esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Performance of Corroded Reinforced Concrete Columns Strengthened with Basalt Fiber Sheets
## 2 Specimen Design and Fabrication
### Material Properties  
The measured compressive strength of the concrete standard cube was 40.34 N/mm\({}^{2}\). Before the longitudinal bar was corroded, its yield strength and ultimate strength were 440 N/mm\({}^{2}\) and 640 N/mm\({}^{2}\), respectively. A balance with an accuracy of 0.1 g was used to weigh the longitudinal bars to obtain the weight per unit length of 1.58 g/mm. After the test of the corroded specimen was completed, the main bars, away from the failure section of the damaged specimen, were removed. The main bars were derusted with steel brushes to study the mechanical properties in order to obtain the nominal yield and ultimate strength, as shown in Table 2. The high-performance unidirectional basalt fiber sheet was used and the fiber-bonding impregnation adhesive TLS-503 was selected as the binder. The mechanical properties of the basalt fiber sheet are shown in Table 3. The primer and adhesive used in the reinforcement process were tested by the National Chemical Building Materials Testing Center (Construction Engineering Testing Department). The measured results of colloidal properties and bonding properties are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{**Test Items**} & \multicolumn{2}{c}{**Test Results**} & \multirow{2}{*}{**Test Results**} \\ \cline{3-4} \cline{5-5}  & & & **Class A** & & **Class B** \\ \hline \multirow{4}{*}{1} & Colloidal properties & Tensile strength (MPa) & \(\geq\)40 & \(\geq\)30 & 45.79 \\  & & Elastic modulus (MPa) & \(\geq\)2.5 \(\times\) 10\({}^{3}\) & \(\geq\)1.5 \(\times\) 10\({}^{3}\) & 2.6 \(\times\) 10\({}^{3}\) \\ \cline{3-5}  & & Elongation (\%) & \(\geq\)1.5 & 3.47 \\ \cline{3-5}  & & Normal bonding & \(\geq\)2.5 (C30 Concrete & \multirow{2}{*}{5.87} \\ \cline{3-5}  & properties & strength with concrete & (MPa) & failure) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test results of colloid and bonding properties.  
Figure 3: (**a**) A roll of fiber sheets. (**b**) Shape of the basalt fiber sheet. (**c**) bonding between BFRP and corroded concrete column.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Specimens**} & **Actual Corrosion** & **Nominal Yield** & **Nominal Ultimate Strength** \\  & **Rate** & **Strength (N/mm\({}^{2}\))** & **(N/mm\({}^{2}\))** \\ \hline Z1 & 0 & 440 & 640 \\ Z2 & 8.4\% & 399 & 587 \\ Z3 & 9.1\% & 376 & 547 \\ Z4 & 7.6\% & 415 & 592 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material properties of main reinforcement.
# Integration of Geoscience Information for Disaster Resilience in Kuala Lumpur, Malaysia
## 5 Conclusions  
Kuala Lumpur aspires to be a well-integrated, sustainable, and resilient metropolitan city that serves everyone. This aspiration is challenged by issues such as landslides, floods, and unfavourable ground conditions. A novel approach has been used where geoscience information is integrated to determine engineering ground conditions and susceptibility to multiple geohazards, which are then combined to demarcate suitable zones for urban development. The combination of both engineering geology conditions and susceptibility to multiple geohazards contributes to better understanding for building resilience to disasters influenced by climate change. The findings reveal that 20% of the city has high suitability for development and is generally not prone to climate hazards. About 80% of the land area in Kuala Lumpur has medium to high ground constraint, and this includes around 25% of the city area that is susceptible to landslides and floods. In the worst-case scenario where no action is taken, communities and urban assets within these susceptible areas would be exposed and vulnerable to more hazard events due to climate change. It is suggested that additional development be limited in such areas to minimiserisk. Where already developed, hazard-specific measures can be taken in targeted areas to build resilience. Examples include disaster risk transfer and insurance schemes for exposed communities, social safety nets for lower-income groups, area-based business continuity plans for industries and the commercial sector, early warning alerts and public warning systems, as well as increasing public awareness and preparedness.  
Funding acquisition, J.J.P.; resources, J.J.P.; conceptualisation, methodology and analysis, T.F.N. and E.A.; writing--original draft preparation, E.A.; writing--review and editing, T.F.N. and J.J.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Newton-Ungku Omar Fund (Application ID: 59348-455144, UM Project No. IF002-2017, UKM Project No. XX-2017-002) for supporting this research.  
Not applicable.  
The datasets generated during and analysed are available from the corresponding author on reasonable request.  
The authors would like to thank the anonymous reviewers and the academic editor for their constructive input to this manuscript. The assistance of project members from multiple institutions is gratefully acknowledged.  
The authors declare no conflict of interest.
# A Survey on Recent Advanced Research of CPS Security
## 3 Reviews
### Security Strategy  
A security strategy is a higher level of application design. Researchers try to balance security and availability in the whole cycle of development and maintenance.  
[PERSON] et al. [133] proposed a software security framework, called PLuggable and Reprogrammable (PLAR). PLAR remotely monitors all devices plugged in the IoT network and reprograms the devices to match the security standard through its life cycle. Moreover, PLAR checks a device's security policies and overrides weak configurations. The authors implemented a prototype on an IP camera, and the prototype successfully detected the authentication vulnerability while mitigating it.  
In a complex IoT network, users need to establish security by minimizing the cost with bounded rationality. [PERSON] and [PERSON] [49] therefore introduced the Gestalt Nash equilibrium (GNE) solution concept, which models users with a sparse cognition vector. With the help of the GNE, users can measure the security level of neighboring IoTs, make mature decisions, and build their cognitive networks holistically. They further computed the GNE with a proximal-based algorithm, and the results revealed several situations that match real-world observations.  
[PERSON] et al. [134] proposed a Pareto-optimal security resource allocation scheme to guarantee the proper functioning of an IoT-based system with reasonable resource overhead. They calculated the best defender strategy by formulating it as a linear optimization problem. Then, they measured its efficiency and effectiveness aspects. For large network topologies, they proposed a divide-et-impera method that decomposes the initial problem into smaller subproblems. After that, they used parallel computing techniques to solve the subproblems as small instances.
# Deep Convolutional Neural Network-Based Approaches for Face Recognition
## 4 Methodology and Experiments
### Experiments and Results
#### 4.2.1 First Experiment: Pre-Trained CNN AlexNet with SVM  
This experiment was conducted by extracting the learned image features from a pre-trained CNN AlexNet, and to train SVM using those features, as shown in Figure 4. As mentioned before, for AlexNet net we needed to resize all images to \(227\times 227\) and convert any grayscale images to RGB. In our implementation, we split the data into 80% training and 20% test data and randomized the split to avoid biasing the results. AlexNet is made up of numerous layers but it is crucial to note that not all these layers are essential for extracting features. The first layer performs the extraction of features such blobs and edges, as displayed in Figure 5b. Therefore, using deep layers gives better distinct features [56]. Figure 5c shows the extracted features of layer 'fc7'. In this experiment, we extracted the feature from each layer in a fully-connected layer 'fc6', 'fc7', 'fc8', and compared the performance with different features. Then, we fitted SVM to perform the classification task. The output of feature extraction from 'fc8' layer was a 4096-dimensional feature vector. For SVM kernel function, we used linear kernel function without any optimization. The kernel function is used to take vector data as input and transform it into the optimal form. The 'MiniBatchSize' was set 20. We trained by stochastic gradient descent (SGD). The results of this method were evaluated with all provided datasets. The extracted features from layers 'fc6', 'fc7', and 'fc8' are illustrated in Figure 6. As it can be seen from Figure 6, the features from the 'fc7' layer had the highest recognition accuracy. This result clearly confirms that an optimal feature can be extracted from 'fc7'. The layer 'fc7' had additional distinguished power for same-class recognition. As results for this experiment, from Figure 6, we can observe that the network achieved a higher accuracy of 100% on YTF datasets, and 99.55% and 99.17% for the GTAV face and ORL datasets, respectively, whereas for the F_FLW dataset the model obtained 98%. Finally, FEI achieved 97.50%, Georgia Tech face dataset was 96%, and LFW was 94%.  
Figure 4: AlexNet convolutional neural networks with SVM.  
Figure 5: Feature visualization for CNN AlexNet on Georgia Tech face dataset: (**a**) input image; (**b**) features of first convolutional layer ’conv1’; (**c**) features of layer ’fc7’.
## 3 Experimental Results
### Acoustic Emission Characteristics  
#### 3.4.1 Acoustic Emission Count and Energy  
The acoustic emission ringing count and cumulative energy statistics during the loading process are shown in Figure 10. Before the fracturing of the specimen, the acoustic emission ringing count gradually increased with increasing load; however, from the perspective of the change in the energy, the energy of the early acoustic emission specimen was very small. When the specimen approached the occurrence of fracturing, the acoustic emission ringing count significantly increased, and the maximum value occurred before the peak load. Based on the analysis of the evolution of the interlayer deformation field, this was the result of the combined effects of the interlayer sliding and rock fracture expansion.  
Figure 10: Statistics of the acoustic emission events of the rock specimens.  
Figure 9: Variation characteristics of interlayer slip: (**a**) interlayer slip displacement; and (**b**) interlayer slip acceleration.
## 3 Results and Discussion
### Chemical-Physical Parameters
#### 3.1.2 pH and Electrical Conductivity  
An important requirement for the use of material in indoor cultivation or hanging garden is the pH between 6.5 and 8, which is the optimal range that would make it compatible with most plant species.  
In Figure 3 the pH values measured both in an aqueous extract of fertilizer glasses and LWAs are plotted. It can be noted that all glasses compositions displayed pH values higher than those corresponding to LWAs. The glasses compositions were properly designed by the authors to achieve a controlled release over time of P and K and to use them to functionalize LWAs with a specific percentage of K and P as required by the Italian legislation on fertilizers [24]. It is possible to hypothesize that since the glasses contain high percentages of K (12%) and Ca (26%), the release of these elements clearly involves alkaline pH values. Regarding the LWAs samples, all compositions showed pH values within the optimal interval (6.5-8). The highest value corresponded to the CFG2 composition containing the addition of 50% of fertilizer glass.  
Figure 2: Correlation between Porosity vs. App. Density for the LWAs prepared.  
Concerning electrical conductivity (EC), all manufactured materials were within the appropriate range for growing plants (0.2-2 dS/m), as shown in Figure 4. It is noteworthy that CNUT2 composition showed the highest EC value, since it contained ashes as source of P and K nutrients with high presence of Ca and Mg oxides, so the mobility of ions was elevated in solutions. Different behavior was shown by CNUT3's composition, which contains the non-vitrified nutrients, but the K source was potassium carbonate.
# Time Domain Spherical Harmonic Processing with Open Spherical Microphones Recording
## 5 Simulation Results and Analysis  
In this section, we evaluate the result of the proposed algorithm for time domain spherical harmonic analysis using a fourth order (\(N=4\)) system. We consider 32 microphones regularly placed on an open spherical array of \(R_{Q}=0.16\) m, where the analysis region of interest is inside the array. A point source is placed at \([1,2,1]\) m with respect to the origin which coincides with the origin of the microphone array. The sampling frequency is 48,000 Hz, and the filter length \(2L+1\) is 501. A noise signal at 40 dB SNR is added to each microphone to reflect thermal noise. Considering the application of the proposed method to be spatial ANC, we construct the desired frequency band to cover the target noise band, and construct the radius of the region to be wide enough to fit one human head.  
It is difficult to validate our method in time domain directly because the coefficients are time dependent and no ground truth has been given. Therefore, we first validate our proposed time domain spherical harmonic coefficients in the frequency domain. Thus, we compare the Fourier transformation of the time domain coefficients to the theoretical frequency domain coefficients given in (4). Next, to clarify that our proposed method has the ability to record a sound field in the region of interest in the time domain, we reconstruct sound pressure at an arbitrary point as well as over a plane inside the region of interest with the captured time domain spherical harmonic coefficients by (6). Finally, the time delay of the proposed method is given.
## Materials and Methods
### Statistical Evaluation  
Several statistical analytical methods were used to evaluate the reliability of the results. For model evaluation, the mean values of simulated crop yield and LAI were compared with the measured values. A two-sample t-test and two statistical indices of root mean square error, _RMSE_ and model efficiency, _ME_[53] were applied for the analyses using R software version 3.4.1 [54]. The physical meaning of the _ME_ is that it is a normalized statistic that determines the relative magnitude of the residual variance compared to the observed data variance. _ME_ indicates how well the plot of observed versus simulated data fits the 1:1 line. _ME_ values range from \(-\infty\) to 1; the model is more accurate when the value is closer to 1. When values are close to zero, the model predictions are less than or as precise as the observed mean.
# Biopurification of Oligosaccharides by Immobilized _Kluyveromyces Lactis_
## 3 Results  
### Sugar Utilization in the Nutrient-Rich and Defined Salt Media  
To reduce burdens from the biological elimination of lactose, we tried to avoid or minimize the organic components, such as peptone, amino acids, or vitamins. Firstly, we examined the utilization of glucose, galactose, and lactose in the nutrient-rich (YPD) and defined salt media (YNB) by _K. lactis_.  
As shown in Table 1, the highest optical density (OD\({}_{600}\) = 7.9) and maximum specific growth rate (0.33 h\({}^{-1}\)) were achieved when glucose was used as a substrate for cell growth in YPD media. With the lactose as a substrate, the growth of _K. lactis_ was maintained at a similar level as shown in glucose. Though maximum OD was lower (OD\({}_{600}\) = 6.9) in YPD media with lactose, the maximum specific growth rate (0.31 h\({}^{-1}\)) and substrate consumption (20.0 g/L) were similar to those observed in glucose. In contrast, the substrate consumption and maximum specific growth rate were the lowest (11.0 g/L and 0.20 h\({}^{-1}\), respectively) when cells were cultivated on galactose as a substrate.  
In YNB media with the addition of amino acids, the maximum specific growth rate was around 0.31 h\({}^{-1}\) when cells were utilizing glucose, lactose, or even galactose as a substrate. However, the maximum OD and substrate consumption, compared to YPD media, were decreased 26-38% and 5-38%. The lowest decrease in substrate consumption was lactose.  
We further analyzed the lactose utilization by _K. lactis_ in YNB without amino acids. Although the maximum specific growth rate and substrate consumption were decreased to 0.27 h\({}^{-1}\) and 14 g/L, the maximum OD (OD\({}_{600}\) = 5.0) and ethanol production (9.5 g/L) were similar in comparison with cells grown in YNB media with amino acids.  
Considering the substrate consumption and ethanol production in the YPD and YNB media, we could conclude that _K. lactis_ uses lactose efficiently as well as glucose. In addition, we could determine that the addition of amino acids to the YNB media had minimal effect on the viability and lactose consumption of _K. lactis_.
## 5 Influence of Urban Thermal Effect on Thunderstorm Processes and Lightning Activity  
During the mid-1970s, some scholars conducted the Metropolitan Meteorological Experiment in Saint Louis, revealing that the city significantly increased the frequency and duration of thunderstorms [81; 82]. When [PERSON] et al. [17] studied lightning activity in Chicago, they were the first to note the impact of cities on lightning frequency. [PERSON] et al. [83] discovered by analyzing lightning detection data that the peak lightning activity in Houston occurs in the afternoon and attributed this to the diurnal variation in the UHI effect. Scholars have utilized lightning location data to investigate the thunderstorm activity characteristics of the Pearl River Delta urban agglomeration and observed that the lightning frequency is significantly higher compared to other regions [84]. [PERSON] et al. [23] postulated that the UHI effect enhances the instability of stratification within a city, thereby facilitating thunderstorm formation. The UHI effect not only alters the thermal field and boundary layer structure within urban areas, but also impacts the development of convective systems through local circulation. The vertical movement of airflow within a city is intricately correlated with the stability of the boundary layer. When the boundary layer is less stable, the vertical velocity and ascending height within the city increase, favoring the initiation and intensification of thunderstorms [85]. [PERSON] et al. [20] posited that under weak synoptic conditions, the UHI effect can trigger local convergence and convection by causing disturbances. [PERSON] et al. [12] contended that the urban thermal effect promotes the development of a mixed boundary layer and, ultimately, leads to thunderstorm system activation (as demonstrated in Figure 7). Figure 6: Lightning flash rate as a function of the aerosol optical depth (AOD) in the dust-dominant (orange dots) and smoke-dominant regions (blue dots). ACI and ARI represent the aerosol–cloud interactions and aerosol–radiation interactions, respectively [78]. Unstable conditions, water vapor conditions, and vertical shear are important environmental factors that affect the development of thunderstorm systems [86; 87]. Vertical shear, in particular, can cause air masses with diverse properties to undergo vertical mixing, and its strength in the middle and lower layers has a direct impact on the intensity of thunderstorms [88]. Additionally, [PERSON] et al. [89] examined how the cold pool outflow from thunderstorms affects the vertical shear due to thermal differences as it passes through cities, without considering the influence of the Coriolis force.
## 5 Constellation of Formations
### Dedicated Control
**Note on the control law:** In [15], several relative orbit control laws are formulated in terms of the linearized Clohessy-Wiltshire equation as:  
\[\underline{a}_{2R}=\underline{a}_{0R}-k_{F}\cdot B^{+}\cdot\delta\underline{ \alpha}_{02R} \tag{10}\]0  
and the control is obtained as:  
\[\underline{a}_{2R}=\underline{a}_{0R}-k_{F}\cdot B^{+}\cdot\delta\underline{ \alpha}_{02R} \tag{10}\]1  
for a given desired coordinate \(\underline{x}_{d}\). As the relation between the control and the error \((\underline{x}-\underline{x}_{d})\) can be considered linear as in (9) and (10), the same approach can be implemented to determine the relative control component associated with the formation objective, restricted to determine a null deviation of the center of the mass formation. **Note on the saturated control law:** The thrust control authority must be selected so that the constellation objectives can be achieved with a large enough margin. In this way, it is always possible to select a small enough gain \(k_{F}\) for the formation control which achieves stabilisation of the formation objective. As there might be time and propellant consumption restrictions, this gain and the thrust and satellite masses allocation in the formation should be selected carefully (see [31; 35]). In particular, the gain \(k_{F}\) could be selected specifically for each chief/follower pair in order to consider different features of each follower satellite and associated objective. However, to make the presentation simpler on (12)-(14), a unique gain \(k_{F}\) is chosen for all the followers.