esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
We estimated the relationship between the annual average probability distribution \(P(A)\) of exceeding a given rain attenuation \(A\) (dB) and the carrier frequency \(f\) (GHz) in the range 16 to 100 GHz, in the zenith paths of GeoSurf satellite constellations and reported results oriented to system design. For illustrating the general characteristics of this relationship, we have reported the results concerning sites located in different climatic regions. The rain attenuation expected in GeoSurf radio-links is independent of the particular GeoSurf design (e.g., altitude and number of satellites), and was calculated with the Synthetic Storm Technique.  
We modeled the relationship according to Equations (2) and (3) by defining a suitable outage probability factor. The mean error of the outage probability factor, Equation (4), depends on site. The main result was that this factor tends to saturate at about 100 GHz, therefore giving about the same outage probability, for a fixed attenuation/power margin, as frequency increases. For a fixed power margin, in wideband radio-links, there is very likely a linear distortion due to in-band outage probability.  
Further work should be conducted to apply these concepts to very large bandwidth radio-links, such as those envisaged with spread-spectrum techniques, by making more detailed predictions within the large bandwidth with the SST, as discussed above, especially with the purpose of establishing the main features of the radiofrequency transfer function.  
Both authors have equally developed the software, modeled the results and written the paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data and software are available from the authors, on request.  
Figure 8: In-band probability factor \(I_{p}\), Equation (4), versus frequency, Equation (5), at the indicated sites for \(B_{w}=1\) GHz: Spino d’Adda (SP, blue continuous line); Gera Lario (GL, black continuous line); Fucino (FC, cyan continuous line); Madrid (MD, green continuous line); Prague (PR, magenta continuous line); Tampa (TM, cyan dashed line); Norman (NR, magenta dashed line); White Sands (WS, green dashed line), in zenith paths.  
**Acknowledgments:** We wish to thank our colleagues: [PERSON], researcher at NASA some years ago, for providing the rain rate data of the North America sites; [PERSON], Institute of Atmospheric Physics in Prague, for providing the rain rate data of Prague; [PERSON], Universidad Politecnica de Madrid, for providing the rain rate data of Madrid.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Two-Way Multi-Antenna Relaying with Simultaneous Wireless Information and Power Transfer
## 3 PSR Protocol  
Figure 2 illustrates the key parameters in the PSR protocol for energy harvesting and information processing at the relay node R and the block diagram of the corresponding receiver. In Figure 2a, the first block time \(N\) is used for multiple access (MA) where the end nodes T\({}_{1}\) and T\({}_{2}\) transmit their signals simultaneously, and \(P\) is the total signal power. In the second block time \(N\), the relay node processes this signal (according to the schemes that will be presented below) and broadcasts it. During the MA phase, the fraction of the received signal power \(\rho P\) is used for energy harvesting, and the remaining received power \((1-\rho)P\) is used for information transmission, where \(0<\rho<1\) is the power splitting ratio. In other words, as shown in Figure 2b, the portion of the received radio-frequency (RF) signal, denoted by \(\sqrt{\rho}y_{r,m}\), is sent to the energy harvesting receiver and the remaining signal strength, denoted by \(\sqrt{1-\rho}y_{r,m}\), drives the information receiver. In the following, we describe three multiple-antenna relaying schemes, which correspond to the PSR protocol.  
Figure 2: (**a**) Key parameter in the PSR protocol for energy harvesting and information processing at the relay node; and (**b**) Block diagram of the relay receiver (with a focus on its \(m\)-th antenna) in the PSR protocol.
## 3 Technical Background on SCHC and Cellular IoT  
### Ng-IoT  
After being standardized by the 3 rd Generation Partnership Project (3 GPP) [32], NB-IoT is currently one of the leading LPWAN technologies to connect IoT applications [33]. It reuses the LTE design, where the complexity of its channels and signals are reduced to fit constrained devices [34]. Furthermore, NB-IoT is designed to occupy a frequency band of 180 kHz (one resource block in the LTE system) and to handle repetitions to achieve long-range and deep indoor communication [35]. Even though NB-IoT is simpler than LTE, data can be sent via different paths with different characteristics in terms of bandwidth, acknowledgments and reliability [36]. In fact, NB-IoT allows for communication over both the IP and the non-IP (NIDD). For more information on NB-IoT, please refer to [35] and the references within.
# Feature Drift in Fake News Detection:
## 4 Feature Drift  
### Definition and Experimental Setup  
Recent studies [25; 33] show that different features play different roles in news propagation, especially for the early stage. These results imply that the detection time deeply influences fake news detection algorithms. To explain the results more clearly, in this work, we define _feature drift_ as a phenomenon in which fake news features perform differently in different datasets over time.  
Figure 1 demonstrates the accuracy results of four basic machine learning algorithms under different _cut-off times_ for the Twitter and Gossipcop datasets. Here, the four basic machine learning algorithms are Random Forest, SVM, Naive Bayes, and KNN. The _cut-off time_ is defined as the observed time window for the event. In this work, we choose five different _cut-off times_, i.e., 2.4 h, 4.8 h, 7.2 h, 9.6 h, and 12 h. The input features are the same as those mentioned in Section 3, including temporal features and structural features. Furthermore, five-fold cross-validation is applied for all results. As shown in Figure 1a, one can find that Random Forest, Naive Bayes, and KNN achieve similar accuracy as the _cut-off time_ changes, suggesting that there is no significant feature drift phenomenon in the Twitter dataset, and the role of these features has not changed. Conversely, in the Gossipcop dataset, one can find an increasing trend of classification accuracy as the _cut-off time_ increases, especially in the algorithm of Random Forest (Figure 1b). This phenomenon implies that there does exist a feature drift phenomenon in the Gossipcop dataset, and these features' roles change over time. Furthermore, the unclear phenomenon under the KNN algorithm also suggests that the feature drift phenomenon is not universal in the models, and the more the model depends on the feature, the more significant the phenomenon. To sum up, the evaluation indicator of 'accuracy' is poor in interpreting feature drift. Furthermore, it is difficult to identify which features change in importance over time. Thus, it is necessary to use interpretable models for further analysis.
# Modeling of Historical Marine Casualty on S-100 Electronic Navigational Charts
## 5 Discussion and Limitation
### Data Classification and Harmonization Challenges  
The integration of marine casualty data from heterogeneous sources such as GISIS, MAIB, and MTIS revealed substantial differences in terminology, format, and classification logic. These discrepancies posed a significant challenge in mapping incident records to the proposed feature types and subtypes. For example, while some sources provided structured tables with coded values, others used free-text narratives, requiring interpretive judgment to determine appropriate classifications.  
To achieve semantic consistency, expert reviewers manually matched each record to predefined code list values based on the model's controlled vocabulary. However, this process was labor-intensive and not immune to subjectivity. This limitation indicates the need for standardized vocabularies or ontology-based alignment across reporting systems to support more consistent data transformation. Without harmonization, cross-jurisdictional integration may yield inconsistencies that reduce the reliability of the model in large-scale applications.
# Predictive Business Process Monitoring Approach Based on Hierarchical Transformer
## 4 Results and Discussion
### Comparison Experiments
#### 4.3.2 Remaining Time Prediction  
The following method are selected as the comparison method in the remaining execution time prediction task.  
* LSTM [10]: remaining execution time prediction using LSTM.
* Accurate-LSTM [23]: using LSTM to model activity sequences, role sequences, and relative time sequences separately to enhance the effectiveness of LSTM models.
* Transformer [16]: remaining execution time prediction is directly implemented using the traditional Transformer.  
Table 3 shows the MAE values of the above three comparison methods and the method in this paper (HiP-Transformer) on the remaining execution time prediction task. Overall, the HiP-Transformer model achieves the lowest error on six of the seven datasets.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Datasets** & **LSTM [10]** & **GRU [11]** & **CNN [21]** & **DNC [22]** & **Transformer [16]** & **HiP-Transformer** \\ \hline BPI2012 & 85.46 & **86.65** & 83.25 & 77.70 & 85.20 & 86.30 \\ BPI2012W & 85.35 & 84.78 & 81.19 & 60.15 & 89.30 & **89.88** \\ BPI2013i & 70.09 & 74.69 & 46.03 & 51.91 & 62.11 & **79.33** \\ BPI2015 & 68.58 & 71.02 & 58.43 & 70.56 & 71.98 & **73.02** \\ BPI2017 & 83.15 & 84.25 & 78.45 & 84.75 & 81.88 & **85.88** \\ Hospital & 78.50 & 79.46 & 75.89 & 70.26 & 80.98 & **84.70** \\ Sepsis & **64.22** & 63.50 & 56.15 & 21.01 & 45.98 & 62.54 \\ \hline Avg. & 76.48 & 77.76 & 68.48 & 62.33 & 73.92 & **80.24** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy of next executing activity prediction (accuracy: %).  
Furthermore, comparing the two Transformer-based methods with the two LSTM-based methods, we can find that the Transformer model is significantly better than the LSTM model. Compared with the next execution activity prediction task, the prediction target of the remaining execution time prediction task is the state of the next stage of business process instance execution, which is more difficult to predict and puts forward higher requirements on the business process instance modeling method. The results of this experiment reflect the advantages of Transformer in modeling event sequences. In each dataset, the methods have poor prediction results on Hospital and BPI2015, and a common feature of these two datasets is the existence of a large number of events executing repeated activities consecutively, which indicates that deep learning models in modeling this phenomenon are still more room for improvement.
## 7 Summary, Conclusions, and Future Work
### Key Findings Include:  
_Predictive Performance:_ The RBF network achieved the highest overall accuracy (median R\({}^{2}\) = 0.975; RMSE = 1.313 MPa) and significantly outperformed both BR and SCG models in test-set MSE (Friedman adjusted \(p\) < 0.01). Bayesian regularization yielded robust generalization (MAE = 1.164 MPa; R\({}^{2}\) = 0.967) without requiring a separate validation subset, while the SCG and LM methods converged faster but showed slightly lower predictive power (Tables 5 and 6).  
_Statistical Validation:_ Pairwise Friedman tests with Benjamini-Hochberg correction confirmed the RBF network's superiority over BR and SCG, and demonstrated that BR and SCG both significantly outperform LM on median MSE (\(\alpha\) = 0.01). No significant difference was observed between BR and SCG or between RBF and LM in specific comparisons, underscoring trade-offs among accuracy, convergence speed, and robustness.  
Figure 19: Relative contribution of input features to uniaxial compressive strength predictions.  
Feature Sensitivity: Partial derivatives sensitivity analysis revealed that interconnected porosity is the dominant driver of UCS predictions (54.4%), followed by water content (30.9%) and real density (14.7%). This prioritization aligns with geomechanical expectations in karst-influenced carbonates (Figure 19).
# Automatic Optimization of Deep Learning Training through Feature-Aware-Based Dataset Splitting
## 4 Experimental Results
### Attention Mechanisms Assessment with External Imagery Sources
Figure 15: Attention visualization for \(D_{Ext\_Tst}^{GLI}\) is performed using the top Xception/Nadam model trained with the documented splitting techniques. Model1, Model2, Model3, and Model4 correspond, respectively, to the models trained with the \(D_{Ext\_Tst}^{GLI}\) variants split using SCDIS, FSCIS, HO, and KFCV10. In the first column, the red rectangle represents the most prominent attention area determined through Grad-CAM, while the green rectangle corresponds to the hand-crafted ground-truth. Figure 16: Attention visualization for \(D_{Ext,Tst}^{BDI}\) is performed using the top Xception/Nadam model trained with the documented splitting techniques. Model1, Model2, Model3, and Model4 correspond, respectively, to the models trained with the \(D_{Ext,Tst}^{BDI}\) variants split using SCDIS, FSCIS, HO, and KFCV10. In the first column, the red rectangle represents the most prominent attention area determined through Grad-CAM, while the green rectangle corresponds to the hand-crafted ground-truth. Instead of closely examining the parameters for each specific image, the focus was on calculating the mean values of key parameters for each class within both datasets, aiming to provide an overview of the models' performances at a more generalized level across different classes. Tables 7 and 8 indicate the numeric results associated with the mean values of key parameters corresponding to Xception/Nadam pairs. Table 7 presents the outcomes obtained regarding the \(D_{Ext\_Tst}^{GLI}\), wherein both SCDIS and FSCIS outperform the opponents. In particular, the SCDIS split method was the best in this series, yielding an accuracy of 38%, an \(\mu\)IoU = 0.19, a \(\mu\)GCS = 7220, and a \(\mu\)Dis = 68. Additionally, Table 8 presents the results for the \(D_{Ext\_Tst}^{BDI}\), relying on the same metrics. Once again, the most effective models originated from both of the proposed dataset splitting methods. Notably, these models attained similar performances: \(\mu\)IoU = 0.18, \(\mu\)Acc = 75%, \(\mu\)GCS = 37,604, and \(\mu\)Dis = 135 for FSCIS; and \(\mu\)IoU = 0.18, \(\mu\)Acc = 75%, \(\mu\)GCS = 37,538, and \(\mu\)Dis = 134 for SCDIS. On the other hand, the results show a spotlight pattern--greater IoU scores align with higher classification accuracies, particularly when the region of attention is closer to the ground-truth center. This pattern underscores the relationship between precise localization and the model's effectiveness in correctly classifying objects. These observations across the tables emphasize the significance of spatial awareness in improving the overall accuracy of the classification process.
## 4 Materials and Methods
### Energy Calibration and Dead Layer Estimation Strategy
The numerical values and the related level of approximation are presented as follows: the proton stopping power at \(E_{0}=0.6\) MeV is \(dE/dx|_{Al}=48\) keV um\({}^{-1}\) in aluminium [45], leading to an energy deposition in the \(T_{Al}=0.5\) um thick aluminium layer varying from \(E_{dep,Al}(\theta=0^{\circ})=31\) keV to \(E_{dep,Al}(\theta=45^{\circ})=E_{dep,Al}(\theta=0^{\circ})/\cos 45^{\circ}=44\) keV. At this lowered energy of \(E^{\prime}=E_{0}-E_{dep,Al}(\theta)=565\) keV, the stopping power at the end of the aluminium layer increases by 4.8%. Similar values are obtained for silicon, thus Equation (3) becomes the following:  
\[E_{dep,eff}(\theta,T_{DL})=E_{0}-\underbrace{\left(\frac{dE}{dx}\bigg{|}_{Al} (E_{0})\cdot T_{Al}+\frac{dE}{dx}\bigg{|}_{Si}(E^{\prime})\cdot T_{DL}\right) }_{\text{slope }m}\cdot\frac{1}{\cos\theta} \tag{4}\]  
where \(E^{\prime}=E_{0}-E_{dep,Al}\). Finally, by irradiating the detector at several angles and plotting the deposited energy \(E_{dep,eff}\) vs. \(1/\cos\theta\), one can extract the slope \(m\), from which the dead later thickness can be obtained as follows:  
\[T_{DL}=\frac{m-E_{dep,Al}(\theta=0^{\circ})}{dE/dx|_{Si}(E^{\prime})} \tag{5}\]  
It is clear that the procedure to compute the dead layer thickness \(T_{DL}\) via Equation (5) requires a valid calibration of the Timepix3 Radiation Monitor. In turn, the final calibration can only be obtained by a proper evaluation of the energy lost in the dead layer, requiring  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Particle**} & \(E_{beam}\) & \(E_{dep,Al}\) & \(E_{dep,DL}\) & \(E_{dep,eff}\) & \multirow{2}{*}{\(\Delta E\)**[\%]**} & \multirow{2}{*}{**No. Pixels**} \\  & [**MeV**] & [**keV**] & [**keV**] & [**MeV**] & & \\ \hline proton & 0.597 & 32 & 19 & 0.543 & 9.2 & 8.2 \\  & 0.623 & 31 & 18 & 0.573 & 6.6 & 8.4 \\  & 0.996 & 23 & 14 & 0.954 & 6.3 & 11.1 \\  & 2.070 & 13 & 8 & 2.047 & 1.0 & 18.2 \\  & 3.000 & 9 & 5 & 2.978 & 0.6 & 21.9 \\  & 4.997 & 6 & 3 & 4.978 & 1.0 & 24.9 \\ \hline alpha & 0.981 & 169 & 106 &the value of \(T_{DL}\) as an input. As further described in Section 5, the solution to this puzzle is to set up an iterative process, where a first preliminary calibration of the sensor is derived by assuming \(T_{DL}=0\,\mathrm{\SIUnitSymbolMicro m}\), and is used to obtain an estimate of \(T_{DL}\). The latter value is then used to update the sensor calibration, iterating the process until both quantities converge to a stable result.
## Appendix A  
To calculate \([R]_{n}\), \([S]_{n}\), \([H]_{n}\) and \([D]_{n}\), a backward sweep process is used. For \(j=n\),  
\[[R]_{n}=[Q]^{T}[W][Q]\] (A1)  
\[\{S\}_{n}=-2[Q]^{T}[W]\{d\}_{n}\] (A2)  
\[[H]_{n}=2[T]^{T}[R]_{n}\] (A3)  
\[[D]_{n}=\left[2[B]+2[T]^{T}[R]_{n}[T]\right]^{-1}\] (A4)  
Start from \(j=n-1\) to 1.  
\[[R]_{j-1}=[Q]^{T}[W][Q]+\left[\widetilde{M}\right]_{j-1}^{T}([R]_{j}-[H]_{j} ^{T}[D]_{j}[H]_{j}/2)\left[\widetilde{M}\right]_{j-1}\] (A5)  
\[\{S\}_{j-1}=-2[Q]^{T}[W]\{d\}_{j-1}+\left[\widetilde{M}\right]_{j-1}^{T}([I] -[H]_{j}^{T}[D]_{j}[T]^{T})\{S\}_{j}\] (A6)\[[H]_{j-1}=2[T]^{T}[R]_{j-1} \tag{11}\]  
\[[D]_{j-1}=\left[2[B]+2[T]^{T}[R]_{j-1}[T]\right]^{-1} \tag{12}\]  
The forward sweep starts from \(j=1\) to \(n\):  
\[\{\widetilde{X}\}_{j}=\left[\widetilde{M}\right]\{\widetilde{X}\}_{j-1}+[T]\{r \}_{j-1} \tag{13}\]  
\[\{r\}_{j}=-\Big{[}2[B]+2[T]^{T}[R]_{j+1}[T]\Big{]}^{-1}\{[T]^{T}\{S\}_{j+1}+2[T ]^{T}[R]_{j+1}\Big{[}\widetilde{M}\Big{]}_{j}\{\widetilde{X}\}_{j}\} \tag{14}\]
## 4 Conclusions  
In this study, carbon blocks were fabricated using coal-based isotropic coke and coal tar pitch while varying compression pressure and holding time during compression molding. The study then analyzed the bulk density, porosity, mechanical and electrical properties, and microstructure of the resulting carbon blocks. The major findings of this study are as follows.  
Overall, as the compression pressure increased, the bulk density increased and the porosity decreased. The decreased porosity resulted in improved mechanical and electrical properties. Indeed, the carbon block fabricated at a compression pressure of 130 MPa exhibited the best performance. Microstructural analysis indicated variations in the connectedness of granules, with the size of pores decreasing as the compression pressure increased.  
With an increase in holding time under compression pressure, bulk density and electrical resistivity increased, but the flexural strength decreased. In the microstructure, pores connected in elongated shapes were located along the interfaces of large granules. Some pores were observed within granules and their size was 20 um or less. This microstructure is attributed to the coalescence of kneaded particles, affecting the properties of carbon blocks.  
The major findings of this study confirm that variations in compression pressure affect the connectedness of granules, along with the size of pores. Additionally, variations in holding time under compression pressure are closely associated with the size and shape of both granules and pores.  
Conceptualization, S.-U.G. and U.-S.Y.; Methodology, S.-U.G. and S.-H.L.; Validation, S.-U.G.; Data curation, S.-U.G.; Writing, original draft, S.-U.G.; Writing, Review and editing, S.-H.L. and J.-S.R.; Supervision, J.-S.R.; Funding acquisition, J.-S.R. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a National Research Foundation of Korea grant funded by the Korea Government (MSIP) (NRF-2018R1A6A1A03025761). This work was supported by the project for Industry-University-Research Institute platform cooperation R&D funded Korea Ministry of SMEs and Startups in 2022 (S3310863). This research was supported by the Demonstration Program (C210301001) funded by the Korea Carbon Industry Promotion Agency (KCarbon, Korea).  
Not applicable.  
Data are contained within the article.  
The authors declare no conflicts of interest.
## 4 Parameter Estimation
### Proposed \(\kappa\) Estimator  
Inspired by the OM-LSA algorithm [8], this letter proposed an adaptive \(\kappa\) estimator using a probability-based framework. Given two hypotheses, \(H_{0}(l)\) and \(H_{1}(l)\), which indicate, respectively, direct sound absence and presence in the \(l\)th frame, as in Equation (9)  
\[\begin{array}{l}H_{0}(l):Z(k,l)=Z_{r}(k,l),\\ H_{1}(l):Z(k,l)=Z_{d}(k,l)+Z_{r}(k,l).\end{array} \tag{9}\]  
When the direct sound is absent, the desired \(\kappa\) can be directly estimated according to Equation (6). Accordingly, the proposed \(\kappa\) estimation strategy is to recursively average past estimated \(\kappa\) during periods of direct sound absence, and hold the estimate during direct sound presence. Specifically, the proposed \(\kappa\) estimator is as follows in Equation (10)  
\[\begin{split} H_{0}(l):\kappa(l+1)&=\alpha_{\kappa} \kappa(l)+(1-\alpha_{\kappa})\widehat{\kappa}(l),\\ H_{1}(l):\kappa(l+1)&=\kappa(l),\end{split} \tag{10}\]  
where \(\alpha_{\kappa}\) denotes a smoothing parameter, and \(\widehat{\kappa}(l)\) denotes the estimated \(\kappa\) in the \(l\)th frame. Under direct sound uncertainty, the frame conditional direct sound presence probability \(p(l)\) can be employed by \(p(l)\triangleq P(H_{1}(l)|Z(k,l),k=0,1,\ldots,K)\), and the recursive averaging can be carried out in Equation (11)  
\[\begin{split}\kappa(l+1)&=p(l)\kappa(l)+(1-p(l))[ \alpha_{\kappa}\kappa(l)+(1-\alpha_{\kappa})\widehat{\kappa}(l)]\\ &=\widetilde{\alpha}_{\kappa}(l)\kappa(l)+(1-\widetilde{\alpha}_ {\kappa}(l))\widehat{\kappa}(l),\end{split} \tag{11}\]  
where \(\widetilde{\alpha}_{\kappa}(l)=p(l)+\alpha_{\kappa}(1-p(l))\) is a time-varying smoothing parameter which is adjusted by the frame conditional direct sound presence probability \(p(l)\).  
Now, there are two remaining parts in the proposed \(\kappa\) estimator that need to be determined: (1) the frame conditional direct sound presence probability, \(p(l)\); (2) the estimated \(\kappa\) in the \(l\)th frame, \(\widehat{\kappa}(l)\).