esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Fire Smoke Distribution and Safety Evacuation of Subway Station Based on BIM
## 4 Fire Smoke Analysis of Subway Station
### Fire Model of Subway Station
#### 4.1.2 Heat Release Rate of Fire  
The heat release rate of fire refers to the amount and speed of heat released by the fire source. The greater the heat release rate, the higher the heat released in the combustion process and the faster the fire combustion speed, that is, the shorter the time it takes for the fire to reach the dangerous state that affects the safety evacuation of personnel [25]. In this study, based on the consideration of the safety factor and combined with the relevant literature at home and abroad [26], the heat release rate of a station fire is set as 5 MW, and the \(t^{2}\) growth coefficient is selected as 0.04689. According to the fire growth formula given by NFPA, the effective growth time of fire is determined. The formula is given in Equation (1):  
\[Q_{f}=\alpha t^{2} \tag{1}\]  
where \(Q_{f}\) is the heat release rate of fire source, \(\alpha\) is the growth coefficient of fire source and \(t\) is the effective growth time of fire source combustion.  
According to the formula, the effective growth time of fire is 327 s, and the simulation time in the fire model in this study is set as 360 s.  
In addition to the heat release rate of the fire source, another important parameter in the fire source setting is the smoke generation rate of the fire. There are many combustion materials in the subway station, accompanied by a large amount of black smoke during the combustion process, which is not conducive to the safe evacuation of people. In combination with the layout of the subway platform and the thermodynamic property parameters of the materials, in reference to the SFPE Handbook of Fire Protection Engineering, the soot yield rate (y\({}_{\text{S}}\)) is set as 0.08 g/g (which is 8%) in the calculation model.
## 3 Experimental Results and Discussions
### Damage Evolutions and Failure Patterns
As the lateral displacement increased from 10 to 30 mm, the number of cracks at both ends of the column gradually increased. New cracks at the bottom occurred within 350-800 mm from the bottom seam, and cracks at the top emerged within a range of 0-600 mm from the top seam. Notably, the number of cracks at the cap-column joint significantly exceeded those at the column-footing joint. As the lateral displacement increased, existing cracks continued to develop and expand, extending from the L2 and L4 sides to the L1 and L3 sides. At \(\delta\) = 35 mm, both the top and bottom seams opened. When the displacement reached 40 mm, the development of cracks in the column essentially stabilized, with no new cracks forming. When the displacement increased from 45 to 65 mm, the cover concrete at the top seam of the column was crushed, exposing the unbonded zone of longitudinal rebar. The cover concrete at the bottom of the column was crushed slightly. When the displacement rose from 70 to 95 mm, the continuous spalling of the cover concrete occurred at both ends of  
Figure 6: Failure patterns of bent B2-PGS. the column along the direction of the actuator. The damage range continually expanded, and the concrete at the joints was severely damaged. When the displacement reached 90 mm, the lateral load-carrying capacity dropped to less than 85% of the maximum. The longitudinal rebar fractured at the cap-column joint, and the bent was destroyed. The failure patterns of bent B2-PGT are shown in Figure 7.
## 3 Set Up Buildings' Settlement Prediction Model
### Set Up LSTM Model
#### 3.2.2 Network Evaluation Metrics  
In order to examine the accuracy of the prediction results of the LSTM neural network, four indexes, namely root-mean-square error (RMSE), mean absolute error (MAE), coefficient of determination (\(R^{2}\)), and relative error (maximum error between predicted and monitored values \(\times\) 100%, expressed as the letter E), were selected to evaluate the accuracy of the prediction model. Among them, the root-mean-square error can visualize the dispersion between the predicted values and the monitoring data of the network model and is easy to calculate; the mean absolute error can measure the average modal length of the error but does not take the direction into account; and the coefficient of determination can measure the explanatory ability of the model for the dependent variable. The formulas for root-mean-square error, mean absolute error, and coefficient of determination are as follows:  
\[\text{RMSE}=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}{(\hat{y}_{i}-y_{i})^{2}}}} \tag{2}\]  
\[\text{MAE}=\frac{1}{n}{\sum\limits_{i=1}^{n}{|\hat{y}_{i}-y_{i}|}} \tag{3}\]  
Figure 4: LSTM model. (**a**) Model prediction flowchart. (**b**) LSTM unit structure.  
\[R^{2}=\frac{\sum\limits_{i=1}^{n}(\hat{y}_{i}-\overline{y})}{\sum\limits_{i=1}^{n}(y _{i}-\overline{y})} \tag{4}\]  
\[\text{E}=\left|\frac{\hat{y}_{i}-y_{i}}{y_{i}}\right|\times 100\% \tag{5}\]  
where \(\hat{y}_{i}\) is the predicted value; \(\overline{y}\) is the average value of the monitoring data; \(y_{i}\) is the monitoring data; \(n\) is the number of monitoring points; RMSE, MAE, the smaller the predicted value and the monitoring value, the more consistent and accurate the model; \(R^{2}\) is the overall evaluation of the model's goodness of fit, the value of the range of [0, 1], the closer the \(R^{2}\) is to 1, the more accurate the model.
## 3 A New Approach for Bias Detection Combining Rule Mining and Regression Analysis
### Recommendation Extraction  
The selected regression models \(\mathcal{M}_{sel}\) obtained from step 3 provide the best 'explanation' of the decisional process. Each model comprises a set of coefficients \(C_{i}=\{\beta_{i,1},\beta_{i,2},\ldots,\beta_{i,k}\}\) together with an output of a statistical test determining whether each element of \(C_{i}\) is significantly different from zero (i.e., whether the associated variable in \(V_{i}\) is likely to have a significant effect on the outcome variable). The minimum level of statistical significance generally considered is 5% (\(p\leq 0.05\)).  
By inspecting each model \(M_{i}\in\mathcal{M}_{sel}\), in this phase, we extract regressors and associated coefficients \(\langle\beta_{i,j},Var_{i,j}=v_{i,j}\rangle\) with \(p_{i,j}\leq 0.05\) for which there is enough evidence to consider possible effects on the outcome variable. Each extracted pair \(\langle\beta_{i,j},Var_{i,j}=v_{i,j}\rangle\) conveys information on the _direction_ and _size_ of the identified bias towards the group \(Var_{i,j}=v_{i,j}\), represented, respectively, by the sign and magnitude of the coefficient \(\beta_{i,j}\). The interpretation of this coefficient, in the case of discrete (as opposed to continuous) variables, is to be interpreted as the change in the outcome variable for observations that belong to the relevant category relative to observations in the baseline category (cf. Appendix A for a more detailed discussion).  
We stress that 'hand-picking' variables with significant \(p\)-values is _not_ a meaningful approach for model selection and interpretation. Differently, the goal of the proposed approach is to identify variables (possibly appearing across several selected models) for which there exists some evidence of correlation with the process outcome, and that may require additional, more rigorous investigation by an analyst or policy maker. To evaluate the strength of the emerging evidence, an analyst can, for example, compare how the associated coefficients for a variable vary across models (see analysis reported in Section 4.2 for an example), or evaluate cross-correlation effects with other variables in subsequent analyses. The output of the proposed approach serves, therefore, as an indication to guide further investigations of the data and the respective generative processes, and should _not_ be considered as a means to automatically generate robust explanations for the data.
## 3 Results  
### Sensor NDVI Value Agreement  
To investigate the degree of sensor agreement when producing NDVI for the same pixel at the same time, we developed one-to-one scatterplots of observed L8 versus observed S2A and observed ASTER individually, along with their counterpart plots of observed L8 versus adjusted S2A and adjusted ASTER (Figure 7). The NDVI images used to generate Figure 7 were acquired in May 2018. Figure 7A,C shows scale inconsistencies between L8 and observed S2A and observed ASTER, respectively, demonstrated by the different data ranges for each sensor. Figure 7A,C also shows a positive yet significantly scattered relationship between observed L8 and observed S2A and observed ASTER. Figure 7B,D show improved (less scattered) relationships after S2A and ASTER have been adjusted by the RF to estimate L8. A lower NDVI saturation point, at around 0.1, is evident for both S2A and ASTER compared to 0.2 for L8. On the higher end, L8 and S2A appear to saturate at approximately 0.8 and ASTER at approximately 0.6. There was less data dispersion around the trendline through the adjusted data shown in Figure 7B,D for both S2A and ASTER (\(R^{2}\) = 0.81 and 0.77, respectively), when compared to the observed NDVI for S2A and ASTER (\(R^{2}\) = 0.52 and 0.56, respectively) shown in Figure 7A,C. Note that the \(R^{2}\) values shown in Figure 7 were calculated from the 1000 point sub-sample used to generate that plot, however the trends are consistent with the results from the entire data set stated previously.
# Using Graph Embedding Techniques in Process-Oriented Case-Based Reasoning
## 6 Experimental Evaluation
### Discussion  
The two experiments show the suitability of sGEM and sGMN and their different variants in similarity-based retrieval. We would like to discuss some points in particular. First, sGEM and sGMN present inconsistency in the effects that different variants have on different domains. Compared to the base models, these effects can lead to completely different results in different domains. For instance, in the second experiment, sGEM\({}_{\text{tree}}\) shows a lower MAE than the base model for CB-II but a significantly higher MAE for CB-I. This leads to the need for individual testing of different methods and subsequent hyperparameter tuning. Since the hyperparameters of the models in our experiments are only tuned for the base models, this can certainly lead to further performance improvements. The performance of the sGEM and sGMN variants can further be improved by performing inference on the GPU instead of the CPU (as done in these experiments). Since computations on the GPU are not possible for all other measures, this is a unique characteristic of the neural-network-based models. Furthermore, it is shown that the additional integration of the semantic information of sGEM and sGMN and the usage of Graph Neural Networks (GNNs) can outperform the simple, structural measure, EBM. This confirms the assumption that partly motivated this paper. The effect might be amplified by domains that are even more complex than CB-II, such as argumentation [25] or flexible manufacturing [4]. Additionally, it is shown that sGEM and sGMN, although integrating rich semantic information, are not able to consistently outperform FBM in MAC/FAC retrieval tasks. The usage of manually modeled features and the integration of expert knowledge seems to be superior to automatic learning. Nevertheless, automatically learned measures such as sGEM, sGMN, and EBM still have the advantage of greatly reduced effort when adapting to changes in the similarity definition or the underlying domain models. The overall effort of knowledge acquisition is greatly reduced when using an automatically learned measure. Automatic learning is also helpful when dealing with user-labeled data, since users are usually not capable of labeling cases with concrete similarity values but rather with binary indications, e.g., similar or not similar. The neural networks can learn similarity functions based on these labels with a modified loss function [23; 37]. In contrast, this is not straightforward for A*M and FBM as it involves manual configuration effort.
## 4 Discussion  
The goal of our study was to estimate the degree of robustness of DStr and DSize against image binarization without focusing on the specific problem statement of morphological image analysis. It has transpired that the results of the experiments we performed do not guarantee that applying the EM to the morphological analysis of any grayscale images will always result in a high level of robustness for DStr and DSize. Thus, when the EM is applied, the robustness of DStr and DSize has to be assessed. Moreover, it was demonstrated that the watershed algorithm and Gaussian blur smoothing are tools that can be used to create the binarization of a grayscale image with low edge gradient and make it possible to apply the EM for such image analysis.  
Areas of application of EM and its limitations with respect to analysis of binary images are described in our previous work [1; 2; 3], which is also applicable for GAI in addition to two new limitations. It is also necessary to point out that the robustness of DStr and DSize depends on various program settings, which have not been included in our experiments. This is a limitation of the present work. Another limitation is that the contours which make up the contour map are manually chosen to quantify DStr and DSize, since some contours have noise and negligible image details. Gaussian blur is one of the possible tools that allows us to reduce noise in a contour map.  
Overall, we consider the results of the present work as the first step toward justifying the applicability of the EM to the processing of a GAI. Different known and unknown assumptions and limitations still have to be explored with respect to various image categories and the goals of each category's morphological analysis.
## 1 Introduction
### Novelty and Contributions  
The main contributions of the paper are summarized as follows.  
* We propose the partitioning process, for the first time, in fine-grained computational offloading in MEC. The proposed work considers the cost of partitioning a task into multiple components and selects the possible partitioning option with minimum cost in all possible partitioning options;
* We combine the selection of task partitioning from \({}^{m}\mathbb{Z}_{n}\) possible options and partial offloading policy from \(2^{n}\) possible options and model as a multi-label classification problem. The computational overhead of finding minimum cost in terms of energy consumption and execution delay while considering the offloading policy and partitioning simultaneously becomes \(O(^{m}\mathbb{Z}_{n}2^{n})\). Therefore, to avoid this huge computation complexity, we propose a supervised deep learning approach to solve both problems simultaneously with a complexity of trained DNN of \(O(1)\). We formulate a comprehensive cost function, which considers multiple parameters, namely, network fluctuations and computing resources of MESs, propagation delay, the time delays, and energy consumptions due to partitioning, transmission, execution, and reception;
* Through extensive simulation results we demonstrate the superiority of the proposed technique, compared with total offloading technique (TOT), random offloading technique (ROT), deep learning-based offloading technique (DOT), and energy efficient deep learning-based offloading technique (EEDOT), in terms of energy consumption and execution delay of UEs;
* The UEs can use the trained DNN to find the offloading policy and partitioning for \(n\) number of components with minimum cost. Since the cost function depends on both energy consumption and time delay, therefore, the end-user will consume minimum energy with faster decisions on selecting the best partitioning and offloading policy for \(n\) number of components per task.  
The rest of the paper is organized as follows. In Section 2, we present the system model. Section 3 presents the proposed technique. Simulation results are presented in Section 4, and Section 5 concludes the paper.
# Discrete Time Model for Process Meta Language with Fictitious-Clock
## 4 Experimental Results
### Fischer's Mutual Exclusion Protocol
Additionally, to check the effect of higher timer values, we have increased the value of deltaC from 2 to 16. The deltaB variable was always equal to the default value of 1. When deltaC was increased to 16, the fictitious clock of DTMP has generated a noticeably lower number of states. Additional communication and execution of the Monitor process generated more transitions. As expected, for this type of protocol, DT Spin's low-level implementation of discrete time outperforms the high-level approach of the DTMP. Figure 3: Formal verification with Spin and SpinRCP. However, DTMP is part of the SDL framework for the semi-automated model extraction from the SDL system. ### Positive Acknowledgment with Retransmission Protocol

The PAR protocol was described in [38]. It is a one-way data-link protocol used over unreliable transmission channels StoR and RtoS that connect the sender and the receiver. When a message is sent to the channel, three things can happen [36]:

1. the message is transfered correctly,
2. the message is corrupted in transit,
3. the message is lost. Each time the message is sent to the StoR channel, the sender starts a timer. The sender waits for an acknowledgment before the new message is sent. Protocol uses only a positive type of acknowledgment. The message is re-transmitted at period of _To_ until the receiving host acknowledges reception of the message. Premature expiration of the timer can disturb the functioning of the protocol, since duplicates of the message would be sent before positive acknowledgment managed to reach the sender. Consequently, there can be multiple acknowledgments in the channel RtoS. If the next message is lost in the channel StoR, the sender will mistakenly interpret the received acknowledgment as a confirmation of the successful transfer of the lost message. Therefore, the re-transmission period should be greater than the delays in both channels and the message processing time at the receiver. The DT Promela model of the PAR protocol is shown in Listing 8 [20]. It consists of two process definitions, Sender and Receiver. Process instances are created during system initialization. Delay of the StoR channel is defined by the \(dK\) variable, while variable \(dL\) defines the delay of the RtoS channel. The value of the variable \(dR\) defines message processing time at the Receiver. The sum of both channel delays and the Receiver's message processing time defines the minimum period of re-transmission, \(To>(dK+dL+dR)\). Message values are calculated as modulo of number 8, which is defined by the MAX.
# Emergence of White Organic Light-Emitting Diodes Based on Thermally Activated Delayed Fluorescence
## 3 Approaches for WOLEDs Based on TADF
### WOLEDs Based on All TADF Emitters  
#### 3.2.1 WOLEDs with Conventional Fluorescent Hosts and all TADF Materials  
To realize WOLEDs based on all TADF emitters, the most directed way is the use of conventional fluorescent hosts and all TADF materials. For this way, all blue, green and red emitters are TADF materials. Besides, the conventional fluorescent host should possess high T\({}_{1}\), particularly higher than the blue TADF materials. Otherwise, the triplet excitons of emitters would be quenched by the host, resulting in low performance. Moreover, high-T\({}_{1}\) charge transport materials should be selected, confining the triplet excitons [116; 117; 118].  
In 2014, [PERSON] et al. used this approach to realize high-efficiency TADF WOLED [46]. Figure 2 depicts the device structure: ITO/1,4,5,8,9,11-hexaazatriphenylene hexacarbonitrile (HAT-CN, 10 nm)/9,9\({}^{\prime}\),9\({}^{\prime\prime}\)-triphenyl-9H,9\({}^{\prime}\)H,9\({}^{\prime\prime}\)H-3,3\({}^{\prime}\):6\({}^{\prime}\),3\({}^{\prime\prime}\)-tercarbazole (Tris-PCz, 35 nm)/10 wt. % 1,2,3,4-tetrakis(carbazol-9-yl)-5,6-dicyanobenzene (4 CzPN): 3,3-Di(9H-carbazol-9-yl)biphenyl (mCBP, green EML) (x nm)/6 wt. % 4 CzPN: 2 wt. % 41,4-dicyano-2,3,5,6-tetrakis (3,6-diphenylcarbazol-9-yl)benzene (CzTPN-Ph): mCBP (red EML) (y nm)/10 wt. % 9-(3-(9H-carbazol-9-yl)-9-(4-(4,6-diphenyl-1,3,5-triazin-2-yl)phenyl)-9H-carbazol-6-yl)-9H-carbazole (3 CzTRZ): 2,8-bis (diphenylphosphoryl) dibenzo-[b,d] thiophene (PPT, blue EML) (z nm)/PPT (50 nm)/LiF/Al. In this device, the T\({}_{1}\) level of mCBP host is 2.9 eV, which is much higher than that of blue, green and red TADF emitters, ensuring the high efficiency. Besides, PPT has a high T\({}_{1}\) of 3.1 eV, suggesting a good confinement of the triplet excitons. By optimizing the charge generation zone via the adjustment of different EML thickness (the total thickness is set to be x + y + z = 15 nm), the WOLED achieved a maximum EQE of over 17%, a peak PE of 34.1 lm/W with CIE coordinates of (0.30, 0.38).
## 4 Experiments
### Performance Comparison
#### 4.4.2 Comparative Experiments on the LEVIR-CD Dataset  
The quantitative results of the comparison experiments conducted on another public dataset LEVIR-CD are exhibited in Table 5. As Table 5 shows, our algorithm was significantly better than the rest of the algorithms in the main metrics of performance, the F1 score, IoU, and OA, and was 0.82%, 1.37%, and 0.06% better than the second best algorithm, respectively. Figure 8 depicts the visualization results of the comparison experiments. As the spectral information of the images taken at different times may be different, it may cause misdetections or missing detections; as shown in the first line, with respect to the changed area at the bottom right corner, because of the influence of the spectral information, some other methods showed red and some showed green, while our method detected the changed area more accurately. As seen in lines 2 and 7, our method detected both large target regions and small change regions relatively accurately, due to the proposed idea of combining the local features from the encoder as well as the more global features from the decoder. In lines 3, 4, 5, and 6, the shadowed parts of the images caused the comparison methods to easily detect non-changing regions as changing regions; however, our method successfully avoided such pseudo-change.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Model** & **Params.(M)** & **Flops(G)** & **Precision** & **Recall** & **F1** & **IoU** & **OA** \\ \hline
**FC-EF** & 1.29 & 2.92 & 82.28 & 70.66 & 76.03 & 61.33 & 97.92 \\
**FC-Siam-conc** & 1.93 & 4.55 & 40.09 & 73.84 & 51.97 & 35.11 & 93.63 \\
**FC-Siam-diff** & 1.75 & 3.99 & 38.82 & 71.80 & 50.40 & 33.69 & 93.40 \\
**CDNet** & 1.36 & 21.52 & 92.16 & 83.18 & 87.44 & 77.68 & 98.88 \\
**STANet** & 16.93 & 6.58 & 91.25 & 86.18 & 88.64 & 79.61 & 98.97 \\
**BiT** & 3.55 & 10.6 & 86.07 & 85.61 & 85.84 & 75.19 & 98.68 \\
**SNUNet(48)** & 28.34 & 97.87 & 88.35 & 87.80 & 88.07 & 78.69 & 98.89 \\
**ChangeFormer** & 267.90 & 129.27 & 93.44 & 85.70 & 89.40 & 80.84 & 99.12 \\ \hline
**DETDNet** & 56.33 & 18.08 & 93.84 & 91.59 & 92.70 & 86.40 & 99.32 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison results on the BCDD Dataset.  
Figure 7: Visualization results of several CD methods on the BCDD dataset. (**a**) T1 images. (**b**) T2 images. (**c**) Ground truth. (**d**) FC-EF. (**e**) FC-Siam-conc. (**f**) FC-Siam-diff. (**g**) CDNet. (**h**) STANet. (**i**) BIT-CD. (**j**) SNUNet. (**k**) ChangeFormer. (**l**) DETDNet.