esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Novel Equivalent Beam Model Calibration
### Estimation of Parameters and Genetic Algorithms  
The mechanical characteristics of the e-TBM described in Section 2.1 are calibrated minimizing a suitable function of \(nm\) natural frequencies and \(nd\) static displacements of the e-TBM and the e-SBM in Section 2.2 that has general expression:  
\[f(D_{1},\ldots,D_{m})=\sum_{i=0}^{nm}w_{i}\left|1-\frac{\omega_{i}^{e-TBM}}{ \omega_{i}^{e-SBM}}\right|+\sum_{j=0}^{nd}w_{j}\left|1-\frac{u_{j}^{e-TBM}}{u_ {j}^{e-SBM}}\right|, \tag{53}\]  
where \(D_{l}\), \(d=1,\ldots,n_{d\
u}\), are the \(n_{d\
u}\) e-TBM design variables (e.g., mechanical parameters such as cross-section area, second moments, bending, shear and torsional stresses); \(\omega_{i}^{e-TBM}\) and \(\omega_{i}^{e-SBM}\), \(i=1,\ldots,nm\), are the e-TBM and e-SBM natural frequencies; \(u_{j}^{e-TBM}\) and \(u_{j}^{e-SBM}\), \(j=1,\ldots,nd\), are the e-TBM and e-SBM static response displacements; \(w_{i}\) and \(w_{j}\) are suitable weights to drive the solution on the desired target response parameters. At each step of the minimization procedure, a check on the modal shapes is performed order to match both natural frequencies and mode shapes of the e-SBM.  
Genetic algorithms [16] are chosen to minimize the function in Equation (53). This choice is usually done when the optimization problem cannot be solved using classical algorithms, for example because of the discontinuous, not derivable or stochastic objective functions. Classical minimizing algorithms were not successful in the case study that will be presented in this paper because of the significant influence of the initial design variable values. Sensitivity analysis was also performed to have information both on the influence of the variation of the design parameters on the quantities of interest and on the presence of local minima of Equation (53). The genetic algorithms were successful to overcome the problem due to their inherent feature to randomly search in the solution domain and to easily follow decreasing values of the objective function. The classic genetic algorithm can be summarized as follows [17]:  
1. generation of a random set of values for the design variables (initial population);
2. evaluation of the objective function and selection of the best solutions;
3. generation of a new population of hybrid solutions by means of genetic operators (cross-over and mutation);
4. iterative repetition of points 2. and 3. with this population;
5. termination of the algorithm when a specified condition is reached (e.g., convergence criteria, maximum number of generations, time limit).  
A crucial point to find the optimal solution is to give a wide range for the initial population, which is related to the initial domain of the design variables.
# A Video-Based Fire Detection Using Deep Learning Models
## 3 Proposed Method
### Fire Object Detection Based on Faster Region-Based Convolutional Neural Network (R-CNN)  
Faster R-CNN is a CNN-based object detection method that combines both the Fast R-CNN and the Region Proposal Network (RPN) to share a convolutional network after excluding the fully connected layer. So Faster R-CNN shares a similar structure for object detection with Fast R-CNN, except that Faster R-CNN includes RPN [18] to generate region proposals for objects. Based on the proposals, Faster R-CNN extracts the spatial features through the ROI pooling operation, and then calculates the object positions with class scores by fully connected layers. Usually, Faster R-CNN provides a higher mean Average Precision (mAP) than the single-stage object detection models such as SSD(Single Shot Multibox Detector) [19] and YOLO(You Only Look Once) [20]. Reportedly [21],  
Figure 1: The proposed network architecture.  
Figure 2: Timing diagram of fire detection.  
Faster R-CNN showed mAP as high as 34.9% for the MS-COCO dataset, when the shared CNN feature extractor is equipped with ResNet 101 [22], which we have adopted in our work.  
In our method, Faster R-CNN provides the bounding boxes of flame, smoke, and non-fire regions in an image, as shown in Figure 3. Figure 4 represents the sample images of flame, smoke, and non-fire objects. The non-fire objects resemble real fire objects, such as chimney smoke, sunset, and cloud. In addition, the image containing objects that are not related to a fire is itself treated as a non-fire object.  
When the Faster R-CNN detects the classes of flame, smoke, and non-fire objects, false detections may arise due to the possible presence of various types of non-fire objects in a single frame that is similar to a fire. The non-fire objects shown in Figure 4 that resemble fires include sunset, chimney smoke, cloud, etc.  
As aforementioned, however, applying this deep object detection model to find SRoFs and non-fire objects offer an advantage. Because the consecutive convolution enlarges the effective area of the operation, a bounding box resulting from the deep object detection model can include the larger area of a receptive field than it encloses. So, the more context information around objects can be captured in the boxes. This implies that SRoF detection becomes robust because it better reflects the context information around the bounding box.  
Figure 4: Sample images for training Faster R-CNN; (**a**), (**b**) and (**c**) are flames, smoke, and non-fire images, respectively.  
Figure 3: Faster Region-Based Convolutional Neural Network (R-CNN) structure for fire detection.
# Energy Efficient Determinism in WSN through Reverse Packet Elimination
## 6 Performance Results
### Distribution of RPE-Cancelling Packets  
Clearly, our RPE method creates additional traffic in the network compared to a single path. In order to investigate the negative effects of increased traffic, we investigated where the packets are eliminated. Figures 10 and 11 presents the findings. They show where, along the paths, the RPE packets are eliminated. Figure 10 shows how different PRR values change the elimination location. The reduced link quality causes the elimination to spread across all nodes.  
Figure 11 shows at which node the replicated packet is eliminated when the link quality is 80%. In the figure various RPE delays are compared with RPE Overprovisioning (OP). It shows how \(\tau=816\) and \(\tau=1624\) yielded similar results. However, \(\tau=816\) experiences that a few packets were dropped along Path B, but over 30 runs the average value recorded was less than 0.1%. Although, at node B3 an average of 4 packets were eliminated accounting for 0.24% of the drops. Summarizing, \(\tau=816\) and \(\tau=1624\) shows 99.76% and 100% packet elimination occur at the sink. Delay effects are discussed in more detail in the following text.  
Figure 10: Packet elimination distribution for \(\tau=8\).  
Figure 9: Packet reception ratios for Single Path and RPE.
## 3 Experiments and Analysis of Results
### Intelligent Setting of Indoor Temperature and Humidity
#### 3.3.4 Setting Values Based on the _PMV-Bpd_ Model  
The results of the indoor temperature and humidity settings based on the _PMV-BPD_ model are shown in Table 9. The lowest value of the thermal dissatisfaction rate predicted by the _PMV-BPD_ model was 16.28%, and the value of the thermal dissatisfaction rate predicted based on the _PMV-BPD_ model was added to the intelligent settings for different reinforcement learning, and the learned room temperature settings differed. The room temperature setting value of 25.5 \({}^{\circ}\)C and the relative humidity setting the value of 45.6% were learned according to the DDPG algorithm, and the final learned thermal dissatisfaction rate was 16.58%, which was 0.3% different from the lowest Bayesian thermal dissatisfaction rate value expressed in thermal discomfort body language. The learned indoor temperature setting value is closer to the indoor temperature corresponding to the lowest thermal dissatisfaction rate than the learning result without the inclusion of body language expression.  
The room temperature setting value learned according to the DQN algorithm is 25.8 \({}^{\circ}\)C and the relative humidity setting value is 45.3%, and the final learned thermal dissatisfaction rate is 16.9%, which is 0.62% different from the lowest Bayesian thermal dissatisfaction rate expressed by thermal discomfort body language. Compared with the learning results without the inclusion of body language expression, the room temperature setting value is closer to the room temperature corresponding to the lowest thermal dissatisfaction rate, the learned room temperature is lower and the human comfort level is higher. Comparing the indoor temperature and humidity setting values obtained based on Q-Learning and SARSA algorithms, the same conclusion can be obtained.
## 4 Results and Discussion
### Effect of Relative Density  
Figures 6 and 7 show the relationship between the cyclic stress ratio and the mean number of cycles to liquefaction for loose and dense sand for \(\sigma_{\mathrm{v0}}\)\({}^{\prime}\) = 100 and 200 kPa, respectively. The positive and negative error cycle numbers for each mean number of cycles are indicated by the error bar. Each figure shows a comparison of the relationship for different loading frequencies. As expected, the liquefaction resistance increases with the relative density. For the case of \(\sigma_{\mathrm{v0}}\)\({}^{\prime}\) = 100 kPa, the liquefaction resistance of dense sand was greater than that of loose sand by about 64% for 0.05 and 0.1 Hz. In particular, for \(f\) = 0.5 Hz, the liquefaction resistance of dense sand was higher than that of loose sand by about 82%. Therefore, the effect of the relative density on the liquefaction resistance became stronger at the loading frequency of 0.5 Hz. For the case of \(\sigma_{\mathrm{v0}}\)\({}^{\prime}\) = 200 kPa, as shown in Figure 6, the liquefaction resistance stably increased (by approximately 38%-47%) with an increase in the relative density from 40% to 80%, regardless of the loading frequency.  
Figure 6: Comparison of cyclic resistance curves (**a**) \(f\) = 0.05 Hz, (**b**) \(f\) = 0.1 Hz and (**c**) \(f\) = 0.5 Hz at different density states for \(\sigma_{\mathrm{v0}}\)\({}^{\prime}\) = 100 kPa.  
A comparison of Figures 6 and 7 shows that the effect of the relative density reduced as the initial vertical effective stress increased. This is because unlike loose sand, the liquefaction resistance of dense sand decreased dramatically when the initial vertical effective stress increased. On the basis of triaxial tests, [PERSON] et al. [26] reported that the confining stress on loose Fraser River sand had little effect on the liquefaction resistance of the sand.
## 7 Discussion and Conclusions  
This paper proposes a robust planning model considering network reconfiguration and \(DG\) permeability. The planning scheme dispatches the existing resources in the active distribution network to meet the load demand. Moreover, the \"wind-light-load\" uncertain sets are introduced in the planning model to describe the uncertainty of clean energy. Additionally, the two-level optimization is utilized to decompose the planning model.  
The results of the test systems are shown as follows:  
When planning \(DG\) in ADN, considering network reconfiguration will not only affect the planning results of \(DG\), but also affect the economic costs of the system. When considering network reconfiguration, in some scenarios, it is not necessary to add a large number of \(DG\)s to meet the demand of load growth, just adjust the switch state and optimize the power flow to meet the safe operation of ADN. Although a small amount of reconstruction cost will be generated, the network loss cost will be greatly reduced, and the comprehensive cost of \(DG\) will also be reduced, thus reducing the annual comprehensive cost of the system and improving its economy. In addition, considering the uncertainty of wind speed, light intensity and load, the planning system has better adaptability and stability, and also greatly improves the reliability of the system. Moreover, when \(DG\) permeability is considered at the planning stage, the utilization rate of \(DG\) increases, and \(DG\) can be consumed locally, reducing network losses, thereby reducing the purchase of electricity from the large power grid and making the system more economical.  
In summary, the planning method proposed in this article can effectively guide \(DG\) planning under the high proportion of renewable energy access, thereby improving \(DG\) consumption and delaying \(DG\) investment. However, this paper only plans \(DG\) in ADN, but does not plan and analyze grid structure and energy storage devices of distribution network, which is also the next research direction.  
Conceptualization, [PERSON]; methodology, [PERSON]; software, [PERSON]; validation, [PERSON], [PERSON] and J.L.; formal analysis, [PERSON]; investigation, J.L.; resources, [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON]; visualization, [PERSON]; supervision, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fundamental Research Funds for the Central Universities, grant number 2021 QN1067.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Scenario A** & **Scenario B** \\ \hline Computation Time & 1312.83 s & 2116.96 s \\ \hline \hline \end{tabular}
\end{table}
Table 6: The computation time of Case 1 to Case 4.
* The developed empirical stochastic model was compared with the theoretical model describing the nominal precision of GNSS receiver performance due to thermal noise. * The impact of the stochastic model elements on the positioning solution was examined based on the relative carrier-phase model for a very short baseline and Kalman Filter estimation method. The analysis considered the impact of including: different variance models, cross- and time-correlation characteristics and specific parameters for different satellite blocks. * The comprehensive analysis of the positioning accuracy and ambiguity resolution results was performed for various GNSS systems and signals combinations using the developed empirical stochastic model and the standard elevation-dependent model. The ultimate goal of the presented analyses was to improve the quality of the multi-system and multi-frequency solution by refining the empirical stochastic model of Multi-GNSS measurements. The paper is organized as follows. In Section 2 we present GNSS measurement noise characteristic and its theoretical impact on observation quality. In Section 3 the empirical stochastic modeling methodology is presented, which allows to estimate variance and covariance components based on zero-length and very short baseline observations. In Section 4 the positioning model used to evaluate the developed empirical stochastic model is described. The applied estimation method using a modified Kalman Filter, considering the time-correlation of observations, is also outlined. In Section 5 the data set, results of stochastic modeling, the impacts on AR and positioning solutions for different scenarios are analyzed. Finally, in Section 6, the summary and concluding remarks are made. The following symbols and notation for the analyzed systems and signals were used in the paper: for GNSS systems the following abbreviations were used: G for GPS, R for GLONASS, E for Galileo and C for BeiDou; observation codes for pseudo-range and carrier-phase measurements were adopted according to RINEX 3.04 format [77]: GPS--C1C/L1C for L1 C/A, C1W/L1W for L1 P(Y), C2W/L2W for L2 P(Y), C2L/L2L for L2 CL, C5Q/L5Q for L5(Q); GLONASS--C1C/L1C for L1 C/A, C1P/L1P for L1 P, C2C/L2C for L2 C/A, C2P/L2P for L2 P, C3Q/L3Q for L3(Q); Galileo--C1C/L1C for E1 OS(C), C5Q/L5Q for E5a(Q), C7Q/L7Q for E5b(Q), C8Q/L8Q for E5 ab AltBOC(Q), C6C/L6C for E6(C); BeiDou--C2I/L2I for B1-2(I), C7I/L7I for B2b(I), C6I/L6I for B3(I). The analyses performed for individual satellite blocks distinguished the following blocks and types: GPS Block IIR (type A with legacy antenna panel and type B with improved antenna panel [78]), IIR-M, IIF; GLONASS M and K1; Galileo IOV and FOC; BeiDou-2 GEO, IGSO and MEO.
## 2 Materials and Methods
### Solid Target Design and Preparation  
Four aluminum and niobium coin target holders/degraders were custom-designed and manufactured at the Department of Physics's machine shop of the Universite de Sherbrooke (Figure 1). Both aluminum and niobium coins have an outer diameter of 24 mm and a thickness of 2 mm to fit in the small solid target station, which can accommodate no more than 2.2-mm thick targets. Initially, the first target body Al-21-1, presenting a hole diameter of 21 mm and a thickness of 1 mm, was made from 6061-T6 aluminum alloy to accommodate a thickness of 0.254 mm of \({}^{89}\)Y-foil at maximum (Figure 1A). As the proton beam size of both cyclotrons is \(\sim\)10 mm, the hole diameter of the niobium degraders was reduced to 13 mm (Nb-13-0.35) and 6 mm (Nb-6-0.35) to accommodate a 1-mm thick \({}^{89}\)Y-foil/pressed powder (Figure 1B,C). An aluminum open window holder with a hole diameter of 13 mm (Al-13) was also designed to be used with a degrader (Figure 1D). As shown in Figure 1B-D, small aluminum disks (13 and 6 mm diameter) were used when needed to fill the gap between \({}^{89}\)Y-pressed or foil targets and the aluminum back support for improvement of targets cooling during the irradiation. The aluminum back supports were machined with a thickness of 0.5 mm for Al-13 holders when compared to a thickness of 1.0 mm for all other degraders. Target assembly was performed without applying pressure on target material, foil, or pellet. The procedure to assemble the targets is illustrated in Figure S1 of the Supplementary Information.  
Before pressing, inert gas (N\({}_{2}\)) was applied into the hole in the die. The \({}^{89}\)Y powder was inserted and pressed into either 6 or 13 mm diameter pellets ranging in thickness from 0.3 to 0.5 mm. The maximum pressure applied to 6 and 13 mm dies was 3500 and 18,000 pounds per square inch, respectively, while using a digital hydraulic carver press (Module number: 3912, Carver, Inc, Wabash, IN, US). The pellets were placed in the target holders and covered with a degrader. \({}^{89}\)Y-foil targets of various thicknesses ranging from 0.25 to 0.51 mm were mounted on the custom-made holders (Figure S1). Niobium and aluminum degraders were installed on the He/beam side to avoid problems, while mounting and dismounting the disc set in the cyclotron solid target station. For the optimization of the production yield, seven 0.89-mm thick \({}^{89}\)Y foils were mounted together in a round shaped 6-mm diameter Nb-6-0.35 degrader.
## 2 Materials and Methods
### Defining Comparison Parameters: Aligning Questionnaire Responses with Database
To select the appropriate test scenarios, the vector created from the questionnaire (**q**) can be compared with each vector (**r**j) created from the database rows. We used weighted cosine similarity (WCS) for the procedure [30]. The weights (w) were defined in an array with the same number of dimensions as the vectors because the parameters stored in each vector have different importance for case selection. The weighting and explanations for the weights are also provided in Table 12. However, in the table, we proposed reasonable weights but kept the option to handle these flexible to be able to adapt better to specific vehicles. The used formula for the WCS to each test run (WCSRj) is as follows:  
\[\text{WCS}_{\text{Rj}}=\frac{\sum_{i=1}^{\text{n}}\text{w}_{i}\cdot\textbf{q} _{i}\cdot\textbf{r}_{\text{j}i}}{\sqrt{\sum_{i=1}^{\text{n}}\text{w}_{i} \cdot\textbf{q}_{i}^{2}}\sqrt{\sum_{i=1}^{\text{n}}\text{w}_{i}\cdot\textbf{ r}_{\text{j}i}^{2}}} \tag{1}\]  
In Formula (1), j is the No. of the test run, and n is the dimension of the vector, which is 22 in our case. This calculation is performed with every vector created from the database, essentially comparing the vehicle-related parameters with the parameter sets generated from each test run in the database. Based on this comparison, each test run will be assigned a value between 0 and 1. If this value is close to 1, the test run is considered relevant. It is important to establish a threshold value below which a test run is no longer considered relevant. The threshold can be selected in an adaptive manner, for example, based on the vehicle's level of driving automation, defined in the SAE J3016 TM standard. In the case of higher levels, it may be advisable to examine a larger number of test cases to ensure more robust results. However, it is also important to consider the testing environment and the type of vehicle involved. For instance, in the case of larger and heavier vehicles that could potentially cause greater harm--particularly in urban environments where Vulnerable Road Users (VRUs) may be at risk--it may be beneficial to examine more test runs, thereby lowering the threshold. Conversely, in highway environments or for passenger cars, fewer test cases may be sufficient. During the vectorization of test cases described earlier, it may occur that two different test cases have identical vectors, resulting in the same cosine similarity value. This typically happens with test cases that are very similar to each other. To filter out overly similar test cases, we have implemented additional steps, which are detailed in the following section (Section 2.4).
## 2 Experimental Setup Using the Explosive Driven Shock Tube
### Time Interval \(\Delta t\)  
In this section, the time interval \(\Delta t\) is investigated. All images obtained from the HSC are processed using the photon fastcam viewer (PFV) [31]. The reference image is chosen as the last frame recorded immediately before the appearance of the blast wave in the FOV. In a first step, the position of the blast wave front \(d_{bl}\) after its first appearance in the FOV is determined as shown in Figure 12a. Then, its velocity \(v_{bl}\) is calculated. In a second step, the number of frames \(N_{f}\) between the first appearance of the blast wave front and the first appearance of the projectile in the FOV is registered. After that, the position \(d_{p}\) of the  
Figure 11: Percentage of energy transmitted from the charge to the 5 and 7 mm projectiles as function of charge mass.  
Figure 10: Mean projectile velocity with standard deviation for projectiles with diameters of 5 and 7 mm as a function of the charge mass.  
projectile is determined in the image in which it first appears as shown in Figure 12b. Its velocity \(v_{p}\) is obtained using the FTS as explained in Section 2.4.  
Finally, the time interval \(\Delta t\) is obtained by applying Equation (1):  
\[\Delta t=\left(\frac{N_{f}}{frame\ rate}\right)-\left(\frac{d_{bl}}{v_{bl}} \right)-\left(\frac{d_{p}}{v_{p}}\right) \tag{1}\]  
Table 1 summarizes all required values to obtain \(\Delta t\) for charge masses of 10, 20, and 30 g of C4 and both projectile diameters. It is highlighted that for the same charge mass, \(\Delta t\) increases with increasing projectile diameter. It is found that challenges exist in recording and analyzing data, including the short load duration, the high pressures, triggering challenges, and the bright explosive flash. Despite of these challenges, the versatility and the efficacy of high-speed imaging used in the experimental setup for visualizing the blast wave front and the projectile are of interest [32].  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Test n** & \
\begin{tabular}{c} **Charge** \\ **Mass [g]** \\ \end{tabular} &
\begin{tabular}{c} **Projectile** \\ **Diameter [mm]** \\ \end{tabular} & \(N_{f}\) & \(d_{bs}\) [mm] & \(v_{bl}\) [ms] & \(d_{p}\) [mm] & \(v_{p}\) [ms] & \(\Delta t\) [ms] & **Mean \(\Delta t\pm\sigma\) [