esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experimental Set-Up  
The equipment used to conduct the thermal characterization tests in the laboratory under steady-state conditions consists of the following elements (see Figure 3):  
* Climatic generator: This equipment allows for precise control of relative humidity and temperature values in an enclosed environment, ensuing constant cold, heat, and humidity.
* Calibrated hot-box: A fully insulated 1 m\({}^{3}\) capacity hot-box is connected to the climatic generator equipment (see Figure 3). The calibrated hot-box is used to create an environment under controlled temperature and humidity conditions for each test. The above-mentioned equipment operates as indicated in the UNE-EN ISO 8990:1997 [51] and ASTM C1363:19 standards [52].
* Test-box: This is the container in which the tested green swale cross-sections were introduced and set up according to the experimental procedure. It has interior dimensions of 608 \(\times\) 408 mm\({}^{2}\) at the bottom. The test-box is installed over the hot-box (see Figure 3) so that the heat flux passes perpendicularly through the surface of the studied green swale cross-section from its bottom layer. The test-box is thermally insulated from the outside with 20 cm of extruded polystyrene (0.033 \(\pm\) 0.003 W/mK) in order to minimize lateral heat losses.  
For measuring the parameters and collecting and storing the data, the following equipment and sensors were used:  
* Temperature and heat flux sensors: Fourteen type K thermocouples (TCxy) were installed according to the distribution shown in Figure 4. Additionally, two extra thermocouples were used to determine the heat loss through the walls of the Test-box. Eight thermal flux sensors (HFx) Huiskeflux HFP01 were employed, and they were installed at the interfaces of all the layers (see Figure 4). The technical specifications of the thermal flux sensors are as follows: sensitivity, \(60\times 10^{-6}\) V/(W/m\({}^{2}\)); thermal resistance of the sensor, \(71\times 10^{-4}\) K/(W/m\({}^{2}\)); nominal operating temperature range, \(-30\) to \(+70\)\({}^{\circ}\)C; and measurement range, \(-2000\) to \(+2000\) W/m\({}^{2}\).
* TRSYS equipment: This system was used to acquire data from the aforementioned sensors, and it was programmed to collect information in 10 min interval. It also allows for data storage and transmission for further analyses.
# Fourier Neural Operator Network for Fast Photoacoustic Wave Simulations
## 3 Results
### Generalizability of Trained FNO Network  
The FNO network was trained on photoacoustic simulations of breast vasculature. To evaluate its generalizability, the trained FNO network was used to simulate photoacoustic wave propagation with initial photoacoustic sources for the Shepp-Logan, synthetic vasculature, breast tumor and Mason-M logo phantoms. These phantoms contain many features not observed in the training dataset. For example, the breast vasculature phantoms typically occupy a majority of the space in the computational grid and have a mixture of large and small vessels, whereas the other phantoms occupy a fraction of the space and have small vessels or non-vasculature structures.  
The FNO network and k-Wave simulations for each phantom tested were highly similar, but small visual differences could be observed (Figure 6). For example, the Mason-M logo has a mostly uniform grayscale background in the k-Wave simulation at t = 1, but a small gradient or shading could be seen in the FNO network simulation (average RMSEs across all time steps in the FNO network simulations: Shepp-Logan (\(9.5\times 10^{-3}\)), synthetic vasculature (\(1.5\times 10^{-2}\)), tumor (\(1.1\times 10^{-2}\)) and Mason-M (\(1.9\times 10^{-2}\)) phantoms). The Mason-M FNO simulation likely had the highest RMSE because it was a non-biological phantom unlike the other phantoms and the training dataset.  
These results were promising and provided evidence that the trained FNO network is generalizable to other initial photoacoustic sources not in the training data. However, the FNO network did overfit the training data, as shown by the larger RMSE of the additional phantoms. Having a more diverse and larger training dataset can further improve the generalizability of the FNO network.  
Figure 5: FNO and k-Wave were used to simulate wave propagation for the ground truth image. Sensor data were sampled from the resulting simulations and reconstructed into PAT images. The k-Wave and FNO images are almost identical. RMSE (0.011), SSIM (0.97) and max error (0.06).
# A Triple Deep Image Prior Model for Image Denoising Based on Mixed Priors and Noise Learning
## 3 Methodology
### Backbone Network  
In this study, we implement image denoising based on the DIP network architecture. Figure 4 depicts the overall TripleDIP framework. There are three encoder-decoder sub-networks named DIP1, DIP2 and DIP3 in model, where DIP1 is used to generate denoised images \(I_{Content}\), DIP2 and DIP3 are assigned to generate two noises independently: \(I_{Noise1}\) and \(I_{Noise2}\). In addition, the \(I_{Noisy}\) represents the inputted noisy image, and the \(I_{External}\) represents the external prior image generated by the Restormer model. The loss function in the figure represents the MSE loss between the parts indicated by the arrows.  
The prototype of the encoding-decoding structure in Figure 4 is a U-net type architecture, where DIP1 is the standard DIP denoising network. It adopts Gaussian noise as input and complies with the relationship: \(z_{1}\sim N(0,\sigma^{2})\), where \(\sigma^{2}\) indicates the variance of the Gaussian distribution and the Gaussian noise obtained by random sampling has the same size as denoised images \(I_{Content}\). In contrast to the network architecture of DIP1, both DIP2 and DIP3 change the final activation function in the decoding phase, replacing the rectified linear unit function (RELU) with the hyperbolic tangent activation function (tanh). This can be attributed to the object of noise learning being the Gaussian noise distribution of the image, which can have negative values. The employment of tanh can prevent the truncation of negative values compared with the RELU. Moreover, DIP2 and DIP3 use the noise image \(I_{Noisy}\) as an input to reduce the learning difficulty from random to image noise. The encoder contains multiple convolution modules, each consisting of two \(3\times 3\) convolutions with a nonlinear ReLU; additionally, a downsampling operation connects the modules. The decoder consists of the same convolution modules; however, it is connected by an upsampled operation implemented by a \(2\times 2\) convolution (i.e., up-conv) between the modules. In DIP2 and DIP3, we supersede the convolution operation employed to produce the feature vector of the expansion path with a \(3\times 3\) convolution and a hyperbolic tangent activation function (tanh). In addition, there are skip connections between the encoder and decoder of the same level, which integrates deep semantic features with shallow statistical information to minimize the loss caused by upsampling and downsampling. Finally, the feature vector is mapped to the output image using a \(1\times 1\) convolution. As a prevalent encoder-decoder structure, the U-net network can collect multi-scale features and obtain rich details effectively, which causes our models to produce more stable, high-quality results.  
Figure 3: The effect of the automatic stopping criterion on the performance of the model.
# Polling Mechanisms for Industrial IoT Applications in Long-Range Wide-Area Networks
## 4 Results and Discussion  
Different factors should be considered when choosing a polling mechanism for a particular application, based on the results obtained for each alternative. The message size was fixed to 51 bytes, which is LoRaWAN packet's maximum payload size for SF10. It is enough to contain several sensor measurements and a timestamp.  
Figure 8: Solution with unicast polling on Class C.  
Figure 9: Solution with multicast polling on Class C.  
SF for downlink is also fixed to SF9 following the standard's recommendations as it provides a good compromise between time on air (which impacts coverage and resilience to noise) and data rate. The simulation also assumes the gateway has an eight-channel radio, which is the most extended option off-the-shelf. Table 2 shows all the related parameters.
# A Compact Broadband Planar Inverted-F Antenna with Dual-Resonant Modes
## 2 Geometry  
Figure 1 shows the configuration of the proposed PIFA, which is implemented on an RO6006 substrate with a permittivity of \(\epsilon_{r}\) = 6.15 and a height of \(Hz\) = 2.54 mm. It consists of a rectangular patch (\(W\times L\)), a row of shorting pins, and a ground plane (\(G\times G\)). The ground plane is printed on the bottom surface of the substrate, while the patch is printed on its top surface. There is a row of shorting pins near the top edge of the patch, which is used to short the patch to the ground plane. A pair of horizontal slots (Slots 1) is etched on the rectangular patch to adjust the resonant frequencies and improve the radiation patterns of the two target modes of TM\({}_{3/2,0}\) and TM\({}_{1/2,2}\). Slots 2, which are orthogonal to Slots 1, are introduced onto the patch to achieve good impedance matching. A 50 \(\Omega\) coaxial probe is used to feed the antenna. The outer conductor of the probe is connected to the ground, and the inner conductor is connected to the patch.  
All the antenna parameters are listed in Table 1, and the following simulation results are obtained with HFSS 18.9 [29].
## 4 Case Study
### Sensitivity Analysis
#### 4.4.2 Investment Cost and Cycle Life of BESS  
As shown in the above analysis of Case 3, wind-energy union system can achieve positive returns under the condition of \(\mathrm{C_{E}}\) being 250 $/kW\(\cdot\)h and \(\mathrm{L_{cyc,N}}\) being 4000 times. In other words, the additional benefits for wind farm configuration BESS is greater than the additional investment costs. To find the balance point of the additional benefits and costs with \(\mathrm{C_{E}}\) and \(\mathrm{L_{cyc,N}}\) varying, the impact of \(\mathrm{C_{E}}\) and \(\mathrm{L_{cyc,N}}\) on the optimization results is shown in Figures 8 and 9, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline
**On-Grid Price (\$/kW\(\cdot\)h)** & \(\boldsymbol{f}\) (\$) & \(\boldsymbol{P_{cap}}\) (MW\(\cdot\)h) & \(\boldsymbol{S_{cap}}\) (MW\(\cdot\)h) \\ \hline
0.084 & 2990 & 58 & 122.4 \\
0.080 & 2321 & 54 & 102.4 \\
0.076 & 1845 & 35.25 & 63.25 \\
0.072 & 740 & 27.75 & 60.25 \\
0.068 & 322 & 7.15 & 14.25 \\
0.064 & 0 & 0 & 0 \\ \hline \end{tabular}
\end{table}
Table 5: Optimal results under different on-grid prices of wind power.  
(1) It can be seen from Figure 8 that under the condition of Lcy,N being 4000 times, if BESS can be provided a certain compensation for participating in the scheduling, the wind-energy union system will reach the payment balance with C\({}_{\rm E}\) being 360 $/kW\(\cdot\)h. That is to say, C\({}_{\rm E}\) < 360 $/kW\(\cdot\)h can guarantee the positive benefits of this wind farm. The auxiliary service compensation for BESS is more effective to stimulate the wind farm configuring BESS and promote the early arrival of the \"BESS generation\".  
(2) Similarly, from Figure 9, we can see that the capacity of BESS is 0 when Lcy,N reducing to 2800 times under the condition of C\({}_{\rm E}\) = 250 $/kW\(\cdot\)h. In other words, compared to the above mentioned in Section 2.3, the cycle life should being more than 4000 times in large-scale BESS application, it will be more effective to incentive wind farm configuring BESS with taking the auxiliary service compensation of BESS into account.
## 3 Proposal of Positioning Approach in Nondestructive Environments
### Estimation Algorithm  
Let us consider a specific system model of UWB-PPM systems illustrated in Figure 9, in which the environment is assumed to have a nondestructive structure that has two layers with relative permittivities of \(\varepsilon_{1}\) and \(\varepsilon_{2}\). To determine the environmental parameters and also the location of the target \"T\", the transceiver generates UWB pulses with rotation angles of the transceiver antenna are \(\alpha_{i}\) and get the reflected pulses. Figure 8: Correlation functions of proposed UWB-PPM-ATS with different additional time shift. Figure 9: Target system model in an environment with nondestructive structure of two layers. The traveling time \(\tau_{1i}\) corresponding to the \(i^{th}\) time of emission is calculated according to (11), the geometric relationship between \(\tau_{1i}\), \(d_{1}\), and \(\varepsilon_{1}\) is indicated in (15). \[\tau_{1i}=\frac{2d_{1}\sqrt{\varepsilon_{1}}}{c.cosa_{i}}, \tag{15}\]  
where \(\alpha_{i}\) is the rotation angle of antenna and:  
\[\alpha_{i}=i.\Delta\alpha; \tag{16}\]  
where \(\Delta\alpha\) is the minimum rotation angle of the transceiver antenna. Thus the values of \(\varepsilon_{1}\) and \(d_{1}\) in (15) are estimated based on known pairs (\(\alpha_{i}\), \(\tau_{1i}\)). Then, to locate buried objects, the device is moved horizontally, and the transmitter emits pulses in the perpendicular direction after every movement step of \(\Delta x\). Denote \(\tau_{2i}\) is the traveling time of the transmitted signals, reflected from the buried object and returned to the transceiver at position of \(x_{Di}\), it is computed by:  
\[\tau_{2i}=\tau_{10}+\frac{\sqrt{\varepsilon_{2}(d_{2}^{2}+(x_{T}-x_{Di})^{2})} }{c}, \tag{17}\]  
where \(x_{T}\) is the horizontal coordinates of the buried object and \(\tau_{10}\) is the traveling time of reflected signal at the interface between two layers of the environment in \(d\) direction. \(\tau_{2i}\) also computed according to (11), the values of \(\varepsilon_{2}\), \(d_{2}\), and \(x_{T}\) in (17) are estimated based on known pairs (\(\tau_{2i}\), \(\tau_{10}\), \(x_{Di}\)), and  
\[x_{Di}=i.\Delta x. \tag{18}\]  
The proposed algorithm to estimate the values of those parameters is indicated in Figure 10 and described in detail as below  
In Figure 10, the unknown parameter vectors are denoted by:  
\[\mathbf{X}_{1}=(\widehat{\varepsilon}_{1},\widehat{d}_{1});\mathbf{X}_{2}=( \widehat{\varepsilon}_{2},\widehat{d}_{2},\widehat{x}_{T}), \tag{19}\]  
Figure 10: Flowchart of estimated algorithms. and the index \(m\) in Figure 10 is used to replace for 1, 2.
## 3 Results and Discussion
### Analysis of Factors Affecting Frost-Heave Crack Propagation
#### 3.3.3 Influence of Initial Crack Moisture Content  
The average value of the strain curve after peak stabilization should be regarded as the maximum generalized strain at the crack tip, as shown in Figure 11c. The relationship between the maximum generalized strain and the initial crack moisture content of the concrete specimens is shown in Figure 17. Exponential model fitting was employed in this study, as it could better fit the experimental data. The expression between generalized strain and crack initial moisture content (\(n_{0}\)) is obtained as follows:  
\[\varepsilon=0.078\mathrm{e}\frac{n_{0}}{0.399}-0.106\ (0\ \leq\ n_{0}\ \leq\ 1) \tag{22}\]  
By inserting \(\varepsilon^{ini}\) into Equation (22), it was concluded that when the moisture content of the crack is larger than 20%, cracking is initiated. The frost-heave crack propagation is highly sensitive to the moisture content in the cracks. Crack-sealing measures are usually adopted to prevent fracture damage to slab-tracks. Therefore, before such crack sealing, the crack moisture content should be strictly controlled to avoid frost-heave damage caused by residual water in slab-track cracks.  
Figure 16: The relationship between the maximum generalized strain at the crack tip and the external temperature (\(T\)).  
Figure 17: The relationship between the maximum generalized strain at the crack tip and the initial moisture content of the crack (\(n_{0}\)).
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
###### Abstract  
In this paper, a detailed review of microcontroller unit (MCU)-based wireless sensor node platforms from recently published research articles is presented. Despite numerous research efforts in the fast-growing field of wireless sensor devices, energy consumption remains a challenge that limits the lifetime of wireless sensor networks (WSNs). The Internet-of-Things (IoT) technology utilizes WSNs for providing an efficient sensing and communication infrastructure. Thus, a comparison of the existing wireless sensor nodes is crucial. Of particular interest are the advances in the recent MCU-based wireless sensor node platforms, which have become diverse and fairly advanced in relation to the currently available commercial WSN platforms. The recent wireless sensor nodes are compared with commercially available motes. The commercially available motes are selected based on a number of criteria including popularity, published results, interesting characteristics and features. Of particular interest is to understand the trajectory of development of these devices and the technologies so as to inform the research and application directions. The comparison is mainly based on processing and memory specifications, communication capabilities, power supply and consumption, sensor support, potential applications, node programming and hardware security. This paper attempts to provide a clear picture of the progress being made towards the design of autonomous wireless sensor nodes to avoid redundancy in research by industry and academia. This paper is expected to assist developers of wireless sensor nodes to produce improved designs that outperform the existing motes. Besides, this paper will guide researchers and potential users to easily make the best choice of a mote that best suits their specific application scenarios. A discussion on the wireless sensor node platforms is provided, and challenges and future research directions are also outlined.  
Internet-of-Things (IoT); wireless sensor networks; sensor node; microcontroller unit (MCU); power consumption +
Footnote †: journal: Sensors
## 6 Challenges and Limitations
### Integration into Clinical Practice  
Implementing AI tools in clinical practice presents both technical and cultural challenges. Clinicians may be skeptical of AI technologies, particularly if they perceive them as black boxes with opaque decision-making processes [62]. Concerns about job displacement or loss of professional autonomy may also hinder acceptance. From a technical standpoint, integrating AI tools with existing electronic health record (EHR) systems and workflows requires significant effort [63]. Compatibility issues, data interoperability, and user interface design are critical factors that influence usability.  
To facilitate adoption, the following should be considered:  
Education and Training: Providing clinicians with education about AI technologies, their capabilities, their limitations, and how they can enhance patient care;  
User-Centered Design: Involving clinicians in the design and development of AI tools to ensure they meet clinical needs and integrate smoothly into workflows;  
Transparency and Explainability: Ensuring that AI models are transparent and that clinicians can understand and interpret their outputs;  
Demonstrating Value: Conducting studies that demonstrate the efficacy and cost-effectiveness of AI applications in improving patient outcomes.
# Harnessing Biotechnology for the Remediation of Organic Pollutants in Coastal Marine Ecosystems
## 3 Organic Pollution in Coastal Marine Ecosystems
### Microplastics and Synthetic Polymers
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Sources** & **Effects on Marine Ecosystems** & **References** \\ \hline \multirow{6}{*}{Persistent Organic Pollutants (POPs)} & Industrial chemicals (PCBs), combustion byproducts & Bioaccumulate and biomagnify, causing reproductive disorders, immune suppression, endocrine disruption. \\  & (dioxins, furans), pesticides & Mutagenic and carcinogenic; accumulate in sediments; affect benthic organisms; microbial degradation by _Pseudomonas_ and _Alcanivorax_. \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \hline \multirow{6}{*}{Pharmaceutical and Personal Care Products (PPCPs)} & Wastewater effluents, hospital discharge, improper medication disposal. & Cause antibiotic resistance, physiological dysfunction in aquatic organisms, endocrine disruption leading to reproductive failure. \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \hline \multirow{6}{*}{Pesticides and Herbicides} & Agricultural runoff, urban landscaping, industrial discharge. & Endocrine disruption in fish and birds, nervous system disorders, disruption of photosynthesis in phytoplankton, leading to hypoxia and biodiversity loss. \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \hline \multirow{6}{*}{Microplastics and Synthetic Polymers} & Breakdown of plastic waste, microbeads in personal care products, synthetic fibers from textiles. & Act as carriers for pollutants like PAHs and PCBs; disrupt hormone regulation (e.g., BPA and phthalates); alter microbial community structures. \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Major organic pollutants in coastal marine ecosystems and their impacts. Figure 1: Distribution of research focuses on organic pollutants in coastal marine ecosystems based on the study by [PERSON] et al. [51].
## 5 Example Analysis
### Calculate the Weight of Differentiated Service Communication Requirement Indexes
The deployment of high-performance communication infrastructure further highlights the significance of communication costs for this service. The power market transaction service encompasses participants distributed across various geographical locations, necessitating the transmission of substantial volumes of confidential transaction data. Additionally, it must respond to market changes in real time. To guarantee the efficient operation of the electricity market, communication technology must exhibit high security, extensive transmission distances, and minimal latency. In summary, the weighted values derived from a combination of subjective and objective weighting are consistent with the communication requirements of differentiated services, validating the effectiveness of the proposed combined weighting method.