esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### High-Aggregation Sampling  
In SBP, all nodes transmit the position belief of the node itself. However, the belief of nodes is the aggregation of paired potential functions based on mixed Gaussian, and its form is very complicated. For the convenience of expression, we can consider approximating the accurate distribution of nodes by sampling and re-fitting. We propose a highly condensed sampling method, which can simultaneously reduce the amount of calculation, improve the sampling efficiency, and improve the effectiveness of samples.  
Sampling is divided into five steps: uniform sampling in sampling space, probability calculation, node filtering, resampling, and importance sampling.  
(1) Determine the sampling space and obtain uniform sampling \(\tilde{x}\sim U(x\_min,x\_max)\) in the space.  
\[x\_min=\max\{[x\_min;\max(x_{g}-d_{g})]\} \tag{20}\]  
\[x\_max=\min\{[x\_max;\min(x_{g}+d_{g})]\} \tag{21}\]  
(2) Apply the belief of the node \(\tilde{B}\) to the sampled sample particles, and obtain the particle probability \(P(\tilde{x})\)  
(3) Filter \(P_{gate}\) for nodes with particularly low probability to obtain effective nodes \(x_{gate}\)  
\[x_{gate}=\tilde{x}\quad if\quad P(\tilde{x})>P_{gate} \tag{22}\]  
(4) When the number of valid samples is insufficient, resample by sample expansion and random walk.  
Figure 3: Node potential function under different distributions. (**a**) indicates belief of node a (\(\mathcal{N}_{a}\)), where \(\mu=[0.5,0.5]\), \(\sigma=[0.1,0.1]\), \(\rho=0\); (**b**) indicates the potential function \(\Psi_{ac}\) with \(dis_{ac}=0.35\); (**c**) indicates belief of node b (\(\mathcal{N}_{b}\)), where \(\mu=[0.5,0.5]\), \(\sigma=[0.1,0.1]\), \(\rho=0.8\); (**d**) indicates the potential function \(\Psi_{bc}\) with \(dis_{bc}=0.35\).  
Expand the sample by copying the sample:  
\[\tilde{x}_{gate}=\left[x_{gate};\underbrace{\ldots;x_{gate}}_{floor(\eta)} \right]; \tag{23}\]  
where \(\eta=n/size(x_{gate})\),\(n\) is the total number of samples of the node.  
Through random walk of the expanded samples, resampling of the samples is realized.  
\[x_{tem}=\tilde{x}_{gate}+\beta\tilde{\eta}\sigma v \tag{24}\]  
among \(\tilde{\eta}=\max\{3,\eta\}\), the combination of \(\tilde{\eta}\) and \(\sigma\) is used to limit the distance traveled. \(v\) is used to indicate the direction of random walk. In a two-dimensional environment, \(v=[\cos(\theta),\sin(\theta)]\beta\sim U[0,1]\) means uniform sampling in distance. \(\theta\sim U[0,2\pi)\) means uniform sampling in angle.  
(5) Importance sampling of the obtained new particles.  
\[x_{sample}\sim P(x_{tem}) \tag{25}\]  
In this algorithm, uniform sampling is used to obtain sufficient effective particles, which ensures the expressiveness of the samples as a whole. Stepping uniform sampling by importance sampling can realize the approximation of distribution by particles.
## 5 Discussion
This decline was attributed to the poleward shift of mid-latitude storm tracks, among others. The potential influence of climate change on grain production suggests a decline in world food supply [68] and an increase in the level of exposure of the global population to the risk of hunger [69]. It is, therefore, important to ensure the accuracy and efficiency of yield prediction at anytime, anywhere, and at any scale. A more precise description of grain weight patterns in time and space provides more accurate information for precision agriculture to improve its production and sustainability. The semi-empirical carbon turn-over model used for crop yield prediction is based on historical yield data, and, in the future, relevant drivers (e.g., precipitation and temperature) and their interactions may change under climate change, thus requiring model re-calibration [4; 6; 70; 71]. Our approach was tested with C-Crop but could be extended to other semi-empirical models [72] and models based on machine learning [73]. Development of a national-scale yield prediction system requires time series of HSR and HTF imagery to describe the crop growth; therefore, next-generation HSR and HTF satellite data like Sentinel-2 [74; 75] should be tested for nationwide yield estimates post the growing season of 2015. Harmonized Landsat and Sentinel-2 surface reflectance products should be tested for national-scale crop yield prediction [76] when observed yield data for more recent years are available. However, spatio-temporal fusion of MODIS and Landsat data is still necessary for long-term studies that involve historical satellite images collected before 2015 (back to the year 2000, when MODIS was launched). Future studies should also focus on using active RS technologies, such as radio detecting and ranging (Radar) [77; 78] and light detection and ranging (LiDAR) [79], for a national yield estimation because of their ability to penetrate clouds and detect a plant's three-dimensional structural characteristics. Data blending between HSR optical imagery and active RS data (e.g., Radar and LiDAR) [80] warrants further study.
# Enhanced Path Planning by Repositioning the Starting Point +
## 2 Background and Related Work
The algorithmic approach navigates the solution space through incremental, localized modifications, transitioning from one solution to another until discovering an optimal result or satisfying predetermined stopping criteria (such as maximum iteration limits or convergence thresholds indicating no further improvement). The operational mechanism as described in Algorithm 1 involves deploying various local search operators to systematically modify current solutions, generating new candidates for evaluation. Each newly generated solution undergoes comparison with its predecessor; superior solutions replace existing ones, while inferior solutions are discarded. This iterative refinement continues until achieving an optimal solution set or reaching convergence conditions where no further improvements are detected. ```
1 begin
2\(optFound\longleftarrow\) true
3\(newGraph\longleftarrow\) currentGraph
4 while\(optFound\)do
5\(optFound\longleftarrow\) false
6 foreachroute in newGraph.Routesdo
7 Optimise route using 2-Opt
8 end foreach
9 Optimise newGraph using Cross-Opt
10 foreachroute in newGraph.Routesdo Optimise route using Or-Opt
11 ifnewGraph.TotalDistance\(<\) currentGraph.TotalDistancethen
12\(optFound\longleftarrow\) true
13\(currentGraph\longleftarrow\) newGraph
14 end if
15 endwhile
16
17 end while
18
19 end while
20
```  
**Algorithm 1**mTSP using local search operators  
Our implementation incorporates three specialized local search operators: Or-Optimization (Or-Opt), Two-Optimization (2-Opt), and Cross-Exchange-Optimization (Cross-Opt). The Or-Opt operator functions by selecting consecutive station sequences within individual routes and repositioning them to alternative locations within the same route, optimizing local arrangements [42]. The 2-Opt operator focuses on intra-route refinement, systematically examining station pair exchanges within individual routes to minimize total travel distances [43]. In contrast, Cross-Exchange operates at the inter-route level, facilitating sub-route exchanges between different drones to enhance overall solution quality through strategic station reassignment [40]. This inter-drone exchange capability extends the search beyond single-route modifications, enabling exploration of diverse route combinations that can escape local optima [44]. The diversification introduced by Cross-Exchange significantly enriches the search process, revealing improved solutions that remain inaccessible through purely localized operators like Or-Opt and 2-Opt [38]. The synergistic interaction among all three operators drives continuous solution improvement through systematic incremental refinements. Strategic integration of these operators, potentially combined with additional heuristic methods, can further enhance solution quality for complex mTSP instances [37; 39; 40].
## 4 X-ray I0-Correction Optimization  
In this section, we describe the proposed method and formulas for performing the I0-correction directly on the acquired raw sensor data, without converting them to intensity domain images.  
As explained in Section 2.2, most of the commercial CT scanners provide the projections, directly converted in the intensity domain, as real or integer number values, and the total attenuation correction is applied with Formula (2). This formula is computationally complex to implement because the logarithm operation usually is not a primitive operation in the math co-processors. In addition, for using this formula, the collected data must be converted from raw sensor data to intensity data inside the CT scanner; the conversion determines an additional latency between the DMS and the reconstruction system. In fact, for converting data from raw sensor data to intensity data, Formulas (1) and (3) are used. Formula (1) comprises an exponential operation, which is also not a primitive operation in most of math co-processors.  
In our proposed optimization, we consider raw sensor data as input for the I0-correction. In this way, we have merged Formulas (1)-(3) which are usually separated and implemented in the CT data acquisition system and the reconstruction system. By merging them, we obtain the following formula:  
\[P(u,v)=\log_{e}(\frac{c\cdot exp(-(f\cdot Raw_{0}(u,v)))}{c\cdot exp(-(f\cdot Raw (u,v)))}) \tag{6}\]  
If we implement Formula (6) as is, the logarithm and the power operations should be implemented. However, since we implement it inside the data acquisition system of the CT, we apply the mathematical simplification that results in the equivalent formula, shown in (7).  
\[P(u,v)=\log_{10}(2)\cdot f\cdot(Raw_{0}(u,v)-Raw(u,v)) \tag{7}\]  
Therefore, as shown in Formula (7), the I0-correction can be performed directly on raw sensor data with basic operations provided by most of the math co-processors. This mathematical optimization determines the decreasing of the resource utilization and execution time, compared to the implementation of Formula (6).  
Furthermore, to perform the I0-correction and the whole pre-processing step on the fly, we propose the implementation of the algorithm in a dataflow architecture. To describe how Formulas (6) and (7) were implemented in the dataflow architecture, we used the data flow graph representation ([PERSON] et al., 2016), as shown in Figures 4 and 5. In the data flow graph the square boxes represent the input/output data and constants, the circular boxes, the operation and the arrows the flow of data. The dataflow graph shows the flow and the data and dependency of the operations. The various operations in the boxes have different latency and hardware costs in a math co-processor. The values of these metrics depend on the implemented operation and the selected data format. In the next sections, we focus on the hardware implementation of the dataflow architecture, its integration in the data acquisition system of the open-interface CT and how different data formats influence performance.
# Coordinated Dispatch of Multi-Energy Microgrids and Distribution Network with a Flexible Structure
## 2 Mathematical Model Formulation
### Multi-Energy Microgrids
#### 2.1.2 Gas Boiler (GB)  
A gas boiler (GB) is a gas-fired boiler directly supplying heating load by natural gas combustion. The mathematical model can be expressed as:  
\[\begin{array}{l}H_{GB}(t)=\eta_{GB}F_{GB}(t)R_{G}\\ H_{GB}^{\min}\leq H_{GB}(t)\leq H_{GB}^{\max}\end{array} \tag{5}\]  
where \(H_{GB}(t)\) is the output heating power of GB at time slot \(t\). \(F_{GB}(t)\) is the natural gas consumption of GB at time slot \(t\). \(H_{GB}^{\min}\), \(H_{GB}^{\max}\) are minimal and maximal output heating power of GB respectively
# Development of Antibiofilm Therapeutics Strategies to Overcome Antimicrobial Drug Resistance
## 3 Mode of Action of Antibiofilm Agents
### Nucleotide Second Messenger Signaling Modulating Molecules  
The second messenger cyclic di-guanosine monophosphate (c-di-GMP) has emerged as a signaling molecule in Gram-positive and Gram-negative bacteria governing the process of biofilm formation, biosynthesis of EPS, virulence and suppression of cell motility. The enzyme diguanylate cyclase is essential for synthesis of c-di-GMP, inhibition of which has proven to terminate biofilm formation, alluding to the significance of c-di-GMP in the bacterial signaling process [128; 129; 130; 131; 132; 133; 134; 135].
# Data Augmentation Method Using Room Transfer Function for Monitoring of Domestic Activities
## 3 Proposed Data Augmentation Method  
To prevent the model training from being biased towards specific classes due to imbalanced datasets, we employed data augmentation, which is a technique that artificially augments the existing dataset. In particular, for MDA, we devised a data augmentation method that applied the room's acoustic properties. If the training dataset is only recorded at a single location within a space, the model can become susceptible to variations in RTFs. This sensitivity arises from the acoustic properties, where sound is transformed through different RTFs depending on the microphone's location. To address this issue, [19] conducted recordings with microphones placed in various locations. Furthermore, the dataset was categorized based on microphone locations, enabling the microphones at specific locations used solely during evaluation to assess the model's generalization performance. This approach makes sense because when the trained model is implemented into a real device, the device will operate in diverse locations within the room. By leveraging this aspect of RTFs, our proposed method aims to minimize performance reduction caused by variations in RTFs.  
Typically, if the microphone's location and the corresponding RTFs are known, the sound at that specific location can be reconstructed using the sound source and convolution with the RTFs. However, since RTFs are usually unknown, we attempt to augment the data by considering the statistical characteristics of RTFs. When training on sound, using frequency domain features that capture frequency changes over time provides more information than using one-dimensional signals. This can be achieved through the process depicted in Figure 2. Here, the waveform is converted into a frequency signal using Fast Fourier Transform (FFT), and the magnitude information is primarily used by taking the absolute value of the signal. Then, this is processed through a non-linear filter bank, such as the Mel or gammatone filter banks. The proposed algorithm can be applied after feature extraction, as displayed in Figure 3.
# Roles for Exosomes in the Pathogenesis, Drug Delivery and Therapy of Psoriasis
## 3 The Immunological Function of Exosomes That Are or Are Not Associated with Soriasis
### Immune Cell-Derived Exosomes
#### 3.1.2 Neutrophil-Derived Exosomes  
As important innate immune cells, neutrophils can exert antibacterial effects through phagocytosis, degranulation and neutrophil extracellular traps (NETs). An increase in neutrophil migration was found in generalized pustular psoriasis (GPP) [49]. GPP is a type of psoriasis driven by the IL-36 cytokine family [50]. The IL-36-neutrophil loop plays a critical role in the pathogenesis of GPP [2; 51]. IL-36 cytokines are primarily expressed by keratinocytes and subsequently broken down by cathepsin, proteinase 3 and elastase in NETs [52]. The biologically active forms of IL-36 can further promote the production of neutrophil chemokines, such as CXCL1, CXCL2 and CXCL8, by keratinocytes, thus attracting more neutrophils to the skin lesions [2; 53].  
In response to different stimulation, exosomes derived from neutrophils contain different bioactive cargoes, including granule proteins, lipids, microRNA, cytokines and chemokines, which can promote the immune-mediated proinflammatory responses in psoriasis. Exosomes are critical mediators of neutrophil-keratinocyte crosstalk in the pathogenesis of GPP. A previous study found that neutrophils from GPP patients secreted more exosomes than those from control subjects. These exosomes were then rapidly internalized by keratinocytes, which increased the expression of some proinflammatory molecules, such as IL-1\(\beta\), IL-36G, IL-18, TNF-\(\alpha\) and C-X-C motif chemokine ligands (CCLs), in keratinocytes by activating MAPK and NF-\(\kappa\)B signaling pathways [12]. Moreover, olfactomedin 4 (OLFM4) in the exosomes released from neutrophils in turn promoted the release of exosomes containing CXCL1, CXCL2, CXCL8 and CCL20 from keratinocytes. These chemokines led to an increase in the migration of neutrophils and other immune cells to the psoriasisiform skin lesions [12]. These findings suggest that OLFM4 from neutrophil-derived exosomes is a critical proinflammatory protein. However, it remains unclear how neutrophil-derived exosomes interact with other cells involved in the pathogenesis of psoriasis. Thus, further studies are needed in the near future.
## 4 Convolutional Neural Network
### Regression CNN
#### 4.2.4 Influence of Noise  
Figure 13 compares the results of the regression CNN model for noisy input (SNR = 20) with those of the noise-free scenario described in Section 4.2.3. With the presence of noise, the \(R^{2}\) values for predicting \(r_{p}\) and \(d_{p}/t\) decrease somewhat compared to the noise-free scenario. The decrease in \(R^{2}\) values is more prominent for \(\mathrm{P_{ex}}\) and \(\mathrm{PIC_{ex}}\) cases, indicating that the model is more affected by noise when predicting the sizes of external corrosion. Despite the decrease in the accuracy in predicting the corrosion sizes, the model maintains \(R^{2}\) = 1 for the location parameters (\(h\) and \(\phi\)) across all corrosion types except for \(\mathrm{P_{ex}}\), indicating its robustness in capturing the spatial information even in the presence of noise.  
Finally, it is important to point out that the CNN classification and regression models developed in this study are specifically tuned to certain pipeline and MFL tool attributes, such as the pipe wall thickness, magnet properties, lift-off values, and defect shapes. Therefore, further investigations are needed to determine how well the CNN model performs with various pipe wall thicknesses and different MFL tool parameters. Further studies are also needed to take into account the movement of the MFL tool into the prediction models.
# Visual Navigation Systems for Maritime Smart Ships: A Survey
## 3 Visual Perception Navigation Technology
### Single Perception  
#### 3.1.1 AISs  
AIS is the acronym for a universal shipborne automatic identification system; it is a kind of maritime mobile-band VHF broadcasting system, which is mainly used in the field of ship navigation and monitoring, realizing the data exchange between ship and ship and between ship and shore through the VHF radio band. The core function of an AIS system is to send and receive the dynamic and static information about a ship. Dynamic information includes the ship's position, speed, heading, destination and expected arrival time, while static information involves the ship's name, call sign, type, size, deadweight torange and other basic attributes [81]. The real-time updating and exchange of this information enables the ship to understand the surrounding environment and the dynamics of other ships in a timely manner so as to make more accurate navigational decisions. AISs track ship movements through the electronic exchange of navigational data between ships via shipboard transceivers and terrestrial or satellite base stations and uses the information collected for maritime security [82]. In order to detect multi-vessel encounters, [PERSON] et al. (2022) proposed a model to mine multi-vessel encounter situation awareness, analyze the spatial process of encounters and make collision avoidance decisions from automatic identification system (AIS) data, which overcomes the problem that the traditional collision risk assessment methods are only applicable to the differences between two ships and ship awareness [83]. Meanwhile, [PERSON] et al. (2022) proposed a new method based on an improved sliding window algorithm to automatically identify ship collision avoidance behavior from ship trajectories, which can be used to accurately detect ship collision avoidance behavior from AIS trajectory data [84]. With continuous research and discoveries, researchers started to analyze AIS data using visual analysis tools. [PERSON] et al. (2021) proposed a tangible visual analysis tool to analyze the changes in maritime traffic using the spatio-temporal basis of AISs--a novel way to advance the development of judging the safety of navigation of vessels and waters [85]. [PERSON] et al. (2021) focused more on the visualmodel and human-computer interaction and designed and applied a visual analytics (VA) method called AIS Data Quality Visualization (ADQvis) to assess and explore the data quality of AISs [86]. Navigation technologies for smart ships utilize collected information to generate computer images, but these images also need to be converted into usable navigation data through human interpretation, increasing the likelihood of human error. To address this problem, [PERSON] et al. (2023) designed an AIS-based visually enhanced simulation system for ship navigation that superimposes computer-generated navigation information onto real-world scenarios in real time, improving the safety and usability of the navigation system [87].
# Estimation of Inter-System Biases between BDS-3/GPS/Galileo and Its Application in RTK Positioning
## 3 Datasets and Experiments
### Performance of RTK Positioning
#### 3.4.2 LCM RTK vs. TCM RTK
The same as for the ratio performance, with the higher cutoff elevation angle of 40\({}^{\circ}\), more improvement in ADOP occurs in TCM RTK. With such a high cutoff elevation angle, the mean value of ADOP of LCM RTK reaches up to 0.15 cycles, whereas that of TCM RTK remains lower than 0.1 cycles. The average numbers of visible satellites with cutoff elevation angles of 20 and 40\({}^{\circ}\) are 17.16 and 9.77, respectively. The series of ratio and ADOP reflect the advantage of TCM in ambiguity resolution. With the same satellites, better performance and more reliable results can be guaranteed with TCM RTK. Figure 8: Horizontal scatters (**top**) and vertical time series (**bottom**) of the positioning errors of loosely-combined model (LCM) (**a,c**) and tightly-combined model (TCM) (**b,d**) DF RTK of baseline GODE-USN7 on DOY 120, 2021 with a cutoff elevation angle of 20\({}^{\circ}\) (**a,b**) and 40\({}^{\circ}\) (**c,d**). On the right of the horizontal scatterplot and on the bottom of the vertical time series, details are provided about the fixed solutions with the 95% confidence interval. The float solutions, correct solutions, and incorrect solutions are indicated in grey, green, and red, respectively. Figure 9: Time series of ratio (**top**), ambiguity dilution of precision (ADOP) (**middle**), and number of visible satellites (**bottom**) of LCM and TCM RTK of baseline GODE-USN7 on DOY 120, 2021 with cutoff elevation angles of 20 and 40\({}^{\circ}\). The blue and magenta indicate LCM and TCM, respectively.