esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Generally, metro emergencies could lead to delays and seriously affect passengers' trips. The dynamic congestion propagation process under metro emergency-caused delays could be regarded as the aggregation of passengers' individual travel choices. This paper aims to simulate the congestion propagation process without intervention measures under the metro emergency-caused delays, which is integrated with passengers' route choice behaviors. First, using a stated preference survey data collected from Guangzhou Metro (GZM) passengers, route choice models are developed based on random regret minimization (RRM) theory under metro emergency conditions. Then, a simulation environment is established using graph cellular automata (graph-CA) with augmented GZM network structure, where an ASEIR (advanced susceptible-exposed-infectious-recovered) model with time delay is proposed as the evolution rule in graph-CA. Furthermore, considering passengers' routing preferences, a quantified method for the congestion propagation rate is proposed, and the congestion propagation process on a subnetwork of the GZM network is simulated. The simulation results show that metro congestion during peak periods has a secondary increase after the end of the emergency-caused delays, while the congestion during nonpeak hours has a shorter duration and a smaller influence range. The proposed simulation model could clearly reflect the dynamic process of congestion propagation under metro emergencies.  
metro emergency-caused delays; route choice behavior; congestion propagation simulation; ASEIR model; quantified congestion propagation rate
# Cattle Weight Estimation Using Fully and Weakly Supervised Segmentation from 2D Images
## 3 Results and Discussion
Therefore, from 0.5 to 0.9, the applied value was increased by 0.1. When the WSS mask was used, since the largest features had a correlation coefficient of 0.763, the applied value was increased by 0.1 from 0 to 0.7. Our model outperformed the SVR model, improving the MAPE by 2.2% and 1.2% when FSS and WSS were used, respectively. In this experiment, we were able to obtain an MAE of 35.01 kg and MAPE of 9.8% using WSS. The FSS mask features yielded the best performance when the correlation coefficient was 0.5. When the value was 0, the performance was slightly degraded. Moreover, as the number of features decreased, the performance tended to decrease. This trend was also true for the WSS mask. The best performance was obtained when the value was 0.2 and 0.3, and the performance decreased as the number of features decreased. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mask** & **Correlation Coefficient** & **Number of Features** & **MAE (kg)** & **MAPE (\%)** \\ \hline \multirow{4}{*}{FSS} & 0 & 12 & 28.23 & 7.9 \\  & 0.5 & 10 & 27.57 & 7.7 \\  & 0.6 & 9 & 28.28 & 7.9 \\  & 0.7 & 7 & 30.92 & 8.7 \\  & 0.8 & 6 & 30.11 & 8.4 \\  & 0.9 & 4 & 28.56 & 8.4 \\ \hline \multirow{4}{*}{WSS} & 0 & 12 & 40.12 & 11.3 \\  & 0.1 & 11 & 40.76 & 11.4 \\ \cline{1-1}  & 0.2 = 0.3 & 10 & 40.90 & 11.5 \\ \cline{1-1}  & 0.4 & 9 & 41.27 & 11.6 \\ \cline{1-1}  & 0.5 & 7 & 44.06 & 12.4 \\ \cline{1-1}  & 0.6 & 5 & 50.36 & 14.1 \\ \cline{1-1} \cline{2-5}  & 0.7 & 2 & 61.62 & 17.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance of RBF kernel-based SVR according to the correlation coefficient interval. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mask** & **Correlation Coefficient** & **Number of Features** & **MAE (kg)** & **MAPE (\%)** \\ \hline \multirow{4}{*}{FSS} & 0 & 12 & 19.14 & 5.3 \\  & 0.5 & 10 & 17.31 & 5.1 \\  & 0.6 & 9 & 18.37 & 5.1 \\  & 0.7 & 7 & 23.20 & 6.5 \\  & 0.8 & 6 & 23.21 & 6.5 \\  & 0.9 & 4 & 23.77 & 6.6 \\ \hline \multirow{4}{*}{WSS} & 0 & 12 & 35.19 & 9.9 \\  & 0.1 & 11 & 35.53 & 10.0 \\  & 0.2 = 0.3 & 10 & 35.01 & 9.8 \\ \cline{1-1}  & 0.4 & 9 & 36.69 & 10.3 \\ \cline{1-1}  & 0.5 & 7 & 35.91 & 10.1 \\ \cline{1-1}  & 0.6 & 5 & 37.65 & 10.6 \\ \cline{1-1} \cline{2-5}  & 0.7 & 2 & 40.49 & 11.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance of the proposed method according to correlation coefficient intervals. If the number of features is large, even those features with small correlation coefficients are included in the input of the model, and a large amount of information can be conveyed to the model. If the number of features is small, only features with large correlation coefficients should be used for analysis. Because the machine learning model can determine the optimal weight, it can be concluded that including a feature with a small correlation coefficient in the model input can help the model perform better than entirely excluding the coefficient.
## 3 Recent Literature Analysis
### Cyber-Physical Systems
#### 3.2.4 Applications to Meet Field-Specific Niches  
Another subset of the most novel recent literature focused on using CPS research to meet challenges unique to specific niches or fields. Unlike the other research, which applied CPS concepts to use cases, these papers specifically focused on issues related to their fields, with CPSS research being applied to address those issues. The field-specific niches identified in the literature included health diagnostics, plastic production, and prefabricated housing.  
In the field of CPS for healthcare, technologies such as residual networks have been utilized for data classification for diseases, providing practitioners with decision support and leveraging existing data that exists on distributed nodes and cloud servers [19].  
CPS was also identified as a key driver for sustainable development in the context of Brazil's plastic industry [53]. The study identified that CPS technologies assisted in increasing safety, reducing manufacturing costs, saving energy, predicting production performance, providing new opportunities for optimization of resources, and reducing pollutants [54; 55; 56; 57; 58].  
Within the prefabricated housing industry, one novel study leveraged CPS in order address sustainability improvement challenges for prefabricated house construction use cases, such as stakeholder interoperability, resource traceability real-time data visibility, and security [59]. In this context, technologies such as the IoT and blockchain were utilized to improve the construction of these dwellings, with the potential for future applicability to wider supply chain case studies.  
While these studies provide novel insight into case-specific issues of unique domains, their application across larger research is potentially limited by scenario constraints and scope. Finding research gaps within this literature theme would be better suited to additional literature searches and reviews that focus on each specific scenario in isolation.
## 2 Materials and Methods
### Data and Pre-Processing  
The data used in this study are from the public domain and span the period 2001-2022. In accordance with the NEP, WUE, CUE, and DSI calculation requirements, we obtained the NPP, GPP, PRE, TEM, ETa, potential evapotranspiration (PET), and normalized difference vegetation index (NDVI) data, and used these in conjunction with the land cover data for the present analysis (Table 1).  
Here, the land cover types in the MRYR were categorized into a total of five land classes: grassland, cropland, forest, shrubs, and other (Figure 2). The land cover data used the 30 m annual land cover dataset for China, which was constructed based on Landsat imagery [36], and for which the data from 2001 to 2022 were selected. Figure 2 shows the land cover in 2012. We mainly studied the grasslands, croplands, shrubs, and forests. The PRE and TEM data were obtained from the National Tibetan Plateau Science Data Center, which is the 1 km monthly precipitation and mean temperature dataset for China (1901-2022) [37]. These two datasets were generated by the Delta spatial downscaling program in China based on the global 0.5\({}^{\circ}\) climate dataset released by the Climate Research Unit (CRU), and the global high-resolution climate dataset released by WorldClim. The dataset was validated against the observations (TEM and PRE) of 496 national meteorological stations between 1951 and 2016, and the validation results were credible [37]. The data have been  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data** & **Source** & **Time Resolution** & **Spatial Resolution** & **Download Site** \\ \hline \multirow{2}{*}{Land cover} & National Cryosphere & \multirow{2}{*}{Annual} & \multirow{2}{*}{30 m} & [[http://www.ncdc.ac.cn/portal/](http://www.ncdc.ac.cn/portal/)]([http://www.ncdc.ac.cn/portal/](http://www.ncdc.ac.cn/portal/)) \\  & Desert Data Centre & & & (accessed on 1 April 2024) \\ \hline \multirow{2}{*}{PRE and TEM} & National Tibetan Plateau & \multirow{2}{*}{Monthly} & \multirow{2}{*}{1 km} & [[http://data.tpdc.ac.cn/](http://data.tpdc.ac.cn/)]([http://data.tpdc.ac.cn/](http://data.tpdc.ac.cn/)) \\  & Science Data Center & & & (accessed on 1 April 2024) \\ \hline \multirow{2}{*}{ETa and PET} & MOD16A2 & 8 days & 500 m & \\ \hline NDVI & MOD13A1 & 16 days & 500 m & [[https://eartheengine.google.com/](https://eartheengine.google.com/)]([https://eartheengine.google.com/](https://eartheengine.google.com/)) \\ \hline GPP & MOD17A2H & 8 days & 500 m & (accessed on 1 April 2024) \\ \hline NPP & MOD17A2H & Annual & 500 m & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data details and sources.  
Figure 2: (**a**) An overview of the MRYR; (**b**) land cover (2012).  
updated to 2022. The MODIS data used in this paper were the ETa, PET, NDVI, GPP, and NPP. To maintain consistency in the spatial resolution, all were resampled to 1 km.
## 3 Methodology
### Residual Connections  
Residual connections [34] are a widely used technique in deep learning models, particularly in architectures like ResNet and its variants, to address the challenges of gradient  
Figure 1: Embedding representation of RoBERTa model, with input token length (variable, e.g., 128, 256, etc.).  
vanishing and explosion. The core idea behind residual connections is to add a direct path that bypasses some layers of the network, allowing the input to be passed directly to the output. This design facilitates the flow of gradients during backpropagation, which improves the training stability in deep networks.  
The primary benefit of residual connections is that even when some layers learn near-identity mappings (i.e., not significantly altering the input), the overall network can still learn complex, nonlinear mappings. By enabling the direct passage of residual information through the layers, the model mitigates the risk of gradient vanishing or explosion, which is especially prevalent in very deep networks. In our model, residual connections are incorporated after the multi-head attention layers and before the linear classification layer. This design helps to preserve the integrity of the features extracted by the attention mechanism, ensuring that both the original and transformed features are utilized for more accurate and robust fraud detection. Equation (3) provides the calculation formula for residual connections as follows:  
\[x_{l+1}=x_{l}+F(x_{l},W_{l}) \tag{3}\]  
where \(x_{l}\) is the input of the \(l\)-\(th\) layer, \(x_{l+1}\) is the input of the \(l\)+\(l\)-\(th\) layer, and \(F(x_{l},W_{l}\) ) is the residual function of the \(l\)-\(th\) layer.
# A Novel Anti-Jamming Technique for INS/GNSS Integration Based on Black Box Variational Inference
## 2 Problem Description
### Estimating MNCM with VBAKF
According to (5) and the logarithm of the Inv-Gamma PDF, we can see that \(\mathbf{R}_{k}\) still obeys the Inv-Gamma distribution, which can be described as  
\[q(\mathbf{R}_{k})=\prod_{i=1}^{d}\text{Inv-Gamma}\Big{(}\mathbf{R}_{k,i};\mathbf{\hat\mathbf{{\kappa}}}_{k,i},\mathbf{\hat{\mathbf{\rho}}}_{k,i}\Big{)} \tag{7}\]  
According to the characteristics of the Inv-Gamma PDF, the expressions of the parameters are given by  
\[\mathbf{\alpha}_{k,i}=\mathbf{\alpha}_{k|k-1,i}+\frac{1}{2} \tag{8}\]  
\[\mathbf{\beta}_{k,i}=\mathbf{\beta}_{k|k-1,i}+\frac{1}{2}\text{E}\Big{[}((\mathbf{Z}_{k}- \mathbf{H}_{k}\mathbf{X}_{k})_{i})^{2}\Big{]} \tag{9}\]  
with  
\[\text{E}\Big{[}((\mathbf{Z}_{k}-\mathbf{H}_{k}\mathbf{X}_{k})_{i})^{2}\Big{]}=\Big{(}\left( \mathbf{Z}_{k}-\mathbf{H}_{k}\mathbf{X}_{k|k-1}\right)_{i}\Big{)}^{2}+\mathbf{H}_{k}\mathbf{p}_{k} \mathbf{H}_{k}^{\text{T}} \tag{10}\]  
where \(\mathbf{P}\) is the predicted error covariance matrix. According to the characteristics of the Inv-Gamma distribution, the MNCM estimation can be expressed as  
\[\hat{\mathbf{R}}_{k,i}=\text{E}[\mathbf{R}_{k,i}]=\mathbf{\beta}_{k,i}/\mathbf{\alpha}_{k,i} \tag{11}\]
## 3 Results and Discussion
### Structural and Morphological Consequences of Processing
#### 3.1.2 Thermal Properties and Crystalline Structure
Although injection and compression molding reduced the glass transition of bio-nanocomposites, more significant percentages of NFC contributed to retaining higher \(T_{g}\). This appreciation can be correlated to better nanoparticle dispersion as a function of the processing stages. The NFC nanoparticles also removed the cold-crystallization phenomenon, increased the degree of crystallinity, and reduced the lamellar thickness after reprocessing. The previously described nucleating behavior for low NFC percentages (1 and 3 wt%) was corroborated in the dog-bone and film specimens, especially regarding the degree of crystallinity, which reached maximum values of nearly 40% for the PLA-400-NFC1 and 39% for the PLA-1500-NFC1 as thermo-compressed films. Furthermore, the melting transition remained as a double-melting peak after compression molding and revealed a minor reduction of the lamellar thickness of the more prominent crystalline population. It also generated a small percentage of a crystalline region with a lamellar thickness of around 10 nm, both in the PLA-400-NFC and PLA-1500-NFC bionanocomposites. Such crystalline domains with lower lamellar thickness may be correlated to the crystallization of low molar mass oligomeric segments shown in the distributions discussed in the previous section. In summary, the chain scission caused by processing significantly influenced the thermal behavior and crystalline morphology of the bionanocomposites. Although some variances could be observed when considering PEG-400 or PEG-1500, differences were non-critical in the crystalline structure. While signs of improved dispersion of NFC were identified as processing advanced in terms of glass transition, the use of NFC in low percentages enhanced nucleation and crystallization with lower lamellar thickness, with a limit concentration of 1 and 3 wt%. From a technological perspective, it can be highlighted that the high degrees of crystallinity reached by the blends and bionanocomposites compared to pure PLA after reprocessing suggest that thermal history and processing circumstances, especially the conditions during cooling (10 \({}^{\circ}\)C/min in this study), are of high relevance for achieving a crystalline morphology that allows for the desired performance. In this regard, fast cooling procedures during processing may permit an adequate balance between amorphous and crystalline fractions to avoid the fragile performance of such highly crystalline structures.
# Large-Scale Automatic Vessel Monitoring Based on Dual-Polarization Sentinel-1 and AIS Data
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Case Study**} & \multicolumn{4}{c}{**Detection Method**} \\ \cline{2-5}  & \(\mathbf{C_{VV-VH}^{un-norm}}\) & \(\mathbf{I_{VH}^{CFAR}}\) & \(\mathbf{I_{VV}^{CFAR}}\) & \(\mathbf{C_{VV-VH}^{un-norm}}\) & \(\mathbf{I_{VH}^{CFAR}}\) \& \(\mathbf{I_{VV}^{CFAR}}\) \\ \hline English Channel & nb. of vessels & 1711 & 1960 & 1815 & 1420 \\ Mexico & & 63 & 80 & 92 & 56 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: SAR detections: coherence based detections and constant false alarm rate (CFAR) vertical transmission with vertical (VV) and horizontal reception (VH) Intensity detections. **Author Contributions:** Conceptualization, R.P. ; formal analysis, R.P., M.C., R.H., P.M., C.L.-M., M.N., P.R. and G.E. ; investigation, R.P., M.C., R.H., P.M., C.L.-M., P.R. and G.E. ; methodology, R.P., M.C. ; validation, R.P. and M.N. ; writing--original draft, R.P. ; writing--review and editing, R.P., M.C., R.H., P.M. and C.L.-M.  
**Funding:** This research was funded by the Luxembourg National Research Fund (FNR) through the AFR-PP SKUA Project under the Grant 2017-1, project reference 11610378. **Conflicts of Interest:** The authors declare no conflict of interest.
## 6 Theoretical and Security Analysis
### Security Analysis
#### 6.2.3 Byzantine Attack  
Committee nodes might exhibit Byzantine faults during the consensus execution phase, such as proposing or validating incorrect blocks. In the PBFT consensus algorithm, a system with \(N\) nodes can tolerate up to \(f\) faulty nodes, provided that \(N\geq 3f+1\). In PoAP, when \(N\) committee members vote to validate a block, at least \(M\) honest committee members need to correctly perform validation, and these correct blocks must receive more than two-thirds of the total committee votes to properly operate the consensus mechanism, as stipulated by (7).  
PoAP can tolerate Byzantine fault from committee members whose total weight does not exceed one-third, and due to the randomness introduced by the cryptographic sortition algorithm for committee member weights, it is considerably difficult for members to collude and form a Byzantine attack that exceeds one-third of the weight. Additionally, after the committee reach consensus, the generated block must be verified by other maintainers not elected as committee members. If more than half of the nodes fail to verify the committee-generated block during the verification process, the consensus fails and restarts, prompting users to vote again to elect a new committee. In the event of consensus failure, committee members will be recorded by all nodes in the network, and they are unlikely to receive support in subsequent elections.  
Overall, PoAP defends against Byzantine attacks through the introduction of election randomness and the trust relationship between users and maintainers. PoAP with \(N\) maintainers and \(C\) committee members tolerate up to \(f=\left\lfloor\frac{3N-C-2}{6}\right\rfloor\) faulty maintainers; due to the randomness introduced by the cryptographic sortition algorithm, more than \(f\) malicious maintainers are needed to launch a Byzantine attack.
## 1 Introduction
### Adversary Model  
We adopted the widely known Dolev-Yao (DY) threat model [8] to evaluate the safety of proposed protocol. According to the DY model, a malicious attacker can intercept, delete, modify, and insert the transmitted data over insecure channel. In addition to the capabilities of these attackers, the specific assumptions of the threat model are as follows:  
* A malicious adversary can steal or obtain the SG device of a legal user and can extract secret parameters stored in the SG device utilizing power-analysis [9; 10]. We also assume that a malicious adversary is able to capture as many SG device as possible.
* A malicious adversary may attempt various attacks, including masquerade, man-in-the-middle (MITM), session key disclosure, and replay attacks [11; 12].
* Trusted authority (TA) and UCs are assumed to be fully trusted and semi-trusted entities, respectively, and cannot be compromised by a malicious adversary.
## 1 Introduction  
The thermoluminescence (TL) dosimetry technique is a well-established radiation dosimetry technique. However, despite the range of commercially available TL dosimeters, the need for a single robust, tissue-equivalent and highly sensitive thermoluminescence dosimeter with excellent signal stability is always desirable for an extended range of dosimetric applications. The development of a highly sensitive tissue-equivalent thermoluminescence dosimeter with excellent signal stability remains a challenge for researchers. Researchers, over the years, have laboriously explored different compounds to develop a single TL dosimeter for a wide range of dosimetric applications.  
Magnesium borate (MgB\({}_{4}\)O\({}_{7}\)) remains a host matrix of interest due to its unique dosimetric characteristics and has been extensively explored since it was first developed 50 years ago [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]. Most of the literature focused on the effectiveness of rare earth dopants [1, 2, 6, 7, 8, 11, 12, 13, 14, 15, 16, 17]. While the dosimetric properties were improved to some extent with the introduction of various dopants, the crystalline nature of the reported materialsrestricted the user due to the need for careful handling of the material [18]. The low effective atomic number (Zeff = 8.4), which is close to human muscle (Zeff = 7.64), makes the MgB4O7-based thermoluminescence dosimeters suitable for clinical radiation dosimetry, whereas the high neutron capture cross-section of 10B attracts researchers to develop a MgB4O7-based neutron dosimeter [8; 19; 20]. The optical fading of these detectors remains a serious concern that affects the signal stability significantly and restricts the material to limited dosimetric applications. The suitability of glass-based luminescence dosimeters in postal dosimetric audits is well recognized, thus attracting attention and more focus on glass-based luminescence dosimeters [21; 22].  
In this study, we report the synthesis, structural and dosimetric properties of a novel MgB4O7:Zn glass-based TLD. To the best of the authors' knowledge, this is the first report of the use of this material for radiation dosimetry.
## 3 Localization Algorithm  
In this section, we will present a dynamic reference selection-based self-localization algorithm for DUANs (DRSL). The proposed algorithm is elaborated from 3 aspects: (1) The optimal reference selection scheme; (2) The dynamic reference selection-based self-localization algorithm; (3) The implementation of DRSL algorithm. Now, we will describe the three aspects in detail.