esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This text proposes a Vivaldi structure array antenna, using a power divider structure. The composition includes an antenna array with four antennas, suitable for a wideband array structure antenna in the 100 GHz frequency band. The goal is to address the challenges faced by monolithic systems in modern wireless communications, particularly the issue of the inapplicability of antennas on silicon substrates. The Vivaldi antenna was chosen for its wide bandwidth, high efficiency, and stable radiation pattern. It combines the characteristics of a wide scanning angle and ultra-wide bandwidth. Through integration with CMOS technology, the developed antenna achieved a bandwidth of 85.47-102.40 GHz. The peak gain reached \(-4\) dBi, corresponding to a bandwidth of 17.7%. And the antenna volume was only 1.2 mm \(\times\) 1.2 mm, demonstrating its immense potential in high-frequency wireless applications.  
CMOS process; Vivaldi; antenna on chip +
Footnote †: journal:  
0000-0002-4500-4150][PERSON]. [PERSON] and [PERSON]  
0000-0002-0002-3999-2291]K.-C. A Wide Bandwidth Vivaldi Antenna Suitable for 5G/6G Communication Utilizing a CMOS 0.18 \(\mu\)m Process. _Plascom_**2024**, 5, 400-415. [[https://doi.org/10.3390/1400-4150-4](https://doi.org/10.3390/1400-4150-4)]([https://doi.org/10.3390/1400-4150-4](https://doi.org/10.3390/1400-4150-4))
## 3 Results and Discussion
### Effect of Carbonation by SCD on Converter Slag
#### 3.1.2 Absorption and Density  
Table 4 summarizes the absorption capacity, apparent density, and skeletal density of converter slag before and after the reaction with SCD. The absorption capacity of as received converter slag was 1.78%, the apparent density was 3.07 g/cm\({}^{3}\), and the skeletal density was 3.41 g/cm\({}^{3}\). The absorption capacity of reacted converter slag was 2.52%, the apparent density was 3.15 g/cm\({}^{3}\), and the skeletal density was 3.54 g/cm\({}^{3}\).  
\begin{table}
\begin{tabular}{c c c} \hline \multicolumn{3}{c}{**Sieve Size**} & \multicolumn{1}{c}{**Mass (\%)**} \\ \hline
**Passing** & **Retained on** & **Mass (\%)** \\ \hline
4.75 mm (No.4) & 2.36 mm (No.8) & 10 \\ \hline
2.36 mm (No.8) & 1.18 mm (No.16) & 25 \\ \hline
1.18 mm (No.16) & 600 \(\upmu\)m (No.30) & 25 \\ \hline
600 \(\upmu\)m (No.30) & 300 \(\upmu\)m (No.50) & 25 \\ \hline
300 \(\upmu\)m (No.50) & 150 \(\upmu\)m (No.100) & 15 \\ \hline \end{tabular}
\end{table}
Table 3: Gradation of converter slag used in this work (as specified in ASTM C 1260).  
The reaction with SCD increased absorption capacity of converter slag. Such result indicates that the volume of porous network (most likely crack in this case) has been increased by the reaction with SCD. It is due to the expansive reaction with SCD from free lime and periclase that is similar to the case from crystallization of portlandite or brucite in pore structure [39]. As water and dissolved CO\({}_{2}\) migrates into the site where free lime or periclase is located, reaction occurs with SCD, and CaCO\({}_{3}\) or MgCO\({}_{3}\) is produced either from direct reaction or through secondary reaction, which takes two separate processes by (1) earlier formation of Ca(OH)\({}_{2}\) and Mg(OH)\({}_{2}\) and (2) their later conversion into CaCO\({}_{3}\) and MgCO\({}_{3}\). Since density of the portlandite, calcite, aragonite, brucite, and magnesite are 2.23, 2.71, 2.93, 2.39, and 3.00 g/cm\({}^{3}\), respectively, all these reaction products occupy more space than free lime and periclase whose densities are 3.34 and 3.78 g/cm\({}^{3}\), respectively, thereby causing expansion in converter slag after reaction with SCD. The increase in apparent and skeletal densities after reaction with SCD is mainly associated with transition from portlandite to calcite and aragonite.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Absorption (\%)**} & **Apparent Density** & **Skeletal Density** \\  & & **(g/cm\({}^{3}\))** & **(g/cm\({}^{3}\))** \\ \hline As received & 1.78 & 3.07 & 3.41 \\ \hline Reacted SCD & 2.52 & 3.15 & 3.54 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Absorption, apparent density, and skeletal density of converter slag reacted with supercritical carbon dioxide.  
Figure 3: XRD pattern of converter slag reacted with supercritical carbon dioxide.
# Denial-of-Service Attacks on Permissioned Blockchains: A Practical Study
## 4 Methodology
### Threat Model for HLF
#### 4.1.1 Threats to Ordering Service  
The ordering service is a crucial component that is responsible for managing the sequencing of transactions and creating blocks for validation by peers. If this component is compromised, the entire network's transaction flow can be disrupted. The ordering service in HLF groups a certain number of transactions into a block before it is sent for validation. An attacker may manipulate the block size configuration by increasing it to an abnormally large value, delaying the block formation [54]. Alternatively, setting a very small block size forces the orderer to create and distribute blocks frequently, consuming network and computational resources unnecessarily. As a result, the transaction throughput decreases significantly as large blocks introduce latency, and smaller blocks create processing overhead for validating nodes. Similarly, an attacker with privileged access can programmatically discard blocks before distribution. This attack causes transaction inconsistencies, delays in consensus, and incomplete ledger states [55]. Block discarding can be selective or global. In the former case, only specific clients or transactions are tar geted, affecting their processing. In the latter case, all transactions face block loss, creating major disruptions.
# Data Driven Approach for Eye Disease Classification with Machine Learning
## 3 Methodology Adopted
### Data Pre-Processing  
In order that machine learning be performed in accordance with medical rules, pre-processing is required that involves data cleaning and normalization, noisy data filtering and handling of missing values [16]. It is important to mention that data pre-processing highly affects the performance of machine-learning algorithms and, if not performed properly, it may produce biased output [49]. The Weka knowledge analysis tool provides various pre-processing and transformation algorithms.  
Feature selection and extraction are aimed at a more robust machine-learning process by identifying and removing irrelevant attributes from the dataset to reduce dimensionality and improve performance. However, ignoring a small symptom may have serious consequences. Fortunately, standard taxonomies eliminate unnecessary data and, therefore, no feature is ignored in analysis and diagnosis.  
Missing values may also adversely affect machine learning. In this study, two techniques were used for handling missing values, namely, deletion of records with more than 60% missing values and a two-step diagnostic method using segmentation, which will be explained later.
## 4 Discussion  
Both the presence and the level of volatile compounds in kiwiberry juice and its juice mixes were crucial. It was found that the pattern of volatile substances influenced the sensory image of the examined samples. In juices, the diversity of sensory perceptions reported results from qualitative and quantitative perceptual interactions between odorants. According to [PERSON] and [PERSON] [28], the aroma is not just the sum of individual constituents, but the result of complex interactions between numerous chemical compounds. Volatile compounds can interact, showing either additive or competitive effects, which may turn even into synergistic or antagonistic effects [29]. Fruit and juice aroma can vary considerably in different fruit cultivars [30; 31; 32] and origins [17; 33] as well as in different growing system types (organic vs. conventional) [34; 35], and types of postharvest treatment and processing [36; 37; 38]. According to the literature, the esters, alcohols, aldehydes, ketones, and ethers are the most dominant chemical groups detected in the headspace of different apple cultivars, while in pear, esters are the major volatile components, followed by aldehydes, alcohols and ketones [39; 40]. Comparable volatile compounds' chemical groups were reported previously, which are in close agreement with our results. The aroma profile of kiwiberry juice was dominated by green and grassy aroma notes, similar to the whole fruit, as reported in previously conducted studies [22; 23]. The compounds which are mainly responsible for these characteristic aroma notes is hexanal and 1-hexanol, which are formed by the enzymatic breakdown of unsaturated fatty acid during tissue disruption [41; 42]. Moreover, C6 compounds (C6 aldehydes and C6 alcohols) are regarded as green leaf volatiles and
# Impact of Industrial Application of Fast Carbonation of Recycled Concrete Aggregates
## 1 Introduction  
The main objective of the National FastCarb Project (fastcarb.fr) was to study how to store CO\({}_{2}\) in recycled concrete aggregates (RCAs) to improve their quality by clogging the porosity, increase their use in new concrete, and thus, reduce the CO\({}_{2}\) impact of concrete constructions. The objectives of the project targeted two important points for the construction sector: (i) the promotion of the circular economy by integrating the recycling of construction materials with a goal for the construction sector to recover 70% of its waste by 2030 in France and (ii) the reduction of greenhouse gas emissions by promoting low-carbon-footprint constructions over their entire life cycle.  
The idea was to use recycled concrete aggregates from the demolition of concrete structures as a \"carbon sink\" by taking advantage of the spontaneous tendency of hydrated cement paste to fix atmospheric CO\({}_{2}\). A large number of publications showed the potential interest of this technique in the laboratory (see [1; 2; 3; 4; 5; 6; 7; 8] for instance).  
A part of the results of the FastCarb project was already published [9; 10]. Here, the focus was on two original contributions that were not present in other papers on the accelerated carbonation of RCA. The first one concerned the industrial scale-up of the process with the description of two demonstrators that allowed for carbonating large quantities of recycled aggregates and then the manufacture and characterization of concrete using these aggregates. Then, using data from the industrial demonstrators, the gain in the CO\({}_{2}\) impact of one cubic meter of concrete was estimated.
## 2 System Structure
### Server  
In the proposed system, the server is used to match the Rx and Tx information and deliver it to the user terminals via Wi-Fi. The Rx information includes the pseudorange measurements, corresponding transmitting time, PRN and channel number. This information is collected from Rx and updated on-line since pseudorange measurements and available satellites vary with time. The other reason for on-line updating is that the one-to-one relationship of channel number and its corresponding PRN is dynamically adjustable rather than fixed. The Tx information includes the position of each antenna which are required for position calculation. It also includes the identifier of transmitting antenna which relates to channel number. Using the identifier, the server can match the transmitting antenna for pseudorange measurement and PRN as shown in Figure 3. In addition, the transmitting antenna is likely to be far away from the receiving antenna due to a reasonable antenna distribution. As a result, the signal propagation time in the cable connecting transmitting antenna cannot be ignored. Therefore, the stored information also contains the delay correction of each channel referring to the signal propagation time delay.
# A Simplified Matrix Formulation for Sensitivity Analysis of Hidden Markov Models
## 4 Sensitivity Analysis of Smoothing Probability
### Transition Parameter Variation  
In the simplified matrix formulation, the sensitivity function \(p(\textbf{y}_{\textbf{e}}^{t+1:T}|x_{v}^{t})(\theta_{a})\) for transition parameter \(\theta_{a}=a_{r,s}\) can be represented as follows:  
\[p(\textbf{y}_{\textbf{e}}^{t+1:T}|x_{v}^{t})(\theta_{a})=A(\theta_{a})O^{t+1} p(\textbf{y}_{\textbf{e}}^{t+2:T}|x_{z}^{t+1})(\theta_{a})\]  
For \(t=T-1\), \(p(\textbf{y}_{\textbf{e}}^{T+1:T}|x_{z}^{T})(\theta_{a})=1\), the recursive probability expression reduces to the following:  
\[p(\textbf{y}_{\textbf{e}}^{T:T}|x_{v}^{T-1})(\theta_{a})=A(\theta_{a})O^{t+1}\]  
The proposed technique for the sensitivity analysis of the backward probability sensitivity function \(p(\textbf{y}_{\textbf{e}}^{t+1:T}|x_{v}^{t})(\theta_{a})\) for the transition parameter is formulated in Algorithm 4.  
```
0:\(A,O,\Gamma,\textbf{e}\), and \(\theta_{a}\)
0:\(B^{T},\cdots,B^{t}\)
1:\(\tilde{A}\gets A\)
2:\(\tilde{A}_{r,s}\gets 0\)
3:\(\tilde{A}_{r,s}\leftarrow\tilde{A}_{r,s}\cdot/(1-a_{r,s})\)
4:\(\widehat{A}\gets A-\widehat{A}\)
5:\(\widehat{A}_{r,s}\leftarrow\widehat{A}_{r,s}\cdot/a_{r,s}\)
6:\(B^{T}\leftarrow\textit{ones}(n,1)\)
7:for\(k=T-1\) to \(t\)do
8:\(O^{k+1}\gets diag(O_{:,\textbf{e}^{k+1}})\)
9:\(\textit{Btmp}_{1}\leftarrow[\widehat{A}*O^{k+1}*B^{k+1},\textit{zeros}(n,1)]\)
10:\(\textit{Btmp}_{2}\leftarrow[\textit{zeros}(n,1),\widehat{A}*O^{k+1}*B^{k+1}]\)
11:\(B^{k}\gets\textit{Btmp}_{1}+\textit{Btmp}_{2}\)
12:Return\(B^{T},\cdots,B^{t}\)
```  
**Algorithm 4** This computes the coefficients of the backward probability sensitivity function \(p(\textbf{y}_{\textbf{e}}^{t+1:T}|x_{v}^{t})(\theta_{a})\) in backward matrices \(B^{k}\) for \(k=T,\cdots,t\) using SMF.
## 3 Simulation Results and Analysis
### Overall Three-Dimensional Stress Field of the Model  
The three-dimensional stress field of the model exhibits significant directional variations in distribution, yet the magnitudes and forms of this distribution remain essentially consistent. In all three dimensions, the stress concentrations are notably higher in the vicinity of the shaft. Proximity to the horsehead roadway correlates with an increased density in the contour distribution and elevated stress values, as demonstrated in Figure 6.  
In the Z-direction stress field of the model, the MSD of 0.5 m at the horsehead roadway's roof manifests a stress concentration around the shaft, with contour density and magnitude increasing nearer to the roadway. Here, the peak stress approaches 200 MPa at the shaft-roof interface. The stress contours diverge horizontally and align with the roadway's orientation. Beyond this focal area, the Z-direction stress within the rock mass stabilizes between 15 MPa and 20 MPa, akin to the gravitational load (15.8 MPa) applied at the model's summit, suggesting minimal influence from the roadway's roof subsidence at these locations (see Figure 6a). With a subsidence displacement of 1.0 m at the roadway's roof, the stress field pattern resembles that observed at 0.5 m subsidence, but the maximum stress at the shaft-roof junction escalates to approximately 240 MPa. Beyond a 33 m radial distance from the roadway, the stress imparted on the shaft diminishes to below 60 MPa. Within the rock mass, Z-direction stress levels range from 20 MPa to 30 MPa, representing a marked increase from the 0.5 m subsidence scenario (see Figure 6b). At a subsidence level of 1.5 m for the roadway's roof, the peak Z-direction stress intensifies to nearly 280 MPa. However, similar to previous observations, the stress on the shaft beyond 33 m remains  
Figure 6: Three directional stress field of the model (MPa).  
under 60 MPa, and within the rock mass, stress persists within the range of 20 MPa to 30 MPa (see Figure 6c).  
In the model's X and Y-direction stress fields, significant differentiation is evident only in the directional distribution, whereas the magnitudes and structural patterns of these distributions generally concur. With the horsehead roadway's roof subsidence displacement at 0.5 m, the X and Y-direction stress distributions mirror those in the Z-direction; stress intensifies in proximity to the shaft and escalates closer to the roadway interface. The peak stress magnitudes at the shaft-roof juncture reach approximately 110 MPa in the X-direction and 320 MPa in the Y-direction. As the subsidence displacement increases to 1.0 m, the X and Y-direction stress distributions retain the pattern observed at the 0.5 m level, yet the maximum stress values rise to nearly 160 MPa and 460 MPa, respectively, as depicted in Figure 6e,h. At a subsidence displacement of 1.5 m for the roadway's roof, the stress values augment to about 190 MPa in the X-direction and 500 MPa in the Y-direction, as shown in Figure 6f,i.
## 2 Dynamic Response of a Multi-Story STMF with SMAs
### An Alternative Design Approach
Case DC2 leads to a more flexible structure, but SMA strains do not exceed superelasticity limits. The maximum inter-story drifts diagram (Figure 9b) shows a rather uniform distribution for the DC1 case with reduced drifts at upper floors and an uneven one for the DC2 case. Case DC3 leads, once again, to a special segment with increased strength that cannot rotate freely and lets the SMAs incorporated deform, resulting in the uneven drift distribution depicted in Figure 9, which is an unwanted behavior. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**DC1**} & \multicolumn{2}{c}{**DC2**} & \multicolumn{2}{c}{**DC3**} \\ \hline \multirow{2}{*}{**Floor**} & **Displacement** & \multirow{2}{*}{**Strain**} & **Displacement** & \multirow{2}{*}{**Strain**} & **Displacement** & \multirow{2}{*}{**Strain**} \\  & **(m)** & & **(m)** & & **(m)** & **(m)** \\ \hline
9 & Linear & \multirow{2}{*}{Negligible} & \multirow{2}{*}{0.27} & \multirow{2}{*}{9.9\%} & Linear & \multirow{2}{*}{Negligible} \\
8 & Linear & & & & Linear & \\ response & & & & & response & Negligible \\
7 & 0.05 & 1.8\% & 0.2 & 7.4\% & & \\  & 0.1 & 3.6\% & 0.18 & 6.6\% & & \\
5 & 0.15 & 5.5\% & 0.17 & 6.3\% & & \\  & 0.18 & 6.6\% & 0.16 & 5.9\% & & \\  & 0.18 & 6.6\% & 0.12 & 4.4\% & & \\  & 0.15 & 5.5\% & 0.12 & 4.4\% & & \\  & 0.12 & 4.4\% & 0.1 & 3.6\% & & \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Maximum SMA displacements for K1E for the configurations investigated. Figure 9: Comparison of maximum inter-story drifts for: (**a**) K1E; (**b**) IVE. For the Kobe and Chichi earthquakes, the incorporation of case DC1 SMAs (Tables 16 and 17) shows their activation at all floors. Compared to the corresponding values of \(V_{ne}\) dimensioning (Tables 8 and 9), it can be observed that SMA strains at upper floors decreased and at lower floors increased. Case DC2 SMA strains are also within the limits of superelasticity. Maximum inter-story drift diagrams (Figure 10a,b) show a uniform distribution for the DC1 case, with reduced drifts at upper floors compared to the conventional structure and the structure with SMAs according to \(V_{ne}\) drifts, and an uneven one for the DC2 case. Case DC3 shows an uneven drift distribution for the Kobe earthquake while for the Chichi earthquake, the distribution is uniform. For the Landers earthquake, the incorporation of case DC1 SMAs (Table 18) shows that at upper floors (7 th, 8 th, and 9 th floors), they are activated in their elastic range as the strains that they exhibit are negligible, while at lower floors, they exhibit strains ranging from 1.1% to 1.8%. Case DC2 SMA strains are within the limits of superelasticity. The maximum inter-story drift diagram (Figure 11) depicts again a highly uneven distribution not only when SMAs are incorporated but also for the conventional structure.
## 3 Results
### Light Dependence Curves, Carbon Exchange and Vegetation Indices
Figure 10: Comparison between measured gross primary production (Tower) and MODIS gross primary production (MODIS) in **(a,b)** Monthly GPP boxplot (gC m\({}^{-2}\) day\({}^{-1}\)) for eddy covariance observed data (Tower) and MODIS—derived data (Satellite). The points refer to the outliers of the data, while the central lines refer to the median of each month. Figure 9: Pearson’s linear correlation between the GPP data measured in the tower and the vegetation indices: NDVI **(a)** and EVI **(b)**. The hatched area indicates the 95% confidence interval. The carbon balance on the site (Figure 12a) is associated with local water availability (Figure 12b), where the greatest absorption (most negative NEE) occurred in 2006 (\(-979\) g C m\({}^{-2}\) year\({}^{-1}\)), which is later than the wettest year on the site (2005), which had an accumulated annual rainfall of 2880 mm year\({}^{-1}\). The year with the lowest absorption (least negative NEE) was 2008 (\(-27.6\) g C m\({}^{-2}\) year\({}^{-1}\)), which is after the year with the lowest annual rainfall (2105 mm year\({}^{-1}\)) in 2007, indicating that the site's water availability can control the annual carbon balance in subsequent years.
# Cybersecurity Model Based on Hardening for Secure Internet of Things Implementation
## 3 Analysis of Previous Works
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Top** & **Proposals** & **Description** & **Reference** \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{Based on OWASP} & Leveraging based on OWASP is focus on using & \\  & & the IoT Top 10 2018 to do an analysis of & [9; 12; 13; 17; 18; 26; 27; 28; 29] & \\  & & IoT vulnerabilities. & & \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{Based on CVE} & It is a list of records. With an identification & \\  & & number, description, for known & \\  & & cybersecurity vulnerabilities. & & \\ \hline \multirow{2}{*}{3} & \multirow{2}{*}{Based on ISO} & The use of ISO standards allows determining & \\  & & risks to specific elements of IoT. & & [20; 30; 31; 33; 34] \\ \hline \multirow{2}{*}{4} & \multirow{2}{*}{Based on risk analysis} & It focuses on the analysis of the use of & \\  & & ISO standards. & [16; 19; 21; 24; 25; 32; 35; 37; 38; 39; 43] & \\ \hline \multirow{2}{*}{5} & \multirow{2}{*}{Based on vulnerability management} & The approach allows to identify which are the & \\  & & most known vulnerabilities. & & [22; 23] \\ \hline \multirow{2}{*}{6} & \multirow{2}{*}{Based on NIST} & The NISTIR 8228 standard helps organizations & \\  & & better understand and manage cybersecurity & [36] \\  & & and privacy risks with IoT devices. & \\ \hline \multirow{2}{*}{7} & \multirow{2}{*}{Based on CVSS} & Free and open industry standard for evaluating & \\  & & security vulnerabilities. & & \\ \hline \multirow{2}{*}{8} & \multirow{2}{*}{Based on hardening} & The implementation of hardening allows to & \\  & & reduce the attack surface. & \\ \hline \multirow{2}{*}{9} & \multirow{2}{*}{Based on security validation} & Ensures the security of the entire IoT system & [44; 45] \\  & & implementation process. & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the selected papers to review. level. CIS helps to strengthen security against the most common attack vectors and its focus on establishing specific controls helps to control hardware and software assets; see Table 4.  
The application of CIS allows to quickly cover security issues versus times that can be long in risk analysis processes, especially in IoT systems that are dynamic and grow in number of devices daily, so waiting for the processes that lead identification of vulnerabilities, qualitative or quantitative analysis can generate a considerable time gap where you can receive an attack. Unlike traditional systems, those based on IoT even have a capacity to amplify the attack due to their level of complexity, which leads to more practical solutions, without obviously neglecting the risk analysis processes, which undoubtedly need to adapt to this new reality.