esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Implementation: Galerkin Finite Element Method  
Numerical computations are utilized to resolve mathematical models (10), (11), and (12). The G-FEM implementation is outlined as follows.  
* The residuals for problems (10)-(12) are weighted, and integrals are taken over the typical element in their written form;
* In order to obtain the weak systems, the linear second-order derivative expressions are integrated;
* Galerkin approximations are utilized to estimate the weak systems, and stiffness elements are produced according to those results;
* The nonlinear system is assembled using the forcing vectors and boundary vectors of the stiffness elements;
* The Picard linearization approach is used to linearize the nonlinear system, and the Gauss-Seidel procedure is used to solve the linearized problem repeatedly;
* Furthermore, convergence analysis is accomplished by ensuring that the obtained values are mesh-free.  
Examples of the parametric research are provided to demonstrate the effects of porous media, variable thermal conductivity, thermal radiation, heat source, viscous dissipation, and the rate of heat flow on the study of thermal energy in 2D non-Newtonian third-grade fluid flow. Table 3 shows 300-element mesh-free analysis results. Furthermore, a flow chart of the Galerkin finite element technique is depicted in Figure 2.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**No. of Elements** & \(\mathbf{f}\big{(}\frac{\mathbf{y}_{\text{max}}}{2}\big{)}\) & \(\mathbf{\theta}\Big{(}\frac{\mathbf{y}_{\text{max}}}{2}\big{)}\) \\ \hline
30 & 0.0326964105 & 0.0054392174 \\
60 & 0.3008137336 & 0.00482355489 \\
90 & 0.2787224963 & 0.00452597831 \\
120 & 0.2518385629 & 0.00423211689 \\
150 & 0.2327463208 & 0.00396988127 \\
180 & 0.2164459730 & 0.00357315905 \\
210 & 0.1902813462 & 0.00327698245 \\
240 & 0.1907921025 & 0.00305766541 \\
270 & 0.1906827572 & 0.00274452730 \\
300 & 0.1905266081 & 0.00253240956 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Velocities and temperatures based on a 300-element grid.
# Improving Decodability of Polar Codes by Adding Noise
## 4 Proposed Decoding Algorithms
### Online-PDNE Decoding Algorithm
The details of the Online-PDNE decoding algorithm are given in Algorithm 1. ```
Input: \(\mathrm{y^{N}}\) // Received signal \(\mathrm{Q_{P}}\) // Variance of the perturbed noise \(\mathrm{T_{1}}\) // Maximum number of the perturbed attempts \(\mathrm{T_{2}}\) // Maximum number of generations \(\mathrm{T}\) // Number of individuals in the population Output: \(\mathrm{\hat{u}_{1}^{N}}\) // Estimated codeword 1: Initialization: \(\mathrm{\hat{u}_{1}^{N}}\gets 0\), \(\mathrm{i}\gets 1\), \(\mathrm{k}\gets 1\). 2: \(\mathrm{\hat{u}_{1}^{N}}\leftarrow\mathrm{Polar\_decoder}(\mathrm{y^{N}})\)
3: if CRC(\(\mathrm{\hat{u}_{1}^{N}}\)) == success
4: break
5:else
6: while i \(\leq\mathrm{T_{1}}\) do
7: \(\mathrm{y_{1}^{N}}=\mathrm{y^{N}}+\mathrm{Q_{P}}\cdot\mathrm{randn}(1,\mathrm{ N})\)
8: \(\mathrm{\hat{u}_{1}^{N}}\leftarrow\mathrm{Polar\_decoder}(\mathrm{y_{1}^{N}})\)
9: \(\mathrm{i}\leftarrow\mathrm{i}+1\)
10: if CRC(\(\mathrm{\hat{u}_{1}^{N}}\)) == success
11: break
12: end if
13: end while
14: Initial population \(\mathrm{S_{n}^{(0)}}\)\(=\{\mathrm{n_{ne}^{(i)}}|\mathrm{i}=1,2,\ \ldots,\mathrm{T}\}\)
15: for j \(=1\), \(\ldots\), \(\mathrm{T_{2}}\) do
16: \(\mathrm{S_{n}^{(i)}}\)\(=\mathrm{NE}(\mathrm{S_{n}^{(-1)}})\)
17: while k \(\leq\mathrm{T}\) do
18: Choose \(\mathrm{n_{ne}^{(k)}}\in\mathrm{S_{n}^{(i)}}\)
19: \(\mathrm{y_{2}^{N}}=\mathrm{y_{1}^{N}}+\mathrm{n_{ne}^{(k)}}\)
20: \(\mathrm{\hat{u}_{1}^{N}}\leftarrow\mathrm{Polar\_decoder}(\mathrm{y_{2}^{N}})\)
21: \(\mathrm{k}\gets\mathrm{k}+1\)
22: if CRC(\(\mathrm{\hat{u}_{1}^{N}}\)) == success
23: break
24: end if
25: end while
26: end for
27: end if
28: Return \(\mathrm{\hat{u}_{1}^{N}}\)
```  
**Algorithm 1**Online-PDNE Decoding algorithm
# Augmented Reality for PCB Component Identification and Localization
## 4 Materials and Methods
### Experiment 2-Methods
#### 4.3.6 Position Definitions  
1. Reference Position: The reference position represented the true 2D coordinates of the center of each component on the PCB. These positions were predefined using the original PCB layout or schematic (Figure 7), serving as the baseline for comparison. In Figure 7, yellow squares and circles represent the mapped boundaries of selected components, and the reference position corresponds to the geometric center of each boundary.
2. Augmented Position: The augmented position (Figure 8) refers to the 2D coordinates of the component center as displayed in the AR interface. It reflects the technical performance of the system, specifically marker tracking, spatial co-registration accuracy, and the fidelity of the digital twin. In this context, the coordinates obtained for the red dot rendered on each component represent the augmented position. These positions were extracted from recorded video frames using image analysis techniques, such as centroid detection, calculated for each component, angle, and participant.
3. Identified Position: The identified position indicates where users manually pointed to a component using a stylus. It captures the combined influence of human visual perception and motor coordination, providing insight into real-world usability. In this case, the coordinates represent the calculated location of the stylus tip and were extracted from video frames by locating the stylus tip within the AR display (Figure 9), using similar image processing methods as described above.  
Figure 7: Reference position.
# Improved Algorithm to Detect Clandestine Airstrips in Amazon RainForest
## 2 Materials and Methods
### Data Acquisition  
To generate the dataset for training Artificial Intelligence (AI) techniques, we used the mapping conducted by [3], as shown in Figure 1. This work provides the spatial location of 2869 airstrips as of 2021. To capture the targets in their entirety, we created \(2\times 2\) km square cutouts. The choice of this size is due to the objective of finding illegal airstrips, which are relatively small; therefore, a 2 km square captures most targets to their full extent. In addition, these cutouts were made to make the targets more evident in relation to the size of the analyzed image. Satellite scenes typically cover large areas, meaning the targets would appear very small compared to the total image size. Furthermore, the available hardware was unable to process entire satellite scenes.  
Using the airstrip locations, we selected the \"Image (r) 2023 Planet Labs PB\" dataset from the Planet satellite constellation [34], which provides images with a spatial resolution of 4.7 m, enabling clear visualization of the targets. Additionally, these images consist of four spectral bands, three in the visible region (RGB) and one in the near-infrared (NIR) region, each with 16-bit radiometric resolution. Using the previously obtained squares, we cropped the targets using scenes from June 2021 to ensure temporal alignment between the mapping and the dataset. After this, images in which it was difficult to identify the target were excluded, resulting in a final set of 1989 Planet constellation images of airstrips. Such exclusions were made because, in some images, a large number of clouds obstructed visibility, making it impossible to observe the target.  
Furthermore, the algorithm must detect the target using different satellite imaging sensors, as the most recent data may only be available from a specific constellation, depending on the sensor's temporal resolution. Therefore, to achieve such generalization, the same process was applied to images from the Sentinel-2 satellite [35; 36], selecting the same four bands as those from the Planet constellation. This database contains images with a 10-meter spatial resolution, which still allows for clear identification despite offering a lower target definition. Additionally, for the cutouts, we chose images from June 2021.  
Figure 1: Map of the Brazilian Amazon with the airstrip mapping conducted by [3].  
After the visual verification stage, we obtained 2468 images of Sentinel-2 airstrips. Figure 2 shows an example of the same airstrip for both sensors.  
To generate the dataset of images that do not contain airstrips, we conducted an empirical sampling of the Amazon. The reason for this is that, due to the high forest density of the biome, a random sampling would produce images with very similar shapes, mostly representing forest areas. Therefore, images were empirically selected to contain shapes resembling airstrips, such as rivers, cities, and highways. Figure 3 shows two samples contained in this set.
## 2 Simulation Setup
In the off-line DSP at the receiver, the digitized sequence is down sampled and off-line processed at 2 SpS, using one of the three DSP nonlinear distortion compensation methods shown at the bottom of Figure 1. As detailed in [16], the SQRT exploits a square-rootlike function, i.e., \(y(n)=\sqrt{x(n)+|min(x(n))|}\), and POLY consists of a polynomial function, i.e., \(y(n)=x(n)+\alpha x^{2}(n)\), where \(x(n)\) and \(y(n)\) are the input and output signal of the SQRT (or POLY) block as shown in Figure 1, and \(\alpha\) is a free parameter to be optimized. Since we also showed that a SQRT can provide a similar gain as POLY, but with lower complexity, in this paper we will consider the SQRT. We also analyze a technique based on the VNLE. To keep the complexity low, the VNLE is limited to third order, i.e., linear, quadratic, and cubic terms are considered [29]. In our simulations, we set quadratic memory and cubic memory to be the same, and term them as nonlinear memory (NL memory). For example, _NL memory_ = 5 samples means _quadratic memory_ = _cubic memory_ = 5 samples. The three DSP nonlinear distortion compensation methods are: (i) the SQRT [16,30] in combination with a 120-taps feed forward equalizer (FFE) and a 5 taps decision feedback equalizer (DFE). We indicate it as \"SQRT + FFE + DFE\". (ii) VNLE with 121 linear memory and a variable number of NL memory, ranging from 5 samples to 20 samples. We indicate it as \"VNLE\". (iii) The SQRT in combination with the same VNLE as in option (ii). We indicate it as \"SQRT + VNLE\". The SQRT + FFE + DFE method is equivalent to the VNLE method with _NL memory_ = 0. The transmitted optical power _TOP_ is set to 15 dBm for options with the VNLE (i.e., VNLE and SQRT + VNLE) and 11 dBm for the option without the VNLE (i.e., SQRT + FFE + DFE). The typical metric used in a PON performance evaluation is the achievable ODN loss, calculated as the ratio between the TOP and the ROP. In this manuscript, we consider the required ROP (RROP) to obtain a BER target \(BER_{T}\) = 10\({}^{-2}\) (low-density parity check (LDPC) code forward error correction (FEC) scheme), and we will show system performance in terms of the maximum ODN loss that that guarantees operation equal to or below the \(BER_{T}\) (_Maximum ODN loss [dB] = TOP [dBm] - RROP [dBm]_).
# Monitoring of Cropland Non-Agriculturalization Based on Google Earth Engine and Multi-Source Data
## 1 Introduction
The structure of this paper is organized as follows: Section 2 introduces the study area and research data, with a focus on the data sources and preprocessing methods. Section 3 provides a detailed explanation of the proposed methodology, including the approach for updating cropland information using multi-source LULC products and the method for monitoring cropland conversion through the integration of optical imagery and SAR data. Section 4 presents the experimental results, covering both cropland extraction outcomes and cropland conversion monitoring results. Section 5 offers a discussion, where the advantages of multi-source data for cropland conversion monitoring are validated through comparative experiments. It also analyzes the causes of false changes in the monitoring results and highlights the limitations of this study. Finally, Section 6 concludes by summarizing the key findings of this research.
## 4 Methodology  
### Framework  
In order to better leverage the interactive information among prototypes, we propose a prototype network model for FSRE, termed multi-head self-attention and contrastive-center loss (SACT). To enhance the representational power of the prototype network in scenarios with only a limited number of support instances, we introduce a multi-head self-attention mechanism, facilitating the utilization of the interaction information in each prototype. This approach combines the relationship information and employs an adaptive prototype space fusion technique to generate more enriched prototypes. Additionally, we design a contrastive-center loss to assist the model in effectively aggregating samples from the same category while distinguishing differences between different categories, enabling the model to learn a more discriminative metric space.  
In this section, we will provide a detailed exposition of the primary framework of the proposed model, SACT, as illustrated in Figure 3. The framework comprises the following modules: (1) Input: Initially, the sampled support set, query set, and relation information are input into the sentence encoder. (2) Sentence Encoder: This is responsible for encoding the input support and query sets into corresponding sentence representations. Simultaneously, the relationship information is transformed into relationship representations, and their final representation is obtained through additive operations. (3) Prototype Enhancement: By averaging the sentence representations for each relation class in the query set, basic prototypes are obtained. These prototypes are then enhanced using a multi-head self-attention mechanism to generate more expressive prototype representations. Finally, an adaptive prototype fusion mechanism combines the representations of the relationship information and enhanced prototypes to form the ultimate prototype representation. (4) Contrastive-Center Loss: This module compels the prototype network to learn a more discriminative metric space in the semantic domain. It introduces the contrastive-center loss function, aiding the model in effectively aggregating samples from the same category and distinguishing differences between the different categories. Subsequently, we will delve into detailed explanations of the latter three components in the following sections.
# Transpiler-Based Architecture Design Model for Back-End Layers in Software Development
## 3 Transpiler-Based Design Model for Back-End Layers
### Relationship with Other Architecture Designs
#### 3.7.3 Microservices  
The proposed architecture design based on transpilers can be used in conjunction with microservice patterns. Microservices are software architecture patterns that involve breaking down a large monolithic application into smaller independent services that can be developed, deployed, and scaled independently [6]. The transpiler-based architecture design can facilitate the development of microservices by providing a unified approach to exposing back-end functionality to the front-end layers, regardless of the programming language used in the implementation of each microservice.  
In this context, the transpiler can be used to generate the necessary code for each microservice in the programming language of choice and to expose the corresponding API to the front-end layers using a consistent approach, helping to ensure consistency and maintainability across different microservices even if they are implemented using different programming languages. Additionally, the use of nest implementations can allow the deployment of microservices in different technologies, providing flexibility in terms of supported operating platforms and environments.  
However, it is important to note that the use of a transpiler-based architectural design in conjunction with the microservice pattern may add additional complexity to the development process. The decomposition of a monolithic application into smaller microservices requires careful planning and coordination, and the use of multiple programming languages and technologies may require additional training and support. Therefore, it is important to carefully weigh the benefits and drawbacks of using this combination of patterns prior to their implementation in software development projects.
# Design of Shape Memory Alloy-Based Soft Wearable Robot for Assisting Wrist Motion
###### Abstract  
In this paper, we propose a shape memory alloy (SMA)-based wearable robot that assists the wrist motion for patients who have difficulties in manipulating the lower arm. Since SMA shows high contraction strain when it is designed as a form of coil spring shape, the proposed muscle-like actuator was designed after optimizing the spring parameters. The fabricated actuator shows a maximum force of 10 N and a maximum contraction ratio of 40%. The SMA-based wearable robot, named soft wrist assist (SWA), assists 2 degrees of freedom (DOF) wrist motions. In addition, the robot is totally flexible and weighs 151g for the wearable parts. A maximum torque of 1.32 Nm was measured for wrist flexion, and a torque of larger than 0.5 Nm was measured for the other motions. The robot showed the average range of motion (ROM) with 33.8, 30.4, 15.4, and 21.4 degrees for flexion, extension, ulnar, and radial deviation, respectively. Thanks to the soft feature of the SWA, time cost for wearing the device is shorter than 2 min as was also the case for patients when putting it on by themselves. From the experimental results, the SWA is expected to support wrist motion for diverse activities of daily living (ADL) routinely for patients.  
soft robots; shape memory alloy; wearable; wrist; assistive robots +
Footnote †: journal: Applied Sciences
## 3 Role of Small RNA in Stress Response
### RybB  
The outer membrane (OM) is crucial for the survival of Gram-negative bacteria. In _E. coli_, the sigma factor E, SigE, plays a crucial role in envelope stress responses by detecting signals that are indicative of OM dysfunction and thereby initiates an adaptive response by upregulating genes involved in the biogenesis, transport, and assembly of key OM components, including lipopolysaccharidesaccharides (LPSs), phospholipids, and outer membrane proteins (OMPs) [159]. Additionally, SigE regulates the expression of proteases and chaperones that contribute to OM maintenance and repair, ensuring membrane integrity under envelope stress conditions. One key player in this stress response is a 79-nt sRNA called RybB. The expression of RybB is regulated by SigE and the stress-associated alarmone ppGpp in _E. coli_ and _Salmonella_ sp. [160]. Like other trans-acting sRNAs, RybB's function depends on RNA chaperones, including Hfq and ProQ. Hfq stabilizes RybB and facilitates its interactions with target mRNAs, while ProQ protects it from degradation. Its key targets include mRNAs encoding outer membrane proteins such as _ompC_, _ompW_, _ompA_, and _fadL_[161] (Figure 2d). By targeting these mRNAs, RybB reduces the production of porins and fatty acid transporters, facilitating rapid adjustments to envelope composition. In _Salmonella_, RybB plays a greater role to cope with envelope stress by regulating additional targets like _ompD_, _ompF_, _ompN_, _ompS_, and _tsx_. This ability to modulate OMP expression is crucial for bacterial adaptation to stressors, including changes in osmolarity, pH, and exposure to antimicrobial agents. Interestingly [PERSON] et al. identified FtsO, an ORF-internal sRNA derived from the _ftsl_ mRNA, which plays a crucial role in bacterial cell division, acting as an RNA sponge to negatively regulate RybB. An RNA sponge is a regulatory RNA that sequesters and inhibits sRNAs by base pairing, preventing them from interacting with their target mRNAs. Since _ftsl_ encodes a penicillin-binding protein essential for cell division, the regulation of RybB by FtsO suggests a mechanism linking membrane stress response with the division cycle [162]. RybB also acts as a negative regulator of biofilm formation by negatively regulating the expression of Curlin subunit gene D, CsgD. Beyond biofilm regulation, RybB promotes stationary phase cell lysis, a process critical for population turnover, eliminating damaged cells, and preventing mutation accumulation. Disrupting RybB or the stress-responsive sRNA, MicA impairs lysis, reducing survival and increasing mutation rates, ultimately causing population collapse.  
In summary, bacterial small RNAs play a pivotal role in enabling pathogens to withstand various host-induced stresses, thereby contributing significantly to bacterial pathogenesis and host-pathogen interactions. Moreover, bacteria frequently encounter antibiotics, which are administered to eliminate pathogenic infections. The following section explores the role of small RNAs in mitigating the adverse effects of antibiotic exposure and enhancing bacterial survival.
# Effects of Resonant Electromagnetic Fields on Biofilm Formation in _Pseudomonas aeruginosa_
## 2 Materials and Methods
### Exposure System and Procedure  
A custom-made Helmholtz coil/solenoid was made from a hard PVC tube (diameter of 20 cm and length 60 cm) and windings of copper wire connected to a function generator. The generated fields were monitored with a custom-made magnetometer (accuracy: \(+/-\)56 nT). Static magnetic fields in the range of 10-40 microTesla and alternating fields in the range of 100-300 nanoTesla were used when exposing bacteria. Frequencies of the alternating field were in the range of 3-13 Hz.  
Exposure was controlled by a function generator (model RS Pro 5 MHz AFG-21100) supplying a DC field of 10 to 30 microTesla (corresponding to 0.73 to 2.55 Volt DC) and an overlaid AC field of 130 to 300 nanoTesla (at 3.9 to 11.7 Hz/0.130 to 0.390 V AC).  
The following exposure conditions were used in this study: (1) Exposure inside the magnetic chamber by a specified DC field and a specified AC field of a given frequency (\"exposed\"); (2) Zero exposure or shielding inside the magnetic chamber (\"zero\"); or (3) exposure to ambient field outside of chamber on the lab bench (\"ambient\").  
Most exposures were carried out using a frequency of 3.9 Hz, corresponding to the ICR of K\({}^{+}\) at a static magnetic field of 10 uT according to Equation (1). The selection of the potassium cation K\({}^{+}\) as the primary target was based on findings observed by [PERSON] et al., which showed that the K\({}^{+}\) ICR had significant biological impact [24].  
Figure 1: The bioresonance lab is temperature-controlled at 37 \({}^{\circ}\)C and includes a function generator (left), a magnetometer (center), a triple-layer magnetic mu-metal cylinder (right), and a custom-made solenoid (red/white, bottom) that fits inside the mu-metal cylinder.