esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on the Effectiveness of Multi-Dimensional Approaches to Urban Flood Risk Assessment
## 2 Methodology
### Validation of FRIB  
In this study, a comparative analysis was carried out with existing disaster risk sectors to validate the proposed FRIB based risk assessment methodology. Disaster risk sectors are designated for the systematic management of areas prone to natural disasters, following a comprehensive review of historical disaster data, topographical features, expert evaluations, and other environmental factors. These sectors play a critical policy role by implementing targeted management and preventive measures that protect lives and property.  
The 2022 inundation trace map was used to assess risk levels, specifically by identifying the number of \"very high\" areas in each region. The risk rankings were then determined by comparing these disaster-prone areas with the FRIB-based results from this study. Figure 4 presents (a) the 2022 inundation map and (b) the count of \"very high\" areas alongside existing disaster risk sectors.  
Figure 4: Comparison with disaster risk sectors and 2022 inundation map of Sillim-dong.  
Figure 3: Each result of FRIB element was derived, and subsequently, a single FRIB was determined using the Euclidean distance method.  
Overall, the FRIB-based assessment showed a similar pattern to the current disaster risk sectors, with the highest-risk zones notably clustered in the left portion of the figure (Figure 4b). This indicates that the proposed method effectively captures high-risk conditions in regions already recognized as disaster-prone, suggesting that existing disaster risk sectors appropriately reflect vulnerable areas. However, some locations deemed high-risk through FRIB analysis were not included in the official disaster risk sectors. Because these areas are not under special management yet appear vulnerable, revisiting the designation criteria or conducting further analysis may be warranted. Incorporating these additional areas could lead to a more precise disaster response strategy.
## 6 Case Study Analysis
### Evolution of System Failures Considering the Coupling Effects of Power Flow and Weather
Given the high instability of island quantity and distribution during cascading failures, the maximum island handling approach effectively simplifies the problem's complexity and ensures a reliable overall assessment of the system's reliability. Utilizing the abnormal propagation paths outlined in Section 4.2 and the cascading failure simulation method from Section 5.1, we predicted the cascading fault chains in the power grid under tropical cyclone impacts. Through extensive sampling simulations, the 10 most frequently occurring fault chains are identified, and their risk indices are calculated, as summarized in Table 2:  
Figure 10: Typical cascading failure on IEEE 14 system; branches affected in the cascading failure are highlight. The analysis of the fault chain in Table 2 indicates that the entire fault chain path reflects the cascading response of the system to the severe impact of the typhoon. Extensive sampling simulations reveal that faults initially occur in key branches such as branches 14, 15, and 8, quickly spreading to secondary branches like branches 10 and 11, resulting in multiple localized power outage areas resembling islands. Particularly in the later stages of fault propagation, significant power shortages have emerged in most areas of the system, with the overall chain failure path exhibiting specific patterns, as indicated by the fault chain paths in the table. This highlights that the vulnerability of the power grid during extreme meteorological events like typhoons mainly manifests through the overload of key branches and the island effect. Therefore, in designing preventive measures, the system should prioritize these high-risk paths and fault chains, especially reinforcing branches susceptible to island formation, to improve the overall disaster resilience of the power grid.
# Polar Region Integrated Navigation Method Based on Covariance Transformation
## 2 The Grid SINS
### Dynamic Model of the Grid SINS
 \tag{17}\]  
Substituting \(\widetilde{\mathbf{C}}_{b}^{G}\) from Equation (10) and ignoring the error of gravity vector gives:  
\[\delta\dot{\mathbf{v}}^{G}=\mathbf{f}^{G}\times\mathbf{\phi}^{G}+\mathbf{v}^{G}\times(2\delta \mathbf{\omega}_{ie}^{G}+\delta\mathbf{\omega}_{eG}^{G})-\left(2\mathbf{\omega}_{ie}^{G}+ \mathbf{\omega}_{eG}^{G}\right)\times\delta\mathbf{v}^{G}+\mathbf{C}_{b}^{G}\delta\dot\mathbf{{f}}^{G} \tag{18}\]  
From Equation (7), the position error equation is as follows:  
\[\delta\dot{\mathbf{R}}^{e}=\mathbf{C}_{G}^{e}\delta\mathbf{v}^{G}+\delta\mathbf{C}_{G}^{e}\bm {v}^{G} \tag{19}\]  
where:  
\[\delta\mathbf{C}_{G}^{e}=\delta\mathbf{C}_{n}^{G}\mathbf{C}_{e}^{n}+\mathbf{C}_{n}^{G}\delta \mathbf{C}_{e}^{n} \tag{20}\]  
According to Equation (2), \(\delta\mathbf{C}_{n}^{G}\) and \(\delta\mathbf{C}_{e}^{n}\) can be written as:  
\[\delta\mathbf{C}_{e}^{n}=\left[ \begin{array}{ccc}-\cos\lambda\cdot\delta\lambda& -\sin\lambda\cdot\delta\lambda&0\\ -\cos L\cos\lambda\cdot\delta L+\sin L\sin\lambda\cdot\delta\lambda&-\cos L \sin\lambda\cdot\delta L-\sin L\cos\lambda\cdot\delta\lambda&-\sin L\cdot \delta L\\ -\sin L\cos\lambda\cdot\delta L-\cos L\sin\lambda\cdot\delta\lambda&-\sin L \sin\lambda\cdot\delta L+\cos L\cos\lambda\cdot\delta\lambda&\cos L\cdot \delta\lambda\end{array} \right] \tag{21}\]  
\[\delta\mathbf{C}_{n}^{G}=\left[ \begin{array}{ccc}-\sin\sigma&-\cos\sigma&0\\ \cos\sigma&-\sin\sigma&0\\ 0&0&1\end{array} \right]\delta\sigma \tag{22}\]  
where \(\delta\sigma\) is the grid angle error, and its dynamic equation can be obtained by differentiating Equation (1):  
\[\delta\sigma=\frac{\sin\sigma\cos\sigma\cos L}{\sin L}\delta L+\frac{1-\cos^{2 }\sigma\cos^{2}L}{\sin L}\delta\lambda \tag{23}\]
## 4 Experiments and Results
### Quantitative Performance Evaluation of the Framework  
To quantify the performance of the modular framework, the three components, namely the trajectory generation, congestion prediction, and congestion visualization were experimented on with the New York Grand Central Station (GCS) dataset, collected by [PERSON] et al. [9]. Point-wise individual trajectories were manually annotated by [PERSON] et al. [43]. A detailed description of the dataset can be found in the cited references. The dataset consists of 17,682 trajectories, with 6000 video frames annotated at 1.25 FPS.
## 5 FamiWare Monitoring and Context-Awareness Services
### Code Generation  
In order to instantiate the specific monitoring and context-awareness services for a particular AmI system, we use the model-to-text transformations of FamiWare, which produce the code for deploying the specific middleware to each device. In the case of FamiWare, the microkernel and services are pre-implemented and proved previously, so the automatic code generation entails the selection of FamiWare components and some initialization code. So, the process does not generate code from scratch, it reuses the FamiWare components. However, in the scope of this work we have extended this process for adding new code to monitor and to analyze the new contexts (represented in Figure 2, label C). This code, which refers to the data reading from sensing units or other resources (e.g., GPS), must be implemented previously.  
Since we have designed the architecture of the FamiWare monitoring service in order to be reused easily, then the three generic characteristic defined in the previous section are parametrizable. Then, the application developer must provide the following three parameters: (i) the implementation of the reading function; (ii) the frequency of reading; and (iii) the name of the monitored topic. Therefore, the part of code automatically generated corresponds to the specific middleware configuration indicating which version of which services will be instantiated with which parameters and which topics will be published or subscribed. Our current process generates both code and deployment files for the three platforms described. For TinyOS, it generates the high level configuration file (*.nc) with declarations and connections between the selected services and the makefile used to control the build process of a project. For Android, an Android Activity (*.java) is generated. This activity initiates the selected services by using Android Intents and calls the Android startService() method. Furthermore, our process generates an Android Manifest (*.xml) with the needed middleware information for the Android system. Finally, for Sun SPOT, the process generates a MIDlet (*.java) that initiates the selected services and the Manifest (*.mf) that declares these services. For example, Figure 10 shows an excerpt of the code automatically generated by our process for a TinyOS sensor device. Figure 10(a) shows the configuration neS C file and the associated makefile is shown in Figure 10(b). In the configuration file, firstly the _MainC_ mandatory component of TinyOS (line 07) and the FamiWare microkernel (called _MicroDDSC_, line 08) are declared. Then, a component for the new monitoring of presence used in the cameras is firstly created (line 10) and secondly associated to a _DomainParticipant_, _Publisher_ and _DataWriter_ with the microkernel (lines 11-13). This configuration file (_ConfigApp.nc_) is called in the Makefile (Figure 10(b), line 2).  
Figure 10: FamiWare code automatically generated for TinyOS sensors.
# COSMIC-2 Mission Summary at Three Years in Orbit
## 2 Mission Status and Milestones  
### Satellite Constellation  
All flight models (FMs) were initially launched into a single, 720 km high orbit plane and lowered one at a time to their final operational altitude of ~550 km. This allowed the individual orbit planes to precess relative to one another, such that the final orbits are evenly spaced in right ascension of the ascending node (RAAN). The final orbit configuration was achieved on 20 February 2021. Key orbital parameters, and the timeline for lowering each FM, are summarized in Table 1. During orbit lowering, all payloads for the respective FM were turned off. While the maximum latitudinal extent of the ground tracks is \(\pm\)24 deg, the RO profiles provide both full geographic and local time sampling up to approximately \(\pm\)40 deg latitude.
# Wideband Spectrum Sensing Based on Reconfigurable Filter Bank in Cognitive Radio
## 4 Wideband Spectrum-Sensing Based on RFB  
In this section, we present the energy detection for sub-bands based on RFB and its performance evaluation parameters. According to the sensing result, selection algorithm of RFB sub-bands number is proposed.  
Figure 5: Sub-bands division based on improved P-DFTFB.  
Figure 6: Ideal frequency response of the improved P-DFTFB.
# Novel Semantic-Based Probabilistic Context Aware Approach for Situations Enrichment and Adaptation
## 4 Proposed Approach
### Recommendation Process of Situation Rules  
This section presents a hybrid recommendation strategy for situation enrichment and adaptation based on a weighted linear combination equation. This latter, is a combination of two approaches, the first consists of rule's content-based semantic similarity, while the second is a Bayesian-classifier. The objective of the recommendation process is to provide suitable rules to users. It takes the user's preferences, situational context and device information as input to calculate the situation rules' scores and returns the relevant rules ranked according to their corresponding final scores. On the one hand, a content-based recommender deals only user's preferences and rule item content. On the other hand, we use Bayesian classifier to evaluate rule items regarding user situation context. The proposed recommendation process operates in the following six main steps (Figure 4):  
**Step 1--Calculation of rule semantic similarity based on content.** After the learning process, in the first phase of recommendation, the sorted rules will be used to calculate the similarity between user preferences and rule items. It compares the content of the rule item and the terms of user's explicit and implicit preferences.  
**Step 2--Calculation of rule probability based on situational context.** In the second step of recommendation, we compute the probability for recommending the rule item knowing that we take into account the user situation context.  
**Step 3--Calculation of rule final score and sorting rules.** In the third step of recommendation, we calculate the final score of each rule item including explicit/implicit preferences and Bayesian classifier. Then, we use the final score in order to sort the rule items from the highest score to the lowest.  
**Step 4--Selection of compatible rules based on the device's capability.** For running and adaptation phases, the system selects compatible rules based on the device's capability through an inference engine. It infers the situation rules, which have the minimum requirements (e.g., memory size, CPU and storage) of available devices (e.g., smart TV, smartphone, tablet, actuator, etc.) in user's domain (e.g., home, office, car, etc.) and, finally, add them to the user agenda.  
**Step 5--Recommendation of new rules.** In this step, we take recommended rules with the highest scores and update both the ontology model and the user's agenda based on the user's needs.  
**Step 6--Agenda and ontology update.** In the sixth step, we create a semantic link between the user's agenda and situation rules stored in the ontology model. When the user's situation is evolved, the recommended list is changed accordingly.
# Optimizing Physics-Informed Neural Network in Dynamic System Simulation and Learning of Parameters
## 3 Artificial Neural Network
### Physics-Informed Neural Network  
Physics-informed neural networks represent an advanced machine-learning technique that integrates knowledge from physical principles to provide a robust approximation of function and parameter identification. PINNs have emerged as a powerful tool among data-driven deep neural networks due to their versatility in solving inverse and forward problems [18]. A PINN is a differential system solver discovering the system parameters directly from the available data. Alternatively, it can simulate the differential system when the parameters are already known. Interestingly, PINNs are not restricted to a specific neural network architecture and can utilize architectures like feed-forward neural networks (FNNs) as their foundational framework. PINNs utilize conventional deep learning methodologies, employing standard activation functions and optimization techniques. However, the unique feature of PINNs lies in their sophisticated loss function, composed of boundary conditions, initial values, and physical restrictions. This formulation ensures that the output from the neural network adheres strictly to the underlying system of differential equations by integrating residuals from these equations into the loss function. This algorithm holds immense promise, primarily due to its ability to tackle complex, non-linear systems and process vast quantities of noisy or incomplete data. Furthermore, the duality of PINNs in managing both forward and inverse problems allows them to fit observational data effectively and generate reliable predictions based on identified parameters. Physics-informed neural networks represent a critical leap forward in machine learning. PINNs offer a dynamic and comprehensive approach to data approximation and system parameter identification by amalgamating conventional deep-learning techniques with physical principles embodied in differential equations.
# Optimizing Energy Forecasting Using ANN and RF Models for HVAC and Heating Predictions
## 2 Methodology
### Mathematical Model (RF)  
The Random Forest algorithm is an effective ensemble learning technique that is particularly suited for classification and regression tasks in various fields, including architecture and urban planning. By leveraging historical data, RF can predict outcomes, identify key success factors, and enhance decision-making processes for projects like the Lucia Building and FUHEM. The combination of multiple decision trees minimizes the risk of overfitting and increases the accuracy of RF models, making it especially useful for analyzing complex datasets typical in urban development projects.  
The data collection process for the Lucia and FUHEM Buildings involves gathering both file-based and geometric data. At this stage, thorough data preparation is essential to ensure accurate interpretation by the model. This includes handling missing values, encoding categorical variables, and normalizing numerical features to maintain consistency. Additionally, applying feature selection methods--such as iterative extraction or ranking--helps identify the most influential factors affecting project results. This approach provides both an improved predictive performance of the model and more understanding of the significant variables involved. Once the dataset is thoroughly prepared for modeling, it can be leveraged to train, validate, and measure the performance of the Random Forest (RF) model. When training the RF model, a number of decision trees are developed based on the concept of bootstrapping for sampling, and each decision tree sample has its own predictions; then, they are finally aggregated together. We use measures of performance metrics such as accuracy, precision, recall, and F1-score to assess the reliability of the model across repeated conditions. We can also apply cross-validation methods to validate the model's ability to generalize and predict using unseen data. After the model is fully trained, we can use it to make predictions for outcomes for both the FUHEM and Lucia Buildings to support the stakeholder's plans for informed data-driven decisions, as shown in Table 4[46, 47].
# A Deep Learning Approach to Teeth Segmentation and Orientation from Panoramic X-Rays
## 3 Materials and Methods
### Training and Evaluation  
Experimental setup: All experiments are executed on Google Colab Pro+. For optimization, we use the Adam optimizer [25] with an initial learning rate of 0.001 and weight decay of 1 \(\times\) 10\({}^{-5}\). The learning rate is decreased by a factor of 0.1 when the metric shows no improvement for 10 epochs. Models are trained for 50 epochs, and the best model is stored. We employ a combined loss function, using both dice loss and focal loss with equal weights. Unlike cross-entropy loss, which overlooks contextual information of surrounding pixels in favor of per-pixel calculation, dice loss accounts for local and global information. Dice loss, represented as \(DL\) = (1 \(-\)\(DSC\)), encompasses the dice coefficient (\(DSC\)). Focal loss (\(FL\)) is valuable for addressing class imbalance (such as background >> foreground), in which it prioritizes challenging cases by down-weighting easy examples [26]. So, the final loss function is expressed as follows:  
\[\mathcal{L}=DL+FL \tag{3}\]  
Evaluation metric: For the segmentation task, we use intersection-over-union (IoU), precision, recall, and dice score (DSC). For encoder-decoder-based architecture, these evaluation metrics are widely used. Here are each definition's details:  
\[Precision=\frac{TP}{TP+FP} \tag{4}\]\[Recall=\frac{TP}{TP+FN} \tag{5}\] \[DSC=\frac{2 TP}{2 TP+FP+FN}\] (6) \[IoU=\frac{TP}{TP+FP+FN} \tag{7}\]  
Here, _TP_, _FP_, and _FN_ are true positive, false positive, and false negative, respectively.  
For OBB, we calculate IoU between the ground truth OBB and predicted OBB. In later sections, it will be termed as rotated IoU (RIoU) to distinguish it from the IoU used in segmentation.
## 3 Results and Discussion
### Optimization and Validation of the Predicted Model for Brewing Conditions of Cold Brew Coffee
(2021) reported that particle size exhibited different behaviors according to the different coffee species used, e.g., Arabica and Robusta. For Robusta coffee, total soluble solids extraction was higher in the finer grind, whereas Arabica coffee was reported otherwise [25]. The author concluded that the difference in coffee granular structure makes the extraction process distinctly different [25]. Contradictory to our findings, [PERSON] et al. (2019) also reported that a coarser coffee grind presented a higher TDS in comparison to the finer coffee grinds. This situation was due to the indirect immersion method used by the author. The coffee grinds were placed in a filter bag prior to submerging in water, thus producing a more caking effect for medium grinds that would impede solute extraction [7]. In the brewing process, water temperature is considered the main driving force in extracting the chemical compounds present in the coffee. However, this is not the case for CBC, where the water temperature is at room temperature or lower. Thus, it needs to be compensated with a longer steeping time [7]. [PERSON] et al. (2019) reported that contact time was statistically significant for TDS, e.g., higher TDS at 22 h of extraction [7]. In contrast, the present study revealed that extraction time does not significantly influence the TDS of coffee. This can be explained by the kinetic plots of compound extraction. [PERSON] studied the extraction kinetics of CBC. It was reported that the initial TSS extraction increased over the first 3 h and slowed down to reach equilibrium at approximately 9 h. It is also noteworthy to say that there was no significant difference in TSS for a brewing time longer than 6 h [21]. This explains the result obtained in our study, which found no significant difference in TDS for all brewing at 7, 15.5, and 24 h.
## 4 Proposed Scheme
### Adjusting the Anonymity Level with \(T\)
#### 4.4.3 Implications of Anonymity Adjustment  
The parameter \(T\) gives the proposed method the flexibility of balancing between anonymity and security, which was described in the preamble, leaving it up to the various implementations to establish, this fine-tuning being done according to the particular requirements of each implementation. In applications such as electronic voting, where absolute anonymity is the determining factor, \(T\) can thus be set to a large value to completely hide the voter's identity but additional channel security will be required. In contrast, in applications such as financial auditing, a small value of \(T\) allows the signature to reveal only relevant information, while increasing the degree of attack resistance for sensitive data.