esa-sceva/satcom-chunk-collection huggingface dataset:
# Stimulation Montage Achieves Balanced Focality and Intensity
## 3 Results
### Robustness Study
#### 3.3.3 The Impact on TE by Conductivity Uncertainty  
Regarding TE in Figure 6, the scalp and CSF generally have the least impact capped at 4 mm, which have no clear trend of increasing or decreasing for all scenarios (see Supplementary), while the skull and cortex can alter the TE greatly up to 16 mm. Figure 6B shows an example of TE analysis in a single-target experiment with our SBFI method. Overall, the TE tends to decrease when the skull/cortex conductivity increases. Sometimes, the cortex even induces more TE changes than the skull. However, considering the conductivity change of the skull is 94% smaller than the cortex (\(\Delta\sigma_{skull}=0.039\) S/m and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Tissue** & \(\sigma\)**(S/m)** & **Max Intensity (V/m)** & **Min Intensity (V/m)** & **Rate ((V/m)/(S/m))** \\ \hline Scalp & 0.2–0.6 & 0.598 & 0.242 & 0.890 \\ Skull & 0.001–0.04 & 0.828 & 0.037 & 20.299 \\ CSF & 1.20–2.01 & 0.386 & 0.257 & 0.159 \\ Cortex & 0.05–0.71 & 0.352 & 0.219 & 0.202 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Intensity results of the robustness test.  
Figure 5: **(A)** Maximum intensity deviation from the expected value due to conductivity uncertainty. **Left**: single target. **Right**: synthetic multiple targets, from top to bottom are the results for target 1, 2, and 3, respectively. (Note: \(y\)-axis is log scale in the right column). The effects on intensity in general: skull \(>\) scalp \(>\) CSF \(\approx\) cortex. (**B**) Example of the intensity changes. The large intensity spans on the graph above indicate the strong impact of the conductivity changes. Within the possible conductivity changes, skull conductivity increase greatly increases the intensity, while conductivity increases in scalp/CSF/cortex decrease the intensity.  
\(\Delta\sigma_{cortex}=0.66\) S/m), the rate of change of skull-induced TE is actually six times higher than that of the cortex, as shown in Figure 6B and Table 3. Thus, it is still reasonable to envision that the skull generally has stronger effects on TE than the cortex does.
# Tracking System for a Coal Mine Drilling Robot for Low-Illumination Environments
## 3 The Proposed Method
### Dealing with Low-Illumination Environments
#### 3.5.2 Accelerated Versions for Mobile Drilling Robot  
The LIME method is employed for enhancing a single image, but the method does not consider the real-time performance using video data. Although LIME can preserve image features well and has high tracking accuracy (Section 4.4), its slow running speed makes it unsuitable for mobile robot applications. Therefore, a fast image enhancement method is proposed to ensure that the LLCT can be used on mobile robots. The brightness value is extracted from the three image channels (RGB):  
\[\mathit{Light}=\frac{1}{2}\biggl{(}\max_{c\in\{R,G,B\}}\mathbf{L}^{c}(p)+\min_ {c\in\{R,G,B\}}\mathbf{L}^{c}(p)\biggr{)}, \tag{21}\]  
The average values of the RGB channels are calculated, and the maximum and minimum mean values are used as the brightness values. An effective nonlinear superposition algorithm is iterated to improve the image brightness:  
\[\mathbf{R}^{c}(p)=\mathbf{L}^{c}(p)+k\mathbf{L}^{c}(p)\odot\left(\frac{255- \mathbf{L}^{c}(p)}{256}\right)\ c\in\{R,G,B\}, \tag{22}\]  
where \(\mathbf{L}(\bullet)\) denotes the captured image, \(\mathbf{R}(\bullet)\) denotes the desired image, and \(k\in[0,1]\) is a parameter for controlling the exposure. Equation (22) is iterated until the brightness reaches the threshold value. The target feature is then extracted for filter training.  
Because the global brightness does not reflect whether the target is in a low-illumination environment (Figure 3), we propose a target area illumination detection method. We only consider the illuminance of BB and its surrounding padding area. When the brightness is below the predetermined threshold \(T_{L}\), the exposure of the current frame is adjusted. This step is skipped when the illumination is sufficient.
# Network Defense Strategy Selection with Reinforcement Learning and Pareto Optimization
## 3 Game Model on Pareto Optima and Q-learning
### Game Rules  
This game uses the following actions on a node for the attacker and defender (the cost is shown in brackets):  
_A = [ compromise_service (6), install_virus (12), steal_data (8), nothing (0)]_  
_D = [restore_service (12), remove_virus (12), nothing (0)]_  
A player can perform any of the above action on a single node. The actual action sets defined in Equations (16) and (17) are a subset of the combination of one of the above actions and the nodes in the network. Assuming that n is the nodes of the network as defined in Equation (13). The subset contains all the valid actions for the current state.  
\[A_{s}\subseteq A\times n\mid<n,\ p>\epsilon\;\mathrm{S} \tag{16}\]  
\[D_{s}\subseteq D\times n\mid<n,\ p>\epsilon\;\mathrm{S} \tag{17}\]  
**The game is played as follows:** At a given time \(t\), the game is in state \(s_{t}\;\epsilon\;S\). If all the data is stolen, the game ends. If not, check the player's resource points. If one of the players has enough resource points to do anything other than _nothing_, they can perform an action. The game ends as soon as one of the following ending conditions is met:  
- The attacker brings down all the services (network availability is zero), the game ends.  
- The attacker manages to steal all the data or infect all the nodes, the game ends  
- Either the attacker or the defender can no longer make a move, the game ends.  
Figure 4: Example of a game state.  
**Performing an action:** The attacker chooses an action from \(A_{s}\) and performs it, then the defender chooses an action from \(D_{s}\) and performs it. The game determines the next state \(\text{s}_{t+1}\) according to the transition function \(P\). The rewards for that state transition are calculated and the goals are updated with the rewards. The game transitions to state \(s_{t+1}\) and time \(t+1\). See Figure 5 for a state transition  
**Transitioning to a new state:** Transitioning to a new state involves three things: (1) modify the network state according to the performed actions; (2) subtract the cost of the performed action from the corresponding player's resource points; (3) subtract one resource point for each compromised service and one resource point for each installed virus from the attacker's resource points; this is a cost for keeping the nodes compromised.  
**Valid actions:** compromise_service--an attacker can compromise any service that is not yet compromised.  
install_virus: an attacker can install a virus on any node that is currently virus free.  
steal_data: an attacker can steal data from any node that has a virus installed.  
restore_service: a defender can restore any service that is compromised.  
remove_virus: a defender can remove a virus from any node that has a virus installed.  
nothing: either attacker or defender can choose to do nothing.
# Field Evaluation and Application of Intelligent Quality Control Systems
###### Abstract  
During road construction, the accuracy of compaction work is critical for the structural stability and maintenance of the road. Although the plate load test (PLT) is commonly used for quality inspections, it is impractical to test every section due to time and cost constraints. Therefore, simpler testing methods are being extensively developed. This study compared quality inspection results using the commonly used PLT, the relatively simple dynamic cone penetrometer test (DCPT), the lightweight deflectometer (LWD) test, and an intelligent quality control system equipped with accelerometer and global positioning system (GPS) sensors in intelligent compaction (IC) rollers. The results showed a strong correlation between the conventional tests (PLT, DCPT, and LWD) and the values obtained from the intelligent quality control system. The correlation analysis between the intelligent quality control system and PLT, LWD, and DCPT yielded R-square values of 0.69, 0.91, and 0.95, respectively, indicating significantly high correlations. The implementation of intelligent quality management systems in earthwork construction projects will facilitate a thorough verification of the compaction quality throughout all construction segments, ensuring consistent compaction across the project. By enabling real-time data acquisition and analysis, these systems differ markedly from traditional methods, reducing the frequency and necessity of manual inspections. This approach not only streamlines construction processes, but also enhances operational efficiency. As a result, integrating these intelligent systems is anticipated to significantly increase productivity by optimizing the workflow and resource utilization in earthwork construction.  
intelligent quality control systems; compaction measurement value (CMV); plate load test (PLT); dynamic cone penetration test (DCPT); light weight deflectometer (LWD)
# Battery Management System Hardware Concepts: An Overview
## 4 Measurement
### Temperature Measurement  
Common temperature sensors for the measurement of temperatures in the region that is relevant for battery management applications are of Negative Temperature Coefficient (NTC) (metal oxide) or Positive Temperature Coefficient (PTC) (semiconductor) type. These sensors change their resistance as a function of the temperature--for NTC, the resistance decreases with increasing temperature, for PTC vice versa. The measurement is realized by capturing the voltage drop while a constant current is flowing through the resistor. They are typically usable in regions of \(-50\,\mathrm{\SIUnitSymbolCelsius}\) to \(150\,\mathrm{\SIUnitSymbolCelsius}\), relatively cheap, easily applicable and are linear in a certain range, but behave nonlinearly in adjacent temperature ranges (e.g., very low and very high temperatures) [47], Chapter 19.1. Usually, BMS front end ICs provide one or more ADC channels for temperature measurement (see Section 3.3, [3; 25; 27; 28; 42; 43; 45; 48; 49; 50]), the usage of NTC type thermistors is common (see e.g., [1; 42]). From the nonlinearity, it follows that, in the digital processing chain (e.g., the intelligent cell controller board), a look up table has to be implemented to allow the calculation of correct temperatures from measured voltages. Other sensors that use a digital interface and provide a readily calculated temperature value are more conveniently usable. An example for a sensor with a built in digital interface (\"1-Wire\") is the DS18B20 (Maxim Integrated) [51]. Similar sensors using I\({}^{2}\)C communication can be found at Texas Instruments (TMP100 [52]), LM75 [53], etc.). These sensors can be easily connected to a microcontroller environment. However, it has to be noted that, even though digital sensors are usually known for their robustness, care has to be taken when positioning these sensors close to the high-power path in a battery pack to reduce EMI induced failures.  
Other ways of temperature measurement, e.g., using metal based PTCs, as the well-known PT100, or making use of the thermoelectric effect (Thermocouple) could provide higher accuracies and a wider temperature range; however, this would involve a higher complexity in terms of electronics [47], Chapter 19.1.
###### Abstract  
Microbial-induced calcite precipitation (MICP) is a soil amelioration technique aiming to mitigate different environmental and engineering concerns, including desertification, soil erosion, and soil liquefaction, among others. The hydrolysis of urea, catalyzed by the microbial enzyme urease, is considered the most efficient microbial pathway for MICP. Biostimulated MICP relies on the enhancement of indigenous urea-hydrolyzing bacteria by providing an appropriate enrichment and precipitation medium, as opposed to bioaugmentation, which requires introducing large volumes of exogenous bacterial cultures into the treated soil along with a growth and precipitation medium. Biostimulated MICP in desert soils is challenging as the total carbon content and the bacterial abundance are considerably low. In this study, we examined the biostimulation potential in soils from the Negev Desert, Israel, for the purpose of mitigation of topsoil erosion in arid environments. Incubating soil samples in urea and enrichment media demonstrated effective urea hydrolysis leading to pH increase, which is necessary for calcite precipitation. Biostimulation rates were found to increase with concentrations of energy (carbon) source in the stimulation media, reaching its maximal levels within 3 to 6 days. Following stimulation, calcium carbonate precipitation was induced by spiking stimulated bacteria in precipitation (CaCl\({}_{2}\) enriched) media. The results of our research demonstrate that biostimulated MICP is feasible in the low-carbon, mineral soils of the northern Negev Desert in Israel.  
microbial-induced calcite precipitation; desert soil; biostimulation; erosion mitigation Article
# Caroenoid Production from Microalgae: Biosynthesis, Salinity Responses and Novel Biotechnologies
## 3 Salt-Stress Treatment for Caroenoid Production from Microalgae
### Microalgal Responses to Salt Stress
#### 3.1.1 Early Signaling  
Ca2+ is considered as a universal second messenger for the primary stress signals [71], acting in real-time in response to the imbalance of cell ion homeostasis under NaCl stress in plants [72]. Similar to plants, salt stress can elicit a temporary increase in cytosolic Ca2+ concentration ([Ca2+]cyt), which can regulate the activity of downstream effector proteins, such as calmodulin, Ca2+-dependent protein kinases and CaM-dependent protein kinases [73]. Moreover, the decarboxylation of glutamate to \(\gamma\)-aminobutyric acid (GABA) was regulated by the Ca2+/CaM protein.  
GABA is a recently identified endogenous signaling molecule in plants, participating in cell growth and enhancing abiotic stress tolerance [74]. It is a non-protein amino acid derived from the decarboxylation of glutamate, and exogenous GABA can enhance plant resistance to abiotic stress by activating the GABA bypass pathways and TCA cycle. Under salt stress, GABA helps to maintain C/N balance and even acts as a scavenger of toxic  
Figure 3: Possible mechanisms of responses of microalgae induced by salt stress.  
ROS [75]. It is also related to nitric oxide (NO) accumulation under stress conditions as it amplifies NO stress signaling [74].  
NO is an important molecule involved in plant growth, development and tolerance to abiotic stress. It plays important roles in resistance to drought, temperature (high or low), UV-B and heavy metal stress [76]. NO can also act as a signal in activating antioxidant enzyme defense against oxidative stress induced by salt stress [76]. By applying NO donors, such as sodium nitroprusside, plants under stress conditions showed restoration of chlorophyll to recover their damaged photosynthetic system [77].  
Reactive oxygen species (ROS) are also second messengers induced by salinity stress, which is also associated with Ca\({}^{2+}\) signaling [73]. ROS is a stress indicator in response to abiotic stress of microalgae, which can regulate cell growth and metabolites synthesis. Toxic ROS will lead to lipid peroxidation, membrane deterioration, DNA and protein damage [68]. To eliminate excessive ROS, antioxidant enzymes (such as superoxide dismutase and catalase) and antioxidants (such as carotenoids) in microalgae are two essential mechanisms evolved by organisms [78].
# Impedance Modeling Based Method for Sub/Supsynchronous Oscillation Analysis of D-PMSG Wind Farm
## 3 Impedance-Based Stability Analysis of SSO
### Impedance Sensitivity Analysis  
Based on the correct and effective impedance model, influence of control and circuit parameters can be further discussed. The impedance dynamics effects on SSO stability would be explained, too. It can be inferred from (22) that the impedance characteristics are mainly influenced by initial operating conditions which are decided by power flow, also affected by control parameters of PLL and dq-current controller.
# Efficient Communication Model for a Smart Parking System with Multiple Data Consumers
## 3 Related Work in Literature
In contrast, [PERSON] et al. shows the need for a scheduling mechanism for delivering the aggregated data packet to reduce the latency and proposes a novel relative collision graph algorithm-based scheduler [27]. [PERSON], in a survey paper, categorizes multiple sinks as a viable method to reduce congestion and improve RPL's performance in an IoT network [28]. Many research works propose to increase network performance by defining more than one instance of RPL undera BR. Multi-sink approaches are proposed to handle high traffic volumes, offer safety against BR failure, combat congestion and balance traffic load across various forwarders [29, 30, 31, 32]. A sink is a node that collates data; however, these works refer to the RPL root node as the sink. The coordination between multiple sinks is proposed through a virtual root or through cooperative mechanisms between the different sinks [33, 34, 35]. [PERSON] et al. argued that dynamism in invoking multiple instances is better than static multiple instances in handling different data traffic for multiple IoT applications [36]. Depending on the type of application, the node switches between stored instances to experience a reduction in control messages and power consumption. [PERSON] et al. show that combined metrics offer superior performance when compared to a single metric in a multi-sink scenario [37]. All such works incur modifications to RPL control messages, introduce new layers and increase the complexity. Furthermore, these works do not focus on exchanging data between multiple sinks, as they focus on a particular case of different sinks collating different type of data from the IoT network. Moreover, there is no need for the sink node to be the destination of data and any node in the network can act as a data server. [PERSON] et al. measure RPL's performance under different topologies such as linear, circular, random and grid. They conclude that the topology does not impact power consumption but influences latency [38]. The number of hops needed to reach the destination has an impact on the performance, as congestion is prevalent around the sink node. [PERSON] et al. use a WSN for gathering occupancy data in the central gateway/sink node and apply a self-organizing algorithm for cluster formation [39]. In the clustering approach, there is no explicit insight on the exchange of data between clusters or the latency involved. Although there are a plethora of works in the literature to improve the performance of RPL [40], the simple effects of the position of root node or the usage of multiple servers to collate data are not studied.
## 4 Conclusions  
In this study, the magnetic polymer \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)@GMA@IM was prepared by suspension polymerization and grafting. The magnetic polymer was used as an adsorbent to remove the anionic dye FCF from an aqueous solution. The properties of magnetic polymers were determined based on different characterization techniques. The adsorption effect of the polymer on FCF had no clear change between pH 2-10. By comparing with other data, it was shown that the magnetic polymer mainly adsorbed FCF through electrostatic interaction between protein N on imidazole group and FCF. By studying the adsorption kinetics, it was observed that the adsorption of FCF by the polymer could reach equilibrium within 120 min, which was more in line with the pseudo-second-order kinetic model. Compared with the Langmuir isotherm, the adsorption data were more in line with the Freundlich isotherm model, indicating that the adsorption of FCF by the polymer had some other effects besides ion exchange. By using 10% NaCl to desorb and regenerate the adsorbed polymer, after five adsorption-desorption processes, the adsorption effect could still reach 98.3%, showing a good regeneration performance. The polymer had a maximum adsorption capacity of 445 mg/g for FCF and was a good adsorbent.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**adsorbate**} & \multirow{2}{*}{**T (\({}^{\circ}\)C)**} & \multicolumn{4}{c}{**Langmuir Model**} & \multicolumn{2}{c}{**Freundlich Model**} \\ \cline{3-8}  & & \(\mathbf{K_{L}}\) & \(\mathbf{Q_{max}}\) & \(\mathbf{R^{2}}\) & \(\mathbf{n}\) & \(\mathbf{K_{F}}\) & \(\mathbf{R^{2}}\) \\ \hline \multirow{3}{*}{FCF} & 25 \({}^{\circ}\)C & 0.661 & 369.9 & 0.809 & 6.27 & 162.45 & 0.971 \\  & 35 \({}^{\circ}\)C & 1.146 & 376.4 & 0.830 & 6.76 & 178.23 & 0.960 \\ \cline{1-1}  & 45 \({}^{\circ}\)C & 3.257 & 378.6 & 0.783 & 7.52 & 197.20 & 0.934 \\ \hline \end{tabular}
\end{table}
Table 3: Adsorption isotherm parameters at different temperatures.  
Figure 11: Reusability of \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)@GMA@IM adsorption FCF.  
**Author Contributions:** Conceptualization, M.C. and Y.L.; formal analysis, investigation, Y.L. and Y.S.; resources, M.C.; data curation, Y.S., Y.C. and F.Z.; writing--original draft preparation, Y.L.; writing--review and editing, Y.L. and Y.S.; funding acquisition, M.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (grant number 21976094, 22176100).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Raw data is available upon request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Origin and Function of Structural Diversity in the Plant Specialized Metabolome
## 2 The Four Major Specialized Metabolome Pathways  
### Polyketides and Fatty Acid Derivatives  
Polyketides are a large group of secondary metabolites that result from the condensation of acetyl-CoA (C\({}_{2}\)) with one or more Coenzyme A (CoA) esters, such as malonyl-CoA (C\({}_{3}\)) via chain extension [19] (Figure 1); a process that is catalyzed by polyketide synthases [20]. By losing its carboxyl group, malonyl-CoA condensation leads to a C\({}_{2}\)-unit extension. Different reduction steps generate the myriad of saturated and unsaturated fatty acids in plants. In higher plants, C18 polyunsaturated fatty acids (PUFAs), mainly linoleic acid and \(\alpha\)-linolenic acid, are dioxygenated by lipoxygenase into hydroperoxyl-derivatives. These hydroperoxides serve as substrates for further transformation by different pathways into a series of signaling and defense molecules called phytooxylipins [21; 22]. Allene oxide synthase dehydrates the PUFA hydroperoxides to allene oxides, which are further converted to jasmonates [23; 24]. Jasmonates are signal molecules leading to the expression of proteinase inhibitors and other foliar compounds with negative effects on herbivore performance [23]. The action of hydroperoxide lyase on PUFA hydroperoxides, on the other hand, leads to the production of a group of specialized metabolites, collectively called \"green leaf volatiles\" (GLV), which include C\({}_{6}\) or C\({}_{9}\) aldehydes, alcohols and their esters [25; 26]. An important function of these herbivory-induced GLVs is to prime plant defenses after subsequent herbivory [27]. Young _P. nigra_ trees emitted various GLV upon feeding by gypsy moth (_Lymantria discar_). Among these GLVs, (Z)-3-hexenol and (Z)-3-hexenyl acetate were the most abundant [28]. Increased levels of (Z)-3-hexenyl acetate induced the expression of genes involved in the biosynthesis of phytoalexins, a group of anti-microbial compounds, in poplar leaves [29]. Polyketides are widespread among micro-organisms, yet their biosynthesis pathways are less important in the plant specialized metabolism [19]; they, however, do take part in the biosynthesis of some moieties in particular classes of terpenoids, alkaloids and phenolics, e.g., the biosynthesis of stillenoids and flavonoids (see below).