esa-sceva/satcom-chunk-collection huggingface dataset:
### PNPA in Vitro Diffusion Studies  
In vitro diffusion studies on the developed cotton-based textiles were carried out with the aim of evaluating their ability to release in a controlled manner the synthesized PEA derivative compared to a standard solution of the molecule (Scheme 3).  
In the performed experiments, Strat-M(r) membranes were used as a synthetic alternative, which is predictive of the diffusion process occurring through human skin. The studies involved two different textiles, such as CO\({}_{L}\)G-PNPA and CO\({}_{H}\)G-PNPA, prepared employing the obtained G-PNPA functional sol. The obtained results are expressed as cumulative diffused amount (%) and the diffusion profiles for the tested items are reported in Figure 8.  
Figure 7: AFM micrograph of CO\({}_{L}\)UT (**a**), CO\({}_{H}\)LT (**b**), CO\({}_{L}\)GPTES (**c**), CO\({}_{H}\)GPTES (**d**), CO\({}_{L}\)G-PNPA (**e**), and CO\({}_{H}\)G-PNPA (**f**).  
The two G-PNPA-coated cotton textiles, CO\({}_{\text{L}}\)_G-PNPA and CO\({}_{\text{H}}\)_G-PNPA, show similar results in the performed in vitro diffusion studies. In the case of the CO\({}_{\text{L}}\)_G-PNPA sample, indeed, the amount of released PNPA is equal to 5% within the first hour reaching the 48% and 66% in 6 and 24 h, respectively, while the CO\({}_{\text{H}}\)_G-PNPA sample exhibits a value of 7% after the first hour achieving the 53% and 73% at the time points of 6 and 24 h. On the other hand, the 14% of PNPA is released after the first hour from the control solution reaching the 69% and 89% at 6 and 24 h. The experimental data confirm the ability of both the developed textiles to release in a controlled way the synthesized PEA derivative. These results could be related to the rate-limiting steps in drug release reported in the literature [33; 34]: the drug diffusion within the polymer matrix and the rate of polymer swelling. Depending on the presence of sweat, simulated with the buffer solution in contact with the PNPA-treated fabric, the weak interactions between the treated fabric and the antioxidant molecule slowly disappear until the drug is completely released. The presence of the sol-gel matrix increases the bonding interactions between the network and the drug, slowing down its release.
# Future Directions for Electric Propulsion Research
## 4 Challenges for Electric Propulsion Development
### Low-Power Thrusters  
With the recent popularity and increasing affordability of small satellites, the need for propulsion systems that accommodate modest power budgets has grown. As these spacecraft generally also have little mass budget for propellant, electric propulsion is an attractive option. As a result, many EP technologies have been developed for sub-kilowatt systems or have been downsized to this power level. We depict notional s-curves for many of these types of devices in Figure 3.
# Edge Intelligence: Edge Computing for 5G and the Internet of Things
###### Abstract  
In recent years, the field of artificial intelligence has made a revolutionary breakthrough. The remarkable success of deep learning technologies and large language models (LLMs) has fundamentally reshaped industrial processes and societal dynamics, with their transformative impact poised to persist in the foreseeable future. With the continuous advancement of artificial intelligence (AI) technology, edge intelligence has emerged as a new research hotspot. Just as edge computing was developed to provide faster and lower-latency computing and storage services compared to cloud computing, the development of edge intelligence aims to deliver enhanced responsiveness and diversified intelligent services that surpass conventional cloud-based AI implementations. The emergence of edge intelligence not only brings AI closer to users but also provides new technological support for the development and application of 5G and the Internet of things (IoT).  
Specifically, edge intelligence will promote innovative development in the fields of 5G and the IoT in at least three areas. First, it will reconstruct the network infrastructure of 5G and the IoT, making networks more efficient and flexible, thereby laying a solid foundation for intelligent network services and applications. Second, it will provide faster and more diverse intelligent service support for end-user applications. While enhancing existing applications with faster and more personalized intelligent services, it will also give rise to numerous innovative intelligent applications, such as augmented reality and virtual reality. Last but not least, edge intelligence offers new solutions to the technical challenges faced in the era of 5G and the IoT, for example, how to efficiently and rapidly utilize large amounts of local data generated by numerous IoT devices in a manner that protects user privacy.  
While demonstrating significant potential, edge intelligence remains at a nascent stage of development, requiring in-depth academic research and continuous industrial exploration. To promote the development of edge intelligence, this Special Issue aims to gather recent advances and novel contributions in the areas of edge intelligence and edge computing for 5G and IoT networks, providing opportunities for academic researchers and industry practitioners to exchange creative ideas, with the goal of inspiring further innovation and fostering progress in academia and industry.
# Internet of Vehicles for Sustainable Smart Cities: Opportunities, Issues, and Challenges
## 4 From Basic Connectivity to Full Autonomy (Levels 0 to 5)
### Level 1: Assisted Driving Systems  
The second lowest degree of automation within a vehicle is Level 1 automation. At this level, only one driver assistance automated system will be available, usually dedicated to tasks such as acceleration or steering [63]. However, the human driver is responsible for monitoring other critical aspects of driving, such as steering and braking. It is important to note that at Level 1, the driver must remain prepared to take control of the vehicle at any time. Adaptive cruise control is a common Level 1 driver assistance technology that helps drivers maintain a safe distance without requiring their intervention.  
Figure 5: The range of connectivity and autonomy (Levels 0 to 5).
# MSEANet: Multi-Scale Selective Edge Aware Network for Polyp Segmentation
## 2 Related Work  
### Encoder-Decoder Model  
The advent of convolutional neural networks (CNNs) has revolutionized medical image segmentation, providing the foundation for the development of sophisticated models. Among these, U-Net [5] stands out as a seminal architecture that introduced an encoder-decoder structure, enabling the efficient extraction of rich image features across multiple hierarchical levels, making U-Net a widely adopted solution in medical imaging tasks. Its skip connections effectively bridge the semantic gap between low- and high-level features. Building upon U-Net's success, the DeepLab family of networks [15, 16, 17] has pushed the boundaries of segmentation accuracy by incorporating innovations like dilated convolutions and Atrous Spatial Pyramid Pooling (ASPP). These techniques expand the receptive field without increasing the computational burden, allowing the network to capture global contextual information while preserving spatial resolution. These advancements have laid the groundwork for automated polyp segmentation, demonstrating the potential of encoder-decoder models in addressing complex medical image analysis tasks.  
However, despite their achievements, traditional encoder-decoder architectures that rely solely on convolutional operations face notable limitations. On the one hand, global contextual information is transmitted from deeper stages to shallower stages, and it may be diluted due to the weak feature extraction ability of individual stages. On the other hand, the skip connection in each stage ignores global information and is an indiscriminate combination of local information, which will introduce irrelevant clutter and lead to misclassification of pixels. In polyp segmentation, challenges such as blurred edges and the inability to effectively integrate global and local contextual information often arise. These shortcomings hinder the precise delineation of polyp boundaries and the comprehensive understanding of intricate patterns within complex scenes. Addressing these limitations requires innovative designs that go beyond conventional convolutional paradigms to enhance edge sensitivity and multi-scale contextual understanding. For example, multidimensional signal analysis techniques, such as wavelet transform and Fourier transform, can be leveraged to capture features at multiple scales. These methods have been shown to enhance feature fusion and improve boundary detection by explicitly modeling multi-scale information.
## 3 Assessment of Precise MGEX Orbit and Clock Products
### Orbit Comparisons
#### 3.1.1 Galileo  
For Galileo, the ESM products were taken as the reference due to its smallest standard deviation (STD) values of SLR residuals among all ACs, which will be discussed in the next section. The daily RMS values of Galileo orbit differences with respect to ESM were averaged over all the available satellites for each AC, and the average RMS values in along-track, cross-track and radial directions are depicted in Figure 2. Outliers that were larger than the range of the vertical axes are represented as points on the horizontal axes. For most of the ACs, the RMS values of Galileo orbit differences were between 2 and 5 cm in the three components. COD showed the best agreement with regard to ESM, with the mean RMS values of 1.8, 1.4 and 1.6 cm in the along-track, cross-track and radial components, respectively. TUM showed a small mean RMS value of 1.8 cm in the radial component, while the along-track and cross-track components had the largest mean RMS values of 4.6 and 3.4 cm, respectively. It is worth mentioning that the orbit consistency between GRG and ESM was obviously improved since DOY280 2018, and the mean RMS values descended from 4.0, 3.5 and 3.0 cm to 2.4, 2.0 and 2.5 cm in the along-track, cross-track and radial components, respectively. This is because the GRG Galileo products have been computed via an undifferenced ambiguity fixing strategy since Global Positioning System (GPS) week 2022 (October 2018) [6]. Table 3 summarizes the averaged 3D RMS values of Galileo orbit comparison results among all ACs. Differences that were three times larger than the medians of the series were excluded from the statistics of comparison results. Note that only comparison results after DOY280 2018 were counted for GRG. As shown in the table, the consistency among COD, ESM, GFZ, GRG and WUM was at the 3-5 cm level.  
Figure 2: RMS values of Galileo orbit differences in along-track, cross-track and radial components with respect to ESM.
# Which, When, and How: Hierarchical Clustering with Human-Machine Cooperation
## 6 Experiments
### Algorithms for Comparison  
Here, we denote the traditional agglomerative hierarchical clustering algorithm as TAHC, while the proposed algorithm is denoted as HMCAHC. Then, five different variations of the HMC strategies are used for comparison:  
* Instead of picking the most questionable pair of data points, the machine asks questions based on a committee-based sampling strategy [39]. This algorithm is HMCAHC-which.
* Rather than asking questions upon certainty increases, the machine asks questions upon a certainty reduction (judged by a threshold of 0.1). This is because a large certainty reduction means that a newly merged cluster is not as pure as previous ones. The threshold is empirically determined. This algorithm is HMCAHC-when1.
* Instead of asking questions upon certainty gain, the machine asks questions upon a maximum entropy reduction [40] (judged by a threshold of 0.1). This algorithm is HMCAHC-when2.
* Rather than the back-tracing scheme, the machine uses local adjustments for the \"how\" problem. This algorithm is HMCAHC-how1.
* Instead of the back-tracing scheme, the machine uses global adjustments, in which the machine goes back to the initial state and redoes everything. This algorithm is as HMCAHC-how2. In general, HMCAHC-how2 should have a better result than HMCAHC, at the cost of a higher time complexity. While HMCAHC takes \(O(N^{2}\log N)\), HMCAHC-how2 takes \(O(QN^{2}\log N)\).  
K-Means is also used for comparison as a baseline algorithm. In addition, two constraint-based clustering algorithms are also used, i.e., PCMK-Means [22] and Active PCK-Means [23]. For these two algorithms, questions on random pairs of data points are asked before the algorithm execution, while the human's answers serve as pre-given constraints. Our approach differs from these three classic algorithms for the \"when\" problem and the \"how\" problem. Moreover, the algorithm proposed by [PERSON] et al. [5] is also compared (its set size is 10 for human comparisons).  
In our real data-driven experiments, ground-truth clustering results are available. Therefore, the _mutual information_[41] is introduced to measure the similarity between the current clustering result and the ground-truth result. The mutual information falls into the interval of \([0,1]\), where a higher value indicates a higher similarity (i.e., a better clustering quality). We are interested in the mutual information variance, when clusters are iteratively merged. Considering that our strategy favors asking questions in later steps, we focus on the mutual information variance among the last few clustering steps. The estimation accuracy of the machine's certainty is also investigated. We will observe the difference between the machine's certainty and the true minimum purity.
## 2 Materials and Methods
### Overview of the Network
#### 2.2.1 Sensing, Data Acquisition, and Wireless Communication  
Our focus in this work centers on sensing key environmental factors such as ambient temperature, humidity, air pressure, and air quality. Our sensor node is designed to transmit these data wirelessly using the Bluetooth Low Energy (BLE) protocol, which operates within the 2.4 GHz frequency range of the RF spectrum. Given our emphasis on low-power operation, we propose to operate within a DC power supply voltage range of 2.5-3.6 V, which aligns with the standard voltage range for most low-power microcontrollers, including BLE devices and sensors available on the market. The sensor node is configured to spend the majority of its operational life in sleep mode, with active operation occurring in programmed cycles, as illustrated in Figure 3. In each cycle, the system becomes active for a brief period, during which it carries out all necessary activities. It then enters an extended sleep mode until the start of the next cycle, when it wakes up to  
Figure 2: Illustrative comparison between different wireless protocols for IoT based on [39; 40; 41].  
repeat the process. If the sensor node is active for x seconds and remains in sleep mode for y seconds, we have the following considerations:  
* \(\mathrm{y}>>\mathrm{x}\): this indicates that the sleep time is significantly longer than the active time.
* \(\mathrm{x}+\mathrm{y}\): this represents the total cycle time (in seconds).
* \(1/(\mathrm{x}+\mathrm{y})\): this corresponds to the broadcast frequency (in Hz).  
Our low-power scheme under development aims to limit power consumption to below 1 mW, with the aim of continually reducing it to the absolute minimum level achievable. We explore and investigate various strategies and approaches to achieve this objective. Our system employs a single point-to-point network topology, featuring two interconnected devices: a wireless sensor node at one end and a monitoring system at the opposite end. Our sensor node is capable of sensing, processing, and transmitting environmental data wirelessly in brief bursts of data packets. On the other end of the system, a data monitoring device receives the wireless data and displays them. Subsequently, these data can be utilized for various meaningful purposes.
## 3 Multiple RFI Sources Geolocation Method  
This section introduces the multiple RFI sources geolocation method. Firstly, the RFI detection scheme is discussed. Additionally, the minimum description length (MDL) criteria is applied to RFI source number estimation. Then, the DOA results of RFI signals at each azimuth time are calculated by performing the 2D-ESPRIT algorithm on the contaminated multi-channel SAR echo signal. Finally, on the basis of the geometric relationship between the multi-channel SAR system and RFI sources, a set of equations about the geographic location of RFI sources is established, and the results are obtained by utilizing particle swarm optimization. Figure 3 shows the process of the proposed method in summary.
## 2 Materials and Methods
### Sedimentation Experiments  
Chalk and limestone were evaluated as coagulation and sedimentation agents. Tests were conducted to assess turbidity, total hardness, and water quality parameters following sedimentation with these materials.  
Each container was filled with distilled water from the Red Dam in the chalk sedimentation experiments. Initial turbidity, measured using a turbidity meter, was approximately 12,000 NTU for all containers without chalk. Chalk was added to each container in the following amounts: the small container (0.05 L) received 2.5 g of chalk (concentration of 50 g per liter); the medium container (0.5 L) received 10 g of broken chalk or 10 g of crumbled chalk in separate trials (concentration of 20 g per liter); the larger container (1.5 L) received 10 g (concentration of 6.67 g per liter) or 20 g of chalk (concentration of 13.33 g per liter); and the largest container (6 L) received 20 g (concentration of 3.33 g per liter) or 40 g of chalk (concentration of 6.67 g per liter). The containers were left undisturbed for 24 h to  
Figure 3: Slow sand filter: (**a**) The sand filtration systems that were built in Tanzania; (**b**) schematic figure of the SSF: a thick layer of gravel at the bottom and a large layer of sand above it. The mechanisms the SSF uses are mechanical filtration through sand, biological filtration via biofilm development (schmutzdecke), and pathogen removal along with turbidity reduction.  
allow sedimentation. Final turbidity was measured using the turbidity meter after the 24-h sedimentation period.  
Additionally, sedimentation experiments were conducted using limestone in small (0.05 L) and large (6 L) volume containers. Each container was filled with distilled water, and initial turbidity was measured using a turbidity meter, recording approximately 12,000 NTU for containers without limestone. In the small-volume container, two conditions were tested: one with a substantial amount of limestone dust, and another with no limestone. Three conditions were tested in the large volume container: 300 g of crumbled limestone, 300 g of uncrushed limestone stones, and a control with no limestone. The containers were left undisturbed for 24 h to allow sedimentation. Final turbidity was measured using the turbidity meter to evaluate the reduction in turbidity under each condition.
# An Integration Model for Flux Density Distribution Formed by a Heliostat
## 3 Model Validation  
### Comparison with SolTrace  
SolTrace [17] is a software that simulates the real optical action process based on the traditional ray-tracing method, developed by The National Renewable Energy Laboratory (NREL). In SolTrace, the flux density value calculated from five million random rays is accurate enough, so we set the number of rays to five million in the subsequent calculation using SolTrace.  
SolTrace can only be used to calculate the solar intensity radial distribution model. Therefore, we can compare the results of SolTrace with that of the circular Gaussian model, which were used to verify the calculation results of the circular Gaussian model.  
The origin of the coordinate system is located in the center of the bottom of the tower. The positive direction of the Y-axis is due north, the positive direction of the X-axis is due east, and the positive direction of the Z-axis is the zenith. The tower height is 120 m, the receiver is placed perpendicular to the ground, the solar altitude angle is 45\({}^{\circ}\), the solar azimuth angle is 0\({}^{\circ}\), and the solar light intensity is 1 kW/m\({}^{2}\). All parameters of the heliostat are shown in Table 1. The slope error in the circular Gaussian model is 1.7 mrad, consistent with the slope error setting in SolTrace. After setting each parameter, we calculated the flux density distribution of the rectangular heliostat using the circular Gaussian model and SolTrace, respectively.  
Figure 5a shows the distribution of the flux density on the receiver surface computed by SolTrace and the circular Gaussian model. As SolTrace can only apply the circular Gaussian model, here, we also used it in our model for comparison. Since the reflected  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(X\) (m) & \(Y\) (m) & \(Z\) (m) & \(f_{0}\) (m) & Width & Length & cos\(\lambda\) & \(\sigma_{sum}\) (mrad) & \(\sigma_{tr}\) (mrad) \\ \hline
300 & 500 & 0 & 595.3 & 6 & 9 & 0.8022 & 2.51 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Heliostat parameters.  
Figure 4: Solution of the flux density at a specific point.  
ray is at an acute large incident angle to the receiver surface, the distribution of the flux density on the receiver surface is elliptical, and the average absolute difference computed by the two methods is 0.64%. Figure 5b shows the intercept factor calculated by SolTrace and the circular Gaussian model. On the whole, the intercept factor computed by SolTrace is slightly larger than that. The figures show that the results computed by the circular Gaussian model and SolTrace have a good consistency.