 4 Experimental Design and Setup
### Prediction Models
#### 4.2.3 K-Nearest Neighbors  
KNN, which is called instance-based learning (IBK), is classified as a lazy learner. First, KNN is calculated by selecting the closest neighbors in the training data to generate the target data with regard to the feature space. This selection is employed using the Euclidean (or other specified) distance measure. After that, majority voting, or another decision rule, can be performed to categorize the new instances [40].