esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Characterization of Food-Grade Systems
### Biological Evaluation
#### 3.2.2 In Vivo Studies
The results of the study showed that the nanocarriers remained stable in the gastrointestinal tract, and those obtained from large tea leaves showed the best ability to accumulate in inflamed tissues. In addition, the large tea leaf nanotherapeutics were the most effective in mitigating disease symptoms and reducing inflammation and histological lesions in the colon. In mice treated with the large tea leaf nanotherapeutics, the homeostasis of the intestinal microbial population was maintained almost similar to the healthy control group, confirming the retardant effects against IBD. The nanotherapeutics were also found to be effective in inhibiting the development of colon cancer, as they were able to mitigate inflammatory reactions by downregulating the production of proinflammatory cytokines and upregulating IL-10. The proven safety and  
Figure 18: Fluorescence images at different times of mice administered orally with free DiR or DiR@NPs. Adapted with permission from Ref. [144]. 2022, Frontiers Media SA, Lausanne, CH. efficacy of oral administration make these platforms useful tools for the prevention and treatment of colon diseases [153]. To study diarrhea-predominant irritable bowel syndrome (IBS-D), which is a subtype of IBD, IBS-D model mice induced by chronic restraint stress plus senna alexandrine mill decoction were used. The effects of self-assembled nanoparticles of berberine and baicalin were examined with special focus on the therapeutic influence carried out on the microbiota-gut-brain axis (MGBA) level. Compared with simple mixing of berberine and baicalin, it was observed that self-assembly of the nanostructures ensured a synergistic effect on IBS-D mice. The nanomedicines improved visceral hypersensitivity, depression, diarrhea and anxious behaviors on IBS-D model mice. In addition, administration by gastric gavage reduced levels of brainstem peptides (5-hydroxytryptamine, vasoactive intestinal polypeptide, and choline O-acetyltransferase) in colon tissues and serum, confirming the involvement of MGBA in IBS-D. Treatment with NPs reduced the levels of immune inflammation and restored the homeostasis of intestinal flora, which was examined by 16S rRNA gene sequence analysis in microbial samples isolated from the colon. The synergistic effect of nanomedicines involved three different pathways implicated in the onset of IBS-D, providing a potential and efficient platform for therapies of this intestinal disorder [154]. Although in vivo laboratory animal experiments are subject to numerous ethical, regulatory, and economic restrictions, they are necessary tests to demonstrate the impact of a new drug on the whole organism rather than on isolated or co-cultured cells. Global assessment of potential in vivo interactions could improve the predictivity of efficacy and safety of new therapeutic agents while ensuring a realistic estimation of drug candidates to proceed to clinical trials.
# An Improved Q-Learning-Based Sensor-Scheduling Algorithm for Multi-Target Tracking
## 1 Introduction  
Currently, wireless sensor networks (WSNs) have attracted worldwide attention due to opening new vistas for a wide range of application domains [1, 2, 3]. As one of the attractive applications in WSNs, target tracking has made contributions in many fields such as frontier security, illegal vehicle tracking, space exploration, and pasture protection [4, 5]. In such application scenarios, the tracking network can be constructed from multiple energy-constrained sensor nodes. During the tracking process, some sensors are required to be activated to locate mobile targets, whereas other sensors can switch into a dormant state to reduce energy consumption.  
Most of the existing target-tracking algorithms focus on single-target tracking or tracking of multiple targets with identical properties. However, in practical applications, the system may need to track different types of targets, and the network status where targets are located may also be different. Compared with single-target-tracking scenarios, how to schedule sensors in such complex multi-target-tracking scenarios is a more challenging research work [6]. Therefore, it is necessary to study a novel sensor-scheduling algorithm for multi-target tracking.
## 3 Methodology
### Self-Attention Mechanism  
The Self-Attention technique improves the model's capacity to concentrate on relevant input segments by calculating pairwise relationships inside the sequence. It draws inspiration from human attention, allowing the model to dynamically assign weights to characteristics over various time steps. In this work, a custom Self-Attention layer is introduced following the GRU and dense layers to improve context sensitivity. Given input \(X\), the attention layer computes the output as:  
\[\mathrm{Q}=\mathrm{KW^{Q}},\;\mathrm{K}=\mathrm{XW^{k}},\;\mathrm{V}=\mathrm{ XW^{v}} \tag{6}\]  
\[\mathrm{Attention}(\mathrm{Q},\;\mathrm{K},\;\mathrm{V})=\mathrm{softmax}\;( \frac{\mathrm{QK^{T}}}{\sqrt{\mathrm{d_{k}}}})\;\mathrm{V} \tag{7}\]  
where \(\mathrm{Q}\), \(\mathrm{K}\), and \(V\) are the query, key, and value matrices derived from learnt modifications of the input, representing the dimensionality of the key vectors. The resulting weighted representation allows the model to emphasize the most informative parts of the input sequence. This improves both classification accuracy and model interpretability by highlighting which time steps contribute most to decision-making. The integration of Self-Attention with GRUand convolutional layers results in a hybrid architecture that combines spatial feature extraction, temporal sequence modeling, and adaptive attention-based weighting, effectively addressing the complexities of multi-class intrusion detection in WSNs. The following algorithm outlines the process of training and evaluating a hybrid deep learning-based Intrusion Detection System (IDS) tailored for Wireless Sensor Networks (WSNs). Given a labeled dataset D = {(\(x_{i},y_{i}\))\({}_{i}^{N}=\)1, where each xi\(\in\)Rdx_i \(\backslash\)in \(\backslash\)mathbb[R]'dxi\(\in\)Rd represents a feature vector and yi\(\in\){1,...,C}y_i \(\backslash\)in \(\backslash\){1, \(\backslash\)ldots, C\(\backslash\)}yi\(\in\){1,...,C} denotes the corresponding class label, the algorithm performs data preprocessing, addresses class imbalance using SMOTE, and constructs a hybrid model integrating Conv1D layers, GRUs, and a Self-Attention mechanism. The model is trained to classify network traffic into multiple categories and is evaluated using standard classification metrics, including accuracy, precision, recall, F1-score, ROC-AUC, as well as prediction time and delay, to assess its effectiveness in detecting intrusions in real-time WSN environments. The complete training and evaluation process of the proposed Conv1D-GRU-350 Self Attention-based model is summarized in Algorithm 1.
# Game theory-based Routing for Wireless Sensor Networks: A Comparative Survey
## 3 Game Theory-Based Routing Protocols in WSNs
### Routing Protocols Based on Evolutionary Games
#### 3.3.2 Energy-Efficient Routing Protocol Based on Evolutionary Game (EEREG)
When there is an energy imbalance among the nodes, only the nodes in set \(H\) tend to become CHs. Furthermore, the higher the range of energy for the nodes, the higher the probability of them becoming CHs. When the sensor nodes send \"hello\" messages to the sink node, they generate certain parameters: \(P_{H}\) = the proportion of the nodes in set \(H\), \(P_{L}\) = the proportion of the nodes in set \(L\), \(e\) = the energy consumed when acting as CH, \(\Delta\) = the energy difference between the nodes in sets \(H\) and \(L\), and \(\overline{E_{re}}\) = the average residual energy of the nodes. These parameters are adjusted, taking into consideration the changes in WSNs after a certain cycle \(T\). The sink node broadcasts the value of \(\overline{E_{re}}\) to the sensor nodes. After receiving the broadcast message, a random number is generated by a sensor node that has a range between \(0\) to \(1\). The residual energy is compared with the \(\overline{E_{re}}\) parameter and the class to which a node belongs to (either \(H\) set or \(L\) set) is decided based on this comparison. The nodes falling in set \(H\), which is the set of nodes with higher energy level, are compared with another threshold value, that eventually decides which node would be the CH. EEREG was compared with low energy adaptive clustering hierarchy (LEACH), distributed hierarchical agglomerative clustering [71; 72], threshold-sensitive energy-efficient routing in sensor networks [73], and power-efficient gathering in sensor information systems [73] in terms of the different network lifetime metrics, which were the time until the first node dies, time until half of the nodes die, and time until the last node dies. Technical strengths:  
* Cluster size is maintained optimally;
* Avoidance of the low energy nodes becoming a CH;
* Introducing rationality among the nodes so that the nodes with high energy level are encouraged to become CHs. Technical weaknesses:
* Network lifetime is considered as the time taken for the first node to dies in the network. This is impractical because WSNs may still remain fully functional even after the death of the first node. Applications:
* Animal and plant habitat monitoring. Probable future improvements:
* The complexity of the protocol is high. In particular, complex computation involving optimal cluster size determination could have been simplified.
## 5 Conclusions  
This paper presents a real-time power level data acquisition system for CMLs. The system, installed at the LA.M.E of Joseph KI-ZERBO University, uses SNMP V2 protocol, to collect transmitted and received power data from Telecel Faso's CMLs connected to the system. The number of CMLs accessible to the system has progressively increased from 8 links, in 2019, to nearly 1000 links currently, covering the capital Ouagadougou and the main cities of Burkina Faso (Bobo Dioulasso, Oualhigouya, Koudougou, and Kaya) as well as the axes connecting Ouagadougou to these cities. The attenuation data collected by the system show a good correlation with the data from nearby ANAM rain gauges, showing that the system is working well.  
Future works will focus on increasing the number of CMLs accessible by the system. Another important step will be to process the collected data in order to estimate rainfall amounts and to derive a rainfall map with high temporal and spatial resolution in Burkina Faso. Short-time rainfall prediction will also be investigated.  
Conceptualization, A.D., S.R.S., M.S. and F.Z.; methodology, M.D., S.R.S., M.S., C.C. and H.K.; software, M.D., S.R.S., M.S. and C.C.; validation, S.R.S., M.S., C.C., H.K. and F.Z.; formal analysis, H.K. and F.Z.; investigation, A.D., S.R.S., M.S., I.G., C.C., H.K. and F.Z.; resources, M.D., I.G., W.Y.S.B.O. and N.K.; data curation, M.D., S.R.S., M.S., C.C. and F.Z.; writing--original draft preparation, M.D. and W.Y.S.B.O.; writing--review and editing, M.D., S.R.S., M.S., A.D., W.Y.S.B.O., C.C., H.K. and F.Z.; visualization, M.D. and C.C.; supervision, F.Z.; project administration, H.K. and F.Z.; funding acquisition, H.K. and F.Z. All authors have read and agreed to the published version of the manuscript.  
Thanks to \"Fonds National de la Recherche et de l'Innovation pour le Developpement\" (FONRID) of Burkina Faso for the funding which allowed the experimental zone to cover Ouagdougou and its region, and UNDP who funded TOP-RainCell during the 2020 monsoon season. AgRAIN project funds of one the authors since September 2019. The APC of this article was supported by \"Universite Joseph KI-ZERBO\", UJKZ.  
This work was carried out thanks to several partners. First, we would like to thank Telecel Faso for access to its network to collect CMLs' data and ARCEP for the license to operate the CMLs. Thanks to \"Fonds National de la Recherche et de l'Innovation pour le Developpement\" (FONRID) of Burkina Faso for the funding which allowed the experimental zone to cover Ouagdougou and its region. We also thank the project AgRAIN, funded by the German Ministry of Research and Education (BMBF). We cannot finish without thanking the UNDP who funded TOP-RainCell during the 2020 monsoon season and Universite Joseph KI-ZERBO for the payment of the publication fees.  
The authors declare no conflict of interest.
# Satellite-Based Estimation of Carbon Dioxide Budget in Tropical Peatland Ecosystems
## 4 Discussions
### NBE Reliability Comparing with Other Studies  
The annual averages of the NBE were compared with estimations in other studies. [PERSON] [72] estimated C emissions from degraded Indonesian peatlands as 136.36 Mt C/year in 2008 by using an inventory-based method and reported a 345.27 Mt C/year NBE. In the same study by [PERSON] [72], a set of emission factors was applied to estimate global CO\({}_{2}\) emissions by multiplying the peatland areas for each country. Nation-wide comparisons were conducted in the study. However, the distribution of forest as the only source dataset hardly accurately depicts a carbon budget because SOM decomposition information could be omitted. [PERSON] et al. [13] estimated 172.36 Mt C/year as a median of C emissions (with a possible range of 96.82-233.19 Mt C/year) in Indonesian peatlands in 2006. The satellite-based net C budget (NBE) in this study estimated the value as 411.12 Mt C/year in 2006. In the same study by [PERSON] et al. [13], the C emissions from peatlands were estimated by using a linear relationship between the \(GWT\) and CO\({}_{2}\) emissions from an empirically-derived look-up table that assigned CO\({}_{2}\) emissions to each land cover class. Though the estimation in this study was higher than in the other studies, this study has established a model to estimate various aspects including natural and anthropogenic emissions and absorptions, and this model was carefully validated with in situ observations by conducting spatiotemporal visualizations with satellite-based datasets.
## 3 Results and Discussion
### Empirical Correlation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Correlation** & **Particle Size (nm)** & **Fraction, \(X_{\text{p}}\) (vol%)** & **Temperature (\({}^{\circ}\)C)** \\ \hline
[17] & \(1+2.944X_{\text{p}}/100+19.672(X_{\text{p}}/100)^{2}\) & 33 & 1–4 & 26 \\
[19] & \(0.537852825+0.764481464X_{\text{p}}/100+\) & 36 & 2–10 & 27.2–35.7 \\
[23] & \(0.018688677\) & & & \\
[23] & \(1.0112+0.0454X_{\text{p}}\) & 43 & 1–3 & 25 \\
[25] & \(1+0.135(k_{p}/k_{b})^{0.273}X_{\text{p}}/10467\) & & & \\
[27] & \(1+1.72X_{\text{p}}/100\) & 36, 47 & 0–18 & 21–23 \\
[41] & \(1+7.47X_{\text{p}}/100\) & 13 & 1.34–4.33 & 27 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of empirical correlations for the thermal conductivity ratio of Al\({}_{2}\)O\({}_{3}\)-water nanofluid. \(X_{\text{p}}\) in percentage. percentage error of 2.9%, indicating that the new correlation can be used to obtain accurate predictions in other experimental conditions.
## 2 Materials and Methods
### Data Collection  
#### 2.2.1 Soil Samples  
To train and validate the soil pH prediction models, we collected a total of 150 topsoil samples (0-20 cm) across the study area in October 2021 by the grid sampling scheme (Figure 1b). The measured soil pH varied from 3.62 to 9.44, with a mean of 5.46 and a standard deviation of 0.83. The samples were collected according to the following procedure: First, we created a 2 \(\times\) 2 km fishnet to cover the entire study area. Each grid was regarded as a sampling unit. Second, we adopted a land-cover product, i.e., the global land-cover product with a fine classification system at 30 m (GLC_FCS30), in 2020 to screen the grids outside the cropland [38; 39]. Third, we randomly took ten soil cores from each unit using a 5 cm diameter auger and mixed the soils thoroughly for one composite sample. We recorded their geographic locations with a global positioning system. Fourth, we air-dried and crushed the collected soil samples, filtered them with a 1.0 mm sieve, and stored the preprocessed soils in sealed glass jars for further analysis. Finally, we measured the soil pH (in water, 1:2) using a pH detector.
## 2 Materials and Methods
### Sequence Analyses  
Paired-end reads were merged using FLASH (V1.2.7, [[http://ccb.jhu.edu/software/FLASH/](http://ccb.jhu.edu/software/FLASH/)]([http://ccb.jhu.edu/software/FLASH/](http://ccb.jhu.edu/software/FLASH/))) [21], a very accurate and fast analysis tool, to obtain raw tags. Then the clean tags were obtained by screening the raw tags using the Quantitative Insights Into Microbial Ecology (QIIME) V1.8 pipeline [22]. Then the effective tags finally obtained by removing the chimera sequences using the Uchime algorithm [23]. Sequences analyses were performed by Uparse software (Uparse v 7.0.1001, [[http://drive5.com/uparse/](http://drive5.com/uparse/)]([http://drive5.com/uparse/](http://drive5.com/uparse/))) [24] to be assigned to the same operational taxonomic units (OTUs) at an identity threshold of 97%. For each representative sequence, taxonomic information was annotated by using the Green Gene Database ([[http://greengenes.lbl.gov/cgi-bin/nph-index.cgi](http://greengenes.lbl.gov/cgi-bin/nph-index.cgi)]([http://greengenes.lbl.gov/cgi-bin/nph-index.cgi](http://greengenes.lbl.gov/cgi-bin/nph-index.cgi))) [25] based on the Ribosomal Database Project RDP classifier (Version 2.2, [[http://sourceforge.net/projects/rdp-classifier/](http://sourceforge.net/projects/rdp-classifier/)]([http://sourceforge.net/projects/rdp-classifier/](http://sourceforge.net/projects/rdp-classifier/))) [26] algorithm. Alpha diversity was evaluated to analyze complexity of species diversity for a sample by calculating Good's coverage, ACE richness, Shannon diversity index, and Observed-species with QIIME (Version 1.7.0). Beta diversity analysis was used to evaluate differences of samples in species complexity with QIIME software (Version 1.7.0). Principal Coordinate Analysis (PCoA) was performed to get principal coordinates and visualize from complex, multidimensional data using the QIIME software package, thereby comparing the microbial communities among all samples.  
After assembling and filtering, the raw sequencing data were submitted to the NCBI Sequence Read Archive (SRA), and the SRA records will be accessible with the following link ([[https://www.ncbi.nlm.nih.gov/sra/PRJNA540111](https://www.ncbi.nlm.nih.gov/sra/PRJNA540111)]([https://www.ncbi.nlm.nih.gov/sra/PRJNA540111](https://www.ncbi.nlm.nih.gov/sra/PRJNA540111))) under the accession number PRJNA540111.
## 4 Analysis
### Route Sub-Optimality  
This section provides a quantitative probabilistic analysis of the delay inversion effect. Let \(T_{j}\) be the random variable for jitter values. The delay caused by jitter in an RREQ message traversing a path of \(n\) hops, \(T^{(n)}\), can be then be computed as follows:  
\[T^{(n)}=\sum_{i=1}^{n}{T_{j}}_{i} \tag{1}\]  
Given two paths between a source \(X\) and a destination \(Y\), with lengths \(n\) and \(m\), let \(D^{(n,m)}\) be the inter-path delay difference, _i.e._, the difference between jitter delays suffered by an RREQ flooded through two paths between \(X\) and \(Y\), of \(n\) and \(m\) hops, respectively. It is a random variable that depends on the random variables for the jitter values in the way shown in Equation (2):  
\[D^{(n,m)} = T^{(n)}-T^{(m)}=\sum_{i=1}^{n}{T_{j}}_{i}-\sum_{j=1}^{m}{T_{j}}_ {j}= \tag{2}\] \[= \sum_{i=1}^{n}{T_{j}}_{i}+\sum_{j=1}^{m}{\left(-T_{j}\right)}\]  
The probability that the delay inversion effect occurs in the RREQ flooding corresponds to \(Pr(D^{(n,m)}>0|_{n<m})\), whose expression is detailed in Equation (3):  
\[P\left(T^{(m)}<T^{(n)}\right) = P\left(D^{(n,m)}>0\right)=\int_{0}^{\infty}{f_{D^{(n,m)}}(t)dt} \tag{3}\]  
The probability density function (pdf) of \(D^{(n,m)}\), \(f_{D^{(n,m)}}(t)\), has the following expression:  
\[f_{D^{(n,m)}}(t)=\left(\bigotimes_{i=1}^{n+m}{f_{T_{j_{i}}}}\right)(t+mJ_{m}) \tag{4}\]  
where \(\otimes\) denotes the convolution.
# An Online Reactive Power-Optimization Strategy Based on Load-Curve Prediction and Segmentation
## 2 Reactive Power Optimization Based on Load-Curve Segmentation
### Reactive Power Optimization  
For each time stamp, the changes of tap positions and capacitor banks are considered in the objective function, which can be written in (5).  
\[\text{min}f(t)=\alpha\cdot\frac{P_{loss,t}-P_{loss,\text{min}}}{P_{loss,\text {max}}-P_{loss,\text{min}}}+\beta\cdot\frac{\Delta AT_{t}-\Delta AT_{\text{ min}}}{\Delta AT_{\text{max}}-\Delta AT_{\text{min}}} \tag{5}\]  
where \(P_{loss,t}\) is network loss at time stamp \(t\). For normalization, \(P_{loss,\text{min}}\) and \(P_{loss,\text{max}}\) represent basis values for network losses. \(\Delta AT_{t}\) indicates adjustment times of devices at current time stamp compared with the previous one [15], which can be written in (6). \(\Delta AT_{\text{min}}\) and \(\Delta AT_{\text{max}}\) are basis values for adjustment times. \(\alpha\) and \(\beta\) are optimization weights.  
\[\Delta AT_{t}=\sum_{m=1}^{M}\left(TP_{m,t}-TP_{m,t-1}\right)+\sum_{k=1}^{K} \left(C_{k,t}-C_{k,t-1}\right) \tag{6}\]  
In (6), \(TP_{m,t}\) represents tap position of \(m\)-th voltage regulator at time \(t\), and \(C_{k,t}\) is capacitor number at time \(t\).  
Constraints include three categories, i.e., power flow constraints (7), bus voltage magnitudes not exceeding permissible range (8), and adjustment device state constraints (9) [15; 18].  
\[\begin{split} P_{i,t}&=V_{i,t}\sum\limits_{j=1}^{N}V_{ j,t}(G_{ij}\cos\theta_{ij,t}+B_{ij}\sin\theta_{ij,t})\\ Q_{i,t}&=V_{i,t}\sum\limits_{j=1}^{N}V_{j,t}(G_{ij} \sin\theta_{ij,t}-B_{ij}\cos\theta_{ij,t})\\ &\qquad\qquad\qquad\qquad V_{\text{min}}\leq V_{i,t}\leq V_{ \text{max}}\\ TP_{m,\text{min}}&\leq TP_{m,t}\leq TP_{m,\text{ max}}\qquad TP_{m,t}\text{ is an integer}\\ 0&\leq C_{k,t}\leq C_{k,\text{max}},\quad C_{k,t} \text{ is an integer}\end{split} \tag{7}\]  
where (7) represents constraints of power flow, \(G_{ij}\), \(B_{ij}\) are elements of nodal admittance matrix, \(\theta_{ij,t}\) is angle difference between voltages at node \(i\) and \(j\) at time \(t\), \(P_{i,t}\) and \(Q_{i,t}\) are injected active and reactive power of node \(i\) at time \(t\).  
The voltage magnitude of each node \(i\) at time \(t\), named \(V_{i,t}\), should be within permissible range, as written in (8). Besides, tap position of \(m\)-th voltage regulator at time \(t\), i.e., \(TP_{m,t}\), and capacitor number \(C_{k,t}\), should be discrete values within reasonable range, as described in (9).