esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 AI Fairness in Practice
### Social Administration
#### 7.1.1 Health Care  
AI fairness can also be applied to healthcare diagnosis and treatment recommendation systems to reduce bias and ensure fairness in healthcare delivery, as some healthcare AI systems have been found to differ in the diagnosis of certain diseases among different racial groups [52; 278]. The use of a fairness-aware algorithm improves the performance of the model and provides a fairer diagnosis for all patients [279; 280].  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Method** & **Features** & **Pros** & **Cons** & **References** \\ \hline Disparate Impact Analysis & Measures disparate impact ratios & Easy to implement and interpret & Only captures one aspect of fairness (impact ratios) & [257; 258; 6; 257] \\ \hline Fairness-aware Performance Metrics & Simultaneously evaluates accuracy and fairness & Provides a holistic view of model performance and fairness & Choice of fairness metric may not fully capture desired notions of fairness & [259; 260; 261] \\ \hline Bias Detection Techniques & Identifies biases in data or model predictions & Alerts to potential fairness issues early & May require domain expertise for interpreting and addressing identified biases & [71; 262; 263] \\ \hline Algorithmic Fairness Dashboards & Real-time visualizations and metrics for monitoring & Enables continuous fairness monitoring & Complexity in designing comprehensive dashboards & [264; 265; 266] \\ \hline Model Explanation and Interpretability & Provides insights into decision-making & Facilitates understanding of model behavior and potential biases & May not fully capture complex interactions in the model, leading to limited interpretability & [267; 268; 269; 270] \\ \hline Continual Bias Monitoring & Ongoing and regular assessment & Detects and addresses emerging fairness issues over time & May require significant resources for continuous monitoring & [271; 47; 272] \\ \hline \hline \end{tabular}
\end{table}
Table 12: Regular auditing and monitoring comparison.  
AI applications in healthcare face challenges of interpretability, trust, data privacy, and security. Privacy concerns hinder data sharing, while complex AI models lack interpretability, impacting trust. Transparent and responsible data management is needed for data sharing while protecting privacy. Explainable AI can enhance trust by making AI recommendations understandable [304]. Aligning with healthcare governance measures can ensure trustworthy AI use. Addressing these issues can revolutionize medical decision making, improve outcomes, and foster equitable and patient-centered healthcare.
# Multifunctional Integrated Underwater Sound Absorption Materials: A Review
## 2 Underwater Acoustic Absorption Mechanisms and Research Advancements in This Area
### Research Advancements in Underwater Sound Absorption Materials
#### 2.2.2 Metal-Based Porous Materials  
The acoustic absorption that occurs in metal-based porous materials is caused by viscous losses and thermo-elastic damping as the sound propagates through numerous air cavities within the material. Porous aluminum with a controllable pore structure was prepared, demonstrating that sound absorption coefficients increase with decreasing pore size and increasing sample thickness; the optimal sound absorption occurs at porosities between 75% and 80% [8]. [PERSON] prepared aluminum foams with semi-open cells that improve sound absorption by adjusting processing parameters such as infiltration pressure, which is equivalent to increasing particle size or decreasing surface tension. Figure 5a shows the typical microstructure and Figure 5b shows the sound absorption varies with the effect of air cavity between two 10 mm thick aluminum foam. The results demonstrate that a sound absorption coefficient larger than 0.8 was recorded in the frequency range of 800-2000 Hz [45].  
Figure 4: **(a)** Diagrammatic morphology of SBR/EUG composite [40]; **(b)** SEM images of PSM05+PG30 in high magnification [41]; **(c)** picture of sample of the multi-walled carbon nanotubes (MWCNT-COOH) added to polydimethylsiloxane (PDMS) with steel backing [42]; and **(d)** SEM images of graphene/SBR nanocomposites [44].  
Although non-metallic sound absorption materials are developing rapidly, metal-based materials remain critical when dealing with complex marine environments. They can withstand high pressure underwater and maintain their physical characteristics when exposed to chemical atmospheres or high-speed airflows. Metal-based foam is also required to meet the lightweight requirements of underwater vehicles. The aluminum foam has limited mechanical strength. To overcome this challenge, [PERSON] prepared an impedance gradient underwater sound-absorbing composite with multiphase composites (IGCM) with polyether polyurethane and metal powders such as iron powder, tungsten powder, and mixtures of iron powder and tungsten powder with varying mass density gradients. The sound absorption frequency ranges were concentrated between 6 kHz and 10 kHz under normal incident conditions [46].
## 3 Experimental System Description  
In order to prove the working principle of the system, experimental equipment was built for laboratory testing. For testing, the radiation source--which in real life is the sun--was replaced with an infrared lamp system which emits an equivalent radiation with the one present on the LEO. The design of the experimental equipment destined for the testing of the propulsion principle is presented in Figure 4.  
The testing equipment consisted of a light source (1) which was an infrared lamp system--for generating the parallel infrared rays. The infrared rays were focused by the mirror (2) on the central focal line. The service propellant tank (3) was mounted on the central focal line of the mirror, therefore it was heated up. The cold N\({}_{2}\) gas was fed from the main tank (4) which had N\({}_{2}\) at 200 bar into the pressure regulator (5). The pressure regulator reduced the gas pressure to 20 bar and transferred the gas with this pressure to the service propellant tank.  
When the solenoid (6) valve opened briefly (t = 0.1-0.4 s), a certain a quantity of gas was exhausted through the Laval nozzle (7) generating thrust force. The thrust force was measured by the force transducer (8). Five thermocouples were used for the temperature measurement; one mounted on the propellant tank (9a), three used for the heat dissipation measurement along the distance from  
Figure 3: Hot gas propulsion system working principle.  
the radiation source to the mirror (9b), and one thermocouple was used for the room temperature measurement (9c).  
The pressure transducer was mounted on the service propellant tank. For measuring the irradiation, one pyrometer (10) was used.  
The values of the gas pressure, temperature and thrust force were transmitted to a data acquisition system (11).  
Furthermore, the solar constant at the mean distance of the Earth from the Sun was measured to be S = 1366 W/m\({}^{2}\) (with an uncertainty of \(\pm\)3 W/m\({}^{2}\)) [27]. Therefore, the radiation power used in the experiments was P\({}_{\rm R}\) = 1360 W/m\({}^{2}\). The infrared light source is capable of emitting 5000 W/m\({}^{2}\), being powered with 220 V at 60 Hz. A potentiometer was used to control the intensity of the light. The radiation source was placed at 1 m above the focal axis of the mirror.  
The irradiance value was recorded by the pyranometer. Convection is negligible in space, given the absence of sufficient gas molecules for energy transportation. Therefore, only an approximation for energy loss through radiation (due to mirror heating) can be done. The radiation losses are determined with the Stefan-Boltzmann law [28], see Equations (2) and (3) below in the article.  
The solenoid valve functions between -10 \({}^{\circ}\)C and 125 \({}^{\circ}\)C with air or nitrogen. The shortest open/close time interval is 0.02-0.03 s. It can operate between 0 and 90 bar and is powered with 24 V A.C. current [29].
## 4 Discussion
### Correlations  
Finally, in order to make it easier to use the results in this article for practical or academic purposes, they are also offered in mathematical form. The surface superheat, the mass flow rate, spray height, and the heat sink surface diameter were all mathematically correlated. The correlation was as follows:  
\[\text{Nu}=6.2110\text{Re}^{0.7964}\text{We}^{-0.0965}\text{Bg}^{0.5898} \tag{5}\]  
where Nu, Re, We, and Bg are Nusselt number, Reynolds number, Weber number, and boiling number, respectively, expressed as:  
\[\text{Nu}=\frac{hD}{\lambda}=\frac{q^{\prime\prime}}{\Delta T_{sat}}\frac{D}{ \lambda} \tag{6}\]  
\[\text{Re}=\frac{G^{\prime\prime}D}{\mu} \tag{7}\]  
\[\text{We}=\frac{\rho u_{0}{}^{2}d_{32}}{\sigma} \tag{8}\]  
Figure 17: \(CHF\) and \(h_{\text{max}}\) versus D at \(H=34.5\) mm.  
\[\text{Bg}=\frac{q^{\prime\prime}\,S}{h_{lg}G} \tag{9}\]  
The spheres of application of the correlation are as follows: Re = 800-6000, We = 1200-20,000 and Bg = 0.0005-0.35. The correlations predict values presented in this article within 20.77%.
## 3 System Model and Problem Formulation
### Networks Model  
As shown in Figure 1, in our network architecture, all data are sourced from cloud servers. The core network communicates with the cloud through a backhaul network and with edge base stations (BSs) through frontier networks. Each BS is equipped with an access point that provides wireless connectivity to users. When the requested content is available in the cache of a BS within its service range, it is referred to as a cache hit. \(\mathcal{N}=\{1,\dots,n,\dots,N\}\) represents the set of BSs, each with a cache capacity of \(C_{n}\) for storing content. \(\mathcal{U}=\{1,\dots,u,\dots,U\}\) represents the set of user equipment (UE), randomly distributed within the service range of the BSs. \(\mathcal{F}=\{1,\dots,f,\dots,F\}\) represents the set of content items, each with its request frequency \(r_{u,f}\). \(\mathcal{T}=\{1,\dots,t,\dots,T\}\) represents the epochs of aggregation/distribution for federated learning.  
For the sake of illustration, we assume that the size of all content is equal to \(M\) bits. This assumption has been widely considered in previous studies (e.g., [28; 29; 30]) because large contents are often divided into roughly equal-sized chunks. BSs can directly communicate with other BSs to retrieve the requested content from neighboring BSs. Since retrieving content from neighboring BSs is faster and less costly than retrieving it from cloud servers, priority is given to retrieving content from the cloud last. There are three states when a user requests content, namely local hit, retrieval from a neighboring BS, and retrieval from the cloud. That is, when a request arrives at a local BS, if the requested content is cached locally, the local BS immediately returns the cached content. If the local BS misses, it retrieves the content from a neighboring BS and returns it to the user. If the neighboring BS also misses, the content is retrieved from the cloud to serve the request.
## 2 Material and Methods
### Effect of Ch. sorokiniana Extracts and Fractions on Murine and Human Tumor Cell Growth  
We incubated L5178Y-R and MCF-7 cells (1 \(\times\) 10\({}^{4}\) cells/well) and PBMC (1 \(\times\) 10\({}^{5}\) cells/well) in flat-bottomed 96-well plates (Corning Incorporated, Corning, NY, USA) in respective RPMI-1640 and DMEM complete media for 24 h at 37 \({}^{\circ}\)C in 5% CO\({}_{2}\), after which they were treated with 7.8125 ug/mL to 500 ug/mL of _Ch. sorokiniana_ methanol extracts and CFs for 48 h at 37 \({}^{\circ}\)C in 5% CO\({}_{2}\). We then determined cell viability by incubating the cells with 15 mL of 3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyl tetrazolium bromide (0.5 mg/mL final concentration; Affymetrix, Cleveland, OH, USA) [13] for 3 h at 37 \({}^{\circ}\)C. Formazan crystals were dissolved with DMSO, and optical densities (OD) were measured at 570 nm in a MULTISKAN GO microplate reader (Thermo Fisher Scientific, Waltham, MA, USA) [14]. Cell growth inhibition percentage was calculated as follows: % growth inhibition = 100 \(-\) [(OD\({}_{570}\) in extract/fraction-treated cells/OD\({}_{570}\) in untreated cells) (100)], using 0.05 ug/mL vincristine sulfate (VC; Hospira, Warwickshire, UK) as a positive control.  
In addition, the selectivity index (SI) of _Ch. sorokiniana_ extracts and CFs was determined. For this, we divided the IC\({}_{50}\) in normal cells by the specific IC\({}_{50}\) against tumor cells after 72 h [14]. SI measures the cytotoxic potential of an agent, relative to toxicity to normal cells [15]. Samples with an SI value > 3 were considered to have high selectivity toward tumor cells. SI was calculated using the equation:  
\[\text{SI}=\text{IC}_{50}\text{ normal cells value}/\text{IC}_{50}\text{ tumor cells value}\]
# Classification and Mapping of Paddy Rice by Combining Landsat and SAR Time Series Data
###### Abstract  
Rice is an important food resource, and the demand for rice has increased as population has expanded. Therefore, accurate paddy rice classification and monitoring are necessary to identify and forecast rice production. Satellite data have been often used to produce paddy rice maps with more frequent update cycle (e.g., every year) than field surveys. Many satellite data, including both optical and SAR sensor data (e.g., Landsat, MODIS, and ALOS PALSAR), have been employed to classify paddy rice. In the present study, time series data from Landsat, RADARSAT-1, and ALOS PALSAR satellite sensors were synergistically used to classify paddy rice through machine learning approaches over two different climate regions (sites A and B). Six schemes considering the composition of various combinations of input data by sensor and collection date were evaluated. Scheme 6 that fused optical and SAR sensor time series data at the decision level yielded the highest accuracy (98.67% for site A and 93.87% for site B). Performance of paddy rice classification was better in site A than site B, which consists of heterogeneous land cover and has low data availability due to a high cloud cover rate. This study also proposed Paddy Rice Mapping Index (PMI) considering spectral and phenological characteristics of paddy rice. PMI represented well the spatial distribution of paddy rice in both regions. Google Earth Engine was adopted to produce paddy rice maps over larger areas using the proposed PMI-based approach.  
paddy rice; Landsat; data fusion; paddy rice mapping index (PMI); ALOS PALSAR; RADARSAT-1; time-series analysis Article Classification and Mapping of Paddy Rice by Combining Landsat and SAR Time Series Data
## 2 Network Description  
SCNs is a type of new distributed wireless space system, in which most space missions work cooperatively through ISLs among the satellites. In this section, this paper first presents a proper network description and some definitions and assumptions for SCNs, then puts forward a mathematical model of cooperative work for EO missions.
## 4 Time Gating and Demodulation Algorithms
### Simulation of Basic Pseudo-Doppler Modulator and Gating Arrangement
There it is made clear that these results inspire a recovery algorithm for all the superposed OAM modes, and their spectral shifts will be shown to exist in the envelopes of the spectra. The differences in OAM proportions (more precisely, linear combinations) in the spectral replicas can be algebraically inverted so as to isolate them in final recovery outputs of a signal-processing subsystem. Such a subsystem would be based on least-mean-squares (LMS) optimization techniques and can be made adaptive to optimize their recovery in some statistical sense, much like existing MIMO receivers or adaptive-array systems.
# Soft Segmentation of Terrestrial Laser Scanning Point Cloud of Forests
## 2 Data and Methods
### Soft Segmentation Algorithm
#### 2.2.1 Preprocessing  
Forest point cloud data generally contains a large number of points, resulting in low computational efficiency and high time costs. Therefore, at the preprocessing stage, points that are not within the scope of the study should be removed [30]. Another process is down-sampling, which was often used to accelerate point cloud registration [31].  
We used a down-sampling method [32] to simplify the raw point cloud. For example, after down-sampling, the number of points of Forest A (Figure 2a) can be decreased from 1,068,426 to 90,044 (8.428%) and 38,619 (3.615%) with different sampling intervals, 0.15 and 0.25, respectively, as shown in Figure 5. Although the shape of the stems has become blurred, the silhouette of the crown is kept well, and the density of the simplified point cloud is more unified. By comparing the visual effect and the compression ratio of the number of points, the default value of the sampling interval is set to 0.15 as a constant in our algorithm.  
In addition, before tree identification, the ground points are removed according to the consistency of directions of normal vectors of points [12].  
Figure 4: RUSH06, a laser scan data from the Eucalypt Open Forest [29].  
Figure 5: The down-sampling results with different sampling intervals (up: 0.15; down: 0.25) are illustrated using the point cloud of Forest A. (**a**) The sampling interval is 0.15; (**b**) top view of (**a**); (**c**) the sampling interval is 0.25; (**d**) top view of (**c**).
## 2 Materials and Methods  
### Study Sites  
Two areas in the Cuenca Mountains (Iberian System) separated by a straight-line distance of 14 km were selected for the study (Figure 1): a mixed stand of _Pinus nigra_ (89 \(\pm\) 11%) and _Pinus pinaster_ (11 \(\pm\) 11%) in El Pozuelo and a pure stand of _P. nigra_ in Beteta. The main characteristics of both stands are shown in Table 1.  
According to the data recorded at the Canizares weather station (940 m asl) and provided by the State Meteorological Agency of the Spanish Government [47], the mean annual temperature was 11.3 \({}^{\circ}\)C and the average precipitation was 747 mm in the last 46 years.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{} & **Mixed Stand** & **Pure Stand** \\ \hline Coordinates—Longitude & 40\({}^{\circ}\)33’36\({}^{\prime\prime}\) N & 40\({}^{\circ}\)33’06\({}^{\prime\prime}\) N \\ Coordinates—Latitude & 002\({}^{\circ}\)15’56\({}^{\prime\prime}\) W & 002\({}^{\circ}\)06’32\({}^{\prime\prime}\) W \\ Main species & _Pinus nigra_ (89 \(\pm\) 11\%) & _Pinus nigra_ (100\%) \\ pH of topsoil \({}^{1}\) & 7.3 (clay texture) & 6.9 (loamy-sand texture) \\ Elevation & 1016 \(\pm\) 5 m asl & 1232 \(\pm\) 7 m asl \\ Slope & 3–8\% & 3–10\% \\ Stand density & 627 \(\pm\) 238 treea ha\({}^{-1}\) & 1286 \(\pm\) 339 trees ha\({}^{-1}\) \\ Stand basal area & 25.4 \(\pm\) 9.7 m\({}^{2}\) ha\({}^{-1}\) & 36.6 \(\pm\) 10.7 m\({}^{2}\) ha\({}^{-1}\) \\ Dominant tree height & 18.6 \(\pm\) 0.8 m & 17.0 \(\pm\) 1.6 m \\ Tree height & 12.2 \(\pm\) 2.0 m & 13.2 \(\pm\) 2.7 m \\ Diameter at breast height (DBH) & 19.8 \(\pm\) 2.6 cm & 18.8 \(\pm\) 4.1 cm \\ Bark thickness & 1.7 \(\pm\) 0.3 cm & 1.7 \(\pm\) 0.4 cm \\ \hline \hline \end{tabular} \({}^{\dagger}\) Data from [45].  
\end{table}
Table 1: Main characteristics of mixed and pure stands (mean and standard deviation) [46].  
Figure 1: Locations of the study plots in (**a**) the Iberian Peninsula (1:1,250,000); (**b**) El Pozuelo (mixed _P. nigra-P. pinaster_ stand) and Beteta (pure _P. nigra_ stand) (1:50,000).
# A Comparative Analysis of Anomaly Detection Methods in IoT Networks: An Experimental Study
## 4 Results
### Results Obtained from the IoT-23 Dataset Using a Unsupervised Machine Learning Approach
#### 4.2.3 Research Analysis According to [48]  
Research [48] has investigated anomaly detection in IoT networks, utilizing the IoT-23 dataset. The methodology encompasses several key steps.  
Step 1: Data Preparation and Processing  
The IoT-23 dataset was divided into smaller, more homogeneous subsets. Preprocessing was performed for each subset, tailoring it to the specific characteristics of the data. Contamination reduction was also performed to balance the proportions of malicious and benign samples.  
Step 2: Model utilization  
The model was configured with different parameters such as the number of decision trees, the maximum number of features, and the contamination level. The contamination level was selected individually for each dataset.  
Step 3: Model evaluation  
The Isolation Forest model was evaluated in a binary classification task using the F1-score metric. A 5-fold cross-validation was performed to optimize model parameters and obtain more reliable results.  
Step 4: Result verification  
The results of the Isolation Forest model were compared with other models such as SVM, XGBoost, and others. It turned out that Isolation Forest performed well, especially for smaller and more unbalanced datasets. For larger datasets, Isolation Forest also showed improved performance after optimization.
## 2 Sample Extraction
### Stir Bar Sorptive Extraction  
The basic principles of stir-bar sorptive extraction (SBSE) are identical to those of SPME. When PDMS was the only phase available for SPME, the quantitative analysis was only obtained for the volatile compounds whose phase/water partition ratio was larger than \(10^{5}\). SBSE was developed to enlarge the application range and boost analytical sensitivity by coating more PDMS on the sorption unit [96]. It can be applied to headspace sampling as HS-SPME, constructing a three-phase system, but mostly the sorbent phase is put into the liquid sample, forming a two-phase equilibrium system. Latterly, multiple SBSEs were proposed to increase the application on polar analytes by adding ethylene glycol/silicone phase during the extraction [97]. Both SBSE and multiple SBSE have been successfully applied to beverage VOC analysis [36; 60; 64; 69; 98]. An approach to SBSE in beverages is reverse extraction [60]. After the volatile extraction, analytes on the stir bars can be desorbed by liquid extraction. Back extraction offers possibilities in different aspects, for example, performing the analysis with HPLC, having more choices of sorbent phases [99], downstream odor and taste testing, and allowing free derivatization [100] However, to date, the diffusion of SBSE in laboratory practice has been limited by the need for dedicated and expensive devices for the procedure's automation. Another relevant gap is the need to dip the stirring bar into the sample, which implies carryover phenomena and an early performance drop if compared to headspace techniques.
# Evaluation of Aging Resistance of Graphene Oxide Modified Asphalt
## 2 Materials and Methods
### Evaluation of Anti-Aging Performance
#### 2.4.4 Rheological Property Test  
A SmartPave102 dynamic shear rheometer (DSR) (Graz, Austria) was utilized to test the rheological properties of asphalt binders. The test temperature was in the range of \(-10\) to \(30\)\({}^{\circ}\)C, the rotor diameter was \(8.0\) mm, the sample thickness was \(2.0\) mm, frequency was set to \(10\) rad/s, and the strain was \(0.05\%\).