esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Reconfigurable intelligent surfaces (RIS) and massive multiple input and multiple output (M-MIMO) are the two major enabling technologies for next-generation networks, capable of providing spectral efficiency (SE), energy efficiency (EE), array gain, spatial multiplexing, and reliability. This work introduces an RIS-assisted millimeter wave (mmWave) M-MIMO system to harvest the advantages of RIS and mmWave M-MIMO systems that are required for beyond fifth-generation (B5G) systems. The performance of the proposed system is evaluated under 3 GPP TR 38.901 V16.1.0 5G channel models. Specifically, we considered indoor hotspot (InH)--indoor office and urban microcellular (UMi)--street canyon channel environments for 28 GHz and 73 GHz mmWave frequencies. Using the SimRIS channel simulator, the channel matrices were generated for the required number of realizations. Monte Carlo simulations were executed extensively to evaluate the proposed system's average bit error rate (ABER) and sum rate performances, and it was observed that increasing the number of transmit antennas from 4 to 64 resulted in a better performance gain of \(\sim\)10 dB for both InH--indoor office and UMi--street canyon channel environments. The improvement of the number of RIS elements from 64 to 1024 resulted in \(\sim\)7 dB performance gain. It was also observed that ABER performance at 28 GHz was better compared to 73 GHz by at least \(\sim\)5 dB for the considered channels. The impact of finite resolution RIS on the considered 5G channel models was also evaluated. ABER performance degraded for 2-bit finite resolution RIS compared to ideal infinite resolution RIS by \(\sim\)6 dB.  
3 GPP 5G channel modelling; average bit error rate (ABER); massive multiple input and multiple output (M-MIMO); millimeter wave (mmWave); reconfigurable intelligent surfaces (RIS); sixth-generation (6G) +
Footnote †: journal: Information  
Article
# Utilization of Yacon Damaged Roots as a Source of FOS-Enriched Sweet-Tasting Syrup
## 3 Results and Discussion
### Syrup Production  
Yacon syrup production follows a juice extraction and filtration process to remove solid particles in suspension. To prevent enzymatic browning, an antioxidant is commonly added to the clarified juice. The juice is then concentrated to at least 70 \({}^{\circ}\)Brix [21]. In this study, lemon juice was added as an antioxidant to yacon water-soluble material, inhibiting the polyphenol oxidation reaction by the decrease in pH, promoted by citric acid, and by the reducing power of ascorbic acid [40; 41]. The addition of lemon juice resulted in a decrease in the juice pH from 6.0 to 4.2 (Table 1). Its concentration by boiling at atmospheric pressure yielded a syrup at 72 \({}^{\circ}\)Brix, representing an estimated water loss of about 91%. The dry matter accounted for 155 g/kg of Glc, 394 g/kg of Fru, 46 g/kg of Suc, and 90 g/kg of FOS (Figure 4a). Of the FOS, 26% were of the inulo-type (Figure 4b), representing a 10-fold increase compared to the initial raw material (Table S2). This was attributed to inulin hydrolysis, favored by the acidic pH of the juice combined with the boiling temperatures achieved during concentration [42]. This was also supported by the traceable amounts of inulin detected in this syrup. On a dry weight basis, the theoretical relative sweetness, theoretical glycemic index, and caloric value of the syrup were higher than the raw material by 72%, 90%, and 63%, respectively (Table 2). This was attributed to the higher proportion of sweet-tasting carbohydrates (Glc, Fru, Suc, and FOS) in the syrup [8; 24].  
The commercial yacon syrup (Yacon Portugal) presented a theoretical relative sweetness of 1.05 \(\pm\) 0.07, a theoretical relative glycemic index of 0.28 \(\pm\) 0.03, and a caloric value of 292 \(\pm\) 19 kcal/100 g. The theoretical relative sweetness and caloric value values were higher than those obtained for 5% lemon juice syrup. The commercial agave syrup (Go Bio), produced from an inulin source [12], also presented higher theoretical relative sweetness (1.34 \(\pm\) 0.10), theoretical relative glycemic index (0.53 \(\pm\) 0.03), and caloric value (361 \(\pm\) 25 kcal/100 g). These differences result from the higher prevalence of Glc and Fru in the commercial syrups than in the 5% lemon juice syrup (Figure 4a and Table S2), due to the extensive saccharification of inulin and FOS for commercial sweetening purposes.  
Citric acid and ascorbic acid proportions in citrus juices may vary among species, varieties, seasons, and geographic locations [43]. To understand how these variations might affect the FOS proportion in syrups, citric acid and ascorbic acid were tested as pure components at different concentrations. These tests were conducted using the damaged roots of the yacon Hualqui variety, chosen as a representative source with high free sugar content, and yacon Crespo damaged roots, selected as a representative source of high inulin content.
## 2 Methodology
### Reactor Modelling
#### 2.1.1 Gas Switching Reforming  
Analogously to previous work [22], full fuel conversion in the reduction was assumed and, to prevent kinetic limitations taking place at the end of this step, a minimum fraction (5%) of the oxygen carrier in its oxidized state is specified. The carrier is then fully reduced during the subsequent long reforming step. After the reforming, the degree of oxygen carrier oxidation with the air stream was set to 30%, while the total airflow rate was selected to avoid any accumulation of O\({}_{2}\) in the reactor. The heating value routed to the reduction inlet is controlled to ensure the autothermal operation by tuning the overall steam to carbon ratio at the reforming feed.  
The oxygen carrier in the GSR is based on Nickel, with the formulation taken from [PERSON] et al. [36]. As illustrated in Figure 1, the reactor operates in a cycle consisting of reforming (REF), oxidation (OX) and reduction (RED). The kinetic equations that determine the rate of reaction in each step are expressed in Table 1 below. A value of 0.01 for the relaxation time constant \(\tau\) was selected to reach full conversion (or equilibrium) of the reactants in the reactor volume.  
An illustration of the GSR reactor profile (product stream composition and temperature) corresponding to one of the H\({}_{2}\) plant simulations is presented in Figure 3.  
The equilibrium constants for the WGS and SMR reactions are a function of thermodynamic properties and dependent on temperature [37], as represented by Equations (4) and (5).  
\[K_{SMR}=1.2\times 10^{13}e^{(\frac{-223.080}{RT})} \tag{4}\]  
\[K_{WGS}=0.0177e^{(\frac{36.850}{RT})}. \tag{5}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Step** & **Reaction** & **Kinetics** \\ \hline REF & \(CH_{4}+H_{2}O\leftrightarrow CO+3H_{2}\) & \(R_{SMR}=\frac{1}{\tau}\left(p_{CH_{4}}p_{H_{2}O}-\frac{p_{CO}p_{H_{2}}^{2}}{K_ {SMR}}\right)\) \\  & \(CO+H_{2}O\leftrightarrow CO_{2}+H_{2}\) & \(R_{WGS}=\frac{1}{\tau}\left(p_{CO}p_{H_{2}O}-\frac{p_{CO}p_{H_{2}O}}{K_{nGS}}\right)\) \\ \hline OX & \(O_{2}+2 Ni\to 2 NiO\) & \(R_{O_{2}}=\frac{1}{\tau}n_{O_{2}}n_{Ni}\) \\ \hline RED & \(CH_{4}+4 NiO\to 4 Ni+CO_{2}+2H_{2}O\) & \(R_{CH_{4}}=\frac{1}{\tau}n_{CH_{4}}n_{NiO}\) \\  & \(H_{2}+NiO\to Ni+H_{2}O\) & \(R_{H_{2}}=1\,n_{H_{2}}n_{NiO}\) \\  & \(CO+NiO\to Ni+CO_{2}\) & \(R_{CO}=\frac{1}{\tau}n_{CO}n_{NiO}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Reactions and kinetic expression for GSR.
## 3 Results and Discussion
### Optical Stamp Effects on the Film Transfer  
In our previous paper [16], we performed LIFT of Fn-apatite donor films prepared on a hard C/PET support onto a soft shock-absorbing PDMS receiver. As a result, even the brittle Fn-apatite films could be transferred with keeping their filmy shape. However, in our previous method using the C/PET support, the transferred microchips were crushed on a hard receiver substrate, such as silica glass and PET, and high-quality film transfer was not possible. Therefore, in this work, aiming at the development of a novel technology that can be applied even to hard receiver substrates, PDMS, which is soft and has excellent shock absorption, was introduced for the first time to the transparent support side to prevent the crushing of the donor films during transfer.  
Figure 3a shows schematic image of the LIFTOP process and a confocal laser scanning microscopic image of the Fn-CaP microchips transferred onto the PET receiver substrate using the LIFTOP process. For comparison, the Fn-CaP donor film was transferred by the LIFT process without the optical stamp as shown in Figure 3b. The transfer of the Fn-CaP film with retaining its filmy state was confirmed in the case of the LIFTOP process. Since each laser pulse induces the transfer of the donor film to the receiver substrate [16], the transferred Fn-CaP microchips had a shape corresponding to the laser beam spot. On the contrary, most of the donor film was crushed and deposited sparsely on the PET receiver substrate in the latter case. Therefore, the usage of the PDMS-coated support, namely optical stamp, is effective for higher quality film transfer onto the hard substrate.
## 2 Methodology
### Equipment System Description  
Figure 1 shows the proposed experimental setup of the IoT system used to evaluate the performance of a long-distance IoT communication system operating at 2.4 GHz. The system employs two RF modules, namely an NRF24L01 model; two computers; two microcontrollers (Arduino Uno); and the custom-designed antennas under test. The system comprises two primary units--the transmission unit (TxU) and the reception unit (RxU)--each configured with specific components to optimize overall performance.
# Progress of Organic/Inorganic Luminescent Materials for Optical Wireless Communication Systems
## 2 Luminescent Materials for VLC Systems
### Halide Perovskite Nanocrystals
In this scenario, better color quality in these materials is obtained (related  
Figure 4: (**a**) Photographs under UV light (\(\lambda\) = 365 nm) and corresponding (**b**) PL spectra of different 10–CQDs-X-PNC colloidal solutions, which were generated by varying concentration of perovskite (X). (**c**) CRI measurements achieved for 10-CQDs–X-PQDs based WLEDs, \(X\) = 0.2, 0.5, and 1.5. Reproduced under terms of CC–BY license [90]. Copyright 2021, authors, John Wiley and Sons, Inc. (**d**) Frequency response measurements for green- and red-emitting phosphors converters (CsPbBr3 PNCs + nitride phosphor) in absence and presence of commercial blue-LED. (**e**) BERs measurements by varying data rate, applying forward error correction (FEC) limit labeled. Inset of Figure 4e shows eye diagram of 2 Gbit/s data rate where a clear open eye is depicted. Reproduced under terms of CC–BY license [8]. Copyright 2016, Authors. American Chemical Society. (**f**) BERs measurements for air–stable red– and yellow–emitting CsPb(Br/I)3 and CsPb3 PNCs–PMMA composites (PNCs–PMMA) deposited on a blue-light \(\upmu\)LED. BERs was also estimated in absence of light converter for comparative purposes. Reproduced with permission [9]. Copyright 2021, American Chemical Society. with high PLQY) as covering a broad range of the UV-vis-NIR spectrum. Thus, recent state-of-the-art developments address the types of devices, diverse architectures, and novel strategies required to improve the way that the data/information is transferred [94; 95]. At this point, the rate and capability required to expand the frequency where the communication is travelling is increased. In this actual contribution, we study the most significant luminescent elements, which are the backbone and initial step required to create the suitable integrated system for VLC.
## 4 Case Study
### Influence of P2G Capacity and Carbon Sequestration Capacity on Operation Results  
This paper considers the combination of P2G heat recovery and carbon capture technology. However, in Scenario 4, due to the limitation of carbon sequestration capacity, a large amount of captured CO\({}_{2}\) is re-discharged, while in Scenarios 5 and 6, after removing the CO\({}_{2}\) consumed and sequestration of P2G methane production, some of the captured CO\({}_{2}\) can only be discharged. Moreover, wind abandonment cannot be fully absorbed due to the impact of P2G capacity ceiling in Scenario 6. Therefore, it is necessary to analyze the influence of different P2G capacity and carbon sequestration capacity configurations on system operation. The total system cost operation results under different capacity configurations are shown in Figure 10.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Scenario**} & **Total Cost** & **Wind Abandonment Cost** \\  & **/10\%** & **/10\%** \\ \hline With thermal energy storage & 192.87 & 3.77 \\ Without thermal energy storage & 195.61 & 3.83 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Impact of thermal energy storage on low-carbon economics dispatch.  
Figure 8: CHP heat output of Scenarios 5 and 6.  
As can be seen from Figure 10, when the P2G capacity is lower than 500 MW, the system can absorb more wind power with the increase of P2G capacity, and the total system cost shows a downward trend. When the P2G capacity is greater than 500 MW, the total system cost will not be affected by the further increase of P2G capacity because the wind power has been fully absorbed.  
When the carbon sequestration capacity is low, P2G incurs an extra carbon purchase cost, which affects its gas production efficiency. When the carbon sequestration capacity is high, CO2 that is not sequestered decreases and in consequence the carbon transaction cost is reduced, resulting in a less system operational cost. When the carbon sequestration capacity is greater than 1900 t, P2G methane production and CO2 obtained from sequestration and capture can be fully supplied, and the total cost of the system will not change.  
Figure 9: Effect of heat energy storage on heat output of the system. (**a**) Without heat storage device; (**b**) With heat storage device.
## 2 Rotor Equivalent Three-Phase Winding  
To achieve a low harmonic content in the Magneto Motive Force (MMF), the stator winding of a common IM is well distributed along the periphery with a sufficient number of slots per pole per phase.  
In such a case, only the first harmonic of air-gap flux can be considered, and spatial behavior of induced voltage and current, within the rotor bars, exhibits a sinusoidal waveform, as shown in Figure 1.  
A useful trick is considering a three-phase rotor equivalent winding sinusoidally distributed in the rotor slots, in order to properly set the rotor current and compute  
Figure 1: Induced voltage and current spatial distribution in the slots of a squirrel cage rotor when the air-gap flux density \(B_{\mathrm{g}}\) is sinusoidal. \(v_{sl}\) indicates the relative speed between the stator field and the rotor; \(z_{\mathrm{bar}}=r_{\mathrm{bar}}+\mathrm{j}r_{\mathrm{bar}}\) is the bar impedance; \(i_{\mathrm{bar}}\) and \(e_{\mathrm{bar}}\) are the bar-induced voltage and current; \(L_{\mathrm{sk}}\) is the stack length.  
the rotor flux linkages. The complete procedure to design such a winding is described in [8]. For low rotor frequencies, the rotor bar current distribution can be considered uniformly distributed.  
The number of conductors per phase of the rotor winding is fixed to have the same number of effective conductors as the stator winding:  
\[N_{\rm r}\,k_{\rm wr}=N_{\rm s}\,k_{\rm ws} \tag{1}\]  
This equivalence is made to facilitate the rotor current and flux linkage computation, since no transformation coefficient is needed. The condition (1) makes the stator and rotor magnetizing component of the synchronous inductance to be equal, since the two windings share the same magnetic path for the magnetizing flux:  
\[L_{\rm m}=\frac{3}{\pi}\frac{\mu_{0}}{g}\left(\frac{N_{\rm s}k_{\rm ws}}{2p} \right)^{2}\!D_{\rm i}\,L_{\rm stk} \tag{2}\]  
where \(g\) is the magnetic air-gap, which considers saturation and the Carter coefficient.  
Using this three-phase equivalent winding, it is possible to apply the Park transformation even for cage winding deriving \(d\) and \(q\) quantities in the RFO reference frame.
# Improving the Robustness of AI-Based Malware Detection Using Adversarial Machine Learning
## 7 Results
### Results Obtained after Performing Adversarial Attack  
The classifiers that the authors developed were then attacked using the FGSM adversarial attack. Adversarial samples were generated for each class with different epsilon values, namely, 0.01, 0.1, and 0.15, and then were passed into the model to check its behavior against them. Figure 17 shows the results obtained after performing adversarial attack. The results of this attack are explained below.  
1. Random Forest: The random forest classifier was observed to be relatively more affected by the FGSM attack with an epsilon value of 0.1 and above. With the 0.01 epsilon value of the model, the confidence level was significantly dropped but did not misclassify for maximum classes. There were some classes, such as like Nestha and Other, for which the images were misclassified with a confidence level of 46% and 53%, respectively. The Vilsel class, which was classified with 100% accuracy before the attack, had a confidence level of 68% for the epsilon value of 0.01. The overall accuracy for the class for the epsilon value 0.1 was dropped by around 16% from 100% to 84.52%, whereas the Hlux class' accuracy was observed to be 83.66%, which was 100% before attack. The attack performed with 0.15 epsilon proved fatal as the average accuracy drop was 40%.  
Figure 17: Results obtained after performing adversarial Attack.  
2. CNN: Compared with the random forest classifier, CNN was relatively less affected by the attack for an epsilon value of 0.1 and above. Still, for 0.01, it was similar to random forest classifier. Only the confidence level dropped for classes such as Amonetize, Fasong, InstallCore, Regrun, Stantinko, Visel, etc., which had very high accuracy before attack. However, the classes which had very poor accuracy were misclassified even with a 0.01 epsilon value, such as Expiro and Neshta, whose accuracy dropped from 59.09 to 39% and 72.22 to 63.40%, respectively. For an epsilon value of 0.1, the most affected classes were Autorun, whose accuracy dropped from 86.36% to 52.42%, and Neshta, whose accuracy dropped to 31.58%. The accuracy for each class was dropped around 30-35% for the epsilon value of 0.15.
3. EfficientNet B0: This classifier proved to be least affected by the attack as compared to the previous two models. Only the Neshta class was seen to have misclassification for 0.01 epsilon values. During the attack with 0.1 epsilon, the classes were significantly impacted in terms of misclassification. The most affected classes were Elex, Expiro, and Other (Benign), with 72.34%, 68.42%, and 70.22% accuracy. As seen earlier in the before the attack section and after attack, Neshta is the only class with the highest misclassification rate for all classifiers developed by the authors. The 0.15 epsilon caused a 30% drop on average in the accuracies of all classes of the dataset.  
The graphs of some classes from the MaleVis dataset shown below give a comparative analysis of the effect of the attack for all the three epsilon values on each of the classifiers \(\varepsilon\).
## 5 Advantages of the U-Smile Method
### Robustness to Class Imbalance and Collinearity  
U-smile method's robustness to both class imbalance and collinearity between variables is particularly valuable in real-world applications where imbalanced datasets and correlations between variables are prevalent. VS methods providing global evaluation measures are often biased towards the majority class under imbalanced scenarios, and therefore may fail to detect improvements in the minority class. U-smile method's weighted overall coefficients of Level 3 reflect class-size proportions of the dataset and enable a balanced global evaluation of a variable's usefulness. Table 1 summarizes U-smile method's effectiveness across different levels of class imbalance and collinearity.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Problem Characteristic** & **Example Use Cases** & **How Is the U-Smile Method Effective?** \\ \hline \hline  & Imbalanced scenario & \\ \hline Extreme (severe) class imbalance & & U-smile method detects prediction \\ with event (minority) class & Rare diseases research, financial fraud & improvement in the minority class and \\ prevalence \(\leq\) 1\% & detection, IT systems anomalies detection & reduction in the reference model’s overfitting \\  & & to the majority class \\ \hline High to moderate class imbalance & Predicting post-surgery complications, & Traditional VS methods are often biased \\ with event (minority) class & early detection of machine failures, credit & towards the majority class, while the U-smile \\ prevalence of 10–30\% & risk assessment & method can identify variables that are useful \\  & & for both outcome classes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Effectiveness of the U-smile method across different levels of class imbalance and collinearity between variables.
# An Integration Framework for the Inpainting and Colorization of Arbitrary Masked Grayscale Images
## 3 Methodology
### Component Models
#### 3.2.2 Mask-Aware Transformer (MAT)  
MAT [2] is a state-of-the-art framework tailored for large-hole image inpainting, combining the strengths of transformers and convolutional architectures. It effectively handles high-resolution images by modeling long-range contextual interactions while maintaining computational efficiency.  
The MAT architecture includes three main components: a convolutional head for feature extraction, a transformer body for inpainting, and a convolutional tail for high-resolution reconstruction. The convolutional head employs a series of layers to downsample input images and generate feature tokens, which are then processed by the transformer body. The transformer body incorporates an adjusted transformer block and a novel multi-head contextual attention (MCA) mechanism, ensuring efficient non-local dependency modeling. These innovations enable MAT to process valid tokens selectively, reducing computational overhead and improving performance.  
The adjusted transformer block addresses challenges associated with training on large masked regions by replacing residual learning with fusion learning and eliminating layer normalization. This design improves optimization stability and prevents issues such as gradient explosions. Additionally, MCA utilizes a dynamic mask to focus attention on valid regions, ensuring accurate context aggregation and reducing blurriness in reconstructed images.  
MAT also includes a style manipulation module, which introduces stochastic variations for pluralistic generation, allowing the framework to produce diverse yet semantically consistent completions. The combination of these features positions MAT as a leading solution for high-fidelity inpainting across challenging scenarios.