esa-sceva/satcom-chunk-collection huggingface dataset:
# Physics-Informed Surrogate Modelling in Fire Safety Engineering: A Systematic Review
## 1 Introduction: Addressing Challenges in Fire Safety Engineering with Surrogate Modelling
### Data-Driven vs. Physics-Informed Surrogate Models  
Traditional surrogate models are fundamentally data-driven, meaning they rely solely on the available training data. The resulting trained model thus provides an empirical correlation without explicitly incorporating physical principles. Such purely data-driven approaches have a long history in FSE. [PERSON]'s correlation, for example, is widely  
Figure 2: Main categories of surrogate modelling algorithms.  
used to estimate flame height based on heat release rate and source diameter but is fully empirical [36; 37]. While empirical models offer computational efficiency, they often lack the ability to accurately represent complex fire phenomena across a wide range of conditions.  
Due to the absence of physical constraints, several critical drawbacks can manifest. Firstly, such models are prone to generating physically implausible predictions, especially when extrapolating beyond the bounds of the training data or when encountering novel scenarios not well-represented in the training set. Secondly, they typically suffer from poor extrapolation capabilities, meaning their accuracy degrades significantly when applied to conditions outside the range of the data they were trained on. This is a major limitation in fire safety engineering, where predicting behaviour in extreme or unforeseen circumstances is crucial. Thirdly, achieving and maintaining accuracy often necessitates extensive and high-quality datasets that adequately cover the relevant parameter space. Acquiring such comprehensive data can be expensive, time-consuming, or even practically infeasible for certain fire scenarios [24; 38; 39].  
A fourth significant challenge for more complex data-driven models, particularly neural networks, is their limited interpretability; without a clear connection to the underlying physical laws, it becomes difficult to assess the reliability and trustworthiness of a model's predictions, especially in safety-critical scenarios where being sure of the reasonable nature of the prediction is paramount [40].  
To tackle the problems faced by conventional data-driven models, physics-informed machine learning (PIML) has been introduced. The architecture and training of these models are specifically designed to encourage or even strictly enforce adherence to the underlying physics governing the problem [23; 41]. This integration of physical prior knowledge with available data can lead to improved accuracy, enhanced generalizability, better interpretability, and the potential to work effectively with smaller datasets compared to purely data-driven methods [42; 43; 44; 45; 46; 47].  
Physics-informed surrogate modelling (PISM) is a direct application of PIML whereby the training data is generated by a numerical model [48], as conceptually shown in Figure 3. Within this framework, physics-informed surrogate modelling (PISM) is thus defined as a specific subclass of PIML, based on the source of its training data.
## 4 Simulation Environments
### AI-Driven Resource Management  
In the simulation environment for evaluating AI-based radio resource allocation, the WiLabV2X simulator is employed, supporting the unmanaged mode for Cellular V2X communication. This simulator is primarily utilized to benchmark the performance of existing resource allocation techniques under unmanaged network conditions. For the AI-based resource allocation aspect, custom coding is developed independently, leveraging advanced deep reinforcement learning methodologies.  
The dataset, encompassing parameters such as the distance to neighboring vehicles, forecast indicators, and radio resource occupancy, is meticulously prepared and imported. These parameters define the states crucial for training the reinforcement learning model. The state space incorporates factors like the number of radio resources occupied and the specific actions taken. The reward structure is designed to reflect the efficiency and effectiveness of resource usage, providing positive reinforcement for optimal allocations and penalties for suboptimal decisions.
# A Novel GBSM for Non-Stationary V2V Channels Allowing 3D Velocity Variations
## 4 Time-Variant Parameter Computations
### Time-Variant Path Delays and Powers  
The total delay at a moment of time \(t\) involves the first bounce \(\tau_{n}^{Tx}(t)\), the virtual link \(\tau_{n}(t)\), and the last bounce \(\tau_{n}^{Rx}(t)\), which is obtained by:  
\[\tau_{n}(t)=\frac{\left\|\mathbf{r}^{Tx}(t)-\mathbf{r}^{C_{\pi}^{Tx}}(t) \right\|+\left\|\mathbf{r}^{Rx}(t)-\mathbf{r}^{C_{\pi}^{Rx}}(t)\right\|}{c}+ \tau_{n}(t) \tag{21}\]  
where \(\tau_{n}(t)\) represents the corresponding delay of the virtual link. For multipath scenarios, the propagation power from the transmitter to the receiver varies with different path delays, which depends on the real environment. Based on the measurement results in the 3 GPP channel model, the path power with respect to the delay can be calculated by:  
\[P_{n}^{\prime}(t)=\exp\left\{-\tau_{n}(t)\frac{\eta_{\tau}-1}{\eta_{\tau} \sigma_{\tau}}10^{-\frac{\phi_{n}}{10}}\right\} \tag{22}\]  
where \(\eta_{\tau},\sigma_{\tau}\) denote the delay distribution and delay spread and \(\theta_{n}\) denotes the shadow term, which follows the Gaussian distribution. Note that the total power should be normalized for all the paths. Moreover, the total power at the moment of time \(t\) includes the power of subsisted clusters from the preceding moment of time and the power of recently produced clusters.
## 3 Automatically Choosing the Number of Clusters
### Modularity  
Modularity is one measure of the graph structure. It is designed to measure the strength of the division of a graph into clusters. The networks with high modularity have dense connections between nodes within the same cluster but sparse connections between nodes in different clusters. We calculate the modularity versus the number of clusters or groups. The optimum number of clusters is the one that maximizes modularity [34; 35].  
Given a graph \(G=(V,E)\) and a partition of the nodes \(V\) into \(K\) clusters \(C=\{C_{1},\ldots,C_{K}\}\), the modularity is expressed as follows [34]:  
\[M=\sum_{k=1}^{K}\frac{1}{|C_{k}|}\Bigg{(}\frac{Cut(C_{k},C_{k})}{Cut(V,V)}- \bigg{(}\frac{Cut(C_{k},V)}{Cut(V,V)}\bigg{)}^{2}\Bigg{)}, \tag{47}\]where \(|C_{k}|\) is the size of cluster \(C_{k}\). The term \(Cut(C_{k},C_{k})\) measures the sum of weights within the cluster and \(Cut(C_{k},V)\) measures the sum of weights over all edges attached to nodes in cluster \(C_{k}\). The term \(Cut(V,V)\) calculates the total sum of all edge weights in the graph \(G\).  
We compute the modularity for each value of \(K\), such as \(K\leq K_{max}\). In order to select the number of clusters, we choose the number \(K\) that maximizes the modularity function. A high value of the modularity function means that the edges within the clusters outnumber those in a similar randomly generated graph. Modularity is often used in optimization methods for detecting homogeneous cluster structures in networks.
## 5 Results
### Machine Learning-Based Fault Presence and Type Detection Models
For instance, each performance metric of DT and RFC is 0 with raw data, AP, FFT, ACF, and WT. On the other hand, the CNN and SVM perform best by employing AP, FFT, ACF, and PSD feature extraction techniques. Except WT and Raw Data, each feature extraction technique provides valuable features for CNN and SVM algorithms to classify labels of data with high precision and recall, but their prediction time varies due to the computational complexity of feature extraction techniques. The ML-based fault type detection performance measurement is performed by using different relays and meters' recording data as shown in Figure 15. The predicted outcomes are given in Figure 15 to investigate the response of each ML algorithm and feature extrac  
Figure 14: The prediction accuracy of ML-based fault presence detection (classification layer 1) algorithm with different ML methods and feature extraction techniques. tion method. The 3 LG fault type is considered for the test, and the results are indicated with various power relays and meters to investigate the impact of the sample rate and electric grid location of measurement devices. Outcomes reveal that the prediction accuracy is slightly affected by the sample rate measurement devices. For instance, the CNN performance is decreased with the SEL351S, which has 32 sample rates. However, as obtained in the previous results in Figure 14, the SVM shows significant results by making accurate predictions with each feature extraction method except WT. Further, the FFT feature extraction method provides 100% true prediction with the CNN method. The CNN and AP combination can also achieve reasonable results, but other feature extraction methods (i.e., raw data, ACF, PSD, and WT) cannot provide accurate predictions. In the case of other conventional ML algorithms, both DT and RFC only make one correct estimation. In general, the SVM and CNN algorithms perform well in the ML algorithms, and ACF and FFT provide more accurate results than the others. The ML-based fault type detection (classification layer 2) is also verified with different performance metrics as presented in Table 8. In this case, all 3 LG fault types from testbed are used to measure precision, recall, and F1-Score. Each feature extraction and ML technique is employed. As for pure data (raw data), SVM and RFC provide significant performance; however, the same success of the RFC algorithm cannot be achieved with other feature extraction techniques. Results in Table 8 clearly show that the highest precision and recall can be obtained with the CNN and SVM algorithms along with the AP, FFT, ACF, and PSD techniques. Since ML algorithms are trained on simulated data, their performance is limited by the similar characteristics of labels extracted by using feature extraction techniques. For these reasons, it is necessary to examine not only the prediction accuracy but also the processing times of classification layers 1 and 2.
# Positioning Methods and the Use of Location and Activity Data in Forests
## 4 Location-Based Services, the Internet of Things, Wearable Technology, and Big Data
### Mesh Networking
Whereas positioning devices discussed in the early sections of this paper primarily detect the movements of people, equipment, or other resources, wireless sensor networks (WSNs) are used broadly in forest research and in environmental fields to monitor air masses, soil moisture, and other environmental factors spatially.
## 6 Implications
### Practical Implications  
This study's findings also have some substantial practical implications for government authorities or practitioners who aim to understand the attitudes and sentiments of the public during the COVID-19 pandemic and towards the COVID-19 response strategies via social media platforms. First, authorities could apply the results of this article to improve the current pandemic response strategies by amplifying the donations, help seeking, notifications, cautions/advice types of information that are needed by the public. Second, as suggested by the results of the study, tweets related to donations come from users wit a large number of followers and more positive words and have a large number of retweets. In contrast, tweets related to criticizing the government have more negative words and come from more unverified account users (the most common users of Twitter), propagate most. These results can assist practitioners and authorities to formulate surveillance strategies of public sentiments, so that their opinions can be taken into account, because these users can be rational thinkers and have greater influence on social media. Eventually, these users may clarify the truth and express more anger on social media. Furthermore, this study's findings also indicate that, if authorities want to enlarge the propagation scale of all situational information types, they could focus on the length of tweets because, in all types of situational information except type 5 (criticizing the government), the length of tweets has more impact on retweets.  
Additionally, by analyzing the anger-, anxiety- and sad words-related tweets we found out that they have less or no affect on type 0 (caution and advice) information, and also the existence of a more likable (variable like count) attitude from public; therefore, if authorities want to ease the anxiety of the public during pandemic, they should focus not only on tweet posts with words not only related to anxiety but also on tweet posts with more likes in the type 0 (caution and advice) situational information type. In this way, practitioners or authorities will comfort the sadness of the public during the pandemic and they can also enlarge the propagation scale of this type of information by utilizing this finding.
## 3 Analysis of Test Results
### Rock Mechanical Properties Under the Interaction of Multiple Factors
#### 3.3.1 Analysis of Interaction Effects  
To clarify the interactive effects of each factor on peak stress and elastic modulus, an analysis was conducted using R software (RStudio, version 2023.03.0+386), considering the interactions between factors [28; 29; 30]. The results are presented in Tables 6 and 7.  
Based on the results presented in Tables 6 and 7, the regression models for peak stress (\(\sigma_{max}\)) and elastic modulus (\(E\)), incorporating interaction effects, are as follows in Equations (9) and (10):  
\[\sigma_{max}=26.0712+27.754X_{1}-14.392X_{2}+29.299X_{3}+33.886X_{4}+5.957X_{5}- 11.307X_{1}X_{2}+16.075X_{1}X_{4}+5.111X_{1}X_{5}-10.134X_{2}X_{4}+9.476X_{3}X_{5} \tag{9}\]  
\[E= 10.4785+3.9654X_{1}-6.2846X_{2}+2.1523X_{3}+5.9667X_{4}+1.0471X_{5}+4.4513X_{ 1}X_{5}-2.2635X_{2}X_{3}+6.1807X_{3}X_{4}+2.9091X_{4}X_{5} \tag{10}\]  
In the above equations, \(X_{1}\) through \(X_{5}\) represent the grouting material, number of freeze-thaw cycles, fracture dip angle, confining pressure, and roughness, respectively.  
Tables 6 and 7 show that the \(p\)-values of the single-factor explanatory variables grouting material, number of freeze-thaw cycles, fracture dip angle, confining pressure, and roughness are all below 0.05, indicating that each factor significantly affects peak stress and elastic modulus. Based on the absolute values of the effect coefficients, the order of the single factors' effects on peak stress is confining pressure > fracture dip angle > grouting material > number of freeze-thaw cycles > roughness, while their effects on elastic modulus follow the order of freeze-thaw cycles > confining pressure > grouting material > fracture dip angle > roughness. This aligns with the previous discussion on the impact of individual factors. Further analysis of the \(p\)-values and effect coefficients revealed five significant interaction terms affecting peak stress. Among them, the interaction between grouting material and confining pressure had the strongest effect, with a coefficient of 16.075, surpassing the individual effects of the number of freeze-thaw cycles and roughness on peak stress. Additionally, four interaction terms significantly affected the elastic modulus, with their absolute effect coefficients exceeding the single effect of fracture dip angle on the elastic modulus. This suggests that interactions play a more complex and significant role in determining peak stress and elastic modulus. Single factors alone cannot fully explain changes in rock mechanical properties, highlighting the need to clarify the influence of various interaction effects.
## 3 Results and Discussions
### Results and Discussions of Trajectory Prediction for Turn Phase  
The predicted MEDTWs of the different approaches for the flight turn are reported in Figure 9. The KDA suffers from the largest prediction error because it fails to fully consider the influence of maneuverability of the real-time flight environment when the aircraft is regulating its heading. As the same reason, the Kalman approach also has a large prediction error. Thanks to the learning mechanism of the GP, the GP and DGP approaches obtain higher prediction accuracy compared to other baselines. In addition, the DGP approach obtains more accurate prediction because of the promotion of the hierarchical architecture.  
Figure 8: CIs (95%) of the predicted trajectory for a cruise flight (up: X dimension; down: Y dimension).  
The predicted trajectory and its CIs are reported in the Figures 10 and 11, respectively. Due to the aircraft maneuverability during flight turn, the flight trajectories show huge divergence. Some flight positions are even distributed outside of the predicted CIs. Fortunately, the GP has the ability to capture frequent transition patterns from real operating data, which is further applied to improve the accuracy of the prediction results. Furthermore, the predicted CIs of GP-based approaches allow us to analyze the trajectory patterns in a probabilistic manner, not only for a deterministic one.  
Figure 10: Predicted trajectory for the flight turn (the subfigure is the partial enlargement of the predicted trajectory).  
Figure 9: Experimental results for flight turn.
# Testing Accuracy and Repeatability of UAV Blocks Oriented with GNSS-Supported Aerial Triangulation
## Results and Discussion
### Interaction between Interior and Exterior Orientation Parameters
The mean error change on the CP between **RTK** and **RTK+IGCP** is equal in PhotoScan to 7.3 cm, i.e., most of the 9.3 cm bias reported in Table 2.  
Summarizing, it seems that the balance between the weights of the different observations (GCP and camera center constraints) and the interaction between interior and exterior orientation parameters have different patterns in the three packages: as can be seen in Table 6, the behavior found for the RTK2 flight also applies to the other blocks. With MicMac, the behavior is different, because both principal distance and camera elevations are affected. In the **RTK** case, the RMSE in Z on CP is about 6 cm, with an estimated principal distance significantly higher than those calculated by PhotoScan and Pix4D. This excess in the estimated principal distance is compensated by an average upwards shift of the camera projection centers with a mean value of 26 cm. When one GCP is introduced, the average shift with respect to the measured positions is 15 cm and the principal distance change with respect to the 12 GCP case (8.4 \(\upmu\)m) is equivalent to a depth change of 15 cm.
## 2 D2D Communication System Model
### Communication Link Model
#### 2.5.2 Throughput of D2D Communications  
According to [PERSON]'s capacity formula, the maximum reliable data rate of a direct link can be expressed as  
\[C_{s,d}=B\log_{2}(1+\gamma_{s,d}), \tag{4}\]where, \(B\) is the bandwidth of the channel.  
The data rate of a D2D link having full duplex decode-and-forward (DF) relaying is given by  
\[C_{s,r,d}=B\min\{\log_{2}(1+\gamma_{s,r}),\log_{2}(1+\gamma_{s,d}+\gamma_{r,d})\}, \tag{5}\]  
where, \(\gamma_{s,r}\) is SNR of the signal from the source to the relay, \(\gamma_{s,d}\) is SNR of the signal from the source to the destination and \(\gamma_{r,d}\) is SNR of the signal from the relay to the destination [6,7,33].  
Considering the time spent for probing of candidate relay devices, the throughput of a D2D communication that uses TDMA mechanism can be expressed as  
\[T_{s,d}=\frac{C_{s,r,d}\{t-(\tau\times p)\}}{t}, \tag{6}\]  
where, \(t\) is the time slot duration, \(\tau\) is the probe duration and \(p\) is the number of probes [7,8,12]. The throughput decreases with the increase in duration and number of probes. However, to select a relay transmitting with high power, multiple devices are required to be probed.  
The source compares the throughput offered by the direct link and that offered by the D2D link to improve performance of D2D communications by selecting a relay device whenever possible.  
We consider there are no major obstacles in the propagation path from the source to the destination. We assume the background noise is constant and is interference free. In reality, there might be obstacles in the signal path. The SNR of signal through different candidate relay can be estimated by probing of devices using the protocol presented in the next section.
## 2 Problem Description and Model
### Mathematical Formulation  
The mathematical formulation of the SAOSSP is defined as follows, where the optimization objective is to maximize the profit ratio of the completed tasks and complicated constraints are taken into consideration.  
\[\text{Maximize }F=\frac{\sum_{i=1}^{|T|}\left(p_{i}\cdot\sum_{k=1}^{|W_{i}|}x_{ i}^{k}\right)}{\sum_{i=1}^{|T|}p_{i}}, \tag{10}\]  
subject to  
\[\sum_{k=1}^{|W_{i}|}x_{i}^{k}\leq 1,\forall t_{i}\in T; \tag{11}\]  
\[\left[st_{i}^{k},et_{i}^{k}\right]\subseteq OL(o_{l}),\text{if }c_{i}^{k}=l; \tag{12}\]  
\[est_{i}^{k}\leq ost_{i}^{k}\leq lst_{i}^{k},\text{if }x_{i}^{k}=1; \tag{13}\]  
\[ost_{j}^{o}-oet_{i}^{u}\geq tranT\left(\left|pa_{j}^{o}-pa_{i}^{u}\right| \right)+ tranT(\left|ra_{j}^{o}-ra_{i}^{u}\right|),\text{if }y_{ij}=1; \tag{14}\]  
\[rm\cdot\sum_{i=1}^{|T|}\left(d_{i}\cdot z_{i}^{l}\right)\leq M,\forall o_{l} \in O; \tag{15}\]  
\[\sum_{j=1}^{|T|}z_{j}^{l}\left[rea\sum_{i=1}^{|T|}y_{i}^{j}\left(\left|pa_{j}^{ o}-pa_{i}^{u}\right|+\left|ra_{j}^{o}-ra_{i}^{u}\right|\right)+reo\cdot d_{j} \right]\leq E,\forall o_{l}\in O. \tag{16}\]  
Equation (11) denotes the execution uniqueness constraint that every task is executed at most once. Equation (12) denotes the lighting constraint that every candidate VTW must meet the lighting condition. Equation (13) denotes the image quality constraint that the observation start time must satisfy the minimum image quality requirement. Equation (14), where \(u\) and \(v\) separately denote the VTW indexes of the two tasks, indicates the transition time constraint that the time interval between two observation actions must be sufficient for attitude adjustment. Equation (15) denotes the memory constraint whereby the total memory consumption for observation cannot exceed the maximum in each orbit cycle. Equation (16) denotes the energy constraint whereby the total energy consumption for attitude adjustment and observation cannot exceed the maximum in each orbit cycle.
# Cyberattacks and Security of Cloud Computing: A Complete Guideline
## 7 Countermeasures for Threats in Cloud Computing
### Intrusion Detection Systems (IDS)  
Intrusion Detection Systems (IDS) are indispensable for identifying anomalies by analyzing network traffic patterns and activities within cloud environments. Different types of IDS are deployed at various levels.