esa-sceva/satcom-chunk-collection huggingface dataset:
# Risk Assessment of Drilling and Blasting Method Based on Nonlinear FAHP and Combination Weighting
## 5 Discussion
### Rationality of Evaluation Results  
Through comparative analysis of four weighting calculation methods (FAHP, LFPP-FAHP, FAHP-SPA, and the proposed comprehensive weighting method), this study reveals the characteristic differences between various approaches in risk assessment. As shown in Table 10, the conventional FAHP method demonstrates weight amplification effects in both medium-high and medium risk indicators due to its reliance on subjective expert judgments, confirming the bias accumulation issue in fuzzy mathematics when handling uncertainties. The LFPP-FAHP approach, while optimizing consistency through linear programming, results in more extreme weight distributions, indicating that sole dependence on mathematical optimization may disrupt the equilibrium of group decision-making. In contrast, the FAHP-SPA method yields the most conservative risk assessment outcomes through set pair analysis theory, though its abnormally elevated high-risk indicators expose limitations in handling extreme scenarios.  
The comprehensive weighting method proposed in this study achieves more rational weight allocation while maintaining risk level discrimination. Specifically, the medium-risk value (0.2625) shows a 7.8% reduction compared to FAHP, the medium-high-risk value (0.3001) demonstrates a 14.2% decrease relative to LFPP-FAHP, and the high-risk indicator (0.1429) exhibits a 23.0% reduction versus FAHP-SPA. This balanced performance stems from the method's dual optimization mechanism: compatibility testing to correct subjective expert biases and dynamic weight adjustment algorithms to prevent extreme values. Empirical data confirm that the proposed approach not only overcomes FAHP's subjectivity limitations and LFPP-FAHP's over-optimization issues, but also better preserves risk assess  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Calculation Model** & **Evaluation Result** & **Risk Level** \\ \hline Conventional FAHP & [0.0924, 0.2052, 0.2847, 0.3196, 0.0981] & Level 4 \\ This study & [0.1061, 0.1883, 0.2625, 0.3001, 0.1429] & Level 4 \\ LFPP-FAHP & [0.0753, 0.1698, 0.3095, 0.3497, 0.0957] & Level 4 \\ FAHP-SPA & [0.1152, 0.1749, 0.2448, 0.2796, 0.1855] & Level 4 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of evaluation results of different calculation models.  
ment sensitivity compared to FAHP-SPA, thereby establishing a novel technical pathway for weight determination in complex decision-making scenarios.
## 4 Experimental Results
### Additional Experiments
#### 4.4.2 Calibration with Violated Preliminaries  
Additionally, we aim to assess the impact of calibrating recordings in scenarios where the preliminary requirements are significantly violated. We identify three cases characterized by different types of violations. The first case involves a train positioned directly in front of a switch, where the _ego-track_ is not yet intersected by the merging track (13_station_ohlsdorf_13.1). In the second case, the train is located on a switch, where the _ego-track_ is intersected by the merging track (14_signals_station_14.1). In the third case, the train is located on a narrow curve (19_vegetation_curve_19.1).  
Figure 12b and Table 4 present the results of these scenarios. The analysis reveals that whether the result is improved or deteriorated depends heavily on the type of violation and the backbone model used. This variability suggests that the behavior of calibration under violated preliminary conditions cannot be consistently defined.
## 4 Simulation Experiments, Results, and Discussion
### Simulation Environment  
Table 3 summarize values of all simulation parameters. The number of UAVs is 1, 3, 5, 7 and 9 in five different simulation runs and here we have not considered the possibility of the intersection of multiple-UAV except in the first simulation run where the number of UAVs is 1. As our system transmits from single UAV to multiple-UAV systems and the number of UAVs also increases in various simulation runs, they are able to serve more and more IoT devices improving the scalability of the entire system. That is why the number of IoT devices has also increased in different simulation runs. Our proposed technique EETO-GA is compared with other state-of-art trajectory optimization protocols, such as \"comprehensive energy consumption (CEC)\", \"propulsive energy minimization (PEM)\" and \"Age of information based tragedy planning (AoI-IP)\". Metrics of performance are UAV-Energy-Consumed(UAVE), IoT Device-Energy-Consumed(IoTDEC), Average Delay (AD), and Percentage of Successfully Completed Tasks (PSTK).  
Figure 2: Flowchart of the GATE.
## 7 Conclusions  
In this paper, we have presented a new distributed infrastructure-free congestion avoidance and traffic optimization system for VANET named DIFTOS. The proposed system is completely infrastructure-free and does not rely on any external infrastructure such as roadside units (RSU) or 3G/4G networks. We have proved by means of simulation the scalability and robustness of DIFTOS in different traffic conditions.  
Figure 10: Accident count vs. travel time.  
Figure 9: Accident count vs. computational cost.  
Future Directions: The simulation results prove the effectiveness, robustness, and scalability of DIFTOS. However, DIFTOS still needs many improvements, in this section, we will list our future directions to improve DIFTOS:  
* The inter-vehicle communications are encrypted. However, as long as vehicles' traces are fully disclosed, the user's identity in some cases can be inferred even if pseudonyms are used. Therefore, DIFTOS still needs to be improved from the privacy point of view. Enhancing DIFTOS with an additional security and privacy framework is one of our future research directions.
* DIFTOS's distributed architecture improves the scalability of the system and reduces the load on servers, as the path requests are treated as different servers. However, at upper-level servers, it becomes difficult for a single vehicle to maintain the RRM, due to the large size of data, and the huge amount of computation needed to resolve many path requests simultaneously. Therefore, optimizing DIFTOS to distribute the server calculation as a vehicular cloud server, where many vehicles collaborate to maintain RRM, is one of our future directions.
* The current hierarchical partition still needs improvements, as cross levels paths still need to solicit upper levels servers. In our next work, we will try to change the path request procedure, where two adjacent servers of the same level can cooperate to solve a path request without the need to forward it to higher level servers.  
N.A., W.Z. and Y.A. conceived and designed the system; S.D. and N.A. Analyzed the data-set, mapped the values and performed the simulations. All the authors contributed to manuscript preparation.  
This work is supported by the National Natural Science Foundation of China (Grant No. 61273205).  
The authors declare no conflict of interest.
# A Data-Driven Forecasting Strategy to Predict Continuous Hourly Energy Demand in Smart Buildings
## 3 Methodology and Approach
### Accuracy Metrics  
To evaluate the performance of the different models, accuracy metrics such as coefficient of determination (R\({}^{2}\)), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were used.  
R\({}^{2}\) analyzes how the model approximates the actual data and is calculated according to Equation (1) [35]:  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-y_{i})^{2}}{\sum_{i=1}^{n}(y_{i}-y_{i})^{2}} \tag{1}\]  
RMSE analyze the precision of various anticipating standards and is calculated according to Equation (2) [36]:  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}(y_{i}-y_{i})^{2}}{n}} \tag{2}\]  
MAE indicate the separation between anticipated and real value and is calculated according to Equation (3) [37]:  
\[MAE=\frac{\sum_{i=1}^{n}\left|\frac{(y_{i}-y_{i})}{y_{i}}\right|}{n} \tag{3}\]  
MAPE expresses average absolute error as a percentage and is calculated according to Equation (4) [38; 39]:  
\[MAPE=\frac{\sum_{i=1}^{n}\left|\frac{(y_{i}-y_{i})}{y_{i}}\right|}{n}\times 1 00\% \tag{4}\]  
where \(y_{i}\) is the anticipated value, \(y_{i}\) is the real value, \(y_{i}\) is the average value, and \(n\) is the total number of estimates.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Python** & & **Architectures and** \\  & **Library** & & **Hyper-Parameters** \\ \hline \multirow{7}{*}{TFT} & \multirow{7}{*}{PyTorch} & \(\bullet\) & hidden\_size = 700 \\  & & \(\bullet\) & lstm\_layers = 2 \\  & & \(\bullet\) & attention\_head\_size = 4 \\  & & \(\bullet\) & drop rate = 0.21 \\  & & \(\bullet\) & batch\_size = 128 \\  & & \(\bullet\) & hidden\_continuous\_size = 18 \\  & & \(\bullet\) & log interval = 10 \\  & & \(\bullet\) & learning rate = 0.0598 \\  & & \(\bullet\) & gradient\_clip\_value = 0.048 \\  & & \(\bullet\) & gradient\_clip\_val = 0.048 \\  & & \(\bullet\) & The loss function used was Quantile Loss with seven quantiles. The model \\  & & & with the best epoch in the loss function was selected. The calendar variables \\  & & & were used as time-varying known. \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._
# Graphene-Based Flexible Electrode for Electrocardiogram Signal Monitoring
## 2 Research on Graphene-Based Flexible ECG Electrodes
### Bionic Graphene ECG Electrodes
#### 2.1.3 Avian Nest-Inspired ECG Electrode  
For ECG electrodes that need to be worn for a long time, their mechanical properties should adapt to the multiple demands of the complex wearable environment. Although graphene has intrinsic flexibility, chemical stability, and high electrical performance, the conductive networks formed by graphene can easily be cracked without a proper supporting framework. The mud-based bird nest has marvelous firmness due to the filiform saliva with polysaccharides excreted by the bird [41]. Inspired by the solidification process of the avian nest, [PERSON] et al. reported a durable and non-disposable graphene skin-electrode for long-term ECG signal monitoring [42]. By mimicking the architecture and fabricating process of the avian nest, polymer nanofibers (consisting of CuCl\({}_{2}\), SEBS, and phenolic resin) prepared by electrospinning were captured by a substrate with CVD monolayer graphene film. After annealing, the nanofibers are attached tightly to the graphene film through \(\pi\)-\(\pi\) interactions between phenol side groups and metal cations, which benefits the growth of the crystalline graphene structure. Finally, the whole graphene-polymer nanofibers structure was embedded in SEBS solution to form the electrodes (Figure 1d). The prepared electrode has a structure similar to that of an avian nest, with graphene-polymer-nanofiber-based conductive networks and semi-embedded SEBS for mechanical strength enhancement. When used as an ECG electrode, it shows high conductivity (\(\sim\)150 \(\Omega\)/square) and a high signal-to-noise ratio (30 dB). Besides, the semi-embedded electrode shows excellent washability and repeatability during signal monitoring.
## 3 Model Definition
### Convergence Study  
In order to get the most proper meshing size according to the results and elapsed time during analysis processing, five element sizes are used, 0.15 m, 0.2 m, 0.28 m, 0.35 m, and 0.4 m, to illustrate the best result of the convergence study [38]. The corresponding numbers of elements are 52,236, 50,711, 17,904, 15,046, and 14,988, respectively. The results of these five models illustrate the accuracy of the analysis. Figure 8 illustrates the maximum total deformation results with every meshing size for the first case of loading.  
The results illustrate that the difference between results is not magnificent. In our case, the difference between the minimum result from the coarse meshing and the maximum result from the fine meshing is 1.14%, which is not effective in comparison with the elapsed time in analysis. The difference between the analysis time between the fine meshing and coarse meshing is about 300%. The difference in the elapsed time between the 0.15 m meshing size and the 0.2 m meshing size is about 200%. These results are candidate the meshing sizing of 0.2 m for elements to be the most proper element size.
## 6 Simulation Results
### Multiple Actors  
In this section, we study the performance of TS in the scenario with multiple actors. The energy consumption and search delay are recorded when all the actors receive the search packet. The number of actors grows from 1 to 6. The \(\tau_{A}\) is fixed at 5, and the \(\varepsilon\) is set as 100.  
As depicted in Figure 14, the energy consumption in XYLS rises rapidly with the increase of actors. The reason for this is that every actor has to execute location update individually in XYLS. Moreover, the overhead of location update dominates the energy consumption of XYLS. Therefore, XYLS has poor performance with multiple actors. By contrast, the growth of actors has smaller impact on ERS, TS and TSR. It is because the search strategy is developed based on flooding, such that multiple actors can receive the search packet in one search attempt. Nevertheless, the growth of actors increases the average number of search attempts and hence increases the energy consumption. In addition, with the help of the redundancy reduction scheme, the energy consumption in TS-R performs better with more actors.  
As shown in Figure 15, the search delay in TS is less than that in XYLS when there are fewer actors and becomes larger than that in XYLS when there are four or more actors. The reason for this is that the increase of actors can lead to additional search attempts, which generates more search delay than  
Figure 12: The search delay over the threshold value of trail age \(\varepsilon\).  
Figure 13: The search delay over the waiting time of actor \(\tau_{A}\).  
that in XYLS. The redundancy reduction scheme has no influence on the search phase, thus the search delay in TS and TSR are almost the same.
# Unmanned Aerial Vehicles Enabled IoT Platform for Disaster Management
## 4 Case Study: NDN Architecture for Ground IoT Network
### Proposed Methodology
#### 4.2.1 Results and Discussion  
The evaluation of the pull-push NDN-based real-time wildfire disaster detection mechanism was carried out through nodes average response time (ART) and communication architecture reliability (CAR).
# Chitosan-Coated Nanoparticles: Effect of Chitosan Molecular Weight on Nasal Transmucosal Delivery
## Materials and Methods
### Preparation of the Lipid-Core Nanocapsules Coated using a One-Pot Technique  
Chitosan-coated simvastatin-loaded lipid-core nanocapsules were prepared according to the method of interfacial deposition of pre-formed polymers already reported in literature [35]. An organic phase (25 mL of acetone) containing the polymer (PCL, 0.04 g), sorbitan monostearate (0.016 g), and caprylic/capric triglyceride (0.048 mL) was kept under magnetic stirring at 40 \({}^{\circ}\)C. After complete dissolution of the components, an ethanolic solution (4 mL) containing lecithin (0.025 g) was added into the organic phase, and finally, simvastatin (0.010 g) was added and completely dissolved. The aqueous phase (50 mL) contained 0.1% (\(w/v\)) chitosan, prepared as a dilution from a 0.5% (\(w/v\)) of chitosan solution in 1% (\(v/v\)) acetic acid. The organic phase was injected using a funnel into the aqueous phase under moderate magnetic stirring. The solvents were eliminated at 40 \({}^{\circ}\)C to a final volume of 10 mL, by the use of a rotary evaporator, Buchi(r) R-114 (Flawill, Switzerland). The formulations obtained were named LNC\({}_{\text{SVT-LHMvchit}}\) when low-MW chitosan (viscosity 9 cP) was used, and LNC\({}_{\text{SVT-HMvchit}}\) when high-MW chitosan (viscosity 114 cP) was used. Blank nanocapsules (LNC\({}_{\text{LMWvchit}}\) and LNC\({}_{\text{HMWvchit}}\)) were also prepared, omitting the simvastatin from the organic phase preparation.
## 3 System Model and Problem Formulation  
In this section, we begin with the system model, then provide the definition of the dynamic SFC placement problem. Finally, we formulate the problem in detail. The major symbols and variables used in this paper are listed in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \multicolumn{2}{c}{**Physical Network Symbols**} \\ \hline \(G=(\mathcal{V},\mathcal{L})\) & IoT-MEC network graph with network nodes (\(\mathcal{V}\)) and links (\(\mathcal{L}\)) \\ \hline \(C_{c}^{cpu}\), \(C_{uv}^{bw}\) & CPU and bandwidth capacity of the cloudlet node (\(c\in\mathcal{C}\)) and link (\(uv\in\mathcal{L}\)) \\ \hline \(\omega_{c}^{cpu}\), \(\omega_{uv}^{bw}\) & The available ratios of CPU and bandwidth, respectively \\ \hline \(d_{c}\), \(d_{uv}\) & Delay on the cloudlet node (\(c\in\mathcal{C}\)) and link (\(uv\in\mathcal{L}\)) \\ \hline \(\xi_{i,uv}^{loss}\) & Packet loss rate of the physical link (\(uv\in\mathcal{L}\)) \\ \hline \multicolumn{2}{c}{**SFC Symbols**} \\ \hline \(S_{i}\), \(D_{i}\) & The source and destination nodes, respectively \\ \hline \(R_{i,p}^{cpu}\), \(R_{i}^{bw}\) & The CPU and bandwidth consumption of IoT-SR\({}_{i}\), respectively \\ \hline \(R_{i}^{delay}\), \(R_{i}^{loss}\) & The maximum tolerated delay and packet loss rate of IoT-SR\({}_{i}\), respectively \\ \hline \(F_{i}\) & The VNF set requested by IoT-SR\({}_{i}\) \\ \hline \(\mathcal{P}\) & The set of all VNF types \\ \hline \(\mathcal{P}_{i}\) & The VNF type set that belong to IoT-SR\({}_{i}\) \\ \hline \multicolumn{2}{c}{**Binary Variables**} \\ \hline \(x_{pc}\) & Whether VNF type \(p\in\mathcal{P}\) can be placed on the cloudlet (\(c\)) or not \\ \hline \(x_{i}^{uv}\) & Whether \(uv\in\mathcal{L}\) is traversed by IoT-SR\({}_{i}\) or not \\ \hline \(\delta_{i}\) & Whether IoT-SR\({}_{i}\) is admitted or not \\ \hline \(x_{uv}^{mm}\) & Whether link \(mm\) of IoT-SR is mapped to physical link \(uv\) or not \\ \hline \(x_{p}^{m}\) & Whether VNF \(m\) is of type \(p\) or not \\ \hline \(x_{i}^{c}\) & Whether IoT-SR\({}_{i}\) chooses cloudlet \(c\) to place the required VNF or not \\ \hline \multicolumn{2}{c}{**Other Variables**} \\ \hline \(\mathcal{M}\) & The set of all VNF instances \\ \hline \(n_{c}^{p}\) & Number of VNF instances of type \(p\) placed on cloudlet \(c\in\mathcal{C}\) \\ \hline \(c_{p}^{act}\) & The cost of activating VNF type \(p\) \\ \hline \(\tau\) & The penalty of rejecting an IoT-SR \\ \hline \end{tabular}
\end{table}
Table 1: Notations Table.
## 2 Research Methods and Relevant Theories
### SEM Model Construction and Hypotheses  
This investigation, in contrast to traditional evaluation methods that directly build evaluation models using expert experience, employed the SEM approach to establish hypotheses and validate the significance of each factor before conducting a comprehensive evaluation [24]. The SEM calculation method consisted of two fundamental components: the measurement model and the structural model. The measurement model described the correlations between latent variables and manifest variables, comprising latent variables and observed variables. In contrast, the structural model was taken to describe the causal relationships among latent variables and included only latent variables. In the measurement model, Step 1 involved computing the relationships between indicators and latent variables.  
\[\mathrm{X}=\Lambda\mathrm{x}\xi+\delta \tag{1}\]  
\[\mathrm{Y}=\Lambda_{\mathrm{y}}\eta+\varepsilon \tag{2}\]  
Among these, X represents exogenous indicators, while Y represents endogenous indicators. \(\Lambda\mathrm{x}\) reflects the relationship between exogenous indicators X and exogenous latent variables \(\xi\), while \(\Lambda\mathrm{y}\) denotes the correlation between endogenous indicators Y and endogenous latent variables \(\eta\). \(\delta\) and \(\varepsilon\) are the measurement errors for X and Y, respectively.  
Figure 2: Preliminary theoretical model.  
Step 2: The association between latent variables was calculated using the following formula:  
\[\eta=\text{B}\eta+\Gamma\xi+\zeta \tag{3}\]  
\(\eta\) represents endogenous latent variables; \(\xi\) represents exogenous latent variables; B represents the relationships between endogenous latent variables; \(\Gamma\) represents the impact of exogenous latent variables on endogenous latent variables; and \(\zeta\) represents the residual term of the structural equation, reflecting the unexplained portion of the equation.  
The path coefficient values (also known as factor loadings) obtained from the above formula indicate the degree of influence of measurable variables on latent variables; a larger value signifies a greater impact. Indicator weights were obtained through the following normalization algorithm:  
\[W_{s}=\varphi_{\text{x}_{i}}/\sum_{i=1}^{n}\varphi_{\text{x}_{i}} \tag{4}\]
# Online Deformation Measurement of Laser Repair Substrate Based on Orthogonal Sampling Moire
## 3 Online Measurement System of Substrate Deformation in Laser Repair
The high-power blue LED lamps, CCD camera and designed filter group compose the optical imaging system, which is used to capture the deformed grating images during the repair process, as shown in Figure 3. Additionally, the system parameters are set as follows: the LED light source power is 200 W; the camera resolution is fixed up to 2448 \(\times\) 2048 pixels; and the frame rate is configured as 5-15 frames per second. The deformed grating pattern recorded in the repair process is shown in Figure 7. It can be seen that the gray scale of the image is uniform and that the interferences of laser repair environment has been suppressed. It should be noted that, although the grating has a binary color distribution physically, its gray scale distribution is close to sinusoidal curve by moving CCD to acquire defocus images [23]. Part of the grating pattern (line segment AB in Figure 7) is intercepted and the sinusoidal function is fitted according to its gray distribution, which is \(y=77.3+45.65\sin(0.2686x+2.2085)\). From the fitting results, it can be seen that the gray value of the image in line segment AB coincides well with the sinusoidal curve, proving that, under the prepared orthogonal grating and imaging system, the gray level of grating image presents sinusoidal distribution, which can meet the needs of online measurements of laser repair. It should also be noticed that the imaging system contains only one CCD, which means only 2D deformation in a plane can be measured. Three-dimensional deformation on the complex geometry can be measured based on binocular vision if two CCDs are used, which is not discussed in the paper. Moreover, the displacement measurement range of sampling moire method is generally 0.5-1.5 times the grating period [24]. Thus, the deformation of the cantilever beam should not be too large and exceed the measurement range. Figure 6: Effects of different filter combinations: (**a**) initial picture; (**b**) with attenuator and narrow-band filter; and (**c**) with attenuator, narrowband filter and 1064 nm cut-off filter.