esa-sceva/satcom-chunk-collection huggingface dataset:
# The Effect of Carbon Nanofibers Surface Properties in Hydrogenation and Dehydrogen Reactions
## 2 Materials and Methods
### Catalysts Characterisation  
X-ray diffraction (XRD) analysis was performed with a PANalytical X'PertPRO X-ray diffractometer using a Cu K\(\alpha\) radiation source (\(\lambda\)= 1.5418 A\({}^{\circ}\)) and operated at 40 kV and 30 mA. The diffraction patterns were collected over the 10-80\({}^{\circ}\) 2 \(\theta\) range at a step size of 0.017\({}^{\circ}\).  
X-ray photoelectron spectroscopy (XPS) data were collected with a Thermo Scientific K-alpha+ spectrometer equipped with a monochromatic Al X-ray source operating at 72 W (6 mA \(\times\) 12 kV), and with the signal averaged over an oval-shaped area of approximately 600 \(\times\) 400 microns. The data were recorded at pass energies of 40 eV for high-resolution scans and 150 eV for survey scans, with a 0.1 eV and 1 eV step size respectively. Charge neutralisation of the sample was achieved using a combination of both low energy electrons and argon ions (less than 1 eV) which gave a C(1 s) binding energy of 284.8 eV. The envelopes were fitted after subtraction of a Shirley background using CasaXPS (v2.3.17 PR1.1) using Scofield sensitivity factors and an energy exponent of \(-\)0.6.  
Bare supports, fresh and used catalysts were analysed by Raman spectroscopy with a Renishaw inVia Raman microscope. Typically, 0.01 g of sample was analysed under an IR class laser (514 nm) with a laser intensity of 50% and scanned at an attenuation time of 22 s and 10 scans.  
Transmission electron microscopy (TEM) analysis was carried out using a JEOL JEM 2100 TEM operating at 200 kV. The samples were firstly dispersed in absolute ethanol and sonicated for 5 min. A drop of this suspension was placed on a holey carbon film supported by a 300 mesh copper grid. Mean particle sizes and particle size distributions were obtained by measuring the size of over 500 particles from different areas.  
Pd surface atoms abundance was calculated assuming that all nanoparticles had cuboctahedral morphology with the cubic close-packed structure in this size range; the full calculation of the number of surface-exposed atoms is reported in the Supplementary Information.  
To study the morphology of the samples and determine the Pd content, scanning electron microscope (SEM) images were taken on a Hitachi TM303 PLUS equipped with a Quantax70 energy-dispersive X-ray spectroscope (EDX).
###### Abstract  
Networked microgrids (NMGs) enhance the resilience of power systems by enabling mutual support among microgrids via dynamic boundaries. While previous research has optimized the locations of mobile energy storage (MES) devices, the critical aspect of MES capacity sizing has been largely neglected, despite its direct impact on costs. This paper introduces a two-stage optimization framework for MES sizing, pre-positioning, and re-allocation within NMGs. In the first stage, the capacity sizing and pre-positioning of MES devices are optimized before a natural disaster. In the second stage, the re-allocation and active power output of MES devices are adjusted post-disaster, with boundary switches operated based on the damage scenarios. The framework restores unserved loads by either forming isolated microgrids using MES or re-establishing connections between microgrids via smart switches. The proposed framework is modeled mathematically and solved using a customized progressive hedging algorithm. Extensive experiments on modified IEEE 33-node and 69-node systems demonstrate the model's effectiveness and applicability in improving system resilience.  
resilience; networked microgrids; mobile energy storage; sizing; two-stage optimization
# Efficient Resource Management for Sum Capacity Maximization in 5G NOMA Systems
###### Abstract  
The modern cellular technologies are expected to provide high data rates and massive connectivity in fifth generation (5G) systems; however, this may be impossible through traditional radio access techniques. Recently, non-orthogonal multiple access (NOMA) has emerged as one of the promising cellular techniques for modern cellular communications with its ability to provide access for multiple users to the network over the same system resources. This paper studies resource management problem for downlink transmission of multiuser NOMA system. Our objective is to optimize both frequency and power resources for sum capacity maximization while taking into account each user minimum capacity requirement. Firstly, the problem of resource management decouples into two subproblems, that is, efficient sub-channel assignment and optimal power allocation, respectively. Secondly, for given power at base station, we design two sub-optimal algorithms for sub-channel assignment based on user channel condition and user minimum capacity requirement, respectively. Lastly, for any given sub-channel assignment, the problem first transforms into standard convex optimization problem and then we employ duality theory. To evaluate our proposed NOMA scheme, the enhanced version of existing NOMA optimization scheme is also presented as a benchmark. Results demonstrate that the proposed NOMA resource management scheme outperforms the benchmark NOMA optimization scheme in terms of sum capacity.  
5G; non-orthogonal multiple access; downlink transmission; sub-channel assignment; power optimization; capacity maximization Article
## 3 Results and Discussion
### Variations in the Structure of Fungi during the Tuqu Fermentation of Hunan Light-Flavor Baijiu
#### 3.4.3 Heat Map Analysis of Fungal Community Succession Based on the Species Taxonomic Genus Level  
Figure 9 shows a heatmap of the fungal composition during the fermentation of Tuqu-fermented grains of Hunan light-flavor Baijiu. A color closer to green represents a higher abundance, and a color closer to brown represents a lower abundance. As shown in the graph, as fermentation progressed, there were different dominant fungi at different fermentation stages, indicating a rich microbial structure. The main dominant fungi at the F0 stage were _Trichosporon_, _Saccharomycescoposis_ and _Rhizopus_. The main dominant fungi at the F2 stage were _Pichia_, _Debaryomyces_, and _Cyberlindnera_. The main fungal genera at the F4 stage were _Mucor_, _Aspergillus_, _Kazachstania_, _Monascus_, and _Sarociadium_. The main dominant fungi at the F6 stage were _Meyerozyma_, _Cladosporium_, and _Clonostachys_. The main dominant fungi at the F8 stage were _Rhizopus_ and _Satumispora_, and the main dominant fungi at the F10 stage were _Clavispora_ and _Wickerhamomyces_. These fungi are important fungal genera in the fermentation process of Baijiu. Among them, _Wickerhamomyces_ and _Aspergillus_ play a key role in fermentation. _Aspergillus_ can produce a variety of enzymes and plays a positive role in regulating the _saccharification_, _esterification_, and liquefaction power of Daqu and fermented grains. In addition, it can metabolize organic acids and fatty acids, which can lead to the production of aromatic esters that enhance and improve the flavor of Baijiu [28]. _Wickerhamomyces_ is an important aromatic yeast with a high acidity tolerance and a high ester production capacity, which can impart a rich aroma to the liquor [29].  
Figure 9: Heat map of the genus-based level of Fungi in the Tuqu fermentation process of Hunan light-flavor Baijiu.  
Correlation Analysis of Microbial and Physicochemical System Indicators in the Tuqu Fermentation Process of Human Light-Flavor Baijiu
## 3 Results and Discussion
### Volatile Composition
\begin{table}
\begin{tabular}{c c c c c} \hline
**Compounds** & **RT\({}^{\dagger}\) (min)** & **Chemical Family** & **KI** & **Descriptors 1** \\ \hline \hline
1-Pentent3-None & 4.390 & Ketene & 748 & Vegetable, green sweet \\ Alllyl ethyl ether & 4.552 & Ether & 754 & Green \\
2-Methyl2-butenal & 5.649 & Aldehyde & 792 & Green, fruity \\
2-Pentanal & 6.038 & Aldehyde & 805 & Fruity, vanilla, woody \\ Hexanal & 6.907 & Aldehyde & 836 & Fresh, cell grass \\ (Z)-2-Hexanal & 9.042 & Aldehyde & 907 & Almond, apple, green, sweet, vegetable, leafy \\
1-Hexanal & 9.328 & Alcohol & 914 & Green, herbaceous, woody, sweet \\ Heptanal & 10.373 & Aldehyde & 941 & Gly, fruity, woody, fatty, nutty \\ (Z)-2-Heptanal & 13.290 & Aldehyde & 1014 & Apple, hemon, green, spicy, vegetable \\
1-Octon3-one & 13.659 & Ketene & 1022 & Cucumber, mushroom, earth, vegetable \\ Benzaldehyde & 13.807 & Aldehyde & 1025 & Almond, anise, baslam, cyber, floral, herbaceous \\
6-Methyl5-beppen-zone & 14.119 & Ketene & 1032 & Gly, herbaceous, green, floral \\
5-Methyl2-5(SH)-furanane & 14.494 & Ketene & 1040 & Earthy, green, tobacco, fruity \\ Octanal & 14.809 & Aldehyde & 1047 & Honey, fruity, citrus, fatty \\
2-Isobutylthiazol & 16.034 & Thiazole & 1047 & Fermented, green \\ Phenyi \(\&\)yielddehyde & 17.874 & Aldehyde & 1113 & Green, floral \\ (Z)-2-Occanal & 18.170 & Aldehyde & 1119 & Spicy, herbaceous, green \\ Nonanal & 19.789 & Aldehyde & 1153 & Apple, coconut, grape, lemon, oily, vegetable, rose \\
1-Octon3-ol & 21.215 & Alcohol & 1182 & Creamy, earth, herbaceous, vegetable \\ Phenyi methyl alcohol & 21.832 & Alcohol & 1195 & Honey, rose \\ (Z)-4-Deccanal & 24.483 & Aldehyde & 1249 & Orange \\ Decanal & 24.928 & Aldehyde & 1258 & Wasy, floral, citrus, sweet \\ (Z,Z)-2-A-Deconal & 30.463 & Aldehyde & 1374 & Fatty, citrus \\ Neryi acetone & 37.078 & Ketene & 1519 & Fatty \\ \(\beta\)-Iconome & 39.348 & Ketene & 1571 & Floral \\ \hline \end{tabular}  
* RT = retention time; KI = experimental retention index.
* Sigma-Aldrich flavor and fragments.  
\end{table} 
Table 2: Volatile compounds, retention index, and descriptors in cherry tomatoes as affected by cultivar for pear and round fruits.
# A Unified Machine Learning Framework for Li-Ion Battery State Estimation and Prediction +
## 5 Implementation and Experimental Results
### Use Case 5: SoC Estimation on the Lifetime Dataset  
This use case deals with SoC estimation by processing a dataset created in the context of the aforementioned MARBEL project that describes the whole lifetime of the battery [31]. This dataset is different from the one used in Section 5.4. It consists of two datasets that describe the complete lifetime until the capacity reaches a certain percentage of its nominal capacity. This parameter for the first dataset was 95% and 98% for the second one. Thus, in the first one we can observe the degradation of the battery's maximum capacity, while in the second one, we can observe a smaller degree of fading in this feature. These datasets have been created from different charging profiles. The cycle lives range from 700 to 883 cycles, and the datasets include two different charging protocols. The ambient temperature was controlled by the experimental chamber to be stable at 35 \({}^{\circ}\)C. This dataset has been generated by CALB Prismatic Lithium-ion cells with NMC chemistry and a nominal capacity of 58 Ah.  
For this implementation, raw data and statistics have been selected as features. Table 13 presents the features that have been applied to this use case. In this use case, the most suitable evaluation metric is the RMSE, and it is measured in percentage (%) since it is expressed in the same unit as the target variable, in this case the SoC.  
In this case, four different pipelines have been implemented. The details about the configuration and evaluation of these pipelines are presented in Table 14. Moreover, Figure 17 depicts the RMSE for each pipeline, and Figure 18 illustrates the actual and predicted values of the SoC for pipeline 25. This figure is dense since the testing dataset contains several cycles from the battery's lifetime. In this implementation, as in use case 4, the number of datasets is restricted as well as the range of the batteries' degradation. Nevertheless, pipeline 25 presents satisfactory accuracy in SoC prediction since the model, the features selected, and the training dataset are adequate and efficient for this application.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Features Selection for Use Case 5**} \\ \hline
**Raw Data** & **Statistics** \\ \hline voltage (V) & mean voltage (V\({}_{\text{mean}}\)) \\ \hline current (I) & mean current (I\({}_{\text{mean}}\)) \\ \hline temperature (T) & \\ \hline capacity (C) & \\ \hline \hline \end{tabular}
\end{table}
Table 13: Features applied in use case 5.
# User Utility Maximization in Narrowband Internet of Things for Prioritized Healthcare Applications
## 3 System Model and Assumption
### NB-IoT Network Environment and Application Model  
The NB-IoT is an LTE variant, especially in Internet of Things (IoT)-specific networks. The main mechanism of the NB-IoT network is the evolved packet system (EPS), which employs two types of optimizations: user plane and control plane EPS optimization [6; 27].  
We assume that NB-IoT devices are installed in a hospital environment that generates specific sensed data from the operation theatre, patient ward/cabin, or doctor's chamber (OPD) and sends them to the central server through eNB. Figure 1 represents the system architecture of the NB-IoT-based network, which comprises a set of eNBs connected with various healthcare monitoring applications, including remote patient monitoring, personalized healthcare systems, rehabilitation systems, ambient assisted living, emergency medical systems, and telemedicine systems [8; 12].These applications use various sensors such as temperature, pressure, motion, image, etc., where smart UEs such as watches, weight scales, blood sugar and blood pressure sensors, gastric stimulators, pulse oxymeters, etc., are very likely involved in healthcare applications.  
As small data volumes need to be infrequently transmitted in an NB-IoT environment, there must be some scheduling for UEs. Various UEs generate diverse data types, which demand the necessity of maintaining various priorities \(p\in P\) among them based on the urgency. The communication of uplink users is considered in this model, and usually the NB-IoT uplink transmission occupies 180 kHz of bandwidth and supports two subcarriers spacing, 3.75 kHz and 15 kHz. However, this network model explicitly investigates the feasibility and effectiveness of the event-influenced prioritized access of UEs under good resources to maximize user utility.  
Certain essential issues are concerned to explore the healthcare applications using cellular technologies such as scheduling the access of IoT devices [8; 11] from the environment, then data transmission and management inside the smart hospital environment to communicate with the concerned area. At the same time, interference minimization is also an important issue, as a large number of individuals are expected to connect with the smart hospital system.
# Modelling and Experimental Testing of Passive Magnetic Bearings for Power Loss Reduction
## 4 Results and Discussion  
In this section, the experimental tests and their outcomes are analysed. The experimental campaign consists of two types of tests: static tests aimed at studying the PMB axial force and dynamic tests to investigate the effect of the partial rotor weight compensation on the power losses.
###### Abstract  
Most studies on the effects of plants on reducing pollutants in surface runoff focus on the elimination of a single pollutant by a single plant species. We take into account the fact that natural riparian herbaceous vegetation buffer strips remove multiple pollutants at the same time, and vegetation species need to be selected according to actual conditions. In an indoor simulation, processed soil samples were taken, and collected rainwater was used to prepare standard solutions for simulating surface runoff, which includes five primary water pollutants: total suspended solids (TSS), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH\({}_{3}\)-N), and chemical oxygen demand (COD). Pollutant concentrations were measured after the experiments. We found that the reduction capacity of vegetation buffer strips differs due to the differentiation of biological structure and growth characteristics. _Dichonda reprens Forst_ and _Cynodon dactylon (Linn.) Pers_ had high biomass, and their vegetation was creeping on the ground with well-developed roots, showing a strong comprehensive ability to reduce pollutants. The comprehensive reduction capability of five vegetation buffer strips for pollutants is ranked as follows: _Dichonda reprens Forst_ > _Cynodon dactylon (Linn.) Pers_ > _Zoysia matrella_ > _Festuca elata [PERSON]. [PERSON]_ > _Lolium preemte._  
riparian herbaceous vegetation buffer strips; phytoremediation; surface runoff; primary pollutions; reduction capacity
# Latency Analysis of Blockchain-Based SSI Applications +
## 4 Evaluation Results
### Four-Node Measurements in a Private Cloud
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**TX**}} & \multicolumn{1}{c}{\multirow{2}{*}{**Arrival Rate (TXs/s)**}} & \multicolumn{1}{c}{\multirow{2}{*}{**400**}} & \multicolumn{1}{c}{\multirow{2}{*}{**450**}} & \multicolumn{1}{c}{\multirow{2}{*}{**50**}} & \multicolumn{1}{c}{\multirow{2}{*}{**50**}} & \multicolumn{1}{c}{\multirow{2}{*}{**400**}} & \multicolumn{1}{c}{\multirow{2}{*}{**450**}} & \multicolumn{1}{c}{\multirow{2}{*}{**500**}} \\ \cline{2-2} \cline{6-11}  & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \hline
**Min (W)** & 0.1011 & 1.6937 & 1.8288 & 1.9917 & 4.4555 & 4.8983 & 5.1942 & 5.5093 & 5.7745 & 6.0929 & 6.3293 \\ \hline
**Max (W)** & 2.0067 & 2.1450 & 2.2595 & 4.3912 & 5.0304 & 5.1658 & 5.6114 & 6.2726 & 6.5005 & 6.9145 & 10.1340 \\ \hline
**Avg (W)** & 1.7733 & 1.7860 & 1.9137 & 3.9115 & 4.7391 & 5.0067 & 5.3868 & 5.8393 & 6.1832 & 6.5251 & 7.1020 \\ \hline
**TX** & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \hline
**Type** & **1** & _50_ & _100_ & _150_ & _200_ & _250_ & _300_ & _350_ & _400_ & _450_ & _500_ \\ \hline
**Min (R)** & 0.0178 & 0.0856 & 0.2053 & 0.2155 & 0.4517 & 0.6864 & 0.6904 & 0.7717 & 0.8427 & 1.0742 & 1.135 In comparison with the ledger on the Google Cloud Platform, in terms of the performance in the metrics of reading and writing response latency, we found that the local Indy ledger reads 30-50% faster, and writes 65-85% faster than the Indy ledger running on the Google Cloud Platform. The explanation may be that the network latency is much better in a local Docker environment, than in a close-to-real-life scenario, where the different VMs are in different regions in the Google Cloud Platform.
# A Robust Framework for Real-Time Iris Landmarks Detection Using Deep Learning
## 3 Materials and Methods
### Proposed Methodology
#### 3.4.3 Training and Testing  
The training phase involved two stages. The first was feeding data into the model, known as the learning or training stage. The second was testing the model through training and obtaining results for various parameters such as accuracy, MAE, and loss. We conducted training sessions using the iris landmarks dataset for MobileNetV2 [35], ResNet50 [23], VGG16 [24], and VGG19 [8], and these models were initialized with ImageNet weights [25]. The training of these models was performed over 300 epochs using the Adam optimization technique [26] with a learning rate of 0.001, batch size of 64, and MAE loss function [27]. A complete flow diagram is shown in Figure 4.  
In this research, we used four different deep learning models, i.e., MobileNet version 2, VGG16, VGG19, and ResNet50, over a self-collected and annotated iris landmarks dataset. All models had the same configurations: i.e., the number of layers, optimizers, learning rate, etc. ImageNet weights were used to initialize all the models. For all models, the input shape of 64 \(\times\) 64 was used. All images passed as input to each model had three channels. This is the reason behind the use of three channels for the input shape. Different layers were added such as GlobalAveragePooling2D layers, and the dropout rate was set to 0.3. The last dense layer was added with the value of 10. Adam was implemented as the optimizer for all models, and the mean square error (MSE) was used as a loss function. The learning rate was set to 1 \(\times\) 10\({}^{-6}\). All checkpoints of each model were saved at a specific interval, which was helpful for examining the model performance and can be used in the future. The training of these models was performed over 300 epochs with a learning rate of 0.001 and batch size of 64. The details of the number of parameters selected for each parameter are shown in Table 2.  
Figure 4: Training and testing procedure of proposed model.
# Optimization Design of Coupling Beam Metal Damper in Shear Wall Structures
## 3 The Relationship between Pore Geometrical Parameters and Hysteresis Energy
### Construction of the Kriging Surrogate Model  
Kriging surrogate model-based optimization has been applied in many fields over the past few decades. [PERSON] (1955) applied the method to the design of the space shuttle, and compared it with the calculation accuracy and efficiency of the response surface. [PERSON] (1955) used the Kriging surrogate model to optimize the design of the cylindrical member crashing problem. [PERSON] (2007) used the Kriging model in order to reduce the warping of injection molding process components in order to optimize the design.  
Kriging interpolation, as a semi-parameterized technique, including a regression portion and a non-parametric part, consists of two parts: polynomials and random distributions.  
\[y(x)=F(\beta,x)+z(x)=f^{T}(x)\beta+z(x) \tag{5}\]  
where beta is the regression coefficient, \(f(x)\) is a polynomial in \(X\), an in the design space, the simulated global approximation can be zero-order, first-order, or second-order polynomials; \(z(x)\) is a random distribution error, providing the approximation of the analog local deviations, with statistical characteristics as follows:  
\[E[z(x)]=0 \tag{6}\]  
\[Var[z(x)]=\sigma_{z}^{2} \tag{7}\]  
\[cov\big{[}Z(x_{i}),Z\big{(}x_{j}\big{)}\big{]}=\sigma_{z}^{2}\big{[}R_{ij} \big{(}\theta,x_{i},x_{j}\big{)}\big{]} \tag{8}\]  
in which \(x_{i},\ x_{j}\) are any two points in the training samples, \(R_{ij}\big{(}\theta,\ x_{i},\ x_{j}\big{)}\) is the correlation function with parameter \(\theta\) to characterize the spatial correlation between the training sample points. Thus, the Kriging agent model treated any corresponding value as a random variable following a normal distribution, and the model is not limited to a particular form and is strongly flexible.  
The response surface of the four kinds of metal dampers could be obtained, as shown in Figure 5.  
Figure 4: Parameter sampling for coupling beam metal dampers: (**a**) SP1; (**b**) SP2; (**c**) SP3; and (**d**) SP4.