esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The corrosion rates predicted by the model is compared with corrosion rates obtained using experiments conducted in a deaerated, CO\({}_{2}\)-saturated brine environment. The study was conducted using a three-electrode electrochemical glass cell, and the corrosion rates were measured using LPR and EIS techniques. The flow rates are changed by changing the stirrer speed from 100 to 800 rpm. The authors studied the effect of pCO\({}_{2}\), NaCl concentrations on corrosion rates of the mild steel, and the experimental results show corrosion rates are independent of flowrate at high salinity. The proposed model could not predict this flow insensitive behavior at high flow rates. In this paper, corrosion studies of the pipeline carbon steel (API-5L-X65 QT steel) are done in a flow loop using the LPR method to investigate the parameters which will affect the corrosion rates. Parametric studies have been conducted by changing flow rate, NaCl concentration, temperature, and pH, while bubbling oxygen into the solution. Key to  
Figure 1: Different Corrosion models and predicted corrosion rates (Field data from [22] for the oil well 5). this work, however, was the rigorous control of all parameters save one. This parameter was varied, and the corrosion rate was determined as a function of flow velocity, which was continuously varied from 0 to 0.8 m/s. Upstream pipelines in oil industries exhibit flow fluctuations, and the material degradation will be maximum because of corrosion and erosion. This work focuses on identifying the flow regions where the uniform corrosion is dominating and to predict a range of flow velocities where the corrosion rates can be kept constant. In that flow regions, parametric studies are conducted to develop a correlation with flow conditions and other parameters aiming to develop a predictive equation to obtain corrosion rates. In this research, we intend to measure the corrosion rate in a pipeline system using tight control of parameters. This is necessary, since corrosion rates are affected by so many different parameters, some of which have exponential effects.
## 2 Materials and Methods  
### Device Fabrication  
Shown in Figure 1 is a step-by-step process diagram of the device fabrication. The devices presented in this work were processed on a 6\({}^{\prime\prime}\) Si wafer to demonstrate the scalability of the proposed process. Alignment marks were first etched into n-type silicon wafers (100) for the ASML PAS 5500 i-line stepper, where all standard and lift-off lithography steps were performed thereafter. Following a standard RCA clean, 90 nm of SiO\({}_{2}\) was thermally grown at 1000 \({}^{\circ}\)C. Next, 200 nm of aluminum was thermally evaporated and patterned through a lift-off lithography process to serve as a gate electrode. The evaporation was conducted using a CVC thermal evaporator with 99.95% purity aluminum pellets. A 45 nm layer of PECVD TEOS was then deposited directly on top of the patterned gate electrodes to serve as an alumina etch stop when opening up contact vias. TEOS was patterned and etched away in a Trion Phantom III RIE with 60 sccm of CF\({}_{4}\), CHF\({}_{3}\) and 8 sccm of O\({}_{2}\) with RF power of 200 W to expose the aluminum gate electrode in the active area. Next, 15 nm of Al\({}_{2}\)O\({}_{3}\) was deposited using an Ultratech S200 G2 Savannah atomic layer deposition (ALD) system with 150 cycles. High-quality CVD-grown monolayer graphene on copper foil (6\({}^{\prime\prime}\)\(\times\) 6\({}^{\prime\prime}\)) was commercially purchased from Graphene, Inc. An area of 3\({}^{\prime\prime}\) by 2\({}^{\prime\prime}\) was cut out and transferred onto the surface of the wafer with a double PMMA-assisted process, described in detail in the next section. Graphene was then patterned through standard lithography by oxygen plasma via Trion Phantom III RIE for 60 s. The resist was removed by acetone soak and rinsed with DI water. Ni and Au (5 and 95 nm, respectively) were thermally evaporated sequentially and patterned for source and drain contacts via a lift-off process. Finally, the wafers were coated with photoresist, and contact vias were patterned and etched away to expose the aluminum gate contact pads. This was performed in a two-step process, first etching the Al\({}_{2}\)O\({}_{3}\) in the Plasmatherm ICP Etcher with chlorine-based chemistry and then the etch-stop TEOS layer in the Trion Phantom III with fluorine-based chemistry (details in Section 3). Individual chips containing several graphene FETs and test structures were cut into 1 cm by 1 cm chips using an ADT Dicing Saw 7120 prior to removing the photoresist protecting the graphene in the previous step.
# AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future
## 3 Machine Learning in Physical Layer of IVLC
### Channel Equalization
#### 3.2.1 Pre-Equalization GK-DNN  
Conventionally, one would replace postequalization with pre-equalization to reduce the computational complexity and power consumption at the receiver side. Research works such as a weighted lookup table (WLUT), etc., have been proposed to mitigate the nonlinear distortion in VLC systems [51]. However, LUT-based pre-equalization methods suffer from a massive increase in computational complexity when dealing with high-order and high-ISI communication scenarios. Therefore, researchers have come up with ML-based pre-equalization methods in the field of VLC systems to provide a new way of solving computational problems of LUTs.  
In [52], a pre-equalization method, namely Gaussian kernel-aided deep neural network pre-distortion (GK-DNN-PD), is proposed for a high-order modulated high-speed VLC system. GK-DNN-PD outperforms the LUT-PD in terms of memory depth (MD) and the required training dataset, which leads to lower computational complexity. The experimental results show a 1.56 dB Q-factor gain compared with LUT-PD.  
The proposed GK-DNN-PD method consists of two phases: the training phase and the communication testing phase. In the training phase, the received signal, which is not pre-distorted, will be linearly equalized, giving us the label sets of the GK-DNN channel estimator. Then, the clean transmitted signal with certain MD would be the feature sets. Then, the GK-DNN channel estimator will be trained to obtain the weight and bias of the estimator. Next in the communication testing phase, the weight and bias obtained in the first phase would be used to pre-distort the clean signal that is to be transmitted. Specifically, the difference between the clean signal and the output of the GK-DNN channel estimator is also considered, in addition to the weight and bias during the pre-distortion progress. Additionally, clipping operation is also adopted to reduce the peak to PAPR, which consequently reduces the nonlinear degradation.  
Moreover, an NN-based pre-equalizer is proposed in [53] to mitigate the semiconductor optical amplifier (SOA) pattern effect for 50G PON, confirming the feasibility of NN-based pre-equalizer in intensity modulation and direct detection (IM/DD) system.
## 3 Results and Discussion
### Imputation
#### 3.1.3 Original Data  
Figure 7 shows the count distribution of the ten microbial communities that are most relevant in Shindari fermentation. Original data refers to the raw data we obtained from the Cosmetic Science Center. The count is for 1 st, 3 rd, 5 th, and 7 th day and, as we can see from the figure, the fermentation process makes the counts of the microbial community change continuously.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Regressor Accuracy**} \\ \hline
**Imputation** & \
\begin{tabular}{c} **Mean** \\ **Dissimilarity** \\ \end{tabular} & **SV** & **DT** & **RF** & **SL** &
\begin{tabular}{c} **Kappa** \\ **Error Plots** \\ \end{tabular} \\ \hline GRI-Bag & 0.91 & 65.54 & 78.12 & 79.18 & 73.41 & 0.785 \\ GRI-SE & 0.93 & 67.56 & 76.13 & 79.29 & 72.84 & 0.712 \\ EMB-Bag & 0.86 & 68.98 & 80.29 & 82.51 & 75.82 & 0.854 \\ EMB-SE & 0.82 & 70.12 & 81.66 & 82.78 & 77.15 & 0.844 \\ RFI-Bag & 0.46 & 78.77 & 82.87 & 95.03 & 71.67 & 0.513 \\ RFI-SE & 0.43 & 71.34 & 85.67 & 94.37 & 71.02 & 0.490 \\ MICE-Bag & 0.51 & 71.91 & 88.42 & 91.12 & 78.44 & 0.566 \\ MICE-SE & 0.56 & 75.23 & 88.99 & 93.97 & 76.35 & 0.519 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mean dissimilarity, regressor accuracy and Kappa error plots for different imputation methods. SV—support vector; DT—decision tree; RF—random forest; SL—simple linear; Bag—bagging ensemble; SE—stacking ensemble.  
Figure 7: Original count of the microbial community.
## 2 Material and Methods
### Simulation of Micron-Sized Droplet on Different Powder Bed  
To explore the common rule of droplet impact affected by particle size and wettability of powder, the spreading behavior of micro droplets on powder beds with different contact angles and particle sizes were simulated. And the effects of particle size and wettability of powder by binder on the droplet impact and the dynamic characteristics of droplet spreading were studied and analyzed. The results are supposed to provide process parameter guidance for the application of new materials in 3D printing.  
Since the Weber number (which was 8.58 in Table 1) of this section is close to that of the experiment of Section 2.3 (which was 30.52), based on the similarity theory [50], the validated VOF model can be applied to the study of this section.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & _DPI_ & \begin{tabular}{c} **Layer Thickness** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **Particle Mean Size** \\ **(\(\mu\)m)** \\ \end{tabular} & \
\begin{tabular}{c} **Powder Material** \\ \end{tabular} &
\begin{tabular}{c} **Binder** \\ \end{tabular} \\ \hline A & 80 & 0.1 & 40 & HA & Phosphoric acid/Polyvinyl alcohol \\ B & 100 & 0.1 & 40 & HA & Phosphoric acid/Polyvinyl alcohol \\ C & 120 & 0.1 & 40 & HA & Phosphoric acid/Polyvinyl alcohol \\ D & 140 & 0.1 & 40 & HA & Phosphoric acid/Polyvinyl alcohol \\ E & 160 & 0.1 & 40 & HA & Phosphoric acid/Polyvinyl alcohol \\ F & 180 & 0.1 & 40 & HA & Phosphoric acid/Polyvinyl alcohol \\ \hline \hline \end{tabular}
\end{table}
Table 2: 3D printing parameters.  
In practice, the particle size of the powder used in 3D printing varies from sub-micrometers to hundreds of micrometers. In these simulations, powder bed substrates were built with six different particle sizes, including 200 \(\upmu\)m, 100 \(\upmu\)m, 50 \(\upmu\)m, 25 \(\upmu\)m, 10 \(\upmu\)m, and 5 \(\upmu\)m. The contact angles were selected to be 30\({}^{\circ}\), 60\({}^{\circ}\), 90\({}^{\circ}\), 120\({}^{\circ}\) to define the wettability of powder materials, which covers the typical range of powder materials. The binder droplet impact velocity was 2 m/s and a diameter of 100 \(\upmu\)m. The binder's dynamic viscosity was 1.42 mPa\(\cdot\)s; density was 1.03 \(\times\) 10\({}^{3}\) kg/m\({}^{3}\), and the surface tension was 48 mN/m. The height of the binder droplet from the powder bed was 0.05 mm for all simulations. The two-dimensional numerical simulations of a single microscopic droplet impacting a multi-particle powder bed were carried out.  
In the simulation results analysis, a nondimensionalized \(\delta\) was defined as the ratio of droplet diameter to powder particle diameter, \(\delta\) is as follows:  
\[\delta=\frac{D_{0}}{d_{s}}\]  
where \(D_{0}\) is the initial diameter of the binder droplet, \(d_{s}\) is the particle/sphere size of the powder bed.
## 3 Adaptive Distributed Student's T EKF
### Distributed Student's \(T\) EKF  
Within this subsection, we will derive the distributed Student's t EKF, utilizing the model (2) and (3). In this work, we first design the following joint density:  
\[p\Big{(}\mathbf{s}_{t}^{(i)},\mathbf{v}_{t}^{(i)}|r_{1:t}^{(i)}\Big{)}=St \Bigg{(}\left[ \begin{array}{c}\mathbf{s}_{t}^{(i)}\\ \mathbf{v}_{t}^{(i)}\end{array} \right];\left[ \begin{array}{c}\mathbf{\hat{ s}}_{t}^{(i)}\\ \mathbf{0}\end{array} \right],\left[ \begin{array}{c}\mathbf{\hat{p}}_{t}^{(i)} &\mathbf{0}\\ \mathbf{0}&\mathbf{Q}_{t}^{(i)}\end{array} \right],\eta_{3,t}\Bigg{)}\, \tag{9}\]  
With the EKF, we can obtain the one-step estimation as follows:  
\[\mathbf{\hat{s}}_{t}^{(i)-}\mathbf{=}\mathbf{A}\mathbf{\hat{s}}_{t}^{(i)}\, \tag{10}\]  
\[\mathbf{\hat{P}}_{t}^{(i)-}\mathbf{=}\mathbf{A}\mathbf{\hat{P}}_{t}^{(i)} \mathbf{A}^{T}\mathbf{+}\mathbf{Q}_{t}^{(i)}\, \tag{11}\]  
\(\eta_{3,t}\) is retained, and we obviously recover the EKF time update. Next, we consider the following joint pdf:  
\[p\Big{(}\mathbf{s}_{t}^{(i)},\mathbf{v}_{t}^{(i)}|r_{1:t-1}^{(i)}\Big{)}=St \Bigg{(}\left[ \begin{array}{c}\mathbf{s}_{t}^{(i)}\\ \mathbf{v}_{t}^{(i)}\end{array} \right];\left[ \begin{array}{c}\mathbf{\hat{ s}}_{t}^{(i)-}\\ \mathbf{0}\end{array} \right],\left[ \begin{array}{c}\mathbf{\hat{P}}_{t}^{(i)-} &\mathbf{0}\\ \mathbf{0}&\mathbf{R}_{t}^{(i)}\end{array} \right],\eta_{3,t-1}\Bigg{)}\, \tag{12}\]  
Then, we can compute the following joint pdf:  
\[p\Big{(}\mathbf{s}_{t}^{(i)},\mathbf{v}_{t}^{(i)}|r_{1:t-1}^{(i)}\Big{)}=St \Bigg{(}\left[ \begin{array}{c}\mathbf{s}_{t}^{(i)}\\ r_{t}^{(i)}\end{array} \right];\left[ \begin{array}{c}\mathbf{\hat{s}}_{t}^{(i)- }\\ \mathbf{G}_{t}^{(i)}\mathbf{\hat{s}}_{t}^{(i)-}\end{array} \right];\left[ \begin{array}{c}\mathbf{\hat{p}}_{t}^{(i)-}\\ \mathbf{G}_{t}^{(i)}\mathbf{\hat{p}}_{t}^{(i)-}\end{array} \right],\left[ \begin{array}{c}\mathbf{\hat{p}}_{t}^{(i)-}\\ \mathbf{G}_{t}^{(i)}\mathbf{\hat{p}}_{t}^{(i)-}\end{array} \right],\left[ \begin{array}{c}\mathbf{\hat{p}}_{t}^{(i)-}\\ \mathbf{G}_{t}^{(i)}\mathbf{\hat{p}}_{t}^{(i)-}\end{array} \right],\left[ \begin{array}{c}\mathbf{\hat{p}}_{t}^{(i)-}\Big{(}\mathbf{G}_{t}^{(i)} \Big{)}^{T}+\mathbf{R}_{t}^{(i)}\\ \mathbf{S}_{t}^{(i)}\end{array} \right],\eta_{3,t-1}\Bigg{)}\, \tag{13}\]  
where \(\mathbf{G}_{t}^{(i)}=\frac{\partial g\big{(}\mathbf{s}_{t}^{(i)-}\big{)}}{ \partial\mathbf{s}_{t}^{(i)}}\). The updated parameters of the t EKF can be computed by the following equations:  
\[\eta_{3,t}=\eta_{3,t-1}+m_{r_{t}^{(i)}}\, \tag{14}\]  
where \(m_{r_{t}^{(i)}}\) is the dimension of \(r_{t}^{(i)}\).
# Study About the Performance of Ascon in Arduino Devices
## 5 Performance Analysis of the Ascon Implementation  
Once we presented a detailed description of the Ascon suite in relation to its authenticated encryption and hashing functionalities (Section 2), as well as its main features as a symmetric cryptosystem that make it especially useful in IoT environments (Section 3), and some aspects related to its security, in particular, the assertion that the considered encryption versions can be considered secure (Section 4). In this section, we will present a performance study of the Ascon reference versions in C obtained from the official Github repository [57].
## 2 Framework Design
For data providers, they can share their data resources on demand through data services (for example, they can define a data-processing service to extract part of the data from the specified data set for sharing), so that they can share the data and control the data, so as to ensure the security of data sharing. For users, data services shield the details of data sources and data processing, and when facing diverse data requirements, users only need to focus on data-processing logic, which greatly reduces the difficulty of using data.
# The Architecture of the Access Protocols of the Global Infocommunication Resources +
## 2 Materials and Methods
### Step II. Data Transfer from the Terminal Device to the Base Station  
The process of transferring data blocks from the terminal device to the base station is represented by a two-phase queuing system (see Figure 3):  
* Simulates the process of transferring user data blocks from/to terminal devices to/from the radio network aggregator;
* Simulates the process of transmitting user data blocks from/to the aggregator of access radio networks to/from the base station of the selected radio network.  
The following notation will be used later in the article:  
* Intensity of the flow of user data units coming from a single terminal device [1/s];
* The number of terminal devices that are within the range of a single radio network aggregator;
* Number of radio network aggregators;
* Number of access radio networks;
* Length [bits] of user data blocks to transmit;
* Probability of receiving a refusal to provide the service at the Ist step;
* Probability of getting permission to provide the service at step I;
* Bit rate on phase 1 and 2 [bit/s];
* Mathematical expectation of the allowed time of information aging at phase 1 and 2, respectively [s].  
Consider that:  
* At the output of each terminal device for transmitting over the network access to the base station a uniform simple stream of user data blocks is formed \(l\) [bit]. Flow intensity \(\mu\) [1/s] and it is a Bernoulli flow with the parameter \(q_{1}\) on interval \(T_{1}.T_{1}\) which we will use as the minimum clock cycle for simulating the transfer of user data blocks in phase 1 \(T_{1}=1/V_{1}\) [s];At the output of each radio network aggregator for transmission to the base station, a stream of uniform user data blocks for transmission is formed, which form a Bernoulli-type stream with the parameter \(q_{2}\) on interval \(T_{2}\) which we will use as the minimum clock cycle for simulating the transfer of user data blocks in phase 2 \(T_{2}=1/V_{2}\)[s];
* Direct transmission of user data blocks is performed (i.e., feedback is not provided). Transmitted user data blocks that were damaged in phase 1 and 2 when transmitting over the radio channel are discarded when receiving;
* In the 1 st and 2 nd phases of the second step of the scenario, multiple access of terminal and aggregating devices to the transmission medium is implemented using TDM technology.
## 3 Numerical Results  
In this section, the impact of ICXT on the performance of optically amplified inter-DCI systems with PAM4 format is assessed through the OP. The system simulation parameters used throughout this work are presented in Table 1.  
The attenuation coefficient and dispersion parameters of the MCF cores are assumed to be equal. The electrical and optical receiver filters bandwidth were optimized in back-to-back operation to maximize the receiver sensitivity. Then, for each link length, the signal power at the transmitter output has been set to achieve the BER of \(3.8\times 10^{-5}\) in the absence of ICXT, which is two orders of magnitude below the BER limit in the presence of ICXT, similarly to what has been done in [22]. We consider a BER limit of \(3.8\times 10^{-3}\) in the presence of ICXT, since it is the most common BER limit used for datacenters connections with forward-error correction [30; 47]. The high number of PMPs is chosen to characterize the ICXT statistics rigorously [11]. Two different skew-symbol rate products (SSRPs) are analyzed: (i) \(|S_{mn}\cdot R_{s}|\gg\)1, where the symbol rate of the PAM4 signal is much higher than the ICXT decorrelation bandwidth [14], and the ICXT creates amplitude levels in the received eye-pattern that seem to exhibit a \"noise\"-like behavior [21], and (ii) \(|S_{mn}\cdot R_{s}|\ll\)1, where the symbol rate of the PAM4 signal is much lower than the ICXT decorrelation bandwidth [14] and well-defined amplitude levels in the eye-patterns are created due to ICXT [21].  
Throughout this section, the OP dependence of the inter-DCI PAM4 link with MCF supported transmission on the MCF length, SSRP, ICXT level, and number of interfering cores is studied.
# A Gene Ontology-Based Pipeline for Selecting Significant Gene Subsets in Biomedical Applications
## 2 Materials and Methods  
### Experimental Dataset  
During the simulation procedure, gene expression data from patients with various cancer types were utilized. These data are publicly available on The Cancer Genome Atlas (TCGA) website [[https://www.cancer.gov/tcga](https://www.cancer.gov/tcga)]([https://www.cancer.gov/tcga](https://www.cancer.gov/tcga)) (accessed on 12 June 2024) and were obtained using RNA sequencing (RNA-seq). The classification of cancer types and the corresponding numbers of samples are presented in Figure 1.  
Initially, the dataset comprised 6344 samples and 60,660 genes. Data preprocessing was carried out in the R programming environment (version 4.4.1) [17] using appropriate modules from the Bioconductor package [[https://bioconductor.org/](https://bioconductor.org/)]([https://bioconductor.org/](https://bioconductor.org/)) (accessed on 16 November 2024) [18; 19; 20]. A detailed step-by-step workflow for the formatting, preprocessing, and normalization of gene expression data is shown in Figure 2.  
The dataset was structured as a matrix of size 6344 \(\times\) 60,660, where each row represents a sample and each column corresponds to a gene. Duplicate entries and incorrect gene identifiers were removed based on the organism annotation.  
Gene identifiers in Ensembl format were transformed to Entrez IDs to ensure compatibility with the org.Hs.eg.db database [[https://bioconductor.org/packages/org.Hs.eg.db](https://bioconductor.org/packages/org.Hs.eg.db)]([https://bioconductor.org/packages/org.Hs.eg.db](https://bioconductor.org/packages/org.Hs.eg.db)) (accessed on 22 Desember 2024) [21]. This conversion included extracting gene-specific identifiers, mapping Ensembl IDs to Entrez IDs using the biomaRt package [[https://bioconductor.org/packages/release/bioc/html/biomaRt.html](https://bioconductor.org/packages/release/bioc/html/biomaRt.html)]([https://bioconductor.org/packages/release/bioc/html/biomaRt.html](https://bioconductor.org/packages/release/bioc/html/biomaRt.html)) (accessed on 25 November 2024) [22], and validating the results via org.Hs.eg.db to eliminate incorrect or unmatched entries.  
Figure 1: Distribution of cancer types in the TCGA dataset: number of samples in each category.  
Figure 2: Step-by-step workflow for gene expression data preprocessing and normalization.  
Normalization was applied to raw transcript counts, which originally ranged from 0 to 12,581,910. The pipeline included a counts-per-million (CPM) transformation with log scaling, removal of genes with zero variance, and scaling adjustment by adding a small constant to avoid negative values.
## 3 Mathematical Formulation
### Cooperative Game-Theory Profit Allocation Method
#### 3.4.1 Nucleolus-Based Method  
The goal of the nucleolus allocation method is to minimize the maximum regret, i.e., the worst-case profit increment \(\delta\) among all coalitions. Suppose \(X=\{x_{1},x_{2},\ldots,x_{n}\}\) denotes the set of allocated profit for each player in the game, \(Y=\{y_{1},y_{2},\ldots,y_{n}\}\) denotes the set of given profit allocations, and \(V(S)\) denotes the profit increment produced by the alternation of players in the coalition \(S\). The detailed formulation is shown as follows [18]:  
\[\min\delta\] \[s.t. \ V(S)=\sum_{i\in S_{1}}y_{i} \tag{37}\] \[V(S)-\sum_{i\in S_{2}}y_{i}\leq\delta \tag{38}\]  
Finally, the profit allocation for each player can be calculated by the following:  
\[x_{i}=v(i)+y_{i} \tag{39}\]