esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
This study produced a nationwide LSM for Austria. The LSM is a key step in the analysis and risk assessment of landslides. The main aim of this study was to evaluate the use of an object-based aggregation model for landslide susceptibility along with meaningful regions, showing the impact of each conditioning factor on the resulting LSM. The resulting susceptibility map for Austria at a national level will help in the planning of landslides countermeasures and help to mitigate the risks in the future. Currently, there is no national-level landslide susceptibility map for Austria that includes all the presented conditioning factors and extends across administrative boundaries. In this research, we utilized the geons model, which is based on object-based classification and provides meaningful regionalized units and is more beneficial than pixel-based approaches. We used the landslide data obtained from the Geological Survey of Austria, which may not be complete. We would like to carry out another assessment in the future with a more comprehensive landslide inventory dataset, ideally provided as areas (polygons) rather than points. The higher accuracy of the geons model showed that the object-based assessment could be successfully implemented for the landslide susceptibility mapping for Austria. The geons model can be adapted for other natural hazard susceptibility assessments and in other regions. The resulting LSM maps can be quite useful for national planners to allocate resources in regions that are more impacted by landslides. Overall, this research presents a landslide susceptibility map for the whole of Austria at a national scale. This study also illustrates that geons allow for meaningful units to be obtained, which is more beneficial for planning purposes than pixels. In our methodology, the effectiveness of using the geons model for LSM is dependent on determining appropriate SPs. Working with multiscale segmentation can result in several different LSMs. Here, the introduced methodology allows for the explicit use of multiple scales, and the DST optimized the resulting susceptibility maps. Our future work will focus on the application of data-driven machine learning models for LSM within an object-based software environment.  
Conceptualization, T.G.N. and O.G.; data curation, T.G.N.; funding acquisition, T.B.; investigation, T.G.N.; methodology, T.G.N., S.T.P., and H.S.; supervision, T.B.; validation, T.G.N.; Writing--Original draft, T.G.N. and S.T.P; Writing--Review and editing, O.G. and T.B. All authors read and approved the final manuscript.  
**Funding:** This research is partly funded by the Open Access Funding of Austrian Science Fund (FWF) through the GIScience Doctoral College (DK W 1237-N23).  
**Acknowledgments:** Authors like to thank Geological Survey of Austria for providing the Landslide inventory data. We also would like to thank autonomous reviewers for their constructive inputs on the manuscript. Open Access Funding by the Austrian Science Fund (FWF).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## Appendix A General Considerations in RFE Design
### Thermal Noise of Transimpedance Amplifiers  
Signal current from the photodiode is converted into voltage by a transimpedance amplifier TIA over the feedback resistor, \(R_{TI}\). The inherent thermal noise of this TIA, together with the APDs \(F_{A}\), defines the sensitivity of the RFE.  
Amplifier noise power is classically modeled by a thermal Gaussian noise process over a resistor, \(R_{L}\), times an additional deteriorating amplifier noise figure, \(F_{n}\) (p. 157, [45]):  
\[{\sigma_{t}}^{2}=(4k_{B}T/R_{L})F_{n}B \tag{1}\]  
where the absolute temperature is \(T\), the Boltzmann constant is \(k_{B}\), and the physical (one-sided) bandwidth is \(B\).  
This formula shows the dependence of the noise current, \(\sigma_{T}\), on \(\sqrt{B}\). With today's common use of integrated high-speed TIA circuits, it is, however, more practical to relate this formula to the input-referred current noise density, \(i_{n}\), in A/sqrt(Hz), as stated in the data sheets:  
\[\sigma_{t}=i_{t}\sqrt{B} \tag{2}\]  
Setting \(R_{L}=R_{TIA}\) allows us to relate \(\sigma_{T}\) to the noise figure:  
\[F_{n}=\frac{i_{n}{}^{2}R_{TIA}}{4k_{B}T} \tag{3}\]  
A comparison of TIA noise density values from the data sheets is given in Figure 2, where roughly a \(B^{0.5}\) increase in current noise density can be observed. The exact dependency of noise density on TIA bandwidth is subject to several component-specific parameters, like semiconductor technology, control of stray capacitance, and optimization of amplifier peaking vs. its gain-bandwidth product, etc.
# Intention Recognition for Multiple AUVs in a Collaborative Search Mission
## 3 Description of the Intent Recognition Problem in AUV Collaborative Search Mission  
In multi-AUV collaborative search missions, AUV swarms are tasked with performing target search, tracking, and encirclement in unknown or partially observable complex marine environments. Given the dynamic and uncertain nature of the marine environment, along with the concealment and maneuverability of targets, AUVs must make rapid and accurate collaborative decisions under limited environmental information and communication constraints. During this process, each AUV predicts the intentions of other AUVs based on local sensor observations and inter-agent interaction data (e.g., position, velocity, direction), infers the behavioral objectives of each AUV, and designs multi-AUV collaborative strategies in each decision cycle to ensure the efficiency and robustness of mission execution.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Strengths** & **Limitations** \\ \hline Template Matching & Simplicity, robustness to noise, effective in small datasets. & Relies on predefined templates, high computational costs for large-scale data. \\ \hline Expert Systems & High interpretability, reliable under uncertain conditions. & Depends on domain expertise, not highly adaptable to dynamic environments. \\ \hline Bayesian Networks & Handles uncertainty well, transparent inference process. & High computational complexity, requires prior knowledge and data support. \\ \hline Decision Trees & Fast computation, easy to interpret, adaptable across domains. & Sensitive to data quality, prone to overfitting in noisy datasets. \\ \hline ANNs & High adaptability, good for real-time applications. & Requires large labeled datasets, low interpretability. \\ \hline DL & Strong feature extraction, robust performance in dynamic scenarios. & Needs large-scale data, high computational resource demands, limited interpretability. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Intent recognition methods summary.  
The description of the intent space and intent feature input is shown below.
# T-Move: A Light-Weight Protocol for Improved QoS in Content-Centric Networks with Producer Mobility
## 6 Conclusions  
In view of the wide range of applications which are mobile and require low latency, we propose T-Move, a caching mechanism for content-centric networks. T-Move uses a novel parameter called _trendiness_ of data for caching and handling producer mobility with a light-weight proactive content-pushing module. The cache replacement and content-pushing modules are simple and light-weight, reducing traffic in the core network and latency. Simulation results show increased cache hit ratio, retrieval time and lesser traffic compared to a basic CCN/NDN-like caching policy and MAP-Me. In future, we intend to formulate the cache allocation problem in proactive content-pushing using an optimization formulation.  
Conceptualization, S.K. and S.C.; Methodology, S.K. and S.C.; Software, S.K.; Supervision, S.C.; Validation, S.K.; Writing--original draft, S.K.; Writing--review & editing, S.C.  
The authors thank the anonymous reviewers for their invaluable suggestions that made this paper more meaningful. The authors also thank [PERSON] for his inputs to the development of the original manuscript.  
The authors declare no conflict of interest.
# Mapping Irrigated and Rainfed Wheat Areas Using Multi-Temporal Satellite Data
## Materials and Methods  
### Datasets  
#### 3.1.1 Observation Data from Agrometeorological Stations  
There are nine agrometeorological stations in south central Shanxi Province that observe wheat growth, five of which observe irrigated wheat. Crop growth, development and yield are strongly influenced by climatic factors, such as temperature, precipitation and solar radiation [31; 32; 33]. The water demand of winter wheat during the growing season is approximately between 450.0 and 650.0 mm. The observed precipitation at the agrometeorological stations varies from 83.3 to 190.8 mm during the winter wheat growing period (2011) (Table 1). This amount usually does not meet the water demand of wheat for normal growth, and it is necessary to irrigate crops with supplementary water to reduce water stress at critical growth stages [34]. Generally, irrigated wheat suffers less from water stress compared to rainfed wheat, resulting in distinct differences in growth characteristics.  
In this study, we focus on the wheat growth period from 2010 to 2011. During this period, winter wheat suffered from more severe drought stress than during other years. The precipitation throughout the growing season was 48.8 mm lower than the average precipitation during 2006 to 2014. Field survey data show that growth conditions and yield production between irrigated and rainfed wheat are significantly different. The average yield of irrigated wheat is 2.3-times higher than that of rainfed wheat, based on the agrometeorological station observations.
## 3 Methodology
### Phase 2: Optimal Architecture Synthesis
#### 3.2.5 Training Protocol and Stability Enhancements  
Reproducibility was ensured through the comprehensive control of random processes and computational determinism. Fixed random seeds were applied across all random number generators, including PyTorch (version: 2.6.0+cu124), NumPy (version: 2.0.2), and Python's (version: 3.11.13) random module. CUDA deterministic algorithms were enforced through PyTorch backend configurations, which eliminated non-deterministic GPU operations. Multiple independent training runs were conducted for each configuration, with statistical analysis of the variance of the results to ensure consistent performance patterns. This reproducibility framework enabled reliable comparison across different architectural configurations and supported further result verification.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variant** & **Embedding** & **Attention** & **Transformer** & **Parameters** \\  & **Dim** & **Heads** & **Layers** & **(Approx.)** \\ \hline Small & 96 & 3 & 2 & 0.3M \\ Base & 128 & 4 & 3 & 1.2M \\ Large & 192 & 6 & 4 & 3.1M \\ \hline \hline \end{tabular}
\end{table}
Table 1: Variants of the Proposed Optimal Fake News Detection Model.  
Training Optimization Strategy  
The training protocol incorporates several stability enhancements to address common challenges in transformer training. Smoothing the label with \(\alpha=0.1\) prevents overconfident predictions and improves the model calibration. Gradient clipping at norm 0.5 prevents exploding gradient problems common in deep transformer architectures. Cosine annealing learning rate scheduling provides smooth optimization convergence with warm restarts. Numerical stability monitoring detects and handles NaN values during training, ensuring robust optimization. Early stopping with patience controls prevents overfitting while maximizing model performance. These enhancements collectively ensure stable and reproducible training in different architectural configurations.
# Synthesis, Structure, and In Vitro Biological Evaluation of Semi-Synthetic Derivatives of Betulin
## 3 Results and Discussion
### Computational Studies
#### 3.3.3 Antiproliferative Activity
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Compound**} & \multicolumn{8}{c}{**IC\({}_{50}\) [\(\mu\)M] \(\pm\) SD**} \\ \cline{2-9}  & **MV4-11** & **A549** & **MCF-7** & **PC-3** & **HCT116** & **MiaPaca-2** & **Hs249T** & **MCF-10A** \\ \hline
**6** & \(>\)100 & \(>\)100 & \(>\)100 & \(>\)100 & \(>\)100 & \(>\)100 & \(>\)100 & \(>\)100 \\
**7** & \(>\)100 & \(>\)100 & \(>\)100 & \(>\)100 & \(>\)100 & \(>\)100 & \(>\)100 & \(>\)100 \\
**8** & 59.9 \(\pm\) 23.3 & \(>\)100 & \(>\)100 & \(>\)100 & \(>\)100 & \(>\)100 & \(>\)100 & \(>\)100 \\
**9** & 37.9 \(\pm\) 13.2 & \(>\)100 & \(>\)100 & \(>\)100 & \(>\)100 & \(>\)100 & \(>\)100 & \(>\)100 \\
**10** & 76.5 \(\pm\) 21.2 & \(>\)100 & \(>\)100 & \(76.7\pm 12.2\) & 39.8 \(\pm\) 11.4 & \(>\)100 & \(>\)100 & \(>\)100 \\ \(\alpha\)-lipoic acid & \(>\)100 & \(>\)100 & \(>\)100 & \(>\)100 & \(>\)100 & \(>\)100 & \(98.9\pm 36.8\) & \(>\)100 \\ betulin & 32.8 \(\pm\) 12.9 & 14.0 \(\pm\) 1.1 & 43.6 \(\pm\) 15.8 & 51.5 \(\pm\) 9.9 & 37.7 \(\pm\) 7.5 & \(>\)100 & \(58.5\pm 15.6\) & \(98.5\pm 0.9\) \\ doxorubicin & 0.021 \(\pm\) 0.008 & 0.040 \(\pm\) 0.012 & 0.150 \(\pm\) 0.015 & 1.27 \(\pm\) 0.2 & 0.118 \(\pm\) 0.02 & - & - & 0.114 \(\pm\) 0.013 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Antiproliferative activity of \(\alpha\)-lipoic acid, betulin, and its derivatives **6–10**. This study did not obtain IC\({}_{50}\) values for normal cells (MCF-10A) and cancer cells for compounds **6**, **7** and \(\alpha\)-lipoic acid; therefore, we could not calculate the selectivity index (SI). For the betulin SI, the values were 3 (MV4-11), 7 (A549), 2.3 (MCF-7), 1.9 (PC-3), 2.6 (HCT116), and 1.68 (Hs294T). No activity or selectivity towards MiaPaca-2 cells was observed. The antiproliferative effect may vary depending on the cancer lines tested. Previously conducted studies described by [PERSON] et al. indicated that in the case of leukemia cell lines (CEM, K562), the activity of betulin was lower than that of its 28-acetyl derivative. The introduction of an additional ester substituent at the C3 position in 28-acetyl betuline gave the compound with the highest activity. This relationship was not confirmed in our study [39].
## 3 Preliminaries: Network Model and Problem Definition
### Network Delay and Energy Consumption Model  
The task \(x_{t}^{i}\) generated by the node \(ud_{i}\) is performed on any device at any time \(t\), the delay can be expressed as follows:  
\[T_{i,t}^{\text{comp}}=\frac{\lambda_{i,t}\rho_{i,t}}{f} \tag{5}\]  
where \(\lambda_{i,t}\) is the amount of input data for \(x_{t}^{i}\), and \(\rho_{i,t}\) is the computing density of \(x_{t}^{i}\). That is the number of CPU cycles required to process each bit of data for \(x_{t}^{i}\).\(f\) is the CPU frequency of the computing device.  
The sum of transmission delay and propagation delay generated by communication between \(ud_{i}\) and \(en_{j}\) can be expressed as follows:  
\[T_{i,j}^{\text{space}}=\frac{\lambda}{C_{i,j}}+\frac{d_{i,j}}{V_{\text{space}}} \tag{6}\]  
where \(\lambda\) is the size of the sent data and \(d_{i,j}\) is the distance between \(ud_{i}\) and \(en_{j}\).  
The sum of transmission delay and propagation delay generated by communication between adjacent edge nodes can be expressed as follows:  
\[T_{\text{fiber}}=\frac{\lambda}{C_{\text{fiber}}}+\frac{d_{\text{edge}}}{V_{ \text{fiber}}} \tag{7}\]  
where \(d_{\text{edge}}\) is the distance between adjacent nodes.  
The delay caused by edge node communication with cloud server can be expressed as follows:  
\[T_{\text{cloud}}=\frac{\lambda}{C_{\text{fiber}}}+\frac{d_{\text{cloud}}}{V_{ \text{fiber}}}+T_{\text{delay}}^{\text{cloud}} \tag{8}\]  
where \(d_{\text{cloud}}\) is the distance between edge nodes and cloud server, and \(T_{\text{delay}}^{\text{cloud}}\) is the forwarding delay of core network.  
Local calculated energy consumption for \(x_{t}^{i}\) can be expressed as follows:  
\[E_{i,t}^{\text{comp}}=\kappa\lambda_{i,t}\rho_{i,t}(f_{i}^{\text{user}})^{2} \tag{9}\]where \(\kappa\) is the energy consumption efficiency coefficient of user equipment. \(f_{i}^{\text{user}}\) is the CPU frequency of \(ud_{i}\). If data are sent from \(ud_{i}\) to \(en_{j}\), the duration \(T_{i,j}^{\text{space}}\) can be calculated by (6), and the energy consumption of user equipment during the period can be expressed as follows:  
\[E_{i,j}^{\text{tran}}=P_{i}T_{i,j}^{\text{space}} \tag{10}\]  
where \(P_{i}\) is the transmission power of \(ud_{i}\).
# A Split Ring Resonator-Based Metamaterial for Microwave Impedance Matching with Biological Tissue
## 3 Design
By considering the strong change of scattering parameter modulus \(|S_{11}|\) with respect to the distance \(D\), the following considerations can be inferred: (i) the distance \(D\) change induces the radiation impedance transformation of the applicator; (ii) the distance \(D\) could be optimized starting from the nominal value of \(D~{}=~{}10\) mm, by considering the particular biological tissue; (iii) suitable mechanical spacers could be finely controlled, e.g., by ad-hoc dielectric screws to optimize the microwave radiation. Figure 7b illustrates the simulated scattering parameter modulus \(|S_{11}|\) of the SIW cavity-backed patch antenna with SRR superstrate placed at distance \(D=10\) mm from the human tissue (continuous line) and of the SIW cavity-backed patch antenna with SRR superstrate without tissue (dashed line). The reduction in the scattering parameter modulus \(|S_{11}|\) can be observed over almost the entire frequency range, the metamaterial strongly improves the impedance matching. The electric field distribution has been simulated in order to evaluate the ability of the proposed microwave applicator to focus the electromagnetic field into biological tissue. Simulated electric field 2D distribution in the \(yz\) plane at \(f~{}=~{}11\) GHz is reported in Figure 8.  
The metamaterial lens produces a focusing improvement of the electromagnetic field into the skin model. In particular, in the case of SRR superstrate placed at distance \(D=10\) mm from the human tissue, the electric field modulus better penetrates along \(z\)-direction, in both the skin and fat layers, with respect to the case without SRR superstrate. Moreover, the region where the electric  
Figure 8: Simulated electric field 2D distribution in the \(yz\) plane at the frequency \(f=11\) GHz; (**a**) SIW cavity-backed patch antenna with SRR superstrate placed at distance \(D=10\) mm from the human tissue; (**b**) SIW cavity-backed patch antenna placed at distance \(D=10\) mm from the human tissue without SRR superstrate. Figure 7: Simulated scattering parameter modulus \(|S_{11}|\) (**a**) of the SIW cavity-backed patch antenna with SRR superstrate placed at different distances from the biological tissue and (**b**) of the SIW cavity-backed patch antenna with SRR superstrate placed at distance \(D=10\) mm from the human tissue (continuous line) and of the SIW cavity-backed patch antenna with SRR superstrate without tissue (dashed line). field modulus is close to \(|E|=300\)\(\mathrm{Vm}^{-1}\) is more homogeneous in the presence of SRR superstrate (Figure 8a). It is worth noting the nodal surfaces due to a stationary electromagnetic wave in absence of SRR metamaterial (Figure 8b).
## 2 Related Work  
### Incremental Learning  
Undoubtedly, it is either too ineffective or too inefficient to handle a complex evolving data stream using a pre-trained fixed model or repeatedly creating a new model [9]. A common method is to build an initial model and incrementally update the model over the arriving data. Thus, Incremental Learning (IL) adapts a model to only the latest data, regardless of how data streams evolve. According to the different learning factors, the IL method can be categorized into three types, Sample Incremental Learning (SIL), Class Incremental Learning (CIL), and Feature Incremental Learning (FIL) [10]. SIL aims to continuously learn internal attributes to maintain model presentation ability for dynamic data streams and extract new knowledge to enhance model accuracy. CIL attempts to learn a new class of arriving data and add the new class into the historical class set so the classification performance can be improved. For the FIL method, such an algorithm is devoted to adding new features of evolving data to construct a new representation space to achieve the goal of improving classification accuracy. Due to the scarcity of anomalies in the anomaly detection tasks, it is difficult to define the boundary of normal and abnormal samples, and data labels are lacking for building a supervised model. Therefore, the CIL approach has difficulty performing well in anomaly detection of data streams. Additionally, excellent and distinguishable features in anomaly detection tasks are challenging to extract dynamically, especially in the modeling task of data streams [11]. Thus, SIL is better suited to constructing unsupervised models for the anomaly detection of evolving data streams.
# Advances in Remote Sensing and Propulsion Systems for Earth Observation Nanosatellites
## 5 Discussion
### Directions for Future Research  
Future research in nanosatellite remote sensing and propulsion systems could explore several directions. For remote sensing, considering the big data, increasing the integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques to automate image processing and enhance data analysis can lead to faster and more accurate decision-making, especially in environmental monitoring and disaster management applications. Additionally, nanosatellites can benefit from advancements in multi-sensor fusion, where data from different sensors, such as optical, thermal, and hyperspectral, are combined to offer a more comprehensive assessment of Earth's conditions.  
For propulsion systems, research should focus on miniaturizing electric propulsion systems such as ion thrusters and Hall effect thrusters for more extended missions, enabling precise orbital maneuvers and increased mission longevity. Further exploration into green propulsion technologies, which are less environmentally harmful, is also crucial. Finally, nanosatellite constellations can benefit from autonomous swarm behavior, where satellites communicate and coordinate maneuvers, reducing reliance on ground-based controls and enabling more efficient use of satellite resources.  
Grey literature insights, such as NASA's 'Small Spacecraft Technology State of the Art' report, outline pathways for developing advanced FEEP and electrospray systems tailored for CubeSats with limited power budgets [85]. ESA's ATHENA Project provides further direction, focusing on scalable electrospray thrusters that can support nanosatellite constellations for more complex missions [59]. Such developments are crucial for extending nanosatellite mission lifetimes and enabling precise orbital maneuvers.