esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Comparison of Different Schemes  
Figure 4 compares the spectral efficiency performance of different schemes. The blue curve, TDD cf-mMIMO CB, corresponds to the TDD scheme of reference [8; 13], which uses statistical CSI for power control and transmission. Compared with that of the proposed scheme, the performance of the TDD cf-mMIMO CB is significantly worse. The proposed scheme uses instantaneous CSI instead of statistical CSI, and it improves the spectral effi  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Transmit Power of APs and Terminals & 200 mW \\ System Bandwidth \(B\) & 20 MHz \\ Centering Frequency & 2.0 GHz \\ Height of AP and UE Antenna & 15 m \& 1.65 m \\ Noise Figure (NF) & 9 dB \\ \(\kappa\) & \(1.381\times 10^{-23}\) J/K \\ \(T_{0}\) & 290 K \\ \hline \hline \end{tabular}
\end{table}
Table 3: Key Simulation Parameters.  
ciency by approximately 7.5 bit/s/Hz over the use of statistical CSI, i.e., by approximately three times. Even the ZF scheme using statistical information is not as good as the scheme using instantaneous CSI. The channel is no longer hardened, which has a serious impact on cell-free massive MIMO. Conventional centralized massive MIMO and small cell networks have difficulty in providing uniformly excellent QoS for each terminal under max-min fair power control. This occurs because, in massive MIMO and small cell networks, there are terminals with very poor channel states. The systems incur a considerable cost to guarantee the transmission of the worst terminals. Due to the difference in network structure, the cf-mMIMO system has more advantages than small cell and massive MIMO in providing uniform quality of service. The channel hardening phenomenon is attenuated, and the gain from channel estimation cannot be ignored.
## 2 Materials and Methods
Upon evaluation, each parameter was scored as 0 or 1, where 1 indicated the existence of error (inadequate quality) and 0 for the absence of error (adequate parameter) for each case. Data were collected for 3 months, and the analysis were performed using Statistical Package for the Social Sciences (SPSS; IBM, and Chicago, IL, USA); version 21. Descriptive statistics were used for expressing the frequencies of criteria and the Chi-square (\(\chi^{2}\)) test was used for comparing tooth types, tooth locations, and academic level of students while the difference was considered significant when the \(p\)-value was 5% or less. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Variable** & **Definition** & **Criteria** \\ \hline \multirow{2}{*}{**1**} & \multirow{2}{*}{**Canal recognition**} & The canal/s is recognized and filled & Acceptable \\ \cline{3-4}  & & One or more canals were not recognized & Unacceptable \\ \hline \multirow{4}{*}{**2**} & \multirow{4}{*}{**Length of RCF**} & RCF end 0–2 mm from the radiographic apex & Acceptable \\ \cline{3-4}  & & RCF end more than 2 mm from the apex & Underextended \\ \cline{3-4}  & & RCF passed the radiographic apex & Overextended \\ \hline \multirow{2}{*}{**3**} & \multirow{2}{*}{**Taper of RCF**} & The taper is consistent from the coronal to the apex of the treated tooth & Acceptable \\ \cline{3-4}  & & The taper is lacking from the coronal to the apex & Unacceptable \\ \hline \multirow{2}{*}{**4**} & \multirow{2}{*}{**Density of RCF**} & No voids are recognizable within the canal & Acceptable \\ \cline{3-4}  & & Voids are recognizable within the canal & Unacceptable \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Criteria of calibration of performed root canal treatment.
## 1 Introduction
### Input Data
#### 2.2.3 MODIS Land Surface Temperature  
We used the Collection 6 MODIS level-3 land surface temperature (LST) 8-day composites at 1000 m spatial resolution from both Aqua (MYD11A2) and Terra (MOD11A2) satellites [31; 32]. The MODIS LST data are provided in a sinusoidal grid format and display the mean clear-sky LST in Kelvin observed during the 8-day compositing period. All MODIS data were reprojected and resampled to 30 m using bilinear interpolation into UTM zone 14N to work with the HLS data and into AEA projection to work with the ARD. The LST time series were converted from Kelvin to degrees Celsius for calculation of thermal time used in the LSP modeling.
## 1 Introduction
[PERSON] found that SDCM piles under axial loading might suffer from pile and soil interface failures, end-bearing failures, and pile-core failures through simulated field tests, with the failure mode depending on various factors, including the soil-cement column length and the soil strength [2]. For the failure mode of helical piles, which were usually in cylindrical shear and individual bearing under axial loading, the failure mode depended on the ratio of helix diameter to helix spacing, \(S/D\)[17; 18; 19; 20]. The axial failure mode of helical piles and SDCM piles had been studied, but the failure mode of HSCM piles was less researched. Although the bearing performance of HSCM piles in sandy soils has been investigated by [PERSON] and [PERSON] [4; 8], there is less research on the uplift bearing characteristics of HSCM piles in marine soft clay. [PERSON] [16] proposed several failure modes of SDCM piles, but the failure modes of HSCM piles were unclear. It is challenging to observe the failure modes due to the complex environmental factors that often accompany field tests. Based on the above problems, this paper investigated the uplift bearing characteristics of HSCM piles in marine soft clay through numerical simulation methods based on the existing relevant tests. The reliability of the finite element model was verified using Plaxis 3D, and the effects of soil-cement strength and soil strength on the uplift bearing characteristics and the failure mode of HSCM piles were analyzed.
# Mining Deformation Life Cycle in the Light of InSAR and Deformation Models
## Materials and Methods
### Verification  
#### 2.3.1 Time Aggregation  
In the current study, we applied two stages of time aggregation of the six-day (12-day) displacement maps: Monthly and yearly. The yearly solution calculated the total subsidence and horizontal displacement. In this way, the magnitude of the surface deformation in the light of InSAR and, in particular PSI and DInSAR capabilities, could be assessed. In addition, the yearly result in the case of modelling was used to determine the mask for the predicted range of influence used in the trend removal procedure (Section 2.1.3).  
Since the extraction plan has a monthly frequency, to analyse the life cycle of the mine surface deformation, we combined the subsidence maps from DInSAR and from modelling, respectively, in monthly solutions. On the other hand, these DInSAR stacks hold the advantage of short temporal baselines in support of higher coherence and contribute to decreasing of the influence on the radar measurements from the tropospheric delays. The monthly sequences were combined in cumulative six-month products for the period April-October so they could be compared with the results from the levelling cycle conducted in 2017 over the studied area.
# Routing Schemes in FANETs: A Survey
## 6 Open Issues
### Enhancing Mobility Models for the Investigation of Routing in FANETs  
The mobility pattern of the UAVs are highly dynamic in the three-dimensional space, and they may be spaced far apart, causing link disconnection that reduces route lifetime. In addition to high dynamicity (X.1) of network topology, high cost (X.2), and low residual energy (X.3), there are two new challenges: (a) high mobility degree that causes the next geographical locations of nodes to become unpredictable and (b) movement in the three-dimensional space, which differs from the two-dimensional space in MANETs and VANETs [137]. Nevertheless, some routing schemes have been investigated under two-dimensional space in FANETs, including those in References [102; 103; 112]. To the best of our knowledge, an extensive mobility model that covers the two new challenges are yet to be investigated to support the investigation of routing in FANETs [141]; nevertheless, movement with high mobility degree in a three-dimensional space is the core characteristic of FANETs. Further investigation could be pursued to enhance the mobility models so that the mobility characteristics and flight routes of UAVs emulate real-life scenarios in FANETs [142].
# Three-Dimensional Hyperbolic Mesh Generation Method Based on the Neural Network
## 4 Discussion
It is worth noting that in the method proposed in this paper, appropriate scaling of the model is necessary. This consideration is based on two factors. Firstly, during each training process of the neural network, the computational domain is fixed within the range of [0.1, 0.9] \(\times\) [0.1, 0.9] \(\times\) [0.1, 0.9], and the Jacobian determinant can be regarded as the ratio of the volume of the computational space to the volume of the physical space. If the physical region that needs to be meshed is large, a smaller value of the Jacobian determinant is required. However, if the Jacobian determinant is too small, it may lead to significant round-off errors, making it difficult to achieve the desired constraint on mesh size. On the other hand, a normal Jacobian determinant value would result in a required computational domain that exceeds the fixed computational domain, leading to non-convergence. Secondly, appropriately scaling the model effectively reduces the variation range of the discrete data, thereby enhancing the convergence of the neural network. Finally, it should be noted that the fully connected neural network currently employed requires a longer training time, sometimes the model will be divided into several regions to complete the generation process; these factors will further reduce the efficiency of mesh generation, making the current method less efficient compared to traditional methods. However, there are ongoing studies that address this issue. Firstly, the automatic differentiation mechanism we currently use for computing derivatives in the neural network is not highly efficient. Researchers such as [PERSON] et al. (2018) have improved training speed significantly by converting the automatic differentiation mechanism into a finite difference format. Additionally, a new neural network structure ([PERSON] et al., 2018), which, unlike traditional neural networks, could fit partial differential equations, uses very few nodes, resulting in a substantial improvement in training speed. This area is also part of our future work, aiming to further enhance mesh generation efficiency and make the method more applicable to engineering practice.
## 2 Related Work
### Tvae  
The architecture of the TVAE is similar to VAE (Figure 2a). The loss function is represented as \(\mathcal{L}(\cdot)\). \(E[\cdot]\) represents the expected loss. \(q_{\phi}(z|x)\) is the probabilistic encoder; \(p_{\theta}(x|z)\) is the probabilistic decoder. These modules were both implemented using neural networks. \(p_{\theta}(z)\) is the prior distribution. \(Z\) is the latent variables sampled from the latent distribution (\(N(\mu,\sigma)\)). Figure 2b depicts a graphical model of the VAE. \(\phi\) is variational parameters; \(\theta\) is generative model parameters. Both are parameters of neural networks. Similar to the vanilla VAE [10], the evidence lower bound (ELBO) is used as a loss function; however, it was modified as in the CTGAN framework [20]. The ELBO loss can be expressed as follows:  
\[\mathcal{L}_{\theta,\phi}=E_{q_{\phi(z|x)}}\left[logp_{\theta}(x|z)\right]- KL\big{(}q_{\phi}(z|x)p_{\theta}(z)\big{)} \tag{2}\]  
The first term of Equation (2) is the reconstruction loss. The second term is the Kullback-Leibler loss. Because the ELBO loss function is trained using the Adam, Equation (2) is transformed as follows:  
\[\mathcal{L}_{\phi,\phi}=-E_{q_{\phi(z|x)}}[logp_{\theta}(x|z)]+KL\big{(}q_{ \phi}(z|x)p_{\theta}(z)\big{)} \tag{3}\]  
Figure 1: CTGAN architecture. The figure is based on [PERSON] et al. [20].  
Figure 2: VAE. (**a**) Architecture of VAE. (**b**) Graphical model of VAE.  
To handle tabular data in the VAE, each row \(r\) is pre-processed as follows [25]:  
\[r=cat(\alpha_{Nc},\beta_{Nc},d_{Nd}) \tag{4}\]  
Accordingly, \(p_{\theta}(x|z)\) is expressed as \(p_{\theta}(r|z)\). \(Nc\) is a continuous column. \(Nd\) is a discrete column. \(\alpha_{Nc}\) are normalized values, and \(\beta_{Nc}\) is one hot vector coming from Gaussian distributions. \(d_{Nd}\) are discrete variables. Thus, regarding the TVAE, the optimized probabilistic decoder is eventually expressed as the following joint distribution:  
\[logp_{\theta(r|z)}=\sum^{Nc}log\frac{1}{\sqrt{2\pi\delta}}exp\frac{(\alpha- \hat{\alpha})}{2\delta^{2}}+\sum^{Nc}CE\big{(}\hat{\beta},\beta\big{)}+\sum^{Nd }CE\big{(}\hat{d},\hat{d}\big{)}+constant \tag{5}\]  
Here, \(\hat{\alpha}\), \(\hat{\beta}\), and \(\hat{d}\) are random variables. \(CE(\cdot)\) is the conditional expected loss. \(\delta\) is the parameter in this network and it is trained using the Adam optimizer.
# A Bayesian Network-Based Information Fusion Combined with DNNs for Robust Video Fire Detection
## 3 Proposed Method
### Bayesian Network for Incorporation of all Information into Final Decision
\begin{table}
\begin{tabular}{c c c c} \hline
**H/F** & **Dry** & **Normal** & **Wet** \\ \hline _No_ & 0.2 & 0.3 & 0.5 \\ _Yes_ & 0.5 & 0.3 & 0.2 \\ \hline \end{tabular}
\end{table} 
Table 1: Conditional Probability P(H/F) [26]. \begin{table}
\begin{tabular}{c c c c} \hline
**F** & **H** & **D** & **S = No** & **S = Yes** \\ \hline \multirow{6}{*}{_No_} & _Dry_ & _No_ & 0.9 & 0.1 \\  & _Dry_ & _Yes_ & 0.5 & 0.5 \\  & _Normal_ & _No_ & 0.7 & 0.3 \\  & _Normal_ & _Yes_ & 0.5 & 0.5 \\  & _Wet_ & _No_ & 0.4 & 0.6 \\  & _Wet_ & _Yes_ & 0.1 & 0.9 \\ \hline \multirow{6}{*}{_Yes_} & _Dry_ & _No_ & 0.8 & 0.2 \\  & _Dry_ & _Yes_ & 0.5 & 0.5 \\ \cline{1-1}  & _Normal_ & _No_ & 0.6 & 0.4 \\ \cline{1-1}  & _Normal_ & _Yes_ & 0.5 & 0.5 \\ \cline{1-1}  & _Wet_ & _No_ & 0.4 & 0.6 \\ \cline{1-1}  & _Wet_ & _Yes_ & 0.3 & 0.7 \\ \hline \end{tabular}
\end{table} 
Table 3: Conditional Probability P(S1\(F\), \(D\), \(H\)) [26]. Figure 7: Example of Bayesian Network [26]. Once the state of each node was determined, the posterior probability of root node \"fire\" could be calculated by  
\[P(F|H,D,S)=\frac{P(F,H,D,S)}{P(H,D,S)}=\frac{P(F,H,D,S)}{\sum_{F=yes/no}P(F,H,D,S) ^{\prime}} \tag{1}\]  
where  
\[P(F,H,D,S)=P(H|F)P(D|F)P(S|F,H,D)P(F) \tag{2}\]  
where represents the probability of \"_Fire_\" determined from the ratio of majority voting. Our simple Bayesian network is designed merely to show the effectiveness of information fusion for better fire decision.
## 5 Application of Reinforcement XAI Model  
Over the best-performing RL model, according to the criteria of the previous threshold, a methodology for interpretability was applied. The models trained on a selection of features with an MDI over the threshold of 0.01, and balanced with SMOTE, were used. The SHAP methodology and framework were employed, creating a KernelExplainer with the RL model's action policy and a subset of 100 synthetic summary records constructed via K-means clustering.  
The individual SHAP values of a model prediction help us understand how specific feature values influenced the model's decision to classify an instance into a particular class. From these values, we can extract two key insights. First, the absolute value of a SHAP score indicates the importance of the feature value in the final prediction. Second, the sign of the SHAP value reveals whether the feature contributed positively or negatively to the classification decision. By examining the SHAP values of a specific prediction, we can identify which features were critical indicators of an intrusion. In Figure 2, the features that contributed to classifying a sample as Reconnaissance are illustrated. These features, aggregated from their one-hot-encoding into nominal values, include only the top 10 based on their overall absolute importance.  
These explanations bring insight into the performance of one single prediction, but can be highly dependent on the specific values of the selected sample, and thus have little use in bringing useful replicable knowledge for future instances of a same class. It is of a higher interest to compute a global class specific to feature importance, including both the absolute and direction value of how a feature is generally considered by a prediction model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Balance** & **Threshold** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline DT & No & 0.03 & 0.80 \(\pm\) 0.01 & 0.69 \(\pm\) 0.00 & 0.57 \(\pm\) 0.00 & 0.80 \(\pm\) 0.00 \\ RF & No & 0.03 & 0.81 \(\pm\) 0.00 & 0.73 \(\pm\) 0.01 & 0.57 \(\pm\) 0.00 & 0.60 \(\pm\) 0.01 \\ XGB & No & 0.03 & 0.82 \(\pm\) 0.00 & 0.80 \(\pm\) 0.01 & 0.60 \(\pm\) 0.01 & 0.62 \(\pm\) 0.00 \\ MLP & Yes & 0.01 & 0.72 \(\pm\) 0.01 & 0.49 \(\pm\) 0.01 & 0.61 \(\pm\) 0.01 & 0.49 \(\pm\) 0.01 \\ RL & Yes & 0.01 & 0.70 \(\pm\) 0.01 & 0.48 \(\pm\) 0.01 & 0.60 \(\pm\) 0.01 & 0.47 \(\pm\) 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of different models. Format: mean \(\pm\) std.
# How Resilient Are Kolmogorov-Arnold Networks in Classification Tasks? A Robustness Investigation
## 5 Results and Discussion
### KAN and MLP Models  
Table 1 illustrates the performance of the KAN and MLP models under three training conditions: standard training, adversarial training, and randomized smoothing. The models were evaluated on three datasets--GTSRB, BTSRB, and CTSRD--using various adversarial attack types, including the FGSM, BIM, PGD, and CW, at different perturbation levels (\(\epsilon\)). The key metrics used to assess performance were the Acc, SAR, and DoC, providing insight into the models' robustness under different attack scenarios. In the standard training condition, without any adversarial defense mechanisms in place, both the KAN and MLP models performed well on the BTSRB dataset, achieving notable accuracy rates. KAN reached an accuracy of 88.73%, while MLP outperformed it slightly with an accuracy of 90.08%. However, both models experienced a significant decrease in accuracy when subjected to adversarial attacks, particularly at higher perturbation levels. Under the FGSM attack with an epsilon (\(\epsilon\)) value of 0.01, the KAN's accuracy on GTSRB dropped to 27.03%, while MLP's accuracy fell to 35.34%. This highlights the susceptibility of both models to gradient-based attacks, with MLP showing slightly better resilience. As the epsilon value increased to 1, both models suffered a dramatic decline in accuracy, with the KAN achieving only 0.24% accuracy on GTSRB and MLP achieving 1.24%. The SAR values also rose significantly, surpassing 80%, indicating the models' vulnerability to FGSM attacks. Similar trends were observed with the BIM and PGD attacks, where both models faced substantial reductions in accuracy, especially at higher epsilon values. For example, under the BIM attack with epsilon set at 1, the KAN attained an accuracy of 5.33% on GTSRB, while MLP performed slightly better at 8.56%. Despite this minor advantage, both models struggled to resist adversarial attacks, exhibiting high SAR values and significant changes in their predictions.
# Preparations for Galileo PRS in Poland
## 5 Polish Contribution for the PRS  
At the end of 2016, the European Union Agency for the Space Programme, EUSPA (at that time, the European GNSS Agency, GSA), announced the Joint Test Activities call, which would result in grants awarded to EU Member States involved in tests and measurement of the Galileo PRS service. Poland took part in two international projects: the PRS Pilot Project for Demonstration (acronym-3 PfD) and GNSS Interference Monitoring and Mitigation for End Users-PRS (acronym GIMME PRS). The 3 PfD project, conducted in 2018-2022, was coordinated by the Royal Military Academy from Belgium and involved partners from Germany, Finland, Poland and Sweden. The aim of the project was, on the one hand, to help the PRS participants' CPAs fully comprehend the PRS service and how to interact with the system and, on the other hand, to perform real operational tests of the PRS during dedicated measurement trials and campaigns. One of the most important tasks was to monitor the possible signal interferences across the project partners' territories. The 3 PfD has designated a number of suitable monitoring sites with a high degree of confidence for the purpose of collecting events that would adversely affect the PRS spectrum. Sensors at these sites monitored the GNSS spectrum for an extended period, which made it possible for the consortium to build a waveforms database containing the potential PRS signal interferers. This database and the applied sensors and techniques can now be used by the Member States for better spectral interference management in their respective territories.  
The second project, GIMME, which has been ongoing since 2018, is coordinated by CPA Poland and involves partners from Czech Republic, Finland and Poland. Its main objective is to promote and evaluate the added value of the Galileo PRS in hostile environments where unintentional, as well as intentional, interference with GNSS signals may occur. The conducted field tests will enable validating the PRS in specific use cases and will evaluate the suitability of the PRS to meet the needs of potential users, such as the border control and coast guard, and also will give an opportunity to compare the quality of the PRS signal and its resistance to jamming and spoofing.  
The NIT is involved in both of the above-mentioned R&D grants, and also carries out other activities relevant to the Galileo PRS preparatory actions in Poland. Some of them will be presented below.
# Evaluation of Hidden Cost Risk of Prefabricated Construction Based on SEM-MEECM
## 1 Introduction
Hidden cost risks are difficult to quantify, often prompting managers to take actions only when project costs spiral out of control. Controlling hidden cost risks throughout the project's entire life cycle is crucial for cost reduction and improved efficiency. Therefore, the primary objective of this study is to establish an evaluation index system that identifies the influencing factors contributing to hidden cost risks at each stage of the construction process. This will be achieved by employing a structural equation model (SEM) to determine the weightage of these factors. Additionally, the matter-element extension cloud model (MEECM) will be utilized to evaluate and comprehensively assess the hidden cost risks associated with prefabricated construction. Previous studies on the cost of prefabricated construction have predominantly focused on the total project cost, leading to the application of the MEECM to address issues of fuzziness, randomness, and the conversion of qualitative concepts into quantitative values when evaluating indicators. The unique contribution of this research lies in the novel combination of the SEM and the MEECM, specifically applied in the field of hidden cost risk assessment. The index system will involve a substantial number of observation variables that require direct observation to elucidate the hidden variables. The SEM effectively addresses the determination of relationships among various indicators. Moreover, the MEECM not only accounts for the susceptibility of the index to subjective preferences but also resolves the challenges related to fuzziness, randomness, and the conversion of qualitative concepts into quantitative values during cost risk evaluation. As a result, the evaluation of hidden cost risks becomes more scientifically grounded and reasonable. Additionally, the model allows for reanalysis of the evaluation results and facilitates the implementation of targeted improvement measures based on the evaluation outcomes, ultimately reducing hidden cost risks. Finally, through the utilization of an actual prefabricated construction project as an example, the rationality of the proposed model is verified and appropriate strategies for identifying and controlling hidden cost risks are presented.