esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental and Numerical Evaluation of Calcium-Silicate-Based Mineral Foam for Blast Mitigation
## 3 Numerical Modeling
### Modeling Method  
Figure 13 encompasses the entire set-up for numerical modeling using a Lagrangian approach. Shell elements (Bellytschko-Li-Tsay) of 5 mm \(\times\) 5 mm \(\times\) 15 mm are used to model the steel frame. Aluminum components (front plate and backplate) are modeled using fully integrated (ELFORM-2) solid elements measuring 5 mm \(\times\) 5 mm \(\times\) 0.67 mm. The meshless smooth particle hydrodynamics (SPH) approach simulates the 60 mm thick mineral foam. To ensure the stability of calculations, it is recommended to have a minimum of four SPH nodes along each face of the Lagrangian element that is in contact with the SPH nodes [46]. This requirement, alongside a mesh convergence study (see Figure 14), essentially governs the overall particle number in the model, which is 36,450 particles. All nodes in the front plate and the foam have unconstrained degrees of freedom.  
*CONTACT_AUTOMATIC_NODES_TO_SURFACE card is defined, enabling the transfer of compressive loads between materials with soft characteristics and those exhibiting rigidity [46]. A soft constraint-based formulation is adopted between all aluminum components and the foam to handle significant elastic bulk modulus differences (Foam/Aluminum) [47]. The blast load is applied by using the LOAD_BLAST_ENHANCED card.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Configuration** & **Foam Mass** & \(\mathbf{p_{r}}\) & \(\dot{t_{r}}\) & **Maximum OPD** & **g \({}^{1}\)** \\  & **(g)** & **(MPa)** & **(Pa.s)** & **(mm)** & **(mm)** \\ \hline Without foam & 0 & 7.3 & 235 & 21 \(\pm\) 0.7 & 19.8 \(\pm\) 0.6 \\ (WF) & 595 \(\pm\) 8 & 16.3 & 389 & 11.6 \(\pm\) 0.1 & 8.9 \(\pm\) 0.3 \\ (B) & 508 \(\pm\) 8 & 16.3 & 389 & 10.9 \(\pm\) 0.4 & 6.1 \(\pm\) 0.4 \\ (C) & 446 \(\pm\) 5 & 16.3 & 389 & 10.6 \(\pm\) 0.2 & 5.1 \(\pm\) 1.1 \\ \hline \hline \end{tabular} \({}^{1}\) Mean permanent deflection.  
\end{table}
Table 1: Maximum OPD and mean permanent deformation of the backplate with and without the protective foam.  
Figure 13: **(a)** Front view of the experimental set-up in finite element model and **(b)** One-quarter of the SC solution with the foam in SPH viewed from the rear of the set-up (front skin in green-foam-backplate in red).
# A Construction Method for the Random Factor-Based G Function
## 3 Analysis of the Principles and Limitations of Commonly Used G Functions
### G Function Based on AES Password  
Figure 3 illustrates construction diagram of AES algorithm. In the process of generating a frequency-hopping sequence based on the G function of AES password [28; 29], the initial frequency point and key are randomly determined. The initial frequency point is then converted into a 128-bit binary sequence, with the upper 122 bits set to zero. This sequence is then input into the AES algorithm along with the 128-bit key to obtain the output ciphertext. Next, the lower six bits of the ciphertext are taken and XORed with the data information to be transmitted. The resulting binary data are then converted into decimal frequency point information, which is then converted into a 128-bit binary sequence. This sequence is then input into the next level of the AES algorithm along with the key. This process is repeated to obtain frequency points for each level, creating a frequency-hopping frequency set.  
Although this method has the advantage of variable and equal key length, its processing process is complex, leading to increased computational overhead and implementation difficulty. Additionally, the public nature of the algorithm means that if attackers obtain the key for a certain round, they can use this information to launch further attacks and compromise the security of the system.
## 4 Performance Evaluation in a Capsule Prototype
### Electronic Capsule
Moreover, the Nordic transceiver contains cyclic redundancy check (CRC)-based error detection and retry with auto acknowledgement feature. In auto acknowledge mode, after receiving a data packet, the receiver checks the CRC bits and detects whether there was any error during the transmission of the packet. If there was any error, then it requests the transmitter to resend the data-packet again. This process goes on until the packet is transmitted successfully. So, in auto acknowledgement mode, generally no data loss happens. Experiments have been conducted to show a bit error rate (BER) of less than 0.1% [25]. The Power board contains miniature low dropout regulators (LDO), a magnetic reed switch and three silver oxide button batteries, each having a rated voltage of 1.55 V and capacity of 195 mAh. Figure 8: Architecture of the capsule prototype.
## 5 Results  
In this study, a hybrid methodology utilizing SEM and fsQCA was adopted to empirically investigate the critical success factors influencing the implementation of Industry 4.0  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Industry** & **Number** & **Percentage** & **Respondent’s Title** & **Number** & **Percentage** \\ \hline Machinery & 71 & 35.1\% & CEO/Deputy CEO & 37 & 18.3\% \\ Automobile & 35 & 17.3\% & Digitalization/smart manufacturing manager & 27 & 13.4\% \\ Electronics & 31 & 15.3\% & Production manager & 21 & 10.4\% \\ Aviation & 26 & 12.9\% & Head of production-related department & 19 & 9.4\% \\ Others & 39 & 19.3\% & Technology manager & 14 & 6.9\% \\  & & & Industrial engineering manager & 14 & 6.9\% \\ Years working in the firm & & & Other middle managers & 70 & 34.7\% \\ \textless{}2 & 9 & 4.5\% & Firm size (number of employees) & & \\
2-5 & 34 & 16.8\% & \textless{}500 & 55 & 27.2\% \\ \textless{}10 & 48 & 23.8\% & 500–1999 & 54 & 26.7\% \\ \textgreater{}10 & 111 & 54.9\% & \textgreater{}2000 & 93 & 46.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Sample demographics (\(n=202\)).  
Figure 3: Implementation of Industry 4.0 technologies among firms (\(n=202\)).  
technologies. SEM, a correlational and symmetrical approach, was employed to examine the overall effects of the influencing factors [28]. Complementing SEM, fsQCA offers a nuanced understanding of factor combinations, thereby enhancing the comprehensiveness of our analysis [27; 112; 113].  
This hybrid approach combining SEM and fsQCA offers comprehensive insights into the complex relationships among variables. SEM enables a thorough examination of individual factors' effects, while fsQCA explores interactions among these factors, providing a holistic understanding. This synergy enhances the robustness of the findings by corroborating results and identifying areas for further investigation. However, implementing this approach requires expertise in both methodologies and significant resources, making it challenging for researchers lacking familiarity or adequate resources. Additionally, integrating findings from SEM and fsQCA analyses poses interpretation challenges, demanding careful consideration. Despite these complexities, the approach's benefits in offering nuanced insights and a comprehensive analysis justify its consideration, provided researchers are equipped with necessary skills and resources.  
Prior to analysis, [PERSON]'s one-factor test was conducted to assess potential standard method bias [114]. The results indicated that the first extracted factor accounted for only 31.44% of the total variance, falling below the critical threshold of 50%. Consequently, common method bias was deemed negligible, thus ensuring the validity of our findings.
###### Abstract  
In this study, an impedance tube test was performed to explore the influence of various dimension parameters of Nomex honeycomb sandwich core material on sound transmission loss (STL). The parameters investigated included the size of the honeycomb cells and thickness of the face sheets and honeycomb cores, and the effects of single- and double-layered sandwich structures were also explored. The boundary element and finite element methods were used to simulate test results. The results show that the size of the honeycomb cells has an insignificant effect on STL. Increasing the thickness of face sheets can move the STL valley point of the material at high frequency (around 5 kHz) in the low-frequency direction and increase the STL in parts of the high frequency band. Increasing the thickness of the honeycomb core can improve STL, on the whole, but the magnitude of the improvement effect becomes weakened after the thickness of the core reaches 30 mm. The STL of double-layered structures was found to be superior to that of the single-layered structures. The simulations reveal that the trends in the STL curves of the honeycomb sandwich panels are influenced by the structural mode of the panels, and are related to the resonance of the materials. The results and relevant conclusions obtained through the above research verify that the law of influence of the structure dimension parameters on the STL measured by the impedance tube is similar to that of the large panel. This can provide a reference for the application of the impedance tube test method in structural noise reduction design.  
Keywords:Nomex honeycomb; sandwich structures; sound transmission loss; simulation analysis +
Footnote †: journal: Applied Sciences
# Mean Shift Segmentation Assessment for Individual Forest Tree Delineation from Airborne Lidar Data
## Materials and Methods
### Validation and Assessment Criteria  
In practice, the segmentation is normally validated by checking the locations of segmented trees. That is why the influence of the tree localization method is also investigated. In addition to horizontal locations, the heights of trees can be affected by segmentation, especially when the tree canopy is multistoried or the segmentation method is in 3D. Hence, tree tops (composed of locations and heights) extracted from segmented tree crowns were compared with ground measurements. Even though the segmentation is processed at point-level, the validation is conducted at object-level.  
To determine if a tree is oversegmented or undersegmented, the criteria proposed by [22] is followed. In general, if there is only one segmented tree top within a certain range (e.g., 2 m) in 3D from a ground measured tree top, this segment is considered correct (noted as a match). If there is more than one segment in this range or no segment, then the tree is either oversegmented or undersegmented. When all the tested trees have ground truth, such as in the simulated data, the precision, recall and F\({}_{1}\)-score can then be calculated as follows:  
\[\text{Precision}= \frac{\text{TP}}{\text{TP}+\text{FP}};\ \text{Recall}= \frac{\text{TP}}{\text{TP}+\text{FN}};\ \text{F}_{1}=2\frac{\text{Precision}\text{- Recall}}{\text{Precision}+\text{Recall}} \tag{4}\]  
in which TP (True Positive) is the number of matches, FP (False Positive) is the number of oversegmentations inside or outside of the assessment range, FN (False Negative) is the number of undersegmentations.  
The horizontal position accuracy and height accuracy are assessed by the root mean square error (RMSE) calculated from the horizontal and vertical distances, respectively, between the detected tree tops and ground measurements.
## 5 Results and Discussion
### Results of Outlier Handling and Sample Size Determination Using Coefficient of Variation (CV)
#### 5.1.2 Outlier Handling and Sample Size Calculation for CHF3 Tier 3 DRE Data

Table 10: Requirements for handling outliers in the Burn scrubber Tier 3 DRE during CHF3. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Classification** & **Case 1** & **Case 2** & **Case 3** \\ \hline Sample & 39 & 34 & 39 \\ Mean & 0.985 & 0.995 & 0.993 \\ SD & 0.033 & 0.008 & 0.012 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: DRE value for CHF3 gas according to the outlier case. \begin{table}
\begin{tabular}{c c} \hline \hline
**Classification** & **Value** \\ \hline Q3(75 th Percentile) & 0.999 \\ Q1(25 th Percentile) & 0.954 \\ IQR & 0.046 \\ IQR \(\times\) 1.5 & 0.069 \\ Maximum(Q3 + 1.5 \(\times\) IQR) & 1.068 \\ Minimum(Q1 \(-\) 1.5 \(\times\) IQR) & 0.089 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Requirements for handling outliers in the Burn scrubber Tier 3 DRE during CHF3. To determine the sample size for the CHF3 DRE development, CV values based on the outlier criteria were used, and the results are presented in Table 11. According to the outlier criteria, the sample size calculated was 45 for Case 1, 3 for Case 2, and 6 for Case 3. These results show that Cases 2 and 3 had relatively smaller sample sizes than Case 1, as outliers were treated and adjusted. For CHF3 DRE, the required sample size is lower than that required for CF4 DRE because CHF3 data are more homogeneously distributed than CF4 data, as seen in the outlier handling process.
## 3 Experiments
### Metrics and Statistical Analysis  
As previously described, in **Test 1** the robot is required to reach three targets, providing data for calculating three metrics to assess participants' performance.  
* Distance Accuracy, \(A\): the distance between the center of each of the three targets (\(i=1,2,3\)), and nearest point on the robot, computed as \[A_{i}=1-\frac{d_{min,i}}{D_{max,i}},\] where \(d_{min,i}\) is the minimum distance between the robot and the \(i^{\text{th}}\) target center and \(D_{max,i}\) is the initial distance between the nearest point of the robot and the \(i^{\text{th}}\) target center when \(d_{min,i}\) is achieved. For each subject, the final \(A\) score, normalized to the range \([0,1]\), is computed as \[A=\frac{A_{1}+A_{2}+A_{3}}{3}\]
* Time, \(\bar{t}\): the normalized time required to reach each target, defined as \[\bar{t}_{i}=1-\frac{t_{i}}{T_{max}},\] where \(t_{i}\) is the time elapsed from the test start to achieve \(d_{min,i}\), and \(T_{max}=300\) s is the maximum allowable time given to the participant to complete the test. The final normalized time, \(\bar{t}\), is computed as \[\bar{t}=\frac{\bar{t}_{1}+\bar{t}_{2}+\bar{t}_{3}}{3}\]
* Touch Accuracy, \(A_{t}\): the distance between the robot's base center and the target center for the first two targets and the normalized distance between the robot's end-effector and the target center for the third target. It is calculated as \[A_{t,i}=1-\frac{d_{c,i}}{D_{c,i}},\] where \(d_{c,i}\) is the distance from the robot's base center to the target centers, for \(i=1,2\), and from the end-effector to the target center for \(i=3\). The denominator, \(D_{c,i}\), differs based on the target:
* For \(i=1,2\), \(D_{c,i}\) is \(r+\frac{l}{2}\), where \(r\) is the target radius and \(l\) is the robot base length.
* For \(i=3\), \(D_{c,i}\) corresponds to the offset from the robotic hand's palm center to the fingertip. The target thickness is considered negligible. The final \(A_{t}\) score, normalized to \([0,1]\), is computed as \[A_{t}=\frac{A_{t,1}+A_{t,2}+A_{t,3}}{3}.\] For the targets on \(x,y\) plane (\(i=1,2\)), touch accuracy is categorized as follows:
* Maximum accuracy (\(A_{t}=1\)): when the robot base center is within the target radius.
* Medium accuracy (\(0<A_{t}<1\)): when another point on the robot base, but not its center, is within the target radius.
* Low accuracy (\(A_{t}=0\)): when the target is not reached. The sign test, a nonparametric statistical test, was conducted in MATLAB (MathWorks, Natick, MA, USA) to evaluate differences in performance metrics between the pre-training and post-training phases. Specifically, the analysis focused on the results from **Test 1**, examining whether performance improvements across all three metrics were statistically significant after the training sessions.
## 3 Results
### Effect of Electron Beam Irradiation on Eggs  
The effects of electron beam irradiation on the eggs of the susceptible and three resistant strains of \(T\). _urticae_ are shown in Table 2. The hatching rate of all strains of \(T\). _urticae_ was partially reduced when an electron beam dose of 50 Gy was used. When the electron beam irradiation dose was 100 Gy, the hatching of the eggs of the susceptible strain of \(T\). _urticae_ was completely (100%) inhibited, and the hatching of eggs in the three resistant strains was markedly reduced but not fully inhibited. However, the hatching of all three resistant strains was completely inhibited with an electron beam dose of 150 Gy. The longevity of adults emerging from irradiated eggs tended to decrease at 50 Gy irradiation compared to 0 Gy in both the resistant and susceptible strains of \(T\). _urticae_. The number of eggs laid decreased with increasing electron beam doses for the BR, ER, and susceptible strains of \(T\). _urticae_. However, in the AR strain of \(T\). _urticae_, the number of eggs laid was higher at 50 Gy than at 0 Gy but decreased again at 100 Gy. The \(F_{1}\) generation hatching rate for the AR, BR, and S strains of \(T\). _urticae_ tended to decrease as the electron beam dose increased, but the hatching rate of ER \(T\). _urticae_ was 91.2% at a 100 Gy dose. The emergence rate tended to decrease as the dose increased for all strains of \(T\). _urticae_.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Acaricide** & **Strain** & **LC50 (ppm, 95\% CL a)** & **RR b** \\ \hline \multirow{2}{*}{Acequinacyl} & AR & \textgreater{}2400 & \textgreater{}108.5 \\  & S & 22.0 (19.1–25.5) & 1.0 \\ \hline \multirow{2}{*}{Bifenazate} & BR & \textgreater{}1340 & \textgreater{}705.3 \\  & S & 1.9 (1.6–2.3) & 1.0 \\ \hline \multirow{2}{*}{Etoxazole} & ER & \textgreater{}5000 & \textgreater{}5,000,000 \\  & S & 0.0011 (0.0008–0.0014) & 1.0 \\ \hline \hline \end{tabular}  
* CL, confidence limit. b RR, resistance ratio = LC\({}_{50}\) of the resistant strain/LC\({}_{50}\) of the susceptible strain.  
\end{table}
Table 1: Resistance ratio of \(T\). _urticae_ against three types of acaricides.
# Dist-YOLO: Fast Object Detection with Distance Estimation
## 2 Dist-YOLO
### Preliminaries-YOLOv3  
YOLO--_You Only Look Once_--is a one-stage, multi-scale, anchor-based object detector utilizing a fully convolutional architecture, DarkNet-53. Initially introduced by [PERSON] [28] and evolved by them into the second and third version [4; 29], where the first version utilized single scale detection and lightweight architecture. Currently, the fourth [5] and fifth versions have been introduced by independent groups. The performance boost of the fourth and fifth versions is achieved mainly by new data augmentation and minor architecture changes; the main principle remains the same [5]. The newest version, YOLOX [30] is based on YOLOv3 and modifies it into an anchor-free algorithm which allows use of decoupled heads, leading to a slightly higher accuracy of detection. Furthermore, YOLO has been extended in several ways to improve its precision and capabilities. Namely, object segmentation based on object polygon detection, Poly-YOLO, was introduced in [31], where the original YOLO prediction vector was extended to predict a set of polygon points together with their confidence for every detected object. Furthermore, an approach motivated by a Mask R-CNN \(\times\) Faster R-CNN relation, YOLACT, was introduced in [32] to provide the ability for mask instance segmentation with comparable results to standard two-staged segmentation approaches, but improving their computational demands. Later, this approach was extended into YOLACT++ [33] with additional performance and precision boost.  
In contrast to two-staged detectors [34], YOLO does not use a region proposal network but integrates the whole process into a single architecture. It is split into encoder and decoder parts. The encoder serves as a feature extractor. It is represented by the Darknet-53 backbone, but an arbitrary backbone can be used. The features are decoded on three succeeding scales into three output grids. Each grid consists of cells, where each cell is responsible for detecting objects whose center lies inside the cell. By detecting, we mean the regression on the bounding box coordinates together with class and confidence. In YOLOv3, each cell can detect three objects, where so-called anchors are used for suppressing the problem of detecting three identical objects. By anchor, we mean a prototypical bounding box extracted from training data during preprocessing using \(k\)-means algorithm. During training and inference, a cell is assigned three anchors and detects an object in the output position where the intersect over union (IoU) over the box and the anchor is maximized. Compared to two-stage detectors, YOLO is much faster but yields lower precision of detection.  
The success of the YOLO algorithm family can be illustrated on a wide range of tasks where it was applied, such as face mask wearing detection [35], identification of plant diseases [36], pedestrian detection [37], forest fire detection [38], or safety helmet detection [39]. Currently, YOLO based approaches are used for popular social distancing estimation [40; 41], also combined with mask detection [42].
## 2 Platform Approach
### Evaluation and Optimisation  
The evaluation method deployed within the platform is aimed at translating the qualitative into quantitative which \"is the research topic of multi-criteria decision analysis\" (MCDA) [21; 22]. Thus, MCDA can be used in order to create a framework for decision making when evaluating alternatives for designing a retrofitting solution analysing a set of evaluation criteria (in this case, indicators calculated at district level).  
The use of a weighting method is the main procedure but although there some approaches as ISO standards [23], the aggregation method to be used is not defined. Furthermore, there are also a number of elements that need to be solved when implementing this sort of decision-making approaches [24]. Some of these challenges can be highlighted as, for example, defining the evaluation criteria, normalising and scaling the indicators or defining the objectives and weighting schemes.  
The solution proposed by this evaluation methodology is the one reflected within Figure 2 above. This process considers first the normalisation of the evaluation criteria in order to make them comparable, then an aggregation method is implemented to calculate two functions: the one representing the cost, and the one reflecting the benefit, which allows ordering the scenarios and comparing them to select those most suitable to the targets defined by the user.  
A harmony search optimisation algorithm allows repeating the generation of groups and scenarios and their evaluation in terms of the cost and benefit functions [25]. This algorithm generates new iterations in the form of groups of candidate retrofitting scenarios, which are created based on the results that are obtained from the previous execution and, therefore, optimising them in a continuous loop.