esa-sceva/satcom-chunk-collection huggingface dataset:
# Iterative Analog-Digital Multi-User Equalizer for Wideband Millimeter Wave Massive MIMO Systems
## 4 Analog-Digital Receiver Design  
In this section, we start by deriving the proposed fully iterative multiuser equalizer, and then, a complexity comparison with the sub-optimal two-step approaches designed for full connected architecture [31] is presented.
## 2 Hybrid System and Techno-Economic Analysis Method
### The Design of Hybrid System Components
#### Fuel Cell  
A hydrogen FC is an electrochemical-based power generation system. It combines hydrogen with oxygen to generate the electricity through chemical reaction. There are several kinds of FC technology. In this study, a proton exchange membrane (PEM) FC is used due to essential simplicity and executive reaction with low temperature. The electrolyte is a solid polymer and platinum is the catalyst. The protons are mobile through the polymer to the cathode, while electrons are transferred through the electrode to external load [66]. The FC is fed by hydrogen stored in a tank and oxygen from the air. The capacity of the FC is considered 100 kW. Technical and economic specification is considered 3000 $/kW capital cost, 2500 $/kW replacement cost, 0.03 $/hr O and M cost [42]. The life cycle is considered 40,000 h. The efficiency curve is shown in Figure 3, where the intercept coefficient is 0.0003 kg/hr/kW, and the curve's slop is 0.21 kg/hr/kW.
## 5 Results and Analysis  
HetNets will have an entirely different network topology than the traditional homogenous network as represented in Figure 6. In a traditional network, all BSs space-out (single type-macro only) have distinct areas of coverage with a ubiquitous model for macro BSs locations. However, in HetNets, which has a nested combination of macro cells overlaid by small-cells design, the BSs are placed randomly according to the desired metric (like coverage requirement, high data rate requirements, etc.). Therefore, the nested BS locations need to be modeled by using some random process that corresponds to an entirely different network topology as compared to the traditional macro-only network.  
HetNet comprises collections of multiple/single channels, which are also known as wireless links, sharing both frequency and space. Each channel has its own set of transmitters and receivers. In communication networks, channels can be simple or complicated. Point-to-point (p2P) is known as the simplest one, in which a single transmitter transmits data to only one receiver (like device to device). The broadcasting downlink channel sends information from a single transmitter to multiple receivers. The multiple access channels transmit information from many receivers to a single receiver (e.g., uplink channel), with multiple users transmitting data to associated BS. Figure 7 represents the model for the wireless channel.  
Figure 6: _Cont._
# No-Reference Image Quality Assessment with Convolutional Neural Networks and Decision Fusion
###### Abstract  
No-reference image quality assessment (NR-IQA) has always been a difficult research problem because digital images may suffer very diverse types of distortions and their contents are extremely various. Moreover, IQA is also a very hot topic in the research community since the number and role of digital images in everyday life is continuously growing. Recently, a huge amount of effort has been devoted to exploiting convolutional neural networks and other deep learning techniques for no-reference image quality assessment. Since deep learning relies on a massive amount of labeled data, utilizing pretrained networks has become very popular in the literature. In this study, we introduce a novel, deep learning-based NR-IQA architecture that relies on the decision fusion of multiple image quality scores coming from different types of convolutional neural networks. The main idea behind this scheme is that a diverse set of different types of networks is able to better characterize authentic image distortions than a single network. The experimental results show that our method can effectively estimate perceptual image quality on four large IQA benchmark databases containing either authentic or artificial distortions. These results are also confirmed in significance and cross database tests.  
no-reference image quality assessment; convolutional neural network; decision fusion
## 4 Simulation Result and Discussion  
To verify the merits of the enhanced channel codes, decoding thresholds and BER simulations of the JSCC systems over OD-ISI MR channels are presented in this section.  
Table 1 provides the system decoding thresholds of the enhanced and the benchmark codes. In particular, for the IARA2 channel code in the separate coding system in [22], we employ \(1/2\)-moderate R4 JA code as the source code, and for the benchmark code pairs in JSCC systems, such as \((\mathbf{B}^{\text{s4}},\mathbf{B}^{\text{c2}})\) in [9] and \(\mathbf{B}^{\text{opt}\_1}_{\text{J}}\) in [11], the source codes in the original code pairs are reserved. As seen from Table 1, the system with enhanced channel codes possess lower decoding thresholds compared with the original counterparts.  
Figure 3 plots the BER curves of the proposed system with enhanced channel codes and original counterparts at \(p=0.05\). The number of Turbo equalization iteration is set to 5 and the maximum number of JSCJ iteration is set to 100. The frame length is fixed at 3200 bits and the protograph is generated using the progressive edge growth (PEG) algorithm [29]. It is observed that at a BER of \(10^{-5}\), \(\mathbf{B}_{\text{en}}^{\text{LARA2}}\) achieves coding gain of 0.5 dB over \(\mathbf{B}^{\text{IARA2}}\); \(\mathbf{B}_{\text{en}}^{\text{C2}}\) and \(\mathbf{B}_{\text{en}}^{\text{Iopt},1}\) have 0.5 dB and 0.6 dB gains compared with \(\mathbf{B}^{\text{C2}}\) and \(\mathbf{B}^{\text{Iopt},1}\), respectively, at a BER of \(10^{-5}\).  
Figure 4 shows the BER performance with different channel codes at \(p=0.06\). It can be observed that the enhanced channel codes all have better error performance than the original counterparts in the water-fall region. Clearly, the superiorities of the enhanced channel codes over the benchmark codes in the BER performance are consistent with the analysis results of system decoding thresholds shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(p\) & **Channel Code** & **Threshold** & **Coding Gain** \\ \hline \multirow{3}{*}{0.05} & \(\mathbf{B}^{\text{IARA2}}/\mathbf{B}_{\text{en}}^{\text{IARA2}}\) & \(-3.1/-4.1\) & 1.0 \\ \cline{2-4}  & \(\mathbf{B}^{\text{C2}}/\mathbf{B}_{\text{en}}^{\text{C2}}\) & \(-3.3/-5.3\) & 2.0 \\ \cline{2-4}  & \(\mathbf{B}_{\text{c}}^{\text{Iopt},1}/\mathbf{B}_{\text{en}}^{\text{Iopt},1}\) & \(-3.4/-4.3\) & 0.9 \\ \hline \multirow{3}{*}{0.06} & \(\mathbf{B}^{\text{IARA2}}/\mathbf{B}_{\text{en}}^{\text{IARA2}}\) & \(-2.6/-3.3\) & 0.7 \\ \cline{2-4}  & \(\mathbf{B}^{\text{C2}}/\mathbf{B}_{\text{en}}^{\text{C2}}\) & \(-2.7/-4.3\) & 1.6 \\ \cline{2-4}  & \(\mathbf{B}_{\text{c}}^{\text{Iopt},1}/\mathbf{B}_{\text{en}}^{\text{Iopt},1}\) & \(-2.9/-3.6\) & 0.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Decoding Thresholds of the Proposed System for Different Channel Codes and Source Statistics.  
Figure 3: Simulated BER results of the enhanced and the benchmark codes at \(p=0.05\).
## 5 Models Analysis
Indeed, in terms of parameters to identify, each element of GM requires two parameters (1 pole-zero couple), while for FDGM, each one needs three parameters (1 pole-zero couple and the fractal derivative order \(\alpha\)). This means that a 15-elements GM implies 31 parameters to be identified; the 15 pole-zero couples and the \(E_{0}\). Similarly, a three-elements FDGM needs ten parameters  
Figure 4: Storage modulus and loss tangent for compound A (a), B (b) and C (c): experimental data (dashed line) vs. FDGM model with three (blue line), four (green line) and five (red line) elements. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Compound A**} & \multicolumn{2}{c}{**Compound B**} & \multicolumn{2}{c}{**Compound C**} \\ \hline FDGM & NRMSE E’ & NRMSE tan\(\delta\) & NRMSE E’ & NRMSE tan\(\delta\) & NRMSE E’ & NRMSE tan\(\delta\) \\ model & (MPa) & (-) & (MPa) & (-) & (MPa) & (-) \\ \hline
3 elements & 0.0028 & 0.0273 & 0.0187 & 0.059 & 0.0513 & 0.1054 \\
4 elements & 0.0059 & 0.012 & 0.0176 & 0.0574 & 0.0251 & 0.0822 \\
5 elements & 0.002 & 0.0038 & 0.0178 & 0.0568 & 0.0251 & 0.0821 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: NRMSEs for Fractal Derivative Generalized Maxwell models composed of 3, 4 and 5 elements. to be found; the three pole-zero couples, three fractal derivatives \(\alpha_{i}\) and the \(E_{0}\). Thus, comparing the normalized errors in Tables 2 and 3 and the relative curves, it is evident that the FDGM is able to fit the real viscoelastic behavior with the minimum number of parameters for the three compounds. It is noteworthy that the number of parameters to be identified highly affects the computational load in terms of efficiency and effectiveness. In the reported study, the same function has been used for constrained minimization problems; thus, the nature of the adopted constitutive models in terms of complexity and mathematical formulation are responsible for the variation in efficacy (ability of providing good matching with experimental data) with reasonable computing time and robustness. In light of this, the comparison between the FDGM models with the GM models points out that the FDGM is preferable because of the limited number of parameters to be determined, which means a significant reduction in frequent mathematical difficulties encountered in the ill-posed problem of identification procedures from experimental data. It should be noted that, despite the great number of parameters to be identified especially in for GM model, taking advantages form the pole-zero formulation, no convergence problems have been encountered during the identification procedure.
## 3 Results and Discussion
### Analysis of the CATA Terms  
#### 3.3.1 Correspondence Analysis with 108 Attributes  
Coffee samples and the CATA terms were visually shown through correspondence analysis (Figure 1). Dimensions 1 and 2 explain a total of 59.3%. Dimension 1 was positively associated with caramelized, cocoa, coconut, dark chocolate, and fullness, and negatively related with citrus fruit, grape, lemon, lime, and malic acid attributes. Dimension 2 could be explained positively by blackberry, herblike, orange, raspberry, strawberry, and vanillin attributes, and negatively by animic, clove, musty/dusty, and tobacco attributes.  
RTD samples were positioned in quadrant 1, and Folgers samples and Coffee Shop samples were in quadrant 4. The other Blended, Colombia, and Kenya samples were in quadrants 2 and 3. BabinskiRTD was positioned near olive oil and vanilla attributes and BaristaRTD were associated with coconut and sweet attributes. FrenchCafeRTD was related to fresh, grain, and rose attributes. For Colombia and Kenya beans, brewing methods influenced flavors more than beans, but blending beans may reduce differences obtained from brewing methods. ColombiaCB and KenyaCB were explained mainly by acids and citrus fruit attributes such as acetic acid, citric acid, and malic acid as well as citrus fruit, lemon, and pear. Machine brewed coffees--ColombiaCM and KenyaCM were related to peach and cherry, respectively, and they were also close to pungent, salty, and sour aromas. Blended samples seem to be related to black tea, cinnamon, dried fruit, green, prune, and sour attributes. FolgersCB was situated near acid, bitter, burnt, pipe tobacco, and tobacco attributes, while FolgersCM was correlated with clove, overall impact, pipe tobacco, smoky, thickness, tobacco, and oily attributes. CS samples were related with overall impact and pipe tobacco, especially, TwosomeplaceCS was related to smoky and thickness attributes. CS samples were typically sold with ice in coffee shops. However, CS samples were provided without ice in this study to prevent dilution. Thus, consumers might have felt the thickness and stronger coffeeID in the CS samples, because it would be more concentrated than the regular iced coffee they consume.  
Figure 1: Correspondence analysis of consumers.  
[PERSON] and [PERSON] [24] compared cold brew coffee by dripping methods and cold brew coffee by steeping methods and each sample were extracted 3, 6, 9 and 18 h, respectively. As results of quantitative descriptive analysis (QDA) suggest, acidity, bitterness, and sweetness intensity were different by methods and brewing time. Considering steeping method was used for this study, it seemed different extraction time of each types of samples influenced consumers' sensory perception.
## 3 Results
### On Real-world Proteomic Data
#### 3.2.2 Experiment #2: Protein Solubility Classification  
Data Retrieval and Preprocessing  
The data retrieval process and be summarised as follows:  
1. from the eSOL database (eSOL database [[http://tp-esol.genes.nig.ac.jp/](http://tp-esol.genes.nig.ac.jp/)]([http://tp-esol.genes.nig.ac.jp/](http://tp-esol.genes.nig.ac.jp/))) developedintheTargetedProteinResResearchProject., containing the solubility degree (in percentage) for the _E. coli_ proteins using the chaperone-free PURE system [87], the entire dump has been collected
2. proteins with no information about their solubility degree have been discarded
3. in order to enlarge the number of samples (From the entire dump, only 432 proteins had their corresponding PDB ID.), we reversed the JW-to-PDB relation by downloading all structure files (if any) related to each JW entry from eSOL. Each structure will inherit the solubility degree from the JW entry * inconsistent data (e.g., the same PDB with different solubility values) have been discarded; duplicates have been removed in case of redundant data (e.g., one solubility per PDB but multiple JWs)
* proteins that have a solubility degree greater than 100% have been set as 100%. The (small) deviations from 100% can be ascribed to minor experimental errors. After straightforward normalisation, the solubility degree can be considered a real-valued number in range \([0,1]\).  
This first preprocessing stage leads to a dataset of 5517 proteins. As per the previous experiment, PDB files have been parsed by removing alternate models and alternate atom locations. Finally, proteins with no resolution information or whose resolution is greater than 3A have been discarded as well. This resolution-based filtering dropped the number of available proteins from 5517 to 4781. The solubility distribution within the resulting dataset is summarised in Figure 4.  
Since aim of the classification system is to discriminate between soluble versus non-soluble proteins, a threshold \(\tau\in(0,1)\) must be set in order to generate categorical output values starting from real-valued solubility degrees. Specifically, all proteins whose solubility degree is greater than \(\tau\) will be considered'soluble', whereas the remaining proteins will be considered 'non-soluble'.
## 2 Results and Discussion
### The Headspace Chemical Composition of the Residue (HS-SPME)
\begin{table}
\begin{tabular}{c l c c c c} \hline
**No.** & **Compound** & **RI** & **A** & **B** & **C** \\ \hline
1. & Toluene & \(<900\) & 0.3 & 3.2 & - \\
2. & \(\alpha\)-Pinene & 941 & 73.4 & 66.2 & 52.0 \\
3. & Camphene & 958 & 2.1 & 3.8 & 4.2 \\
4. & Verbenene & 961 & 0.6 & 0.7 & 3.5 \\
5. & Benzaldehyde & 965 & - & 0.2 & - \\
6. & \(\beta\)-Pinene & 983 & 1.0 & 0.9 & 0.3 \\
7. & Myrcene & 994 & 0.4 & 0.2 & - \\
8. & \(\Delta\)-3-Carene & 1016 & 0.3 & 0.2 & - \\
9. & \(p\)-Cymene & 1032 & 0.5 & 1.4 & 0.9 \\
10. & Limonene & 1035 & 2.9 & 4.4 & 1.5 \\
11. & \(\alpha\)-Terpinolene & 1093 & 0.3 & 0.3 & - \\
12. & \(p\)-Cymenyl & 1095 & 0.4 & 2.3 & 1.5 \\
13. & Fenchol & 1123 & 0.1 & 0.2 & 0.7 \\
14. & \(\alpha\)-Campholene aldehyde & 1133 & 0.4 & 0.4 & 0.9 \\
15. & \(trans\)-Pinocarveol & 1148 & 0.3 & 0.4 & 1.0 \\
16. & Camphor & 1153 & 0.2 & 0.1 & 0.3 \\
17. & Borneol & 1175 & 0.2 & 0.3 & 3.8 \\
18. & Terpinen-4-ol & 1185 & 0.1 & 0.1 & - \\
19. & \(p\)-Cymen-8-ol & 1194 & 0.2 & 0.4 & 0.6 \\
20. & \(\alpha\)-Terpineol & 1198 & 0.7 & 1.1 & 2.3 \\
21. & Estragole & 1204 & 0.8 & 0.9 & - \\
22. & Verbenone & 1217 & 0.4 & 0.6 & 2.4 \\
23. & \(\beta\)-Caryophyllene & 1425 & 5.7 & 2.0 & 3.0 \\
24. & \(\beta\)-Selinene & 1460 & 1.1 & 0.4 & - \\
25. & \(\alpha\)-Muuronolene & 1506 & 2.3 & 1.6 & 2.0 \\
26. & Caryophyllene oxide & 1589 & 0.8 & 0.2 & - \\
27. & Methyl dehydroabietate & 2477 & - & - & 2.7 \\ \hline \multicolumn{5}{c}{Total identified (\%)} & 95.5 & 92.5 & 83.6 \\ \hline \multicolumn{5}{c}{RI = retention indices on HP-5 MS column; **A** = solvent-free HS-SPME with the fiber PDMS/DVB; **B** = solvent-free HS-SPME with the fiber DVB/CARPDMS; **C** = the residue dissolved in CH\({}_{2}\)Cl\({}_{2}\); - = not identified; \({}^{*}\) - tentatively identified; \({}^{**}\) - correct isomer not identified.} \\ \end{tabular}
\end{table} 
Table 2: The pine resin VOCs composition obtained by HS-SPME and from the resin CH\({}_{2}\)Cl\({}_{2}\) solution after direct GC and GC-MS analysis. Derivatives of phenanthrene were also identified, but their abundance in the sample is not reliably represented in the headspace due to their low volatility. The most abundant were: 7-(1-methylethyl)-1,4a-dimethyl-1,2,3,4,4a,9,10,10a-octahydronaphthalene (2.7-9.3%), 7-(1-methylethyl)-1,4a-dimethyl-2,3,4,4a,9,10-hexahhydro-phenanthrene (1.0-7.7%), 7-(1-methylethyl)-1,4a-dimethyl-1,2,3,4,4a,9,10,10a-octahydrophenanthrene (3.4-12.2%), 3,6-dimethylphenanthrene (1.0-6.6%), 1,4-dimethoxyphenanthrene (1.7-10.0%), 1-methyl-7-(1-methylethyl)-phenanthrene (1.6-9.9%) and methyl dehydrodietate (1.0-6.6%).
###### Abstract
transfer function and a 3 \(\times\) 3 matrix based on primary chromaticities. These models are interpretable and require fewer measurements. The MDCIM (Mobile Display Characterization and Illumination Model) by [PERSON] et al. [10] is a physical model that takes the following two aspects into account: (i) a device-specific display characterization model and (ii) a method to account for ambient illuminance. It is based on objective measurement data from spectroradiometers (to characterize a particular display), spectrophotometers (for determining the reflectance properties of the objects that need to be displayed), and illuminance meters (for measuring the environmental illumination level). It improves color reproduction accuracy by considering the specific characteristics for a range of mobile displays from the Apple iPad Air 2, iPad 2017, and iPad 2018, as well as for the Samsung Galaxy S4 and iPhone XS Max [10; 11; 12; 13]. The first three displays represent LCD display technology, whereas the others are OLED displays. Good results were obtained for illuminance levels from 600 to 2000 lx. There are now modern display technologies such as virtual reality systems, lithium niobate crystal displays, and high dynamic range screens [14; 15; 16; 17], which exhibit characteristics that differ significantly and offer a different color gamut than CRTs [18]. This evolution further highlights the need to develop a new colorimetric characterization model that can be effectively applied to these emerging technologies. In this study, we focused on the most commonly used displays, namely tablets and smartphones, with predominantly LCD and OLED displays. The sRGB color space, originally designed for cathode-ray tube displays, may be considered limited for modern applications, as it does not encompass the full color gamut of modern LCD and OLED displays. Additionally, the sRGB encoding does not account for variations in ambient illumination when computing the digital values necessary for color reproduction, which can lead to mismatches under real-world viewing conditions. This limitation reduces its ability to accurately represent colors on contemporary devices. Research has consistently shown that the MDCIM overcomes the shortcomings of sRGB by accounting for both ambient lighting conditions and the specific characteristics of individual devices. However, a significant drawback of MDCIM is its dependence on device-specific characterization, which requires costly specialized equipment and a time-consuming process. This study aims to explore the differences and similarities among 11 devices characterized with the MDCIM and evaluate the feasibility of using generic parameters for different types of screens. To achieve this, this article focuses on a specific display, the iPhone XS Max.
## 5 Future Perspectives  
On the horizon of future electronics, hybrid devices emerge as pioneers at the intersection of different technologies and materials, opening new frontiers in flexible, sensitive, and high-performance electronic applications. Among these devices, OECTs, OLEFETs and  
Figure 12: (**a**) D-A naphthalene derivatives (**6**) or (**b**) multidonor benzothiadiazole derivatives (**7**) developed by [PERSON] and col. with application as optical waveguides and organic field-effect transistors with potential application in OFEWs [55; 56].  
OFEWs stand out as outstanding examples of the convergence between organic electronics and other disciplines.  
OECTs represent an exciting class of devices that leverage the electrochemical properties of organic materials to achieve amplification of electrical signals. These devices offer unique advantages, such as high sensitivity to chemical and biological stimuli, making them ideal for applications in biosensors, brain-machine interfaces, and implantable biomedical devices. In the future, OECTs are expected to be further miniaturized and optimized for increased integration into wearable and medical diagnostic devices.  
On the other hand, OLEFETs represent a unique fusion between electronics and optoelectronics, where organic field-effect transistors are combined with organic light emitters to create multifunctional devices. These devices offer higher energy efficiency and flexibility compared to traditional display technologies, making them ideal for applications in flexible displays, lighting devices, and display screens in wearable devices. With ongoing advancements in emission efficiency and material stability, OLEFETs are expected to drive the next generation of display and communication devices, enabling new forms of interaction and user experience in electronic devices.  
Finally, OFEWs represent an innovative class of devices that combine the optical waveguiding properties with the functionality of organic field-effect transistors. These devices promise to revolutionize optical communication and quantum computing by enabling transmission and processing of information at extremely high speeds and with unprecedented energy efficiency. As manufacturing techniques are refined and materials are optimized, OFEWs are expected to play a crucial role in the development of next-generation optical communication networks and scalable quantum computing.  
Together, OECTs, OLEFETs and OFEWs represent just a fraction of the exciting landscape of hybrid devices in future electronics. With continued research and development, these devices have the potential to radically transform how we interact with technology and open up new possibilities in fields ranging from medicine to quantum computing.