esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Climate Dataset  
Multiple climatic variables were employed to help interpret the temporal and spatial dynamics of SIF during the drought event. We utilized monthly mean temperature at 0.5 deg spatial resolution from the Climate Research Unit (CRU) dataset, which was based on monthly observational data, calculating from daily or sub-daily data [37]. The precipitation was taken from Tropical Rainfall Measuring Mission (TRMM) at 0.25 deg spatial resolution and daily temporal resolution from 2007 to 2016. TRMM data have been widely applied to explore the seasonal and interannual variations of precipitation in tropical continents. In terms of solar radiation, we used surface downscaling shortwave flux in all-day conditions at 1 deg spatial resolution over the period 2007-2016 from Clouds and the Earth's Radiant Energy System (CERES). To study the variability of aerosols, monthly mean of aerosol optical depth (AOD) from the Moderate resolution Imaging Spectroradiometer (MODIS) was used. AOD is the degree to which aerosols prevent absorbing and scattering light and can be used as an effective indicator of aerosol concentration [38].  
Figure 1: Land cover map of Southeast Asia, with the northwest region (8–18°N, 98–110°E) and the south region (5°S–5°N, 107–118°E) dominated by crops and evergreen broadleaf forest respectively.  
Gravity Recovery and Climate Experiment (GRACE) observed monthly total water storage anomalies (TWS) in Earth's gravity field due to mass redistribution, which were caused mainly by the movement of water in a variety of surface and groundwater reservoirs [39]. We applied the mascon solutions from Centre for Space Research release 05 (CSR RL05) [40] from 2007 to 2016 to analyse the water content in the study area. The GRACE total water storage anomalies used in these mascon solutions were relative to the baseline average over January 2004 to December 2009 [40]. The Palmer Drought Severity Index (PDSI), an effective indicator of soil moisture in terms of precipitation and temperature [41], was utilized to illustrate the severity of various drought events [42]. The traditional PDSI used a set of empirical weighting factors derived from the average results from the US Great Plains. While the monthly self-calibrating PDSI (scPDSI) determined specific factors for each location separately, individually appropriate to that location [43]. Compared with PDSI data, scPDSI is a more suitable index to compare the spatio-temporal variability of soil moisture globally. The scPDSI data at 0.5\({}^{\circ}\) spatial resolution used in this study were derived from CRU climate dataset [44].
## 3 Plasma Application to Eliminate Organic Pollutants in Water
### Pharmaceuticals  
Another important form of micro-pollutant in water are pharmaceuticals, and even small amounts can accumulate in living organisms and cause serious health problems. Numerous studies have demonstrated that plasma therapy can break down medicines more efficiently than typical AOPs, and with less energy construction than ozone (O\({}_{3}\)) treatment [63; 77; 78]. One of the most important benefits of plasma is its minimal environmental impact. Plasma generates reactive substances, such as -OH, hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) and O\({}_{3}\), which not only react with the contaminants on the surface but also diffuse into the interior of contaminants [79; 80; 81].
## 3 Results and Discussion
### Effects of Reed Carbon Source on Denitrification Function Genes  
#### 3.3.1 Changes in Abundances of _amoA_, _nxtB_ and _hzo_ Genes  
Constructed wetlands mainly remove NH\({}_{4}\)\({}^{+}\)-N from polluted waters mainly through microbial nitrification and anammox. The nitrification process of ammonia nitrogen consists of two steps. First, ammonia-oxidizing bacteria (AOB) oxidizes ammonia to nitrite, and nitrite is further oxidized to nitrate by nitrite-oxidizing bacteria (NOB) [35]. The first step is the rate-limiting step of nitrification, in which ammonia monooxygenase is encoded by the _amoA_ gene, and the nitrite oxidoreductase in the second step is encoded by the _nxtB_ gene [36]. As shown in Figure 7a,b, the mean abundances of _amoA_ and _nxtB_ genes in RW, CW and N-RW were 2.55, 2.74 and 1.90 copies/ ug and 1.04, 1.17 and 0.73 copies/ ug,  
Figure 6: Changes of pH in influent and effluent of three groups of wetland systems.  
respectively. Compared with CW, the abundances of \(amoA\) and \(nxrB\) genes in RW were only reduced by 7.12% and 11.46%. The addition of the reed carbon source in the middle layer of the wetland substrate had no significant effect on the abundances of \(amoA\) and \(nxrB\) genes, and would not inhibit the nitrification reaction in the system. The comparison of RW and N-RW showed that reed planting had a significant effect on the abundances of \(amoA\) and \(nxrB\) genes. The abundances of \(amoA\) and \(nxrB\) genes in RW increased by 34.29% and 41.24%, respectively, compared with N-RW. This suggested that reed planting could increase the abundance of nitrification genes in substrates and thereby improve nitrification in wetland systems.  
Anammox is ubiquitous in aquatic environments. The anammox bacteria use NO2-N in the wastewater as an electron acceptor and NH4+-N as an electron donor to oxidize NH4+-N to N2. The _hzo_ gene is essential for anammox, and controls the conversion of the intermediate hydrazine (N2H4) to the final product N2 [37]. As shown in Figure 7c, the mean abundance of _hzo_ gene in RW, CW and N-RW was 0.22, 0.14 and 0.24 copies/\(\upmu\)g, respectively. Comparison of RW and CW showed that addition of the reed carbon source had a significant effect on _hzo_ gene abundance. The abundance of the _hzo_ gene in RW increased by 52.89% compared to CW. This suggested that the addition of a reed carbon source could increase the abundance of the _hzo_ gene in substrates and thereby improve anammox in wetland systems. Comparing RW and N-RW showed that reed planting had no significant effect on _hzo_ gene abundance. The abundance of the _hzo_ gene in RW was only 8.63% lower than in N-RW. Comparing Figure 7a-c, it was seen that the abundance of the _hzo_ gene was lower than that of the \(amoA\) and \(nxrB\) genes, indicating that the contribution of anammox was lower than that of nitrification.
# Sudden Event Prediction Based on Event Knowledge Graph
## 3 Preliminaries
### Event Prediction by Scenario Model  
For event prediction based on event knowledge graphs, our expectation is for the machine to understand \"what happened\" by perceiving the current scenario, and then give \"what will happen next\". To make it easier to understand, let us imagine a scenario. A boy opens the door of his home, and their perceptual ability makes them instantly aware of some events that are happening. Event 1: his teacher is talking to their parents; event 2: his parents show a frustrated expression. Perhaps the boy has seen such a scenario before, and his experience lets him know that the situation is not good; maybe there will be a \"storm\" behind. It is this ability to understand situations from events and react to them that we want to equip machines with. As mentioned before, the features of the event scenario information are extracted from the initial event and all possible concurrent events. We formalized the event scenario information as follows:  
\[ES=EI_{e}(A_{ce},O_{e},L_{e},T_{ce},P_{e})+\sum_{n}EI_{ce}(A_{ce},O_{ce},L_{ec}, T_{ce},P_{ce}) \tag{5}\]  
\(EI_{e}\) denotes the initial event \(e\) containing the argument information, and \(EI_{ce}\) denotes the event information of concurrent event \(ec\) of \(e\). \(ES\) is the feature vector of the scenario information. We modeled the prediction of event occurrence from the scene information as a multilabel classification task. We designed prediction functions to parse the scenario information to obtain the probability of occurrence for each event category. First, a linear function was used as the projection function to project the scenario information into different result spaces to obtain the result vector. Then, we designed an analytic function to obtain the probability of occurrence of each event class corresponding to the result vector.  
\[W_{R}=Linear(ES_{e}) \tag{6}\]  
\[P(e)=Sigmoid(log\_softmax(W_{R})) \tag{7}\]
# ODGNet: Robotic Grasp Detection Network Based on Omni-Dimensional Dynamic Convolution
## 3 Method
### Loss Function  
The purpose of a grasp detection network is to predict a set of grasp parameter maps \(G=\{G_{1},G_{2},\cdots,G_{k}\}\) from a set of input images \(I=\{I_{1},I_{2},\cdots,I_{k}\}\). For a dataset consisting of objects and rectangle labels, their rectangle labels are processed to obtain the corresponding grasping parameter map \(G_{T}=\{G_{T1},G_{T2},\cdots,G_{Tk}\}\). The goal when training the grasp detection network is to minimize the difference between \(G\) and \(G_{T}\). In this paper, we use smooth L1 [36] as the loss function and the complete loss function can be expressed as  
\[Loss(G,G_{T})=\frac{1}{K}\sum_{i=1}^{K}Loss_{smoothL1}\big{(}G_{i}-G_{T_{i}} \big{)} \tag{11}\]  
where \(Loss_{smoothL1}\) is  
\[Loss_{smoothL1}(x)=\left\{\begin{aligned} & 0.5x^{2},if|x|<1\\ &|x|-0.5,otherwise\end{aligned}\right. \tag{12}\]  
Considering the different roles of grasping quality, grasping angle, and grasping width, the remaining two parameters are generally determined based on the grasping quality.  
Figure 7: The structure of the second primary feature fusion module.  
Therefore, adding weights to the losses corresponding to these four parameters, the total loss function is further expressed as  
\[Loss=\lambda_{Q}L_{Q}+\lambda_{sin}L_{sin}+\lambda_{cos}L_{cos}+\lambda_{W}L_{W} \tag{13}\]  
where \(L_{Q},L_{sin},L_{cos},L_{W}\) are the smooth L1 loss functions of \(Q,sin2\Phi,cos2\Phi,W\), respectively, and \(\lambda_{Q},\lambda_{sin},\lambda_{cos},\lambda_{W}\) are the weights of \(L_{Q},L_{sin},L_{cos},L_{W}\), respectively. Here, \(\lambda_{Q}~{}=~{}1.2\), \(\lambda_{sin}=\lambda_{cos}=\lambda_{W}=1\).
## 3 Results and Discussions  
### Simultaneous \(\varepsilon\)-Polylysine and PHA Production by B. licheniformis PL26  
After complete submerged fermentation of 96 h at an agitation of 220 rpm, fermentation broth was centrifuged to obtain supernatant containing \(\varepsilon\)-polylysine and biomass for PHA extraction.  
Simultaneous production of \(\varepsilon\)-polylysine and PHA by B. licheniformis PL26 was obtained utilizing crude glycerol as the carbon source. However, _B. licheniformis_ is able to produce 0.2 g/L \(\varepsilon\)-polylysine extracellularly in the fermentation broth along with 64.59% PHA with respect to dry cell weight i.e., 1.1 g/L P(3 HB-co-3 HV) having 96 h production age at 37 \({}^{\circ}\)C (Figures S1-S3).  
In the present case, _B. licheniformis_ PL26 is able to produce both \(\varepsilon\)-polylysine and PHA in the same fermentation broth, which is not reported till date. However, _Streptomyces albulus_ (CCRC 11814)is reported to produce \(\varepsilon\)-polyllysine utilizing crude glycerol [3]. _S. albulus_ being an Actinomycetes, it possesses a relatively slower growth rate as compared to _Bacillus_ sp. As previously reported, _S. albulus_ produces \(\varepsilon\)-polyllysine after 120 h in M3G medium containing glucose as the carbon source, 0.2 g/L of \(\varepsilon\)-polyllysine was produced by _Streptomyces albulus_ (CCRC 11814) after 168 h [3], but in present study, \(\varepsilon\)-polyllysine was produced after 96 h by _B. licheniformis_ at a concentration of 0.2 g/L. In addition, fast growth rate and less production age of _B. licheniformis_ which is producing 64.59% PHA with respect to dry cell weight i.e., 1.1 g/L P(3 HB-co-3 HV), which may be considered as the additional advantage of the process. Simultaneously, crude glycerol was replaced with analytical grade pure glycerol with similar concentration of the carbon source and it was found that pure glycerol yielded 0.06 g/L \(\varepsilon\)-polyllysine and 0.4 g/L P(3 HB-co-3 HV).  
0.2 g/L of \(\varepsilon\)-Poly-L-lysine produced from _Bacillus licheniformis_ PL26 is in similar range with respect to its production from a wild strain _Streptomyces albulus_ CCRC 11814 as reported in the literature [3]. However, similar sort of system developed by [PERSON] 2013, wherein using microbial community, simultaneous production of PHA and polyglucose was done [37]. Similarly, using halobacterium _Haloferax mediterranei_, simultaneously poly(3-hydroxybutyrate-\(\varepsilon\)-3-hydroxyvalerate and extracellular polysaccharide (EPS) was produced [38]. Few microbes or microbial consortium have potential to produce two different polymers simultaneously using single carbon and nitrogen source.
## 4 Results
### Criteria for Comparing the Relevant Characteristics  
Table 3 summarizes the comparative analysis implemented for the publication selected to develop the survey. In short, most works follow the general guidelines on monitoring radon, hygrometric variables, and other IAQ pollutants, including threshold-based alerts and further corrective actions taken by human operators. Yet, there is already a study, carried out by [PERSON] et al. in 2021 [74], that goes further in fulfilling risk mitigation needs without human intervention and presents a more comprehensive IoT architecture with a prediction model to act preventively before reaching the risk level. After analyzing the selected radon monitoring systems (architectures and components) with consistent information and experimental results, Tables 4-6 present the extracted data for the comparative study. The data extraction was applied to all selected publications where the criteria for the relevant characteristics were organized in groups as follows:  
* System-on-Chip (SoC);
* Communication technologies;
* Radon gas sensor;
* Environmental sensors included;
* Back-end web architecture;
* Prototype dimensions, estimated power consumption, and price. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Reference** & [68] & [69; 70; 71; 72] & [73] & [74] & [75] & [76] & [77] & [80] & [81] \\ \hline Monitor’s radon concentration. & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline Monitors hygrometric variables (temperature, & & & & & & & & \\ air pressure, relative humidity). & & & & & & & & & \\ \hline Monitors other IAQ pollutants (CO\({}_{2}\), TVOC, & & & & & & & & \\ CO, etc.). & ✗ & ✓ & ✓ & ✓ & ✗ & ✗ & ✗ & ✗ & ✗ \\ \hline Send alerts based on a radon threshold. & ✓ & ✓ & ✓ & ✓ & ✓ & ✗ & ✗ & ✗ & ✓ \\ \hline Prediction model/algorithm interpolation & & & & & & & & & & \\ window capabilities (with radon concentration & & & & & & & & \\ as the air quality metric). & & & & & & & & & \\ \hline Prediction model/algorithm capabilities with & & & & & & & & & \\ other measured variables: energy efficiency, & & & & & & & & & \\ thermal comfort, noise pollution, etc. & & & & & & & & & \\ \hline Corrective actions or measures taken by & & & & & & & & \\ human operators (manual ventilation, & & ✓ & ✓ & ✓ & ✗ & ✓ & ✓ & ✓ & ✗ \\ population alerts, etc.). & & & & & & & & & \\ \hline Equipped with actuators (relays-board to & & & & & & & & \\ interact with airflow control systems). & & ✗ & ✗ & ✓ & ✗ & ✓ & ✗ & ✓ & ✓ \\ \hline Autonomous airflow control system & & & & & & & & & \\ (ventilation system, motorized window, etc.). & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Monitoring systems-summary of most relevant characteristics.
# User Trust Inference in Online Social Networks: A Message Passing Perspective
## 4 Experiments
### Experimental Settings
#### 4.2.2 Evaluation Metrics  
As we construct our mission of trust relationship inference as a binary classification task in this paper, we used the common metrics for classification evaluation. In detail, we used _Accuracy_, _Precision_, _Recall_, and F\({}_{1}\)_score_ for evaluations.  
For the set of user trust relationships that are to be inferred by a model or a method, we denote the number of all elements in the set by C; the number of observed trust relationships that were also predicted to be existent (true positive data points) by tp; and the numbers of false negative, false positive, and true negative ones by fn, fp, and tn, respectively. Then, the Accuracy is defined as  
\[\text{Accuracy}=\frac{\text{tp}+\text{tn}}{\text{C}}, \tag{21}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline Number of users & 14,317 \\ \hline Number of reviews & 24,406 \\ \hline Number of reviews per user & 1.7 \\ \hline Number of trust relationships & Y: 87,804 & N: 78,863 \\ \hline Web of trust density & Y: 0.00043 & N: 0.00038 \\ \hline \hline \end{tabular} For a trust relationship: “Y” indicates an existing trust and “N” a nonexistent one.  
\end{table}
Table 7: Dataset specification.  
and Precision, Recall, and \(F_{1}\) score are, respectively, defined as  
\[\text{Precision}=\frac{\text{tp}}{\text{tp}+\text{fp}}, \tag{22}\]  
\[\text{Recall}=\frac{\text{tp}}{\text{tp}+\text{fn}}, \tag{23}\]  
\[\text{F}_{1}=\frac{2\text{tp}}{2\text{tp}+\text{fp}+\text{fn}}. \tag{24}\]  
From the metric definitions, the higher one metric is, the better performance a model or a method has.
# An Effective Scheduling Algorithm for Coverage Control in Underwater Acoustic Sensor Network
## 3 Energy-Efficient Coverage Control Using Memetic Algorithm
### Algorithm Simulation and Analysis
#### 3.3.2 Local Search  
To further improve the adaptability of the genetic algorithm to calculate the population, we propose a simple local search strategy that makes ESACC converge quickly. For the next generation, this strategy determines whether to keep the modification by modifying the value of each gene in the chromosome (changing the value of a gene from 1 to 0) and comparing it with the adaptability of the original chromosome. This gives us a better node-scheduling scheme than the original node. Population \(k\) is expressed as \(Pop_{k}=\{u_{1},u_{2},\cdots,u_{k}\}\), where \(u_{k}\) is the \(k\)th group of chromosomes, and \(A_{i1},A_{i2},\cdots,A_{iN}\) is the gene sequence of chromosome \(i\), where \(i\in[1,k]\). The pseudo code of the local search strategy in ESACC is as follows in Algorithm 1.  
```
1:Calculate the adaptability \(fit(A_{i})\) of chromosome \(u_{i}\);
2:Statistics of index position \(g\) where gene \(A_{ij}\) equals \(1\) in chromosome \(u_{i}\), \(j\in[1,N]\), \(g\subset[1,N]\);
3:for\(t=1,2,\cdots,length(g)\)do
4:if\(fit\left(A_{i}^{\prime}\right)\)>\(fit\left(A_{i}\right)\)then
5:\(fit\left(A_{i}\right)\)=\(fit\left(A_{i}^{\prime}\right)\);
6: Keep the state of \(A_{it}\);
7:else
8:\(A_{it}\)=1;
9:endif
10:endfor
11:Get the improved chromosome \(u_{i}\) and repeat to obtain the optimal population \(POP_{k}\).
```  
**Algorithm 1** Local search algorithm.
# A Failure Risk-Aware Multi-Hop Routing Protocol in LPWANs Using Deep Q-Network
## 4 System Models  
### Network Model  
Without loss of generality, we consider a network where \(N\) ENs are randomly distributed within an \(L\times L\) monitoring area with nonuniform density. As established in [19; 20; 21; 22], the network model satisfies the following assumptions to construct a standardized scenario:  
1. A gateway (GW) is located in the center of the network and remains powered on. Central placement simplifies the network model, providing a consistent reference point for all ENs while facilitating a more balanced distribution of data flow.
2. All ENs are homogeneous. This configuration minimizes performance variations due to hardware differences, thereby facilitating an unbiased evaluation of the logic and effectiveness of routing protocols under consistent operating parameters.
3. Both ENs and GW are stationary after deployment. This configuration eliminates route fluctuations caused by the mobility of ENs and GW.
4. All ENs are synchronized and can determine their locations via Global Positioning System (GPS) or other self-localization algorithms. Synchronization is essential for ordering control and data packets in negotiation-based protocols, while geographic information is fundamental for distance-based relay selection.
5. The links are symmetric. This assumption ensures bidirectional connectivity and consistent link characteristics, thereby avoiding complications from unidirectional paths that disrupt acknowledgment-dependent routing protocols.
# Dual Quaternion Embeddings for Link Prediction
## 5 Experiments
### Experiment Settings
#### 5.1.3 Baselines  
We compared our model with several state-of-the-art baselines. For transformation based models, we compared our model to TransE [4], TorusE [24], RotatE [5] and HAKE [7]; for bilinear models, we compared our model to ComplEx [13], HolE [12], SimplE [9], DihE-dral [14] and QuatE [6] (to make the comparison fair, we use the version of QuatE without type constraints on the common link prediction datasets considering the requirement of type constraints is too strong).
## 3 Results
### Relative Error at Peak Value  
In fluorescence studies, the peak value is a key index for simulation, as it can reflect the photosynthetic conditions in the plants. Furthermore, the peak values are related to the photosystem FQE II and I in the simulation, so a better fitting peak can create a more accurate simulation. In Figure 8, for the purpose of model validation, we further compared the scatterplots of the measured vs. modeled values and the measured vs. modeled with FQE inversion code values for all 102 leaves at peak wl = 687 nm and 740 nm, respectively. We categorized the fluorescence quantities into two varieties: WT high and MG low for upward and downward fluorescence, respectively. The results clearly show that the modeled plots with FQE inversion dramatically improved at the two peaks. In particular, the peak at 687 nm for low MG downward fluorescence leaves had an R\({}^{2}\) that increased from 0.18 to 0.80 and a PBIAS value that decreased dramatically after adding the FQE inversion code.  
Figure 8: _Cont._We also explored the REs at the two peaks. From Figure 9, it is easy to observe that the REs of the peak values decreased significantly in the first peak for leaves with both high and low chlorophyll content. Correspondingly, the RE for the first peak at 687 nm for MG low downward fluorescence decreased dramatically from 120% to 15% with the inclusion of FQE inversion. Table 3 shows the changes in the FQE values; with only minor changes to FQE I, there was no notable improvement in the second peak, but the RE decreased further by a small margin. Overall, the simulation with the FQE inversion code is favorable, with RE values ranging from \(-\)20% to 20%. For the second peak, the RE in the downward direction for low-chlorophyll-content leaves showed a decrease that improved the simulation. The results with significantly increased \(R^{2}\) (Figure 8) and decreased RE (Figure 9) values at the two peaks provide more evidence that the new FQE retrieval method coupled with the Fluspect model can better simulate the ChIP with greater accuracy under natural conditions.  
Figure 8: Measured and modeled fluorescence values with and without FQE inversion for 102 soybean leaf samples. (**A1–A4**) WT high; (**B1–B4**) MG low; (**C1–C4**) all leaves. The blue circles indicate the measured vs. modeled correlation without the FQE inversion, while the red crosses indicate the measurements vs. the improved model with FQE inversion.  
Figure 9: The relative error at peak values (687 nm and 740 nm) for the downward/upward fluorescence of high-/low-chlorophyll-content leaves: (**A1**,**B1**) high chlorophyll/upward fluorescence; (**A2**,**B2**) high chlorophyll/downward fluorescence; (**A3**,**B3**) low chlorophyll/upward fluorescence; (**A4**,**B4**) low chlorophyll/downward fluorescence.