esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this work, we used satellite remote sensing data and flux site observation data to introduce the GeoMAN model based on an encoder-decoder framework with an attention mechanism for site features, and we obtained good results. According to the experiments on training data selection based on distance and vegetation type, we found that both distance and vegetation type had an impact on GPP prediction results, with vegetation type having a larger impact. Through the feature ablation experiments, we found that different sites showed sensitivity to different factors, with the site located in the alpine swamp meadow being insensitive to changes in environmental factors, while the site located in the alpine meadow steppe showed a different trend since the GPP prediction accuracy decreased sharply with the changes in environmental factors. The GPP prediction accuracy of the site located in the alpine Kobresia meadow also varied with environmental factor changes but was more stable than the other sites. The results of this work show that deep learning models have high accuracy when simulating site-scale GPP and, to some extent, reflect the correlation between a target site's GPP and other sites' distances, vegetation types, and meteorological factors. Our work could be used in the prediction of other factors, for example, AGB (Above Ground Biomass) and RE (Ecosystem Respiration). However, this work has some limitations. Firstly, we do not consider some factors that have an influence on productivity in Tibetan grasslands, such as soil development and drought regimes. Secondly, the data we used in this work only cover a partial area of the Tibetan Plateau region, and this introduces constraints to regional GPP assessment. In our future work, we will add more factors to the training data, for example, soil pH, soil fertility, and soil organic matter (SOM), since high soil pH and a lack of soil fertility limit plant productivity [36], and SOM is able to enhance alpine grassland productivity by improving the soil structure, aggregates, and cation-exchange capacity (CEC) under high aridity conditions [37]. Moreover, ecological factors, such as growing and non-growing seasons, will be considered, and larger regional-scale data will be used in future training and learning processes. These improvements will help us perform more accurate and larger-scale GPP simulations.  
Conceptualization, Q.Y. and Y.W.; methodology, Q.Y. and N.N.; software, Q.Y.; validation, Q.Y., Z.W. and M.W.; formal analysis, R.C.; investigation, Q.Y.; resources, X.R.; data curation, Q.Y. and X.W.; writing--original draft preparation, Q.Y.; writing--review and editing, N.N. and X.W.; visualization, Q.Y. and W.L.; supervision, Y.W.; project administration, Q.Y.; funding acquisition, N.N. and Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (Grant No. 2021 YFF0703902).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Improved APF Algorithm
### Principle of APF Algorithm  
The APF algorithm, as a kind of virtual composite force field, superimposes the attractive force from the target point and the repulsive force from the obstacle together to form a resultant force vector, which acts on the vehicle for local obstacle avoidance. The gravitational field, repulsive field, and combined potential field of the APF algorithm are shown in Figure 2.  
As can be seen from the figure, the gravitational potential field is equivalent to a mountain slope, which will attract the ball to roll towards the bottom of the slope, while the repulsive potential field is like a high mountain, providing repulsive force to ensure that the ball will not be too close to it, and the superposition of the two together will result in the red ball rolling all the way around the high mountain to the bottom of the mountain slope, which is the effect of the APF.  
In the traditional APF, the magnitude of the gravitational potential energy at the current point position increases as the distance and position of the target point increase. Similarly, the potential energy of the repulsive force decreases as the distance and position from the obstacle increase. The calculation formulas of the potential field functions for the gravitational potential field and the repulsive potential field are shown in Equations (1) and (2) as follows [20]:  
\[U_{att}=\frac{k_{att}\rho\Big{(}q,q_{goal}\Big{)}^{2}}{2} \tag{1}\]  
\[U_{req}=\begin{cases}\frac{k_{rep}\Big{(}\frac{1}{\rho(q_{obs})}-\frac{1}{ \rho_{0}}\Big{)}^{2}}{2}&\rho(q,q_{obs})\leq\rho_{0}\\ 0&\rho(q,q_{obs})>\rho_{0}\end{cases} \tag{2}\]  
where \(\rho\Big{(}q,q_{goal}\Big{)}\) is the Euclidean distance from the current point to the target point. \(\rho(q,q_{obs})\) is the Euclidean distance from the current point to the obstacle. \(\rho_{0}\) is the repulsive influence range. katt is the gravitational coefficient, and krep is the repulsive coefficient.  
Figure 2: Three potential field diagrams. They are: (**a**) Gravitational potential field diagram; (**b**) Repulsive potential field diagram; and (**c**) Combined potential field diagram.  
\(F_{att}\) and \(F_{rep}\) can be derived by taking the negative gradients of \(U_{att}\) and \(U_{rep}\). The calculation formulas are shown as Equations (3) and (4) [21]:  
\[F_{att}(q)=-\
abla U_{att}(q)=-k_{att}\rho\Big{(}q,q_{goal}\Big{)} \tag{3}\]  
\[F_{rep}\left(q\right)=-\
abla U_{rep}\left(q\right)=\left\{\begin{array}{l} k_{rep}\Big{(}\frac{1}{\rho\left(q_{obs}\right)}-\frac{1}{\rho_{0}}\Big{)}\frac{1}{ \rho\left(q_{obs}\right)^{2}}\\ 0\end{array}\right. \tag{4}\]
## 5 Conclusions  
In this paper, we propose a probabilistic topology control (PTC) algorithm for underwater cooperating AUV networks which are associated with limited communication capability and high mobility. In PTC, to reduce the transmission power adjustment ratio of the topology control algorithm, when the AUVs' transmission power does not equal the optimal transmission power calculated based on an optimal algorithm, the AUVs must not adjust their transmission power. The AUV calculates the deviation of the transmission power, the residual energy, the degree of AUV, and the queue length firstly; then the AUV calculates the transmission power adjustment probabilities of each parameter based on these deviations. The maximum probability will be chosen as the transmission power adjustment probability of the AUV. Through this approach, the transmission power adjustment ratio of the topology control algorithm can be reduced greatly (by about 40%). Since the transmission power adjustment ratio has been reduced, the network performance also improved remarkably.  
We note that in this paper, the PTC algorithm is combined with the FTC algorithm; however the PTC algorithm could also be combined with topology control algorithms other than the FTC algorithm to reduce the transmission power adjustment ratio. Additionally, while in this paper, the parameter selection is based on the requirements of the SWARMs project, these parameters are not fixed, and they can be easily changed based on the requirements of different applications. This flexibility represents one of the main advantages of PTC algorithm.  
**Acknowledgments:** The research leading to the presented results has been undertaken with in the SWARMs European project (Smart and Networking Underwater Robots in Cooperation Meshes), under Grant Agreement n. 662107-SWARMs-ECSEL-2014-1, partially supported by the ECSEL JU and the Spanish Ministry of Economy and Competitiveness (Ref: PCIN-2014-022-C02-02), and the Fundacao para a Ciencia e a Tecnologia (FCT; ECSEL/0002/2014; ECSEL/0003/2014). Also the China Scholarship Council (CSC), which has partially supported the first author's research described in this paper.  
**Author Contributions:** [PERSON] Concieved the topology control algorithm; [PERSON], [PERSON], [PERSON], and [PERSON] designed and performed the experiments; [PERSON], [PERSON], [PERSON], and [PERSON] analyzed the data; [PERSON] and [PERSON] proofreading the paper and provided important professional feedback; [PERSON], [PERSON], [PERSON], and [PERSON] wrote this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Study on Sensitive Bands of EEG Data under Different Mental Workloads
## 4 Significance Analysis of Mental Workload and Feature Based on SVM Classifier
### Feature Selection Based on Gini Impurity  
The 120-dimensional characteristic distribution of EEG data is: 1-30 dimension is the \(\alpha\) band of each electrode point, 31-60 dimension is the \(\beta\) band of each electrode point, 61-90 dimension is the \(\theta\) band of each electrode point, 91-120 dimension is the \(\delta\) band of each electrode point. Taking the subject 22 as an example, the importance distribution of the 120-dimensional features in the EEG data calculated by Gini's impurity is shown in Figure 9.  
Figure 9: The importance distribution of the 120-dimensional features of the subject 22: (**a**) describes the importance scores of the individual features; (**b**) describes the ranking of the individual features according to the importance scores.  
It can be seen from Figure 9 that the features with higher importance are concentrated in the \(\beta\) band of each electrode point, and the \(\beta\) band plays a major role in the four bands. The results obtained by performing the same feature selection for the remaining subjects are shown in Figure 10. It can be found that the features with higher importance in the EEG are also concentrated in the \(\beta\) band, which means the \(\beta\) band is the most sensitive compared to other bands.  
Figure 10: Distribution of importance of the 120-dimensional features of the remaining subjects: (**a**) describes the importance scores of each feature of the remaining subjects; (**b**) describes the ranking of the characteristics of the remaining subjects according to the importance score.
## 4 Technical Review and Discussion  
### Evaluation Metrics  
In this section, we discuss the various evaluation metrics used to assess the effectiveness of watermarking approaches for LDMs. These metrics are critical for understanding the performance of watermarking schemes in terms of imperceptibility, robustness, and capacity. The following subsections provide an overview of the evaluation metrics used in each of these categories, highlighting their relevance to LDM-based watermarking and contrasting them with traditional approaches.
# Novel Applications of Microbial Fuel Cells in Sensors and Biosensors
## 2 MFC Power Supply for Sensors and Biosensors
### Effect of Temperature  
Different parameters of MFCs, such as activation energy, mass transfer coefficient, and solution conductivity, are related to kinetic and mass transfer processes of the system. Those processes are significantly influenced by changes in temperature, and so they affect the performance of the MFC for energy generation [60; 61; 62; 63]. On the other hand, the influence of the temperature will change depending on the specific nature of the microorganism and the feedstock employed [58; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75]. Therefore, different optimum temperatures will be required depending on the specific characteristics of the MFC. Various investigations have proved a significant enhancement of the power output by increasing the reaction temperature in the anode chamber [58; 62; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73]. That improvement in the power density has been related to an enhancement of the microbial metabolism and membrane permeability, as well as to the reduction of ohmic resistance due to a higher conductivity of the liquid solution by increasing the temperature [62; 63].  
It is worth mentioning that biofilm formation can be also be affected by temperature changes [58; 62; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75], since biofilm development at the anode has an important impact on the anodic biocatalytic activity, and therefore on the energy generation. In that way, some studies show that the initial temperature is crucial in the formation of the biofilm in a MFC, and consequently, in the time needed for the startup. Thus, higher temperatures lead to a stable biofilm and MFC operation in a short period of time, and therefore to a higher performance.  
Regardless of the aforementioned, some microorganisms naturally prefer low-temperature environments to reach optimum performance, which is potentially useful for MFC-driven sensors to be placed in cold areas [62; 63]. However, at extremely low temperatures, microbial reactions slow down and eventually MFCs cannot operate.
## 3 Results
A comparison of the daily NDVI, PRI, F\({}_{\text{red}}\), and F\({}_{\text{far}}\) using the proportion of maximum of each (Figure 3e) highlights the relative degree to which these metrics capture changes in the response of the plants to stress. In this manner we show the large changes in PRI, F\({}_{\text{red}}\), and F\({}_{\text{far}}\) as compared to the change in NDVI. Figure 3e also illustrates the difference in timing of the maximum values, which is the beginning of the experiment for NDVI, Day 2  
Figure 3: Results of plant stress experiments. CF metrics, NDVI and PRI measurement time series for the experiment. (**a**) Daily mean, F\({}_{\text{red}}\) and F\({}_{\text{far}}\), (**b**) Daily mean F\({}_{\text{red}}\)/F\({}_{\text{far}}\), Inset plot of the first five days of the experiment highlighting the trend of increasing F\({}_{\text{red}}\)/F\({}_{\text{far}}\), (**c**) The normalized difference vegetation index (NDVI) taken one time daily for the experiment. (**d**) The photochemical reflectance index taken one time daily for the experiment (**e**) F\({}_{\text{red}}\), F\({}_{\text{far}}\), NDVI, and PRI plotted as the proportion of maximum over the course of the experiment. (**f**) Pearson Correlations among the mean values. Only significant correlations (\(p\)-values < 0.05) are shown along the lower right half. Blank areas in the lower right half indicate insignificant correlations (\(p\)-values > 0.05). Error bars indicate the standard deviation for each day. for PRI, and Day 3 for F\({}_{\text{red}}\) and F\({}_{\text{far}}\), further supporting that NDVI does not respond clearly to the initial stress, PRI has a potential moderate response, but F\({}_{\text{red}}\) and F\({}_{\text{far}}\) capture both the increase in dissipated energy during the subsequent onset of stress and the decline in function. Pearson correlations (\(r\) values) among the metrics are shown in Figure 3f for comparison, especially between the CF metrics and RIs (NDVI and PRI). No significant correlation exists between F\({}_{\text{red}}\) and NDVI, F\({}_{\text{red}}\) and F\({}_{\text{red}}\)/F\({}_{\text{far}}\), as well as F\({}_{\text{far}}\) and F\({}_{\text{red}}\)/F\({}_{\text{far}}\). However, a positive relationship exists between PRI and F\({}_{\text{far}}\) (\(r\) = 0.88, \(p\)\(<\) 0.01) for PRI and F\({}_{\text{red}}\) (\(r\) = 0.7, \(p\)\(<\) 0.05) as well as for NDVI and F\({}_{\text{far}}\) (\(r\) = 0.75, \(p\)\(<\) 0.05). The F\({}_{\text{red}}\)/F\({}_{\text{far}}\) is negatively correlated with both NDVI (\(r\) = \(-\)0.83, \(p\)\(<\) 0.01) and PRI (\(r\) = \(-\)0.79, \(p\)\(<\) 0.05). F\({}_{\text{red}}\) and F\({}_{\text{far}}\) are highly positively correlated and are likely autocorrelated which mathematically explains the lack of relationship to their ratio (F\({}_{\text{red}}\)/F\({}_{\text{far}}\)). PRI and NDVI are also strongly positively correlated (\(r\) = 0.96, \(p\)\(<\) 0.01).
## 3 Materials and Methods
### Benchmark Datasets  
To evaluate the performance of the proposed CnnRBP, six publicly available datasets [15] were utilized as benchmark datasets. These datasets contain RNA-binding and non-RNA-binding proteins from three different species, humans, _E. coli_ and Aalmonella, with two datasets per species. For each species, one dataset was taken as the training dataset and the other one was utilized to perform independent validation. For humans, the training dataset comprises 1625 positive samples (RNA-binding proteins) and 10,834 negative samples (non-RNA-binding proteins), and the independent validation dataset comprises 181 positive samples and 1204 negative samples. For _E. coli_, the training dataset comprises 460 positive samples and 3404 negative samples, and the independent validation dataset comprises 52 positive samples and 379 negative samples. For Salmonella, the training dataset comprises 275 positive samples and 1273 negative samples, and the independent validation dataset comprises 31 positive samples and 142 negative samples. Table 1 summarizes the statistics of these above six benchmark datasets. Please refer to the paper [15] for details of these datasets.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & \multicolumn{3}{c}{**Training**} & \multicolumn{3}{c}{**Independent Validation**} \\ \hline  & **Positive** & **Negative** & **Pos:Neg** & **Positive** & **Negative** & **Pos:Neg** \\ \hline Human & 1625 & 10,834 & 1:6.67 & 181 & 1204 & 1:6.65 \\ _E. coli_ & 460 & 3404 & 1:7.4 & 52 & 379 & 1:7.29 \\ Salmonella & 275 & 1273 & 1:4.63 & 31 & 142 & 1:4.58 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sizes of training and independent validation datasets for all three species.  
Figure 1: The flowchart of the proposed CnnRBP. * For each species, one model is trained on its training dataset and then tested on its testing dataset.
## 1 Introduction
Although it is simple and rapid, the unsatisfactory accuracy and poor separation of TLC limit its application [37; 38]. Detection by GC-MS is a frequently used method, but requires critical and time-consuming derivatization steps [39]. HPLC-MS/MS is regarded as the most promising tool because of its high selectivity and sensitivity [40; 41; 42]. However, the current analytical methods can barely be utilized for the simultaneous detection of ZEN and ZEN-14G in various biomatrices due to the great challenges posed by their different polarities, along with the significant matrix effects. In the current study, a rapid and sensitive HPLC-MS/MS method was established for the simultaneous detection of ZEN and ZEN-14G in the plasma, urine, and various tissues of rats, i.e., the liver, kidney, stomach, small intestine, large intestine, and ovary. The greatest advantage of this proposed analytical method was that identical conditions could be utilized for analyzing ZEN and ZEN-14G in all the above mentioned biomatrices to achieve high sensitivity, accuracy, and reliability. Through use of the methodological advantages, the kinetics and tissue distribution of ZEN-14G after oral administration to rats was successfully investigated.
## 3 Objective and Scope  
At road construction sites, it is common for heavy equipment, workers, and vehicles to be constantly moving, which can often lead to the obstruction of signals. Therefore, it is important to have a real-time safety monitoring system that can accurately track the poses of workers, equipment, and vehicles. When the poses are accurately estimated, the boundaries of vehicles or equipment can be tracked by the safety monitoring system to ensure the safety policies are regulated to secure the workers and equipment. Though UWB-based tracking systems can achieve sufficient accuracy for worker tracking [12; 13] and vehicle tracking [17; 23; 26; 27; 28; 29; 42; 43; 44], when a Non-Line of Sight (NLOS) situation occurs, it reduces the accuracy of these localization systems [20]. UWB radios' communication with one another is necessary for all safety tracking (relative and absolute) approaches. A partial or total blockage by obstacles to the direct path between the sender and receiver radios creates NLOS situations. This situation may affect the estimates for location in the UWB localization systems. Related studies on UWB-based tracking [17; 23; 26; 27; 28; 29; 42; 43] systems did not consider the potential presence of NLOS situations in their experiments. ViPER [44] was the first research to address this issue by studying the accuracy of localization in road construction environments. In ViPER [44], the tracking area was occupied by loaders and trucks creating an NLOS situation. The results of the study indicated that NLOS conditions are unavoidable in construction site environments, and they can degrade the quality of localization as well as the number of successful localizations. This study extends the efficacy of our previous work (ViPER) by developing an improved UWB-based safety monitoring system (ViPER+) for tracking and monitoring the boundary of workers and vehicles on construction sites to evaluate and minimize the NLOS effect on localization. Our new system is evaluated in a real-time environment with equipment creating NLOS situations. The results of ViPER+ were compared against the traditional baseline method and ViPER which is the state-of-the-art solution for vehicle tracking.
# Industrial IoT Monitoring: Technologies and Architecture Proposal
## 4 Proposed Architecture
### Monitoring Logger  
As shown in Table 1, the standards under consideration use different application approaches. Specifically, ZigBee, ISA100.11a, and WIA-PA use object-oriented representation, whereas WirelessHART uses a command-oriented representation. Sensor node monitoring data needs to comply with the representation defined in each standard, which leads to different representations for the same sensor node data. To solve this issue, the monitoring logger is the component that parses distinct standard representations into the same representation.  
The monitoring logger should fulfil the following requirements: support the connection to the different standards \"gateways\"; support the parsing of the distinct application protocols using a sub-component (the log parser); support the representation of sensor node monitoring data in the languages supported by the management protocols; allow access to the monitoring data by management systems, using the management agent; and, lastly, store the sensor node monitoring data in a datastore. As a software component, the monitoring logger can be installed as an extra software module in industrial gateways or in another compatible equipment.
## 3 Deliberate Attacks on IECPS
### Coordinated Cyber-Physical Attacks
#### 3.3.2 A Coordinated Strategy for Prioritizing Cyberattacks  
Prioritization of cyberattacks is the opposite of prioritization of physical attacks, where the attacker first launches a cyberattack, for example, by preventing the control center from receiving operational data, and then launches a physical attack. When the system communication is cut off, the attacker can carry out physical damage at will without any countermeasures if allowed. In the coordinated approach prioritizing cyberattacks, cyberattacks are mostly dominated by LR attacks [57]. In [74], the attacker initially employs LR attacks to alter the system load data. Upon successful execution of the LR attack, a physical attack is swiftly carried out, with subsequent LR attacks used to continuously obscure the ongoing attacks on the line. In [75], a coordinated attack method against generators is proposed, where the attacker first employs an LR attack to change the load demand of the system, and then conducts a physical attack to cause the generator to trip, but this attack is not covert.  
From the attack mode and model analysis of the above attacks, it can be seen that cyberattacks primarily induce erroneous system scheduling decisions or impair system functionality. Meanwhile, physical attacks collaborate with cyberattacks to amplify the attack's impact. However, most of the above studies are based on more idealized assumptions, and most of them focus on power systems. Consequently, the countermeasure  
Figure 8: Process of CCPA.  
strategies proposed in these research results may not effectively defend against real IECPS coordinated attacks. Therefore, it is necessary to more fully consider the characteristics of IECPS and establish more sophisticated defense mechanisms to resist the threat of deliberate attacks.