esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis and Interpretation of Results
### Dependence of the Occurrence of Negative Prices on RES Generation
#### 4.2.3 Poland: Intermittent Growth and Stable Extremes--Figure 4  
In Poland (Figure 4), the impact of renewable energy sources (especially wind) on the occurrence of negative prices has been gradual but increasingly evident over the period analysed. Until 2021, these phenomena were very rare or completely absent, but after 2022, they start to appear prominently at higher levels of RES production--especially above the 12,000 MW threshold of combined solar and wind production.  
The Polish electricity system is undergoing a transformation. On the one hand, the share of wind power plants is increasing (especially in the northern regions of the country). On the other hand, the grid is still facing limits in transmission capacity and flexibility of consumption. As a result, when RES production increases (especially on weekends or holidays with low consumption), surplus situations can occur, resulting in negative spot market prices. A characteristic feature of the Polish market is the strong dominance of coal and the slower integration of flexible mechanisms--such as battery storage, demand-side management, or export capacity. This makes the system more vulnerable to sudden fluctuations in supply. In contrast to Germany, where negative prices appear even at intermediate levels of RES generation, this phenomenon is concentrated only at extreme production peaks in Poland.  
The results of the research suggest that Poland is fast approaching the point at which negative prices will become more common--especially if the construction of wind and solar farms continues without corresponding grid upgrades. It is therefore important to monitor developments not only in absolute terms, but also in the context of RES integration policy, pricing, and cross-border flows.  
Figure 3: Share of negative price in % in France.
## 3 Results and Analysis
### Evaluation Results  
The results presented in Figure 13 enable a comprehensive evaluation of the four designs. Based on the findings, it can be concluded that PVTS and LRER may be more suitable for low LOS, whereas LSI has a wider application. However, these results might fail to provide sufficient decision support for expressway construction, as further details are not available. Thus, to address this issue, Section 2.4 of this paper undertakes a quantitative analysis, presenting a score for each design in each scenario, as shown in Figure 14.  
Figure 13: Results of VISSIM simulation for the investigated LOS, with the better-performing designs occupying the larger square on the radar plot. (**a**) LOS-A. (**b**) LOS-B. (**c**) LOS-C. (**d**) LOS-D.  
The minimum indicator value has been scored as 0 via CE. Consequently, the final score of a design with weak performance in various aspects shows a significant difference from the best one. This hinderance makes it difficult to assess the improvement degree of optimized solution accurately. Therefore, the authors propose that the sum of the product of each indicator improvement ratio and weight would provide a better expression for the quantitative advantage of each design, as shown in Equation (14):  
\[\text{Im}=\sum_{j=1}^{m}\Bigl{[}\left(\left(B_{j}-A_{j}\right)/A_{j}\right) \times 100\%\times w_{k,j}\Bigr{]} \tag{14}\]  
where \(B_{j}\) denotes the \(j\)th indicator of optimized design (LSI, PVTS, and LRER), \(A_{j}\) denotes that of CI, and Im denotes the total improvement ratio of the optimized design. Table 4 shows the final result.  
Table 4 provides some valuable insights and conclusions. It indicates that PVTS and LSI achieve the best effect at LOS-C, but their effectiveness begins to decline subsequently, eventually leading to failure. This failure occurs at the stage when the eight-lane mainline becomes overwhelmed by the crowded traffic. In contrast, LRER produces negative side effects at LOS-A and LOS-B, while as the mainline speed decreases with the drop in LOS, it gradually catches up with the effectiveness of LSI.
###### Abstract  
Carbon sinks in terrestrial ecosystems can be significantly increased by afforestation, which will slow global warming. However, it is still unclear how different plantations influence the carbon sink and how they respond to environmental factors, especially in drylands. In this study, eddy correlation method (EC) was used to measure carbon and water fluxes and environmental factors of two artificial forests (_Larix principis-rupprechtii_ and _Pinus tabulaeformis_) in the dryland of Northwest China, and the responses of evapotranspiration (ET), net ecosystem exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (RECO) to environmental factors were also assessed. Results showed that the _L. principis-rupprechtii_ forest ecosystem had higher water use efficiency (WUE), light use efficiency (LUE), GPP, and RECO than the _P. tabulaeformis_ forest ecosystem. However, the proportion of net ecosystem production (NEP) to GPP in the _P. tabulaeformis_ forest ecosystem (62.89%) was higher than that in the _L. principis-rupprechtii_ forest ecosystem (47.49%), indicating that the _P. tabulaeformis_ forest ecosystem had the higher carbon sequestration efficiency. In addition, the CO\({}_{2}\) and H\({}_{2}\)O fluxes in the _L. principis-rupprechtii_ forest ecosystem were more sensitive to environmental factors, compared with the _P. tabulaeformis_ forest ecosystem. Further, the RECO of the _L. principis-rupprechtii_ forest ecosystem was more sensitive to temperature changes, which implies that the _L. principis-rupprechtii_ forest ecosystem will release more CO\({}_{2}\) than the _P. tabulaeformis_ forest ecosystem with a warming climate. Therefore, the _P. tabulaeformis_ forest ecosystem may have better carbon sequestration potential. These results are important for understanding the effects of climate change on the CO\({}_{2}\) and H\({}_{2}\)O cycles in coniferous plantation ecosystems in drylands.  
eddy covariance; carbon and water fluxes; water use efficiency; light use efficiency; temperature sensitivity; coniferous plantation ecosystems +
Footnote †: journal: Physics Letters B
# A Decision Support System for Crop Recommendation Using Machine Learning Classification Algorithms
## 3 Materials and Methods
### Proposed Model for Seasonal Crop Recommendation  
When a farmer seeks the recommended crops for a specific location based on a set of conditions, such as season, water availability, and other environmental factors, the model will predict the most suitable crop varieties that are most likely to perform well in those conditions. In this regard, a group of classification techniques will be applied to predict suitable crops for the specific field area.  
We have proposed a procedure shown in Figure 3, which mainly focuses on area-wise crop data collected from standard agriculture websites in India. It is considered to have a smartphone application and a cloud memory dataset for the implementation of the support system.  
Figure 3: Proposed model for area-wise seasonal crop recommendations.  
The model consists of two phases. Phase 1 consists of maintaining the dataset in the cloud and using an Android app interface to collect the GPS location from a farmer's land. Then comes Phase 2, consisting of applying the machine learning methods, identifying the best method, and resulting in crop recommendations.
# Research on the Influence of Dust Suppressants on the Coupling Behavior of Dust-Mist Particles
## 4 Results and Discussion
### Influence of Suppressant Materials on Atomization Parameters of Spray  
#### 4.1.1 Size Distribution of Mist Droplet  
The results of the particle size distribution of mist droplets for different suppressant materials, as shown in Figure 8, include water, DA, OP-10, and SDS. For water, the particle size distribution initially remains lower than that of the other dust suppressants; this is attributed to the fact that water has a high surface tension and low viscosity as compared to the other dust suppressants, which makes water particles easy to break [37]. As a result, the mist droplet from water alone is less uniform and consists predominantly of larger droplets [38], limiting its effectiveness in applications requiring fine mist droplet dispersion during spraying.  
Figure 8 also reveals that the particle size distribution of the dust suppressants DA, OP-10, and SDS, due to the reduced surface tension (Figure 2b) and increased viscosity (Figure 2a), remained higher than that of water. A lower surface tension results in larger particle sizes, and a higher surface tension results in smaller particle sizes. The DA suppressant has a higher particle size distribution due to its efficient surface tension reduction and increased viscosity. This results in enhanced mist uniformity and dispersion. Op-10 and SDS also showed a similar trend to DA, which was still better than water.  
Figure 7: Schematic diagram of the form of dust–mist coupling behavior.
# Thermal Control of CubeSat Electronics Using Thermoelectrics
## 3 Results and Discussion
### Temperatures Achieved with Thermoelectric Cooling
The fact that thermoelectric coolers can refrigerate to sub-ambient temperatures makes them an option for such use cases. It is important to note however that they should be considered in comparison to other active cooling solutions.
# Detecting Fatigue during Exoskeleton-Assisted Trunk Flexion Tasks: A Machine Learning Approach
## 2 Materials and Methods
### Classification Algorithms and Model Development  
We developed custom-tailored code in Python to train and test machine learning models. Pre-labeled results of feature engineering were imported as a table, followed by separation based on type of measure. Our dataset consisted of feature groups for the following scenarios: (a) trunk motion measures, (b) muscle activity features from all four sensors (LES, RES, LBF, and RBF), (c) features from lower back sensors, (d) features from LES, \(\mathbf{\upxi}\) features from RES, and (f) whole-body stability features. Four machine learning classification algorithms, namely Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and XGBoost (XGB), were applied to determine fatigued state over a single bending/retraction cycle. As per the literature, these approaches are widely utilized and have demonstrated strong performance in the development of human-movement-based models [37, 40, 64]. Feature normalization through standardization was utilized in the implementation of the SVM and LR methods. A train-test split ratio of 0.8 to 0.2 was employed on the selected datasets to partition the dataset into training and testing subsets for model training and evaluation, respectively. Lastly, we implemented k-fold cross-validation with five folds to ensure robustness in our model evaluation process. The performance of each algorithm was evaluated based on the metrics described in the subsequent section.
## 4 Policy/Intent-Based Security in SDN and NFV Networks
### Advancements in Policy Implementation Techniques
The Extraction Engine or Evaluation Engine is responsible for monitoring incoming network traffic and evaluating it against the stored policies to determine if they are being compiled (notice the similarity of PDPs with the functionality of this component and the Policy Manager). The Policy Enforcer (analogous to the PEPs) is the component that applies the determined flow rules to the network's traffic, ensuring that only policy-compliant traffic is allowed, lastly, the Handle Creator generates handles for packets that travel across the network, enhancing security by ensuring packet authenticity and integrity. This decoupling of network layers based on functionality is expected in SDNs and allows them to be flexible and dynamic. The PbSA takes full advantage of this by being able to manage security policies from a single point of control. This centralization simplifies the administration of complex policy rules across various network devices, enabling consistency and fast adjustments throughout the whole network topology. Since the SDNs allow a comprehensive network view, the PbSA can use this enhanced visibility to monitor all traffic flows across the network more effectively, thus providing a more accurate and timely detection of anomalies or policy violations.
## 2 Methods  
### SUD-CGHP Method Workflow  
This research develops an innovative slope unit delineation framework that integrates traditional hydrological partitioning methods with computer graphics technology, incorporating both hydrological and mechanical parameters as constraint conditions. The method enables fully automated and efficient mapping of scientifically valid slope units in the study area. The technical workflow is illustrated in Figure 1.  
Figure 1: SUD-CGHP method workflow.  
Step 1: Data Preprocessing  
The method employs DEM as the primary terrain data source, followed by advanced digital image processing techniques including spatial filtering for noise reduction and mean curvature computation for terrain characterization. Through systematic thresholding based on computed mean curvature values, the processed DEM is classified into ridge and valley zones, which are subsequently converted into binary images through standardized thresholding operations to facilitate subsequent geometric analyses.  
Step 2: Graphical Analysis  
Morphological skeletonization is applied to the binary image matrix to extract topographic skeleton lines representing ridge and valley networks. This process involves graph-theoretic line simplification techniques to eliminate redundant vertices while preserving essential topographic features, ultimately generating a topologically consistent skeleton graph that captures the fundamental drainage patterns of the study area.  
Step 3: Watershed Delineation  
Building upon the extracted skeleton network, hydrological principles are integrated through iterative flow accumulation analysis and topological sorting algorithms to connect ridge-valley skeletons into coherent hydrological systems. This phase employs specific watershed construction protocols to form closed drainage regions, ensuring both geometric accuracy and hydrological consistency with the original terrain data.  
Step 4: Region Merging and Post-processing  
The initial watershed partitions are refined through hierarchical clustering based on the unit homogeneity principle. Multi-criteria merging criteria including mechanical parameter consistency, hydrological connectivity requirements, and topographical feature preservation guide the optimization process. Final post-processing includes morphological closing operations to eliminate artificial boundaries and achieve a balance between hydrological integrity and mechanical homogeneity, resulting in geomechanically valid slope units.
# Drone and Controller Detection and Localization: Trends and Challenges
## 2 UAV Architecture and Security Concerns  
### UAV Architecture  
UAVs have multiple subsystems integrated to perform various operations, such as launch, fly, operate, process, transmit, and receive commands from remote or ground stations [3; 5]. Four main UAV subsystems should be considered: (1) a power unit, (2) a communication module, (3) the main computing device, and (4) a sensor board. The power unit is designed to provide a longer lifetime for UAV operation without charging it [4]. The high-level architecture of the UAV system is illustrated in Figure 1, including UAV's main computer processes commands based on the collected data from other subsystems or components (GPS, sensors, gyroscopes, accelerometers, antennas, receivers, etc.). These  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Detection Technologies** & **Ref.** \\ \hline UAV Architecture and Security Concerns & [3; 4; 5; 6; 7; 8] \\ \hline RF & [7; 8; 9; 10] \\ Radar & [21; 22; 23; 24; 25; 26; 27; 28; 29] \\ UAE Detection Technologies & Acoustic & [30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40] \\ Electro-optical & [41; 42; 43; 44; 45; 46] \\ Hybrid fusion & [40; 41; 47; 48] \\ \hline Controller Detection and Localization & [12; 18; 49; 50] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of reviewed topics for drones and their controller detection.  
data or commands are transferred through a communication link between the UAV and the ground control station (GCS). This communication is mainly monitored to detect UAVs based on RF and radar-based technologies (details in Sections 3.1 and 3.2).  
A brief description of the major components of UAE architecture is as follows:  
* UAV's structure/airframe: There are many common features of a UAV's chassis, such as lightweight, small size, endurance, aerodynamic flexibility, etc.
* Main computer: The critical part responsible for autonomous functioning and flight control. The computing subsystem processes sensed information, transmits it back, manages flight operations, and communicates with the control base.
* Sensors/payloads: UAVs can be equipped with a range of possible lightweight sensors per the application's needs, including RGB cameras, thermal sensors, LiDAR sensors, and multispectral and hyperspectral sensors. All of them are connected to the flight controller to gather real-time data and process it for the missions' execution.
* Communication link: UAVs are equipped with a high-quality wireless communication unit, including 5G, WiFi, Bluetooth, and radio-frequency identification (RFID), to facilitate communication with the GCS or the internet.
* Ground control station (GCS): This base station is mainly employed to monitor and control the UAV during its operation. Flight operation is continuously monitored and can be controlled to alter the mission.
# Improving Bundle Routing in a Space DTN by Approximating the Transmission Time of the Reliable LTP
## 4 Numerical Evaluation of the Optimal Performance
### Case 1: Homogeneous Network  
In the homogeneous case, all the network links were affected by the same average duration of 100 s for both link disruptions and contacts. Figure 3 depicts the network response time for four different cases. In Figure 3a, results for the case where both the one-way propagation delay and the packet loss ratio were fixed to 0.01. In Figure 3b, the one-way propagation delay was set to 0.01 but the packet loss ratio to 0.1. In Figure 3c these values were reversed. In Figure 3d, both the propagation delay and the packet loss ratio were fixed to 0.1.  
The charts show the results for the average flow response time obtained by calculating the optimal flow routing using each of the four methods for the average service time as described in Section 3.5. The independent variable is the per flow sending rate. Because six flows were tested, the total arriving rate at the network was six times the value depicted in the figures. Given that the values of the propagation delays were selected to be smaller than the bundle transmission time, the sole use of the one-way propagation delay as an estimation of the service time led to response times significantly wor  
Figure 3: Average network response time as a function of the per flow sending rate of six concurrent flows obtained using four alternatives for estimating the one-hop bundle service time. Cases (**a–d**) show results for different communication scenarios with a one-way propagation delay (\(D\)) and the packet loss ratio (\(p\)) set to either 0.1 or 0.01.  
alternatives. Despite the inclusion of the bundle transmission time producing improvements to the average response time, no significant differences were observed between methods 2 and 3 (i.e., excluding or including the impact of the packet loss ratio), at least for the parameter values that were tested. The use of Equation (4) allowed achieving the lowest response time of the four methods for the entire range of flow rate values.
# Training a Minesweeper Agent Using a Convolutional Neural Network
## 3 Model Design
### Model Hyper-Parameters
#### 3.4.3 Batch Processing Parameters in Supervised Learning  
In supervised learning, the following parameters are associated with batch processing during model training:  
1. nEpochs (Number of Epochs): Represents the number of complete training iterations over the entire training dataset. Each epoch corresponds to one full pass through the dataset;
2. nSamples (Number of Samples): Refers to the total number of samples in the training dataset, i.e., the number of data points fed into the model for training;
3. batchSize (Batch Size): Refers to the number of samples used to update the model parameters during each training step. The dataset is divided into equal-sized batches, each containing batchSize samples. The model performs forward and backward propagation on each batch, followed by an update to the model parameters.  
The relationship between the number of training iterations and these three parameters can be expressed as  
\[\textit{Total number of training iterations}=\frac{nEpochs\times nSamples}{ batchSize} \tag{6}\]  
Equation (6) illustrates the total number of parameter updates performed during training. Increasing the batchSize reduces the number of iterations per epoch, but increases the number of samples processed in each iteration. Conversely, decreasing the batchSize increases the number of iterations per epoch, while reducing the number of samples processed in each iteration. Choosing an appropriate batchSize can affect both the training speed and performance of the model.  
In this study, the supervised learning method uses the following batch processing parameters: nEpochs = 10, nSamples = 320, batchSize = 32.
# The Use of Earth Observation Data for Railway Infrastructure Monitoring--A Review
## 2 Railway Infrastructure Monitoring Use Cases and Satellite Data Potential
### Monitoring of Railway Transition Zones (TZ)  
Transition zones in railway networks are locations with significant changes in the track-supporting structure, such as those located near tunnels and bridges. The track geometry continuously degrades faster in transition zones compared to open tracks due to differential settlement, differences in stiffness, and geotechnical issues [30], leading to track damage, amplification of the interaction forces between wheel and rail, and possible derailment and affecting the comfort of the passengers [18]. Transition zones are usually monitored by measuring coaches or by performing measurements using optical instruments. These measurements are carried out occasionally and locally, mainly due to high costs. Satellite technologies could provide an alternative, a non-invasive, high-resolution method for continuous monitoring of transition zones. The challenges of transition zones can also be grouped as belonging to TD and GD use cases so that the same satellite data and data processing techniques are applied, as explained above.