esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental/Numerical Evaluation of Steel Trapezoidal Corrugated Infill Panels with an Opening
## 4 Numerical Analysis
### Parametric Analysis  
The force-displacement curves of several CSPs using the numerical results are shown in Figures 24 and 25. Dimensions, material properties and location of opening are basically similar to specimen 1 except for characteristics that are specified. The width of all stiffeners used around the stiffened opening is 70 cm.  
Figure 23: Final deformation form of specimen 2 (mm).  
the effect of the infill plate thickness and the use of stiffeners around the opening on the performance of CSIPs with an opening was considered by performing numerical analyses. The obtained results are presented below:  
1. The initial stiffness, ultimate strength and ductility ratio of two tested specimens with an opening were 65%, 21% and 39% smaller compared to the specimen without an opening, respectively. The opening percentage of both specimens was equal to 18%.
2. The energy dissipation capacity of the specimen without an opening was 21% larger than the specimen with one opening.
3. The energy dissipation capacity of the specimen with two openings was 8% larger than the specimen with one opening. The beam yielding along with the columns yielding in the specimen with two openings occurred at larger strains compared to the specimen with one opening. Therefore, the performance of the specimen with two openings is somewhat better than the specimen with one opening, while the opening percentage is the same for both tested specimens.
4. Similar to the specimen without opening, both tested specimens with opening dissipated energy by plastic deformations without any pinching in the hysteretic loops.
5. The numerical results indicated that the reduction of ultimate strength and initial stiffness of CSIPs due to the existence of an opening can be compensated by the increase of the infill plate thickness.
6. The numerical results specified that the ultimate strength of CSIPs with and without a stiffened opening is close to each other.  
Conceptualization, M.Y.G. and M.M.; methodology, M.Y.G. and M.M.; software, M.Y.G.; validation, M.Y.G.; formal analysis, M.Y.G. and M.M.; investigation, M.Y.G. and M.M.; resources, M.Y.G. and M.M.; data curation, M.Y.G.; writing--original draft preparation, M.Y.G.; writing--review and editing, M.M.; visualization, M.Y.G.; supervision, M.M.; funding acquisition, M.Y.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Verification of the Scots Pine (_Pinus sylvestris_ L.) Crown Length Model
## 2 Materials and Methods
### Allometric Model  
The study checked the accuracy of the crown length model [14] expressed by Equation (2). The model was derived from a regression equation. The stages of construction of the crown length model are presented in the article [PERSON] and Sporek [14]. The model includes a power exponent \(\alpha\) = 0.25 and a correction factor of 1.33, which was replaced by the value \(\pi\), since 3.14 to the power of 0.25 equals 1.33. Thus, (\(\pi\cdot DGH\cdot H\)) in the power of 0.25 is the volume of the tree. The tree volume raised to the power of 0.25 gives the crown length including the stand density index in the model. The key predictor in this model appeared to be the density index, defined as the actual number of trees per ha (which we know only at the time of measurement) to the expected number of trees of a given age and stand quality class (value read from Tables of Stands Volume and Increment [48]).  
\[CL_{cal}=\left(\pi\cdot DGH\cdot H\frac{1}{\sqrt{D1}}\right)^{0.25} \tag{2}\]  
where  
\(CL_{cal}\)--calculated crown length (m),  
\(DGH\)--diameter of the tree at ground height (diameter of the base of the tree): \(DGH=DBH+1.7\) (cm)  
\(H\)--tree height (m),  
\(DI\)--density index (actual number of trees per ha/normative number of trees (taken from Tables of Stands Volume and Increment [48])  
In order to verify the pine crown length, calculated using the allometric model (2), detailed statistical analyses were carried out on a sample of 300 trees selected from a pool of 10,957 measured diameters at breast height. The verification was divided into two stages. The first stage focused on investigating the impact of physical parameters of 300 trees, such as diameter at breast height, height, and density on the crown length. In the second stage of the analysis (Section 3.3), we adopted the average values from 15 selected sample trees for each plot. This was aimed at reducing random errors that may result, for example, from tree competition for limited resources, crown reduction as a result of folivore feeding, or weather conditions such as damage to the crowns caused by strong winds.
## 3 Results
### Optimal Model Parameter Results  
For the PLSR model, the optimal number of principal components is 7 for the January 15 data and 10 for the August 26 data. For the RF model, the optimal number of decision trees is 140 for the January 15 data and 280 for the August 26 data. For the BPNN model, the optimal number of training epochs is 403 for the January 15 data and 455 for the August 26 data.
# Design and Implementation of Composed Position/Force Controllers for Object Manipulation
## 3 System Description: The Robotic Manipulator  
The robotic manipulator assembled in this research is shown in Figure 2. As can be noticed, this robotic manipulator is composed mainly of the following components:  
* A two-independent-finger gripper. The gripper is composed of two Dynamixel AX-12 servo motors manufactured by CrustCrawler Robotics. Regarding the fingers, each finger is 10.16 cm long and is made of aluminum. Furthermore, they can be open up to 22.86 cm. An FFS-MT piezoelectric force sensor was placed at each fingertip (Figure 2b) The sensing force range for this sensor goes from 0 to 10 N, with an output resolution of 0.1 mV, providing a stable output over the range of force exerted. In addition, the output of this sensor exhibits linear behavior, as described in [36].
* A three-degrees-of-freedom arm. The arm is operated through Dynamixel servo motors. These servo motors each include a micro-controller, which obtains the different states of the servo motor (e.g., speed, position, temperature, and voltage). The maximum lifting capability is 900 g. A data acquisition board (Arduino) and a 12 V battery were placed on the back of the robotic arm (Figure 2a). The data acquisition board processes the signals from each force sensor and sends them to the computer.
* An iRobot Create. The robotic arm and gripper are mounted on an iRobot Create (mobile base). The iRobot allows moving the robotic arm from one place to another; therefore, objects may be repositioned.
## 4 Result and Discussion
### Comparison and Results
As can be inferred by comparing the three cases, the larger the \(|\delta-1|\) is, the better performance MART1 can achieve, and this is for two main reasons: (1) The MART process does not establish a uniform constraint for the electron density in all voxels; therefore, the larger discrepancy between the initial electron density and the background electron density, the worse the reconstruction result MART can achieve. The multiplication of adjustment factor \(\delta\) makes the initial electron density closer to the background electron density to achieve better performance. (2) In the voxels without any transversing rays, the reconstructed electron density remains at initial values, and the smaller discrepancy of optimized initial electron density makes better performance.
## 4 Results and Discussion
### Pressure Measurements
The force reconstruction based on the sensors is given by the expression:  
\[F_{slosh}^{sensors}(n)=\sum_{i}^{n}F_{slosh}^{sensors,i}=\sum_{i}^{n}(p_{i}^{b} -p_{i}^{u})A_{i}=\sum_{i}^{n}(p_{i}^{b}-p_{i}^{u})\frac{Lb}{n} \tag{28}\]  
where \(A_{i}=\frac{Lb}{n}\) is the area associated to each sensor, \(b\) is the tank dimension in the transverse direction and \(n\) the number of sensors. The force \(F_{slosh}^{sensors}\) computed from the pressure signals multiplied by the corresponding associated wall areas of all sensors are added (see Equation (28)), and the result is compared to the pressure component of the sloshing force \(F_{slosh}^{p}\) computed by the SPH formulation; see Equation (20). These two signals are both plotted in Figure 21 for the water and glycerine fluids, the agreement between both signals being very good for both cases. It should be clarified that the liquid weight has been removed from both signals and consequently, all forces tend to zero at the end of the computation. The differences are caused by numerical error due to the limited number of sensors. Figure 20: Nondimensional pressure maxima as a function of time and \(x\) coordinate for the upper (**left**) and bottom wall (**right**) in the water case at \(f=0.5\) (**top**) and glycerine case at \(f=0.39\) (**bottom**). The contour plot has been interpolated using 17 pressure signals for each wall. To quantify the effect on the \(\underset{slosh}{F^{sensors}_{slosh}}\) when the number of sensors is varied, the root mean square error \(RMSE\) between the pressure sloshing force computed by Expression (28) and the SPH pressure component is defined as:  
\[RMSE(n)=\sqrt{\frac{\sum\limits_{j=1}^{N}\left(F^{sensors}_{slosh,j}(n)-F^{p}_{ slosh,j}\right)^{2}}{N}} \tag{29}\]  
where \(N\) is the number of time steps recorded. Figure 22 shows the evolution of the relative difference between the \(RMSE\) values using \(n\) sensors with respect to the case where only two sensors are used. It can be observed that the water tends toward a constant value of \(\sim\)\(-\)39%, meaning that when using more than 30 sensors, the level of approximation is reasonable. In the case of the glycerine, the convergence is not so evident, and even when using 34 sensors the force still has some small variations. Figure 21: Comparison between the pressure contribution to the SPH sloshing force and the sum of the local force measured by the sensors. Figure 22: Relative variation of the root mean square of the sloshing force reconstruction error when the number of pressure sensors is varied.
# On the Robustness of No-Feedback Interdependent Networks
## Simulation and Analysis
### Dependency Strength
In the unilateral failure mode, if \(DS\) was less than 0.5, there was little change in the collapse threshold of the interdependent networks; if \(DS\) was larger than 0.5, the collapse threshold decreased sharply. However, the situation under bilateral failure was different, the collapse threshold uniformly decreased with the increasing of dependency strength from 0 to 1. Finally, for the case of \(DS\) = 1, the weakly interdependent model evolves into fully interdependent networks. Similar to the situation shown in Section 3.1, the threshold of interdependent networks collapse under bilateral failure was 0.26, and the value under unilateral failure was 0.4, which is basically consistent with [1], where 0.39 was derived. The tiny difference is probably due to the finite effect caused by the limited subnetwork size during the simulation. In the end, an interesting phenomenon was observed from the comparison between Figures 4 and 8: the changes of collapse threshold \(f_{c}\) in the partially interdependent and weakly interdependent models were consistent. The main reason is that the dependency link in interdependent networks serves as a failure propagator. Specifically, for double-layer no-feedback interdependent networks, where coupling strength is \(CS_{A}=CS_{B}=CS\), dependency strength is \(DS\). In the initial state, the failure probability of node in one subnetwork is \(p_{1}\), the failure probability of the node (resulted from the losing its dependency link) in the other subnetwork can be generated from:  
\[p_{2}=CS\times DS\times p_{1} \tag{3}\]  
In Section 3.1, the dependency strength of the partially coupling model was 1, thus \(p_{2}=CS\times p_{1}\). In Section 3.2, we considered the weakly coupling model with fully dependency, i.e., the coupling strength was 1, so \(p_{2}=DS\times p_{1}\). Therefore, when the coupling strength is equal to the dependency strength, the failure probability of nodes is the same after initial failure on both failure modes. This explains the consistent results of Figures 4 and 8. Thus, we can conclude that the coupling strength and dependency strength are equivalent to the robustness of interdependent networks. Similarly to in Section 3.1, we conducted additional simulations on the effect of dependency strength and the robustness based on the other two types of interdependent networks, using the BA model and WS model as subnetworks, respectively. The results were shown in Figure S4 of the supplemental material, and the same conclusions were obtained from these results as that in ER-ER interdependent networks. Figure 8: Collapse threshold \(f_{c}\) as a function of _dependency strength_(\(DS\)), each point is obtained by averaging over simulation on 20 independent realizations.
# A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning
## 3 Results
### Refined Classifier Performance Evaluation
#### 3.2.1 Accuracy Evaluation of Different Features and Models
For statistical features in four datasets, using the statistical features of the current time (k = 1) can obtain higher accuracy, while for the time sequence features, the bigger the window size was, we obtained the higher accuracy. * The accuracy of only use statistical features on Dataset_1, Dataset_2, Dataset_3 and Dataset_4 was 0.9893, 0,9344, 0.9319 and 0.9185, respectively. However, in the vast majority of cases, the accuracy was under 0.91 when only used time sequence features. The accuracy reached 0.9662 on Dataset_1 when only used spatial geometry distribution features, while in other datasets, the accuracy was less than 0.81. This result showed that statistical features play the most crucial role in IO detection. Since the number of features contained in spatial geometry distribution and time sequence was small, only using these two kinds of features lead to low accuracy. * It was worth noting that spatial geometry distribution features and time sequence features improved accuracy more than 0.01 on Dataset_2 and Dataset_4. Furthermore, the improved accuracy of the two kinds of features mentioned above was closed to 0.04 on Dataset_3. Therefore, spatial geometry distribution features and time sequence features contributed to IO detection. * In indoor and outdoor scenarios, the optimal accuracy of Dataset1 was 0.0209 higher than that of Dataset3 using the same kinds of features. In IO transition scenarios, the optimal accuracy of Dataset2 was 0.0173 higher than that of Dataset4 using the same kinds of features. While the accuracy of our proposed algorithm in the new test environment was lower than that in the environment where collected training data, the overall accuracy in dataset_3 and dataset_4 were more than 0.9280, that means the proposed algorithm robust among different complex environments (non-sampled environments).
## 4 Results and Discussion
### Traffic Classification and Transmission  
The framework achieved a 100% in-vehicle traffic classification accuracy, indicating the efficient differentiation of critical vehicular data (Camera, LiDAR, and RADAR) into three distinct classes using Slice IDs (Figure 5). In 5G VANETs, this classification is useful for identity management, service differentiation, and QoS prioritisation. Complementing this result, packet transmission analysis (Figure 6) showed that all data transmitted by the SDVs were successfully received at the gNB with zero packet loss, demonstrating the reliability of the SDN VXLAN underlay transport network.  
During preliminary testing, we observed that high transmission bursts from Camera sensors, due to their larger packet sizes and higher data rates, increased flow durations in processing units and led to approximately 50% packet loss. To address this, we conducted a pre-simulation analysis and profiling of sensor transmission characteristics, including packet sizes, current flow durations, and transmission rates. This informed the tuning oftransmission parameters and configurations, enabling the system to achieve 100% transmission reliability in subsequent tests.
## 1 Introduction
### Previous Research
As mentioned above, the method for detecting roof damage using the distribution of blue tarps covered on the roof is widely used after disasters, such as typhoons and earthquakes in Japan. Moreover, the results of the above research show that with the increased blue tarps area, there is an increased loss of residential roofs. However, the current detection methods for roof damage in aerial photos suffer from two primary shortcomings. First, it is impossible to calculate the proportion of roof damage. Currently, the existing basic outline of houses provided by the Japan Institute of land and geography is used. However, in addition to new constructions, reconstruction and expansion of houses, there are also completely destroyed houses caused by the typhoon, resulting in great changes in the roof outline. Therefore, the roof outline must be measured in real time. Second, roofs that are completely destroyed and not covered with blue tarps cannot be detected. If the house is seriously affected by a typhoon, for example, collapses or is damaged due to a secondary fire disaster, it is impossible to continue to live in them. Consequently, many residents tend to move to another safe place temporarily and although the house is seriously damaged, there is no need to continue to cover it using blue tarps. Therefore, this type of house damage has been omitted. Thus, it is incorrect to only count the area of blue tarps, and roofs that are completely destroyed must be detected separately. Consequently, the sum of the area of blue tarps and the area of the roof completely destroyed must be considered as the total damaged area of the roof in the investigation area. In addition, according to the previous deep learning model, only two or three types of the level of roof damage can be classified, for example, damaged or undamaged roofs. Moreover, there is a lack of a specific classification basis for calculating the area of damaged roofs. Therefore, in this study, a deep learning model with a segmentation function was used to detect roof damage after a typhoon. According to the characteristics of roof damage after a typhoon in Japan, this study provides a solution for the rapid detection of roof damage after a typhoon. The innovation of this study is in providing an overall solution for fast and accurate detection and classification of roof damage. Compared with other methods, this method has superior performance in speed and accuracy. The rest of this paper consists of six sections: Research Methods, Materials, Training and Detection using Deep Learning, Classification of Roof Damage Level, Discussion which includes a comparison with other classification methods, and Conclusions.
## 6 Evaluation
### Performance Evaluation
#### 6.1.2 Scalability and Throughput
Notably, for 5 and 10 users, the request rate per second does not fall off as sharply as might be expected given the increased load, suggesting that the system is still managing to process a reasonable number of requests per second even under higher concurrency. However, the increase in processing times with more users indicates that the system's resources are becoming a bottleneck, potentially due to increased contention for network or computational resources. Figure 6 displays the total test time for a verification process across different numbers of users and transactions per user. For a single user, the total time increases from 4073 ms for 50 transactions to 46,898 ms for 500 transactions, which is a significant increase for this number of transactions. With five users, the time taken is initially relatively low, at 816 ms for a single transaction, but this value escalates to 36,842 ms for 100 transactions, showing that the total processing time increases with both the number of users and transactions. For ten users, the total test time begins at 1681 ms for one transaction and reaches 37,905 ms for 50 transactions. These performance results indicate that the system can handle multiple transactions and users, but the total test time rises significantly with increased load. Figure 5: Evaluation of adding a product. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline
**No. of Users** & **Transactions per User** & **Fetch Chain Average (ms)** & **IPFS Average (ms)** & **Requests/s (Incl. Authentication)** \\ \hline \(1\) & \(1\) & 78.00 & 11.00 & 1.00 \\  & \(50\) & 69.46 & 8.95 & 24.80 \\  & \(100\) & 66.66 & 8.42 & 26.48 \\  & \(250\) & 81.54 & 10.08 & 21.61 \\  & \(500\) & 83.61 & 8.91 & 21.34 \\ \hline \(5\) & \(1\) & 316.37 & 25.48 & 18.22 \\  & \(10\) & 317.58 & 101.25 & 22.13 \\  & \(20\) & 270.51 & 114.30 & 24.80 \\  & \(50\) & 288.43 & 111.09 & 24.41 \\  & \(100\) & 260.29 & 102.59 & 27.22 \\ \hline \(10\) & \(1\) & 548.63 & 207.22 & 17.45 \\  & \(5\) & 612.39 & 203.57 & 20.90 \\  & \(10\) & 610.50 & 177.65 & 23.64 \\  & \(25\) & 607.51 & 179.39 & 24.48 \\  & \(50\) & 554.33 & 182.23 & 26.64 \\ \hline \end{tabular}
\end{table} 
Table 3: Verification process performance metrics. The total test time thus increases due to various factors. An increase in time for both single and multiple users suggests significant overheads for the system when handling the verification process and concurrent user sessions. The increase in total test time for 5 and 10 users indicates an additional workload from managing multiple concurrent processes, possibly due to context switching, increased synchronisation overhead, or data contention issues. The findings emphasise the need to optimise concurrency and scaling resources to handle increased loads efficiently.
## 1 Introduction  
As one of the pillar industries of modern industry, the petrochemical industry holds a vital position in the national economy [1]. Ethane serves as one of the most crucial indicators of the development of the petrochemical industry [2]. It is not only used as a fuel for outdoor applications and as a refrigerant but also plays a key role in the feedstock for ethylene production. Currently, ethane accounts for more than 40% of the world's ethylene production feedstock [3]. Ethane storage is an important issue because of its different physicochemical and phase change properties from those of natural gas and crude oil. Ethane storage tanks may experience leakage and diffusion due to causes such as corrosion perforation, overpressure, and failure. Given the high latent heat of vaporization of ethane, when it leaks into the air, it vaporizes, absorbing a significant amount of heat, which can lead to frostbite on humans and damage to equipment. Additionally, ethane exhibitsflammability and toxicity [4]. Despite improvements in safety measures for ethane storage, accidents such as ethane leaks and explosions still occur annually [5]. These incidents, especially in areas with dense storage tank concentrations, can trigger a domino effect, resulting in greater casualties and economic losses [6], as in the case of the disaster at the large ethane storage complex at Mont Belvieu, just outside Houston, USA [7]. In research on ethane leaks, due to the flammable and explosive nature of ethane, which poses high risks, and the high cost of related experiments, numerical simulation remains the mainstream approach for studying ethane leakage and diffusion [8]. Commonly used numerical simulation tools include Fluent, FLACS, COMSOL, PHAST, and FEM. Numerous scholars have obtained some important conclusions through these tools and methods, as shown in Table 1 below.