esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of Aquatic Weed in Irrigation Channels Using UAV and Satellite Imagery
## 5 Conclusions  
Weed growth in irrigation network channels is a major source of flow constraint, which results in slow delivery of irrigation water to farms. This paper has demonstrated the use of remotely sensed multispectral images from UAV and satellite platforms to detect and quantify areas of weed in irrigation network channels. An algorithm was developed, combining unsupervised and supervised classification to robustly identify areas of weed, water and land from images with very little user intervention or overhead in supplying training data for new images. Classification accuracy typically achieved kappa values of greater than 0.85. The required bands to achieve these results were red, green, blue and near-infrared, and resolutions of better than one metre were recommended. UAV images were shown to be useful for intensively monitoring small areas of irrigation channels over time, for example, to quantify the change in weed after chemical or mechanical controls are implemented. Satellite images were processed over very large areas and the total area of weed in the channels throughout the images were automatically calculated. These techniques will facilitate improvement in the maintenance of timely water delivery from irrigation channel networks.  
[PERSON] performed the image capture, analysed the data and prepared the manuscript. [PERSON] wrote the section on vegetation and provided advice on weed types. [PERSON] provided overall project guidance.  
This research was funded by Murrumbidgee Irrigation, Ltd. (Griffith 2680, Australia).  
The authors thank [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] from Murrumbidgee Irrigation, Ltd. (Griffith 2680, Australia) for providing advice and data.  
The authors declare no conflict of interest.
## 3 The Proposed Method
### Searching for Finder Patterns  
First, the binary image is scanned from top to bottom and from left to right, and we look for successive sequences of black and white points in a row matching the ratios 1:1:3:1:1 (\(W_{1}\):\(W_{2}\):\(W_{3}\):\(W_{4}\):\(W_{5}\) where \(W_{1}\), \(W_{3}\), \(W_{5}\) indicates the number of consecutive black points which are alternated by \(W_{2}\), \(W_{4}\) white points) with small tolerance (tolerance is necessary due to imperfect thresholding and noise in the Finder Pattern area, black and white points in a line do not alternate in ideal ratios 1:1:3:1:1):  
\[\begin{array}{lcl}W_{1},W_{2},W_{4},W_{5}\in\langle w-1.5,w+2.0\rangle\\ W_{3}\in\langle 3w-2,3w+2\rangle&\text{, where}\\ W_{3}\geq\max(W_{1}+W_{2},W_{4}+W_{5})&\\ w=\frac{W_{1}+W_{2}+W_{3}+W_{4}+W_{5}}{7}=\frac{W}{7}\end{array} \tag{4}\]For each match in a row coordinates of Centroid (\(C\)) and Width (\(W=W_{1}+W_{2}+W_{3}+W_{4}+W_{5}\)) of the sequence (of black and white points) are stored in a list of Finder Pattern candidates (Figure 5).  
Then, Finder Pattern candidates (from the list of candidates) that satisfy the following criteria are grouped:  
* their centroids \(C\) are at most 3/\(W\) points vertically and at most 3 points horizontally away from each other,
* their widths \(W\) does not differ by more than 2 points.  
We expect, that the Finder Pattern candidates in one group belong to the same Finder Pattern and therefore we set the new centroid \(C\) and width \(W\) of the group as average of \(x\), \(y\) coordinates and widths of the nearby Finder Pattern candidates (Figure 6a).  
After grouping the Finder Patterns, it must be verified whether there are sequences of black and white points also in the vertical direction, which alternate in the ratio 1:1:3:1:1 (Figure 6b). A bounding box around the Finder Pattern candidate, in which vertical sequences are looked for, is defined as  
\[x\in\langle C_{x}\pm 1.3/7W\rangle,\ y\in\left\langle C_{y}\pm 5.5/7W\right\rangle \tag{5}\]  
where \(C\) (\(C_{x}\), \(C_{y}\)) is a centroid and \(W\) is width of the Finder Pattern candidate. We work with a slightly larger bounding box in case the Finder Pattern is stretched vertically. Candidates, where no vertical match is found or where the ratio \(H/W<0.7\), are rejected. For candidates, where a vertical match  
Figure 5: A Finder Pattern candidate matching 1:1:3:1:1 horizontally.  
Figure 6: (a) Group of 8 Finder Pattern candidates matching 1:1:3:1:1 in rows, (b) Finder Pattern candidate matching 1:1:3:1:1 vertically.  
is found, the \(y\) coordinate of centroid \(C\) (\(C_{y}\)) is updated as an average of \(y\) coordinates of centers of the vertical sequences.
# Infill Pattern and Density of 3D-Printed Insoles Alter Energy and Pressure Distribution in Gait
## 5 Conclusions  
The effects of infill pattern and density were investigated on the mechanical properties of 3D-printed specimens and on plantar pressure reduction of 3D-printed insoles. The findings of this study showed that both infill pattern and density notably influence stiffness and energy absorption. Increased infill densities provided higher stiffness and energy absorption. Among five infill patterns, based on test specimens, the honeycomb infill pattern gave the highest stiffness, whereas the gyroid infill pattern showed the lowest stiffness. When applying a honeycomb infill pattern and gyroid infill pattern at 20% infill density, the 3D-printed insoles showed no significant reduction in plantar pressure compared to not wearing insoles. The 3D-printed insoles slightly reduced plantar pressure on the heel during walking, whereas other foot regions had elevated plantar pressure. We suggest that optimized stiffness should be considered when offloading is focused on diabetic feet. In addition, a long-term durability assessment and the effectiveness of 3D-printed insoles on plantar pressure reduction should be conducted in diabetic patients.  
Conceptualization, S.C., T.D. and A.K.; methodology, S.C., A.K. and S.S.; formal analysis, S.C. and M.N.; investigation, S.C.; resources, A.K.; data curation, S.C.; writing--original draft preparation, S.C.; writing--review and editing, M.N., S.S. and D.M.; visualization, S.C.; supervision, T.D. and D.M.; funding acquisition, S.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Science, Research and Innovation Fund (NSRF) and Prince of Songkla University (Grant No. MED6601240S) and Horizon Europe Framework Programme (Grant No. 101086348).  
**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Faculty of Medicine, Prince of Songkla University (REC.66-188-38-2, 25 June 2023).  
**Informed Consent Statement:** Informed consent was obtained from a subject involved in the study.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
**Acknowledgments:** We acknowledge the participants who volunteered for this study. We thank [PERSON] for assisting in the mechanical testing of specimens and [PERSON] for the participant recruitment and data recording.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Pro-Health Potential of Selected Uncommon Oilseed Plants
## 3 Pro-Health Potential of Selected Uncommon Oilseed Plants
### Nigella sativa L.
#### 3.3.5 Antiobesity Effect  
In a crossover, double-blind, placebo-controlled, randomized clinical trial, researchers assessed the effect of supplementing with 2000 mg of black seed oil daily on anthropometric parameters, body composition, and appetite in overweight and obese women. The results showed a significant reduction in body weight, waist circumference, body fat percent, body fat mass, and visceral fat area. Additionally, participants reported a decrease in appetite and an increase in satiety [103]. In a study conducted by [PERSON] et al. [104], researchers investigated the impact of black cumin consumption on HFD-induced obesity in rats. The rats were given 300 mg/kg/day of _N. sativa_ seed powder for six weeks following the induction of obesity. The results showed that the HFD significantly elevated body weight and atherogenic index, as well as liver enzyme levels. However, the treatment with black cumin led to a notable reduction in final body weight. Additionally, it resulted in an improvement in lipid profile, atherogenic index, and ALT levels, as well as a reduction in liver damage [104]. High-fat diet-induced obesity rats were also a model in a study evaluating the antiobesity effects of thymoquinone. The compound significantly reduced body weight gain in rats fed a high-fat diet, improved glucose tolerance, and increased insulin sensitivity. Additionally, hepatoprotective effects were also observed [105]. The authors emphasize the need for further studies with larger numbers of participants, different doses, and longer intervention times to better understand the potential of black cumin in the treatment of obesity.
# The Status of Space Weather Infrastructure and Research in Africa
## 1 Introduction
### The African Context
This paper provides information on space weather infrastructure, human capital development, research achievements, ongoing and upcoming projects, international collaborations, funding sources, gaps in infrastructure, and human resources, as well as suggestions on what needs to be done going into the future. This paper is organized as follows: In Section 2, we present the distribution of space weather infrastructure in Africa and discuss data accessibility and reliability; Section 3 deals with human capital development (HCD), completed and on-going postgraduate research, and various HCD initiatives; Section 4 gives insight into gaps in infrastructure and current and planned space weather projects; and in Section 5, we give the conclusion and way forward.
# Perceptual Quality of Audio-Visual Content with Common Video and Audio Degradations
## 4 Video and Audio Distortions
### Video Degradations
#### 4.1.2 Packet-Loss  
As a first step, video bit-streams for the corresponding H.264 and H.265 coding standards were generated. To simulate a packet-loss effect, packets from the Network Abstraction Layer (NAL) were discarded from the video bit-stream using the NALTools software, as done in similar studies [54]. The packet-loss ratios used for this study were: 1%, 3%, 5%, 8%, and 10%. Although these values were taken from a real transmission scenario found in video streaming applications [55,56], recreating a particular use case scenario was not the main intention. These packet-loss ratios served to define five clear quality levels that could be distinguished by participants.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Low** & **Medium** & **High** & **Very High** \\ \hline
**H.264/AVC** & 500 kbps & 800 kbps & 2000 kbps & 16,000 kbps \\
**H.265/HEVC** & 200 kbps & 400 kbps & 1000 kbps & 8000 kbps \\ \hline \hline \end{tabular}
\end{table}
Table 3: Compression Bitrate values used for each codec.
## 2 Results
### Ab-FAR-1 Interacts with Arabidopsis thaliana AtADP3  
First, we found that Ab-FAR-1\({}^{\mathrm{asp}}\) could not be self-activated in the yeast self-activation assay (Figure S4). Using the Ab-FAR-1\({}^{\mathrm{asp}}\) protein as bait and a cDNA library derived from _A. thaliana_ as prey, we performed the yeast two-hybrid (Y2H) screen of Ab-FAR-1\({}^{\mathrm{asp}}\). Forty independent clones were screened on high-stringency selection medium, and 21 Ab-FAR-1\({}^{\mathrm{asp}}\) candidate interaction genes were identified by sequencing (Figure S5; Table S1). Among them, we found a number of genes involved in the life process of _A. thaliana_, such as actin depolymerizing factor 3 (ADF3), phytochrome-associated protein 1 (PAP1), chloroplast RNA-binding protein 31B (CP31B), etc., indicating that Ab-FAR-1 may affect the host's life activity. We found six colonies that were sequenced for the AtADP3 gene, and all of the colonies contained the complete open reading frame of AtADP3. Previous research has suggested that ADF3 may be involved in the process of pathogen-host interaction [42]. Therefore, we further studied the role of ADF3 in the interaction between RWTNs and _A. thaliana_.  
It was further found that yeast AH109 co-transformed with pGBKT7:Ab-FAR-1\({}^{\mathrm{asp}}\) and pGADT7:AtADP3 could grow on SD/-Leu/-Trp/-Ade/-His + x-\(\alpha\)-gal plates and turned blue (Figure 6a). To further examine this interaction, Co-IP and BiFC assays were performed. The 35S:Ab-FAR-1\({}^{\mathrm{asp}}\):eGFP and 35S:AtADF3:Flag vectors were co-expressed in tobacco leaf cells. Tobacco leaf cells that co-expressed 35S:eGFP and 35S:AtADF3:Flag were used as controls, and the total proteins were extracted for Co-IP analysis. Western blot analysis was used to confirm the expression of the input proteins. The hybridisation bands were at ~27, ~46, and ~16 kDa, which were consistent with the sizes of eGFP, Ab-FAR-1\({}^{\mathrm{asp}}\):eGFP, and AtADF3:flag, respectively. In the cells expressing 35S:Ab-FAR-1\({}^{\mathrm{asp}}\):eGFP and 35S:AtADF3:Flag, AtADF3:Flag was detected using an anti-Flag antibody in the sample immunoprecipitated with an anti-GFP antibody compared with control cells (Figure 6b). For BiFC analysis, 35S:Ab-FAR-1\({}^{\mathrm{asp}}\):nYFP and 35S:AtADF3:cYFP were co-expressed in tobacco leaf cells. The YFP fluorescent signal was detected in the cytoplasm and nucleus (Figure 6c), which corresponded to the YFP activity reconstituted by the interaction between Ab-FAR-1 and AtADF3. At the same time, no YFP signals were detected in cells expressing 35S:nYFP and 35S:AtADF3:cYFP or 35S:Ab-FAR-1\({}^{\mathrm{asp}}\):nYFP and 35S:cYFP (Figure 6c). Thus, both the BiFC and Co-IP assays confirmed the specific interaction between Ab-FAR-1 and AtADF3.
## 3 Results and Discussion
### Viscoelastic Properties
#### 3.2.2 Oscillatory Shear Rheology  
The viscoelastic properties determined by OSR are depicted in Figure 5. The quality of the measurement data was assessed by Lissajous diagrams, which led to an exclusion of frequencies above 10 rad/s. The absolute storage and loss moduli of the mucus samples differed. At rest (\(\omega\) 0.06 rad/s), all mucus samples showed higher storage than loss modulus, see Table 4. The highest storage and loss modulus was found for mucus sample m3, G'(0.06 rad/s) = 2.75 Pa and G\"(0.06 rad/s) = 1.38 Pa. The lowest moduli were found for m5, G'(0.06 rad/s) = 0.48 Pa and G\"(0.06 rad/s) = 0.32 Pa. Four of the five mucus samples consistently showed a higher storage than loss modulus (m1, m2, m3, m5). Sample m4 revealed a higher storage than loss modulus at low, but almost equal moduli at high frequencies. No subdivision was attempted due to limited samples (n = 5). The demographic data of the patients belonging to the samples are given in brackets in the legends.  
\begin{table}
\begin{tabular}{l c c c c c} \hline  & \multicolumn{3}{c}{**Post Hoc Tests (Mann–Whitney-U-Test; \(p<0.017\))**} & \multicolumn{1}{c}{**Kruskal Wallis (\(p<0.05\))**} \\ \hline  & \multicolumn{3}{c}{_Group a vs. b_} & \multicolumn{1}{c}{_Group a vs. c_} & \multicolumn{1}{c}{_Group b vs. c_} & \\ \hline
**MSD Parameters** & MSD & 0.000 & 0.006 & 0.004 & 0.000 \\ \(\tau\) = 5.14 ms: & \(\alpha\) & 0.000 & 0.006 & 0.004 & 0.000 \\ \(\tau\) = 1662 ms: & MSD & 0.000 & 0.006 & 0.004 & 0.000 \\
**Viscoelasticity Parameters** & & & & & \\  & G” & 0.000 & 0.006 & 0.004 & 0.000 \\ \(\omega\) = 0.6 s\({}^{-1}\) (at rest): & G’ & 0.000 & 0.006 & 0.004 & 0.000 \\  & tan\(\delta\) & - & - & - & 0.149 \\ \(\omega\) = 195 s\({}^{-1}\): & tan\(\delta\) & 0.000 & 0.006 & 0.004 & 0.000 \\ \hline \end{tabular}
\end{table}
Table 3: Statistical analysis of characteristic MSD and viscoelasticity parameters. Differences were found for all parameters except the diffusive exponent \(\alpha\) at the higher and the tan\(\delta\) at the lower evaluation limit and comparison of all groups.  
Figure 4: Relationship of the demographic data of the patients to the mucus groups. All groups were found for different gender and larynx status. Whereas group a and group b mucus samples were found for all ages and all smoking behaviors, group c mucus samples were only found for smokers and patients <70 years.  
Figure 5: (a,b): Measurement results of oscillatory shear rheology (OSR). Four of the five investigated mucus samples revealed gel characteristics (m1, m2, m3, m5). One sample revealed close moduli (m4). The abbreviations in the brackets give detailed information about the demographic data of the patients belonging to the samples: 1. digit: gender (f: female, m: male); 1. digit: larynx status (h: healthy, p: pathologic); 3. digit: smoking behavior (s: smoker, f: former smoker, n: non-smokers); 4/5. digit: age.
# Control of _Francisella tulareis_ Virulence at Gene Level: Network of Transcription Factors
## 2 Transcription Factors Involved in Regulation of the FPI
### MgIA/SspA Complex Directly Regulates FPI
The regulons of MgIA and SspA are similar and both proteins are involved in the regulation of virulence genes in Francisella, including FPI genes. MgIA and SspA interact with each other to form a complex with the RNA polymerase, which is essential for activation of the FPI genes [30]. Structural and biochemical analysis showed that MgIA is able to form homodimers, but it exhibits a preference for heterodimer formation with SspA [36]. SspA homologs exist in several bacteria and have consistently been associated with virulence [67; 68; 69; 70], whereas MgIA is a protein unique for Francisella [36]. In _E. coli_, during the stationary phase, SspA inhibits the level of the global regulator H-NS (histone-like nucleoid structuring), which acts mostly as a repressor, and thus SspA induces stress defense mechanisms, including protection against acid and starvation stress [71]. The importance of MgIA for adaptation to oxidative stress was also reported for the _F. tulareis_ live vaccine strain (LVS) strain [72].
## 2 Preliminaries and Problem Formulation  
### Vehicle Dynamics  
An accurate vehicle driving-resistance model is hard to construct. The existing simplified model usually only considers the vehicle's rolling resistance \(R_{r}(k)=m_{g}C_{r}\), gradient resistance \(R_{i}(k)=m_{g}\sin\theta(k)\), and air resistance \(R_{w}(k)=\frac{1}{2}\rho(k)C_{d}Av(k)^{2}\), where \(m\) (kg) is the vehicle's mass; \(g\) (9.81 m/s\({}^{2}\)) describes the gravitational acceleration; \(\theta(k)\in[-\pi/2,\pi/2]\) represents the slope angle of the road at time \(k\); \(\rho(k)\) (kg/m\({}^{3}\)) represents the density of air; \(C_{d}\) represents the air drag coefficient; \(A\) (m\({}^{2}\)) represents the windward area; and \(C_{r}\) represents the rolling resistance coefficient. The torque of the engine can be calculated by combining the corresponding force \(F(k)\) and the transmission ratio of the transmission and vehicle speed \(v(k)\) (m/s). The vehicle dynamics model is usually described as  
\[\left\{\begin{array}{ll}\dot{z}(k)&=v(k),\\ mv(k)&=F(k)-R_{i}(k)-R_{w}(k)-R_{r}(k)+\delta(k)\end{array}\right. \tag{1}\]  
where \(z(k)\) denotes the position of the vehicle; \(z(0)\in\mathbb{R}\), \(v(0)\in\mathbb{R}\); \(\delta(k)\) denotes the bounded disturbances.
# Spatial Correlations of Global Seismic Noise Properties
## 2 Initial Seismic-Noise Data  
The data used were the vertical components of continuous seismic-noise records with a 1 s sampling step, which were downloaded via the address [[http://www.iris.edu/forms/webrequest/](http://www.iris.edu/forms/webrequest/)]([http://www.iris.edu/forms/webrequest/](http://www.iris.edu/forms/webrequest/)) (accessed on 1 January 2023) from 229 broadband seismic stations of three networks, [[http://www.iris.edu/mda/_GSN](http://www.iris.edu/mda/_GSN)]([http://www.iris.edu/mda/_GSN](http://www.iris.edu/mda/_GSN)) (accessed on 1 January 2023), [[http://www.iris.edu/mda/G](http://www.iris.edu/mda/G)]([http://www.iris.edu/mda/G](http://www.iris.edu/mda/G)) (accessed on 1 January 2023), and [[http://www.iris.edu/mda/GE](http://www.iris.edu/mda/GE)]([http://www.iris.edu/mda/GE](http://www.iris.edu/mda/GE))(accessed on 1 January 2023), which belong to the Incorporated Research Institutions for Seismology.  
The duration of the time interval of observations was 26 years, from 1 January 1997 to 31 December 2022. The data were resampled to 1 min time series by computing the mean values within adjacent time intervals of the length 60 s.  
An auxiliary network of 50 reference points was introduced. The positions of these points were determined by cluster analysis of the positions of 229 seismic stations using the hierarchical \"far neighbor\" method, providing compact clusters [19]. The positions of the 229 seismic stations and 50 control points are shown in Figure 1. The numbering of the reference points was carried out in order as the latitude of the point decreases.
## 3 Model Construction  
In this paper, we introduce a convolutional neural network (CNN) structure to enhance the feature extraction capability of the dataset. Our goal is to optimize the feature processing flow to improve the efficiency and accuracy of network-security situation prediction. The network-security situation prediction model proposed in this study combines a convolutional neural network (CNN) with a gated recurrent unit (GRU) and introduces an attention mechanism to enhance the performance of feature selection and time-series analysis. The model structure mainly includes three key components: a feature extraction layer, a time-series analysis layer, and an attention layer. The model structure is shown in Figure 1.  
* Feature extraction layer: Utilizing CNN technology, this layer can accurately capture the spatial characteristics in the network behavior and provide robust data support for subsequent analysis.
* Time-series analysis layer: The time-series analysis layer consists of the GRU module. The GRU module learns the feature vector extracted from the CNN module and captures its internal change rules.
* Attention layer: The attention mechanism is introduced based on the CNN and GRU. By assigning weights to different features, the model can automatically focus on the most influential key information for the prediction task, thereby enhancing the accuracy of predictions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Advantage** & **Disadvantaged** \\ \hline Predictive Methods Based on Uncertain & It is suitable for scenarios involving & The error is significant when dealing with \\ Inference Theory & limited and incomplete data. & irregular and fluctuating data. \\ Machine Learning-based & Good at dealing with small samples, & There are challenges and limitations in \\ Prediction Methods & nonlinear data, high accuracy, and & training and designing models when \\  & easy to understand. & working with large-scale data. \\  & Can handle complex nonlinear & It is difficult to train due to the excessive \\ Neural Network-based & relationships and capture high-level & number of parameters and the \\ Prediction Method & features in the data through a hierarchical & complexity of adjusting them. \\  & network structure. & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of different prediction methods.