esa-sceva/satcom-chunk-collection huggingface dataset:
# Metal-Organic Frameworks Characterization via Inverse Pulse Gas Chromatography
## 3 Applications of IGC for the Characterization of MOFs
### Hkuet-1
For the commercially available MOFs, like HKUST-1 (Basolite C300), Fe-BTC (Basolite F300), MIL-53 (Basolite A100), and ZIF-8 (Basolite Z1200), a preconcentration process for sensitive sensing of nitroalkanes was developed, and the results were associated to those of the Tenax TA porous archetype. In this study, HKUST-1 showed an enrichment factor 109-fold higher than that of Tenax, as revealed by the high value of \(\Delta H_{\text{ads}}\) for nitromethane (Figure 8), and a dispersive element of surface energy values (\(\gamma_{S}^{D}\)) above 100 mJ m\({}^{-2}\). As predicted, the enthalpy of adsorption values for nitroalkanes on HKUST-1 (relatively basic) were higher than those on Fe-BTC (Figure 8) since the nitroalkanes were slightly acidic due to the electron-withdrawing effect of nitro groups. Figure 8: Enthalpies of adsorption for regular and nitroalkanes on all sorbents investigated in this study. Enthalpies in kJ mol\({}^{-1}\). Analytes: nitromethane (NM), nitroethane (NE), 1- and 2-nitropropane (1 NP and 2 NP), as well as pentane (C5), hexane (C6), and heptane (C7). Reprinted with permission from [38]. Copyright (2018) Elsevier. Figure 7: Enthalpy of immersion for Fe-BTC and HKUST-1 for the three adsorbates. Reprinted with permission from [58]. Copyright (2015) Elsevier. To correlate IGC measurements to a frontal technique, the results of IGC experiments were compared to micro-breakthrough ones with downstream thermal desorption experiments. HKUST-1 also displayed impressive improvement factors above 2000 while sampling at a nitromethane source of 1000 ppm.
# Analytical Modeling of the Maximum Power Point with Series Resistance
## 4 Analytical Expression for the Series Resistance
### Accuracy of the Approximation  
The accuracy of Equation (15) decreases with increasing series resistance. For sufficiently large \(r\), the term involving \(z_{\rm{mpp}}\) in the numerator on the right-hand side of Equation (11) will not be small in comparison to \(V_{\rm{mpp}}/V_{\rm{t}}\), implying that Equation (15) will be less accurate. Therefore, it is relevant to determine the value of the series resistance, \(r_{\rm{L}}\), until the derived model gives the acceptable results. The value \(r_{\rm{L}}=r_{\rm{max}}/3\) is proposed as a rule of thumb. This corresponds roughly to \(z_{\rm{mpp}}\approx 1/e\), which seems a natural choice since for \(z_{\rm{mpp}}\geq 1/e\), the Taylor expansion in Equation (9) should not be applicable as \(z_{\rm{mpp}}\) would be larger than the convergence radius of the expansion. Determining the actual value of \(r\) that makes \(z_{\rm{mpp}}=1/e\) would require solving simultaneously \(z_{\rm{mpp}}-1/e=0\) and Equation (8), which is rather counterproductive, since the main point of making use of Equation (15) is to avoid solving Equation (8) numerically.
## 2 Materials and Methods
### Equipment  
The structures of PPY and Ag NPs/PPY hybrid nanocomposites were studied by attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy using a spectrometer Jasco 4200 coupled with an ATR JASCO PRO 470-H accessory. The samples were measured directly by placing them on the ATR device's crystal and pressing with a controlled force, in the following conditions: the spectral range of 4000-500 cm\({}^{-1}\), resolution 4 cm\({}^{-1}\), scans number/spectrum: 50 accumulations. Raman spectroscopy measurements were performed with a Raman-dispersive spectrometer-LabRam HR Evolution, Horiba Jobin Yvone, France, equipped with laser extinction of 633 nm, acquisition time 15 s, and 100 accumulation/spectrum. The Raman spectra were used to characterise the disorder degree in the synthesised nanocomposite. The morphology of the samples PPY and Ag NPs/PPY nanocomposite was studied by scanning electron microscopy (SEM) using a Carl Zeiss SMT FESEM-FIB (Scanning Microscope Tunnelling Field Emission Scanning Electron Microscope-Focused Ion Beam) Auriga type scanner in the following conditions: the tension of acceleration 10 kV and magnitude of 20 and 50 k. The elemental analysis (energy-dispersive X-ray spectroscopy (EDX)) was performed with a dispersive energy probe of Inca Energy 250 type Oxford Instruments Ltd. England coupled to SEM. The electric bioimpedance measurements were carried-out with a precision LCR (Inductance Capacitance Resistance) meter, AGILENT E4980 A type, in the frequency range of 1-300 kHz. The configuration was in two points measurement, the simplest set-up method for electric bioimpedance measurements [4], by using a bioimpedance sensor made of the two obtained electrodes (Figure 3).
# A Spatial Fuzzy Co-Location Pattern Mining Method Based on Interval Type-2 Fuzzy Sets
## 4 Methods
### A Mining Method Based on Spatial Cliques
#### 4.1.5 Filtering Prevalent FCPs  
For a candidate FCP, after obtaining all its row-instances (these row-instances make up its table-instance) according to **Definitions 8** and 9, we can calculate the upper bound participation index and lower bound participation index of the FCP. Then, according to the upper bound participation index, the lower bound participation index, and the prevalence threshold, we can filter prevalent FCPs. Therefore, we propose two pruning strategies to filter complete and correct prevalent FCPs from candidate FCPs.  
**Pruning strategy 1 (Fuzzy feature pruning):**_In a candidate FCP, for any of its fuzzy features \(Fuzz\_f_{i}\), if the upper bound participation ratio of the fuzzy feature is less than the given prevalence threshold \(min\_prev\), the candidate fuzzy pattern is absolutely non-prevalent._  
Proof.: For a candidate fuzzy co-location pattern \(\mathbf{c}\), its upper bound participation index \(\overline{\Pi}(\mathbf{c})=min_{i=1}^{k}\{\overline{\Pi}(\mathbf{c},\textit{Fuzz\_f }_{i})\}\) and \(\overline{\text{PR}}(\mathbf{c},\textit{Fuzz\_f}_{i})\) is the upper bound participation ratio of the \(i\)th fuzzy feature in \(\mathbf{c}\). If the upper bound participation ratio of the fuzzy feature \(Fuzz\_f_{i}\) is less than the given prevalence threshold, that is, \(\overline{\Pi}(\mathbf{c})=min_{i=1}^{k}\{\overline{\text{PR}}(\mathbf{c},\textit{Fuzz\_f }_{i}\mathbf{c})\}<min\_prev\), the upper bound participation index of is less than the given prevalence threshold \(min\_prev\). In this case, according to Definition 10, is absolutely non-prevalent. \(\square\)  
**Pruning strategy 2 (Absolute table-instance pruning):**_For a candidate FCP \(\mathbf{c}\), if the lower bound participation index of its absolute table-instance \(\Delta\mathbf{\mu}(\mathbf{c})\) is greater than or equal to the given prevalent threshold \(min\_prev\), then the FCP is an absolutely prevalent FCP._  
Proof.: For a candidate FCP \(\mathbf{c}\), its absolute table-instance is included in its table instance, so the lower bound participation index of pattern \(\mathbf{c}\) is greater than that of its absolute table-instance API(\(c\)), that is, \(\underline{\text{PI}}(c)\geq\)(\(c\)) API(\(c\)). If API(\(c\))\(\geq\)\(min\_prev\), then \(\underline{\text{PI}}(c)\geq\) API(\(c\))\(\geq\)\(min\_prev\), according to Definition 10, \(c\) is absolutely prevalent. \(\square\)
# Design and Application of New Aeration Device Based on Recirculating Aquaculture System
## 3 Results
### Dissolved Oxygen Distribution Uniformity  
Based on the data obtained from the tests, Table 5 shows the rates of change in dissolved oxygen distribution uniformity under various NAD operating conditions.  
In the absence of artificial intervention, DO in pond water decreases as the water depth increases [30]. According to the test results in Figure 7, it can be concluded that, with the activation of the NAD, not only has the DO in the pond water tank increased, but the uniformity of its distribution has also increased, indicating the exchange among water layers in the RAS tank. On one hand, in the tests where only one of the two components was operated, it was evident that the impeller aerator increased the dissolved oxygen distribution uniformity more efficiently than the microporous aerator. On the other hand, the pond water DO distribution uniformity was greater following the NAD orthogonal tests compared with the experiments testing the individual components.
## 3 Methodology
### Stage 2: Systematic Literature Review  
The second stage involved a systematic review of the selected papers, followed by a scoping review to explore a broad range of themes and identify gaps in the literature. The  
Figure 3: Proportions of research documents by subject area. Source: Scopus.  
Figure 2: Proportions of research documents by types.  
systematic review followed the PRISMA guidelines [31], ensuring that only peer-reviewed journal articles published between 2022 and 2024 were included in the analysis. After this, a scoping approach was employed to explore the emerging themes and research gaps, particularly in areas such as blockchain-AI integration in education.  
This combined approach allowed for both rigorous filtering of high-quality papers and a broader exploration of the key trends, themes, and future directions in blockchain education research [32; 33].  
Our inclusion criteria required that the research titles contain the terms \"Blockchain\" and \"Education\" (or related terms such as \"Higher Learning\", \"Higher Education\", \"Online Learning\", etc.). To ensure the study reflects the most current research, we limited our selection to peer-reviewed journal articles published between 2022 and 2024. Papers published before 2022, non-journal publications, and those not directly addressing blockchain in education were excluded. Additionally, duplicate papers were eliminated to avoid redundancy.  
The selection process followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines [31], as illustrated in Figure 4.  
Figure 4: PRISMA.  
The initial search yielded 164 papers in total. Through our rigorous screening process, we excluded all papers published before 2022, 72 papers that did not meet research title requirements, 36 papers not published in journals, and 6 duplicate papers. This meticulous process resulted in a final selection of 25 research papers that met all our criteria for full-text analysis.
# A Cooja-Based Tool for Coverage and Lifetime Evaluation in an In-Building Sensor Network+
## 4 Implementation
### An Example  
WSN-Maintain is implemented using the Java programming language. Figure 3 shows a snapshot of the implementation of WSN-Maintain and Cooja that simulates a data-gathering application. In the floor plan shown, 15 sensor nodes are placed arbitrarily in seven rooms, where Node Numbers 11 to 15 are coverage redundant nodes. All rooms have a coverage requirement equal to one, except Rooms I and II have requirements equal to two and three, respectively. WSN-Maintain visualises the coverage map with colours ranging from red to green. The red area (darker colour if the figure is in grayscale) means the coverage requirement is not satisfied at all, while green (lighter colour in grayscale) means the requirement is satisfied. For example, when a user turns Node 8 off, WSN-Maintain will turn on redundant Node 12 to maintain coverage. These changes will be informed to Cooja through a file that  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Default Value** \\ \hline Voltage (\(V\)) & 3 V \\ Current draw of microprocessor when running (\(I_{m}\)) & 1.8 mA \\ Current draw of microprocessor in low power mode (\(I_{l}\)) & 0.0545 mA \\ Current draw of communication device in receive mode (\(I_{r}\)) & 19.7 mA \\ \hline \hline \end{tabular}
\end{table}
Table 4: Energy consumption parameters [1,7].  
contains the on/off status of all nodes. Cooja will act accordingly by turning off Node 8 and turning on Node 12.
# Coupled Spatiotemporal Characterization of Monsoon Cloud Cover and Vegetation Phenology
## 4 Discussion
### Implications for Jabal Dhofar
This is especially likely in cases where the species composition of vegetation communities changes appreciably through time (e.g., invasive species), where pests cause species-selective mortality (e.g., bark beetle infestation in conifers), or where critical weather/climate transitions are reached (severe drought or frost). All of these potential causes of nonstationarity are particularly likely in ecosystems that are made susceptible by degradation, such as that of Jabal Dhofar. The EOF analysis that is presented here--like any characterization based on historical data alone--has no power to predict such transitions in the future. Finally, the inferred link between cloud cover and vegetation phenology suggested by this analysis relies on soil moisture. While some measurements of soil moisture have been made in the Dhofar study area [30], many fundamental questions related to the inferred cloud cover-soil moisture-vegetation abundance causal mechanism still require field validation. In particular, continuous in situ measurement of soil moisture at sites controlled to have a minimum of disturbance from humans or livestock could then be compared to ongoing near-daily observations of cloud cover and vegetation abundance, as used in this study. One hypothesis predicts that soil moisture should increase with the onset of monsoon cloud cover and begin to subsequently decrease when the clouds clear and evapotranspiration increases. Regions with higher soil moisture would be expected to have more frequent or longer duration cloud cover, as well as more abundant vegetation and/or slower rates of senescence. As this investigation would only strictly require accurate relative (not absolute) measurement of soil moisture, a network of relatively inexpensive frequency-domain reflectometry probes could be used.
# Biologically Derived Gels for the Cleaning of Historical and Artistic Metal Heritage
## 4 Organogels for Organic Coatings Removal
### Bio-Based Organogels for Cleaning  
#### 4.3.1 Biopolymers  
Biopolymers are naturally occurring polymers (e.g., polyhydroxyalkanoates--PHAs) or derived from bio-based monomers (e.g., polylactide--PLA) [110]. Their most evident green quality is that starting resources are not petroleum-based. Biodegradability is not implicit, indeed only those biologically synthesized are inherently biodegradable. That is the reason for the increasing number of publications on life cycle analysis (LCA) of polymers [87].  
Biopolymers appear as valid candidates to replace petrochemical counterparts; however, the generally high cost of production draws some boundaries for wider diffusion. They are broadly employed for biomedical applications (e.g., suture threads [111], drug carriers [112]) because of their natural deterioration and human-compatibility, without any side effects. However, they have also spread in fields ranging from food industry to waste management, passing through cosmetics and household products [113, 114, 115, 116, 117].  
To develop reliable bio-organogels for metal conservation, a deep investigation on these innovative polymers should be carried out to assess their suitability as thickening agents and the fulfillment of conservation ethics criteria. Regrettably, limited research was carried out in this field and to the best of the authors' knowledge, only one biopolymer has been explored so far: polyhydroxybutyrate (PHB).  
PHB belongs to the family of polyhydroxyalkanoates (PHAs), and therefore, to the esters class. PHAs can be produced by more than 300 bacteria and archaea species, in both oxic or anoxic environments. The synthesis occurs either during the growth phase or under stress conditions (e.g., depletion of vital nutrients) while in excess of carbon [118]. PHA is hoarded as intracellular granules in the cytoplasm and kept as energy storage.  
PHAs generate high appeal in many industrial sectors thanks to their inherent chemophysical features, like well-known and spread petrochemical polymers such as polypropylene (PP) and polystyrene (PS). Notably, PHAs' natural degradation into carbon dioxide, water, and biomass requires about two months [119]. Nonetheless, as reported in up-to-date research, the current cost of PHAs is nearly six times higher compared to fossil-sourced plastics, clearly restricting these biopolymers to a wider use [118].  
PHB is a semi-crystalline polymer synthesized by many microorganisms (e.g., _Alcaligenes_ spp., _Bacillus_ spp., _Azotobacter_ spp., _Pseudomonas_ spp.) from renewable feedstocks rich in sugars [120, 121]. From a green perspective, its core advantages are bio-origin, biodegradability, and biocompatibility. From a hands-on point of view, it owns thermoplastic processability and solubility in polar solvents but not in water [122]. In the sporadic applications in art conservation, the form poly-3-hydroxybutyrate (P3 HB) has been chosen [123, 84, 107, 124] (Figure 2).
# Multiple Feature Integration for Classification of Thoracic Disease in Chest Radiography
## 4 Experimental Results
### Classification Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Feature**} & \multirow{2}{*}{**Dataset Division** (\(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\bmbmbmbmbmbmbmbmbmbmbmbmbmbmbmbmbmbmbmbmbm{ \bmbmbmbm{  }}}}}}}}}}}}}\)) **} & \multirow{2}{*}{**SIFT**} & \multirow{2}{*}{**SIFT**} & \multirow{2}{*}{**SIFT**} & \multirow{2}{*}{**SIFT**} & \multirow{2}{*}{**SIFT**} & \multirow{2}{*}{**SIFT**} & \multirow{2}{*}{**SIFT**} & \multirow{2}{*}{**SIFT**} \\ \cline{5-8}  & & **(\(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\bmbmbmbmbmbmbmbmbmbmbmbmbmbmbmbmbmbmbmbmbmbmbmbm{ \bmbm{ \bmbm{  }}}}}}}}}}}}}}\))** & **SIFT** & **SIFT** & **SIFT** & **SIFT** & **SIFT** \\ \hline \multirow{11}{*}{F1} & \multirow{2}{*}{SIFT+GIST+LBP+HOG} & \multirow{2}{*}{Training:} & 0.7276 & 0.7646 & 0.7924 & 0.7124 & 0.7937 & 0.8110 & 0.8686 \\ \cline{1-1} \cline{5-8}  & & GIST & & 0.7436 & 0.7651 & 0.7921 & 0.7292 & 0.7937 & 0.8101 & 0.8227 \\ \cline{1-1} \cline{5-8}  & LBP & & 0.7532 & 0.7627 & 0.7869 & 0.7454 & 0.7937 & 0.8167 & 0.8167 & 0.8328 \\ \cline{1-1} \cline{5-8}  & & & 0.7532 & 0.7627 & 0.7869 & 0.7454 & 0.7937 & 0.8167 & 0.8167 & 0.8328 \\ \cline{1-1} \cline{5-8}  & & & 0.7532 & 0.7627 & 0.7869 & 0.7869 & 0.7869 & 0.7869 & 0.7869 & 0.7869 \\ \cline{1-1} \cline{5-8}  & & & 0.7532 & 0.7627 & 0.7869 & 0.7869 & 0.7869 & 0.7869 & 0.7869 & 0.7869 & 0.7869 & 0.7869 \\ \cline{1-1} \cline{5-8}  & & & 0.7532 & 0.7627 & 0.7869 & 0. To accurately compare our proposed approach with the reference baseline model in the classification of all 14 pathologies, we divided the dataset as follows; 70% for training, 10% for validation, and 20% for testing used for the pretrained DenseNet-121; the preprocessing step is described in Section 4.1. The feature integration approach reaches 84.62% classification accuracy, which is higher than the 80.97% accuracy of the pretrained DenseNet-121 model. The experiment reveals the effectiveness of the feature integration strategy in extracting representative and discriminative features to describe X-ray images. Table 3 indicates that our pretrained DenseNet-121 model achieved very competitive accuracies in the diagnosis of the 14 thorax diseases. Note that the authors of [61] trained their model with 180,000 images from the PLCO dataset [62] as extra training data.
## 3 Use of AI in Wireless Communications  
For many years computers have assisted humanity in solving a lot of challenging and time-consuming tasks. However, most of these tasks like solving an equation, beating humans at a strategy game, or in our case calculating the best BS placement for optimal coverage do not require any intelligent behavior from these systems. When AI came into the spotlight and the means to implement it were readily available, its potential applications were considered in all disciplines including telecommunications. AI has been used in the telecom industry [22; 23; 24; 25; 26; 27; 28; 29] in multiple ways.  
1. Network Optimization: AI algorithms are used to analyze reception feedback and predict needs before they arise, optimize the network topology as well as improve efficiency and performance.
2. Customer Service: AI is used to provide customer support with personalized interactions by employing virtual assistants and chatbots.
3. Fraud Detection: AI is used to analyze the behavior of users and patterns in data in order to detect fraudulent or exploitative use of the network and protect both the users and the providers.
4. Targeted Marketing: AI is used to create customized advertising and targeted marketing campaigns to maximize customer engagement and reach.
5. Security and Surveillance: Like fraud detection, AI is targeting security threats such as cyberattacks against user data and networks.  
One of the most important applications of AI in telecommunications is Network Optimization. In the past, this term used to mean the configuration of network components to improve some basic characteristics, such as power or coverage. In 6G wireless communications, a huge next step is taken; it consists of applying the AI for the continuous reconfiguration of the hardware used in order to compensate for demands in the network like more connections or higher data speeds. This is envisioned as Intelligent Radio [22]. Nevertheless, the possibilities are even greater, as the environment itself can now be configured [30] to play an important role in the creation of Intelligent Radio. This is made possible  
Figure 2: RIS-aided wireless communications controlled by AI.  
with the creation of SREs. These environments are envisioned, as mentioned above, with the use of RISs.