esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Terpenoids constitute the largest and most heterogeneous class of secondary metabolites, and include monoterenes and sesquiterpenes as volatile constituents [1; 2]. These volatile terpenoids can act both as constitutive and herbivory-induced (Herbivore-Induced Plant Volatiles, HIPVs) defense compounds. Moreover, HIPVs can be emitted either at the site of damage or systemically from undamaged parts of affected plants [3]. Constitutive accumulation of terpenoids occurs in specialized tissues, such as the glandular trichomes [4], which are present in aromatic plants and act as direct defense against herbivores through either a toxic or deterrent activity [5]. In the _Lamiaceae_ family, oregano (_Origanum vulgare_ L.) is characterized by large peltate glandular trichomes that accumulate monoterpenes and sesquiterpenes [4; 6; 7]. The main terpen are the two phenolic monoterpenes: Thymol and carvacrol [8; 9; 10]. These terpenoids are physiologically toxic towards insect larvae [11]. Phytophagous insects represent one of the major causes of biotic stress for plants [12] and herbivore attack can cause both quantitative and qualitative changes in the composition of HIPVs [2; 13; 14; 15]. Monoterpenes such as thymol and carvacrol show LD\({}_{50}\) values of 25 and 43 \(\mu\)g-larvae\({}^{-1}\), respectively, when tested on early four-instar _Spodoptera littura_ and the LD\({}_{50}\) of these monoterpenes increases with the larval development [16]. The toxicity of these monoterepenes is higher versus _Spodoptera littoralis_, _Plutella xylostella_, _Callosobruchus maculatus_ and _Culex quinquefasciatus_, with LD\({}_{50}\) values as low as 0.22 ug-larvae\({}^{-1}\)[17; 18; 19; 20; 21], whereas thymol and carvacrol toxicity versus _Sitophilus granarius_ is lower, with LD\({}_{50}\) above 100 ug-larvae\({}^{-1}\)[22; 23]. Toxic synergistic effects of thymol and carvacrol with other monoterepenes have been also described [24]. The mode of action of these monoterepenes on herbivores is not fully understood, although they have been found to elicit insect detoxification enzymes [25; 21; 26] and other mechanisms of tolerance [27].  
The objective of this work was to study the response of the generalist herbivore _Spodoptera littoralis_ to _Origanum vulgare_ terpenoids. To this aim, we analyzed _O. vulgare_ volatile terpenoids before and after herbivore feeding and evaluated their toxicity by insect exposure during feeding, while the terpenoid deterrent and anti-nutritional effects were tested by behavioral assays. Finally, the detoxification mechanisms of _S. littoralis_ towards _O. vulgare_ terpen were also studied.
# Empirical Evaluation of TLS-Enhanced MQTT on IoT Devices for V2X Use Cases
## 3 MQTT and V2X Use Cases
By explicitly defining the roles of publishers, subscribers, and brokers and incorporating essential security mechanisms for authentication, message integrity, and confidentiality, MQTT enables both reliable and secure communication in the dynamic and latency-sensitive environment of connected and autonomous vehicle systems.
## 1 Introduction
The purpose of this study is to compares four long-term AVHRR-based NDVI datasets (NDVIg, NDVI3g, STAR, VIP) with MODIS and SPOT NDVI datasets for the common period (from 2001 to 2008) and evaluate the interannual variability of the four long-term datasets over their common period from 1982 to 2008. Following previous regional studies [25; 26; 27], we focus on the western and central United States and Mexico that cover many biomes including tropical forests, arid and semi-arid regions, and temperate deciduous and evergreen forests. This study attempts to answer two questions: How consistent are the NDVI data from various groups? How does vegetation vary interannually according to these NDVI datasets? Furthermore, recognizing some of these datasets have very different NDVI values, simple methods to create self-consistent records need to be explored such as converting NDVI to a biophysical variable such as the green vegetation fraction (GVF) [11; 41; 42]. In this study, we will also analyze the effectiveness of GVF in homogenizing the NDVI datasets and providing more consistent long-term vegetation phenology trend analysis. Two additional questions will be addressed: How can we adjust NDVI data from various sources to make them more consistent? How does interannual variability of vegetation change after NDVI is adjusted?
# Non-Contact High Voltage Measurement in the Online Partial Discharge Monitoring System
## 8 Conclusions  
The article presents an innovative system for measuring high voltage with the use of a capacitive probe. This system employs additional equipment of the portable partial discharge monitoring system in the power transformer to ensure its full autonomy and independence from the substation devices.  
The article presents the concept of HV measurement with the use of a capacitive probe and a detailed analysis of the selection of the optimal solution was carried out. The developed monopole probe showed the best features taking into account both the electrical parameters and the design properties. Laboratory tests using a prototype capacitive probe allowed for the development of algorithms for determining the voltage ratio and measurement uncertainty. In laboratory conditions, the measurement uncertainty was in the range of 1-5%, which is a fully acceptable result for the remote (non-contact) method of HV measurement.  
Finally, the results of the field tests are presented. The object of the research was a 250 MVA power transformer. The voltage measurements using the prototype system were compared with the voltage values recorded by the voltage transformer installed at the substation. The comparative test results showed that the maximum difference in measured voltage was only 4.11 kV (1.02%).  
The developed measurement system allows for real-time recording of high voltage values in a safe and non-invasive manner. Importantly, installing the system near the transformer does not require shutting it down. Thanks to the use of advanced methods of digital filtration and noise reduction, the system can work effectively in the presence of strong EM interference. By measuring not only the maximum or root mean square (RMS) value of the voltage, but also by recording the voltage waveform (e.g., during one or more periods), it is possible to precisely correlate the PD activity with the phase angle and create phase-resolved partial discharge patterns (PRPD). Such PRPD images can then be used to recognize the type of defect that is the source of the PD and increase the reliability of the transformer condition diagnostics.  
Conceptualization, K.W. and W.S.; methodology, K.W.; software, K.W. and W.S.; validation, K.W. and W.S.; formal analysis, K.W.; investigation, K.W. and W.S.; resources, K.W. and W.S.; data curation, W.S.; writing--original draft preparation, K.W. and W.S.; writing--review and editing, K.W. and W.S.; visualization, W.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Polish National Centre for Research and Development, within the Applied Research Programme, grant No. PBS3/A4/12/2015.  
The authors declare no conflict of interest.
## 1 Introduction
**} & **Area** & **Image** & **Height (m)** & **Camera** & **Shutter** & **Focal** & **Overlap** & **GCPs-** & **GSD** & **RMSE/GSD** \\  & **(km\({}^{2}\))** & **Type** & **Height (m)** & **Model** & **Model** & **Length (mm)** & **(for-ward-lateral) (\%)** & **CPs** & **(cm/pix)** & **XV** & **Z** & **3D** \\ \hline
[9] & 0.006 & Nadir & 25-50-120 & Sony Exmor R RSI\({}_{1/2}\), 3 & Electronic & 20 & 95–95 & 15–35 & 4.8 & 2.2 & 1.9 & 3 \\
[39] & 0.017 & Nadir & 45 & Phomon 4 & Mechanic & 24 & 85–75 & 5–19 & 0.75 & 2.6 & 4.3 & 5.1 \\  & & Nadir & & & Canon EOS & & & & & & & \\
[40] & 0.09 & and & 50 & 550D & Mechanic & 25 & 90–90 & 18–5 & 1 & 2 & 2 & 2.9 \\  & & Nadir & & & Canon & & & & & & & \\
[36] & 0.09 & and & and & 60 & IXUS160 & Electronic & 28 & 90–45 & 6–41 & 1.4 & 0.9 & 2.6 & 2.8 \\  & & oblique & & & & & & & & & & \\
[41] & 0.25 & Nadir & 140 & Sony NEX 5 & Electronic & 35 & 80–40 & 35–101 & 4 & 1.6 & 1.1 & 2 \\
[42] & 0.370 & Nadir & 92 & Sony NEX-7 & dual & 16 & 75–75 & 11–12 & 2 & 1.8 & 3 & 3.5 \\
[43] & 0.40 & Nadir & 65 & Phantom 4 & Mechanic & 24 & 80–60 & 18–29 & 1.75 & 1.6 & 3.2 & 3.5 \\  & & Nadir & & & & & & & & & & \\
[8] & 0.024 & and & 75–100 & Phantom 4 & Mechanic & 24 & 70–10 & N/A & 2.7 & 0.7 & 1.8 & 1.9 \\  & & oblique & & & & & & & & & & \\  & & Nadir & & & & & & & & & \\
[44] & 1 & and & 53 & Sony Exmor RSI\({}_{1/2}\), 3 & Electronic & 20 & 80–80 & 30–15 & 1.9 & N/A N/A & 4.2 \\  & & & Nadir & & & & & & & & & \\
[37] & 2.1 & and & & 100 & DSC-QX100 & Electronic & 37.1 & 80–75 & 6–7 & 2 & 1.5 & 1.8 & 2.3 \\  & & oblique & & & & & & & & & & \\
[45]\({}^{*}\) & 0.19 & Nadir & 120 & FC 300X & Electronic & 20 & 80–75 & 4–2 & 5.18 & 2.4 & 1 & 2.60.8 km\({}^{2}\), instead of four 20 ha photogrammetric subblocks eventually integrated into one larger one summing 0.8 km\({}^{2}\). Considering the aspects mentioned above, this study aims to evaluate the impact of employing a camera equipped with an electronic rolling shutter with different flight strategies by modifying key parameters in flight planning, such as including transverse strips with different flight heights or oblique perimeter imagery. The following scenarios were studied: (A) forward strips (commonly used); (B) forward strips combined with a strip along the whole perimeter of the block with the camera at 65\({}^{\circ}\) (oblique images); (C) forward strips combined with cross strips on the edges of each block at different heights; and (D) forward strips plus cross strips on the edges plus a strip around the whole perimeter with the camera at 65\({}^{\circ}\). In addition, six configurations were made to select the best location for the GCPs and ultimately ensure the generation of reliable photogrammetric products.
###### Abstract  
In recent years, graph neural networks (GNNs) have achieved great success in handling node classification tasks. However, as data explosively grows in various industries, the problem of class imbalance becomes increasingly severe. Traditional GNNs tend to prioritize majority class nodes when dealing with imbalanced class distributions, which fail to adequately capture the features of minority class nodes, leading to significant difficulties and challenges in data classification. To address issues such as inaccurate edge generation during graph data oversampling, insufficient representation of minority classes, and the presence of noisy samples, this paper proposes the ESA-GCN model. The advantages of this model are as follows: (i) it employs the ENN-SMOTE comprehensive sampling method to balance the dataset by reducing majority class nodes and increasing minority class nodes; (ii) the ENN algorithm reduces the classifier's error rate and improves performance stability by removing low-quality and noisy data; (iii) an attention mechanism is introduced during the edge generation phase between new nodes and original nodes, fully considering the mutual relationships between nodes and concentrating on a subset of key information with high weights, thereby significantly improving classification accuracy while reducing model parameters and computational complexity. Experiments conducted on three public datasets (Cora, Citeseer, and PubMed) demonstrate that the ESA-GCN has achieved significant results in class imbalance node classification tasks.  
graph neural networks; imbalanced data; node classification +
Footnote †: journal: Medical Image Computing and Applications  
1
## 5 Experiments and Results
### Experiment 3: DrivFace Dataset  
Once again, ReSVM outperformed the state-of-the-art approaches when tested on the DrivFace dataset as can be seen from Table 2 and Figure 10. All the approaches, including proposed and existing, underwent the same percentage degradation as can be seen in the case of the BU dataset. For instance, the percentage decrease in accuracy of VGG-19 and ResNet-101 was 57.30% and 46.63%, respectively. The dataset is smaller in size and has high intraclass variations due to the illumination factor of frames, glasses, eye gaze movements, talking to a passenger, picking up something on the dashboard, sleeping, etc. It can further be observed that ReSVM replaces the SVM classifier in ResNet-50, which increases its accuracy from 87.61% to 93.44%. As mentioned in Section 5.3, the SVM classifier shows good performance on small samples and large features [61].
## 2 Materials and Methods
### Data Processing  
The mobility plan divides the Valencia metropolitan area into 560 transport zones. This makes a total of 313,040 possible combinations, discarding the 560 combinations where the origin and destination are in the same location and the trip cannot be calculated. Therefore, the process of geolocation and association of the total number of trips was programmed and executed by machines. The result of this process would give at least one route for each trip combination. This is because there will always be a route solution for every combination by road (car, truck, and motorcycle) but not always by public transport (if there is no suitable infrastructure for that combination). Other route calculators were used to check the integrity of these results.  
The next step was to apply the weighting statistics to the routes obtained. The statistics by type of public transport mode (Table 2), which cannot be applied to trips that are found in areas without public transport infrastructure, are distributed among the statistics where such infrastructure does exist. In this way, multiplying by the total number of trips of each combination, we obtained the total number of trips that go on that route for each route.  
Figure 2: Schematic representation of the methodology.  
The next step was to apply a split tool to obtain the sections that are transcurrent through each neighborhood of each route obtained in the previous step. From this process, we extracted each section that traveled through each neighborhood (Figure 3). For each section, depending on the type of transport mode used, its corresponding emission factor was associated with it (Table 2) and multiplied by the length of the section and the total number of trips on each route calculated above, obtaining the total pollutant emitted by each route, which, adding the result of each route in the neighborhood, yielded the total emissions within it. Finally, the process was replicated for every combination of daily trips to obtain the total emissions emitted within the geographic area of each neighborhood.
## 4 Data Analysis in Maritime IoT Applications
### Role of Data Analysis in Enhancing Maritime Operations
#### 4.2.6 Foster Innovation  
Lastly, data analysis fosters innovation in the maritime industry. The insights derived from data analysis can reveal new opportunities for improving operations, optimizing performance, and creating value.  
From developing advanced navigation systems that leverage real-time data to designing energy-efficient vessels based on fuel consumption data, the potential for innovation is immense. This not only drives the continuous improvement and evolution of maritime operations but also positions maritime companies to stay competitive in the digital age [78].
## 3 Proposed Approach and Methodology
### Coordination Strategy  
The proposed system uses a distributed DRL approach where local agents manage individual intersections while the global agent facilitates indirect communication and coordination. This ensures synchronization of traffic signals across intersections, addressing scalability and congestion issues.  
Different methods using DRL can be used depending on the traffic scenario in hand. For multiple intersections, the existing methods can be classified into two categories, the centralized control methods and the decentralized control methods. For the first method, a global agent is trained to control the traffic signal of the entire network. Each agent observes an intersection and saves its experience to be used to train this global agent, which controls the environment and indicates which actions should be taken at each intersection. The second method employs the decentralized control formulating the multi-intersection signal control as a multi-agent system, in which each agent is trained to control a single intersection and only observes and perceives parts of the traffic environment.  
A global agent coordinating local agents offers significant advantages for achieving network-wide optimization, especially in complex urban scenarios. Some advantages include the following:  
* _Inter-Agent Communication and Coordination:_ The global agent has access to aggregated data from all local agents. This allows it to identify bottlenecks, optimize traffic signal phase timings for high-priority routes, and balance traffic loads. Local agents might have conflicting goals (e.g., prioritizing their intersection's flow versus preventing downstream congestion). It mediates these conflicts by aligning local actions with global objectives.
* _Dynamic and Context-Aware Strategies:_ By observing traffic density and flow patterns, the global agent adjusts signal phases across intersections, creating synchronized green waves that improve throughput and reduce stop-and-go behavior. During events like accidents, road closures, or high pedestrian density (e.g., near stadiums or schools), the global agent coordinates local responses to adapt quickly to changing conditions.
* _Global Reward Integration:_ While local agents optimize using their localized reward functions, the global agent enforces a composite reward that balances local and network-wide priorities by penalizing actions that cause downstream congestion or pedestrian risks and rewarding synchronized actions that optimize overall traffic flow.
* _Incident Management:_ Detects anomalies, such as sudden congestion or accidents, and coordinates local agents to minimize the impact.  
So, without a global agent, the local agents optimize only their intersections independently. An upstream intersection may release vehicles without considering downstream congestion, causing queue spillovers. With a global agent, it monitors the entire corridor, instructing the upstream intersection to hold traffic temporarily to allow downstream intersections to clear. This prevents cascading congestion and ensures smoother flow.
# A Hybrid DNN Multilayered LSTM Model for Energy Consumption Prediction
## 5 Experimental Results
### Model Training and Loss  
The hybrid DNN multilayered LSTM model, DNN model, and LSTM model were implemented. The ReLU activation function was used in the dense layer of the model. Model training involves determining the best values in the datasets for every bias and weight. Model losses often result in poor predictions and indicate insufficient data preprocessing practices. Thus, a perfect predictive model should have zero losses.  
The model losses for the proposed model, DNN model, and LSTM model are shown in Figures 5-7, respectively. To check the prediction performance, actual versus predicted values are graphed for the proposed model in Figure 8, the DNN model in Figure 9, and the LSTM model in Figure 10. The resolution was adapted to a 60 min resolution time step for the first 750 h.  
Figures 5-7 show the loss of the model for training and testing. There was overfitting observed in the DNN model, which is indicated by a large difference between the training and testing results. This suggests that the DNN model alone cannot effectively capture the relationship between predictors and the response variable. However, when LSTM was integrated with the DNN, the model exhibited consistent results, and the issue of overfitting disappeared.  
In addition, there were differences between the predicted values of the DNN model and the actual power consumption, as shown in Figure 9. However, as illustrated in Figures 8 and 10, LSTM improves the effectiveness of the model both as a standalone model and when integrated with the DNN. Therefore, LSTM has the most significant impact on reducing overfitting and improving prediction results.  
Figure 4: Pearson correlations for feature selection.  
Figure 5: Hybrid DNN multilayered LSTM model loss.  
Figure 6: DNN model loss.  
Figure 8: Hybrid DNN multilayered LSTM model, actual vs. prediction.  
Figure 7: LSTM model loss.
# Open Loop Position Control of Soft Hydraulic Actuators for Minimally Invasive Surgery
## 4 Results  
### Positioning  
#### 4.1.1 One Degree of Freedom  
Figure 5 shows the results of the one-DOF positioning tests. Diagrams of the one-DOF tests are shown in Figure 4A-C. The actuators follow the trajectories as expected, with the antagonistic setup appearing to be more accurate. Some lag was observed as the actuators took time to reach the desired targets and this seemed to be longer for the antagonistic actuators.  
The root mean square error (RMSE) and hysteresis were calculated as appropriate for each test. For the minimum to maximum tests, the values were calculated across all points, whereas for the random positioning tests, only the times when the actuators were static were considered.  
The hysteresis was calculated by taking the measured contraction on the forwards and backwards paths, then finding the maximum absolute difference of these values throughout the test. Figure 6 shows the hysteresis of the single actuator, antagonistic actuators, and the single actuator with a pulley. The RMSE, hysteresis values, and minimum and maximum percentage errors are summarised in Table 2.  
Figure 5: Results of path following tests for the one-DOF setup. (**A**) Single actuator, no pulley, min. to max. test, 30 repetitions. (**B**) Antagonistic actuators, no pulley, min. to max. test, 30 repetitions. (**C**) Single actuator, no pulley, random positioning. (**D**) Antagonistic actuators, no pulley, random positioning. (**E**) Single actuator, with pulley, min. to max. test, 30 repetitions.  
Figure 6: Hysteresis results for the tests moving between the limits of the operating range.
# Reducing Soil Permeability Using Bacteria-Produced Biopolymer
## 1 Introduction
Although the use of XG in soil stabilization is more sustainable than the use of Portland cement, the industrial production of commercial xanthan gum is not entirely environmentally friendly, since it includes: processes of fermentation, filtration (or centrifugation), precipitation using non-solvents (such as isopropanol, ethanol, acetone), the use of salts, and pH adjustments [40]. In order to mitigate some of the environmental concerns related to the use of commercial XG, this work studies the ability of a xanthan-like gum obtained directly from the strain of _Stentotrophomonas maltophilis_ Faro439 (LabXLG) to reduce the permeability of the soil treated. The aim of the present work is to compare the effectiveness of LabXLG with commercial XG and the traditional stabilisation with cement concerning the permeability coefficient of sandy soil. Additionally, the effect of the curing time (3, 7, 14, and 28 days), the hydraulic gradient (6.9, 8.2, 9.4, and 10.6), the XG content (0.16 and 0.33%), and cement (0.33 and 1.0%) are analysed. Scanning electron microscopy (SEM) with energy dispersive X-ray (EDX) was also used to study the microstructure and the chemical composition of the soil treated with LabXLG and commercial XG.