esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Finite Element Conversion  
To realize the integration of structural design and analysis on BIM platforms, extensive research has been conducted by numerous scholars, particularly on model conversion between BIM and FEA software. The following section highlights some research in this area.  
[PERSON] et al. [23] proposed a method for converting from BIM models to Midas finite element models based on the Autodesk Revit Application Programming Interface (API) and the C# language, achieving the accurate addition of structural parameter information during the finite element modeling process of prestressed concrete continuous beam bridges. [PERSON] et al. [24] achieved an efficient conversion between Revit models and ANSYS finite element models in the form of ANSYS Parametric Design Language (APDL) command flows. [PERSON] et al. [25] explored the application of BIM technology in the finite element simulation analysis of long-span corrugated steel web continuous beam bridges, proposing methods to extract geometric parameter information from BIM models using Dynamo visual programming software and to convert data between BIM models and Midas Civil finite element models via Python programming. [PERSON] et al. [26] discussed the application of BIM in geotechnical engineering design and construction, using drilled and blasted tunnels as an example. They created tunnel models containing geological information on the Bentley platform and completed the creation of numerical models through data exchange with finite element software. [PERSON] et al. [27] proposed an algorithm based on Industry Foundation Classes (IFCs) for converting building structural geometry models to numerical models and evaluated the reliability of this algorithm in four structural analysis applications (ETABS, SAP2000, ANSYS, and MIDAS). [PERSON] et al. [28] developed a parametric file capable of generating all types of bridges from a single parameter file. The developed file uses parametric algorithms to model bridge elements within design software applications and to generate and analyze models in structural analysis software applications.  
The above research mainly focuses on bridges, but there are some differences in the way bridges and box culverts are modeled in finite elements; for example, their nodes are not defined in the same way, with the former being the bridge routes and the latter being the box culvert cross-section contour lines. Therefore, the BIM and finite element software model data conversion for bridge projects cannot be applied to culvert engineering. Moreover, the aforementioned studies only provide one-way conversion and do not provide an integrated system that encompasses information collection, model construction, structural calculation, and visualization.
## 2 Materials and Methods  
### Reagents and Solutions  
The pure standards of compounds listed in Table 1 were all purchased from Sigma. Deuterium-labelled 2,4,6-trichloroanisole (d5-TCA) was used as the internal standard (IS). These compounds were employed to confirm the identification of the peaks observed in the chromatograms.  
Individual stock solutions of about 500-3000 mg L\({}^{-1}\) were prepared by weight in methanol and stored at 4 \({}^{\circ}\)C. Mixed working solutions were made by diluting the required volumes of the intermediate solutions (prepared from stock solutions, also in methanol) with ultrapure water. In Table 1, the selected concentrations for the working solutions are shown.  
Sodium chloride (99.9%) and HPLC-gradient grade methanol were supplied by Carlo-Erba Reagents. Ultrapure water from a Milli-Q Plus water purification system (Millipore Iberica) was used.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Compound** & **Retention Time (min)** & **m/z 1** &
\begin{tabular}{c} **Concentration** \\ **(\(\mu\)g L\({}^{-1}\))** \\ \end{tabular} \\ \hline
1,8–cineol (eucalypt) & 12.33 & **81**,108,139,154 & 3.95 \\ MDMP 2 & 12.66 & **137** & 0.46 \\ IPMP 2 & 13.55 & **137** & 0.38 \\ Guaiacol & 13.51 & **81**,**109,**124** & 19.58 \\ (\(\pm\))-linalool & 13.88 & **71**,**93,121 & 3.33 \\ (+)-fenchol & 14.33 & **81** & 5.26 \\
1,2-dimethoxy benzene (veratrol) & 14.84 & **95**,**123,**138** & 15.62 \\ Campnor & 15.00 & **95** & 3.66 \\ Sec-IBMP 2 & 15.42 & **124,**138** & 0.68 \\ (-)-borneol & 15.6 & **95** & 4.25 \\ IBMP 2 & 15.65 & **124** & 21.12 \\ Menthol & 15.71 & **95** & 3.82 \\ Methylisoborneol & 16.02 & **95**,**108** & 0.57 \\ \(\alpha\)-terpineol & 16.13 & **93**,**121,136** & 5.54 \\ Benzothiazole & 16.86 & **108**,**135** & 21.89 \\ TCA & 19.30 & **210**,**212** & 0.01 \\ d5-TCA & 19.22 & **215**,**217** & 0.06 \\ Geosmin & 21.50 & **112**,125 & 0.6 \\ \hline \hline \end{tabular}  
* m/z in bold are used for quantification. 2 MDMP: 2,3-dimethyl 5-methoxy-pyrazine; IPMP: isopropyl methoxy pyrazine; IBMP: isobutyl methoxy pyrazine.  
\end{table}
Table 1: Compounds used for target analysis of cork volatiles: retention time, m/z fragments used for the identification and concentration of the compounds in the working solution prepared to confirm the identification.
# Derating of Electrical Contacts with Varying Surface Roughness
## 2 Materials and Methods
And the results are summarised in Table 1.  
The software interface shown in Figure 3 is used to conduct derating tests for electrical components, including electrical contacts. The program allows for the analysis of operating parameters such as maximum current, contact resistance, and operating temperatures to determine the limiting conditions of devices based on the environment and load. The program is divided into several functional sections:  
* INPUTS and RESULTS: A panel for entering test parameters and displaying results. * RESULTS: A table containing test data such as sample number, test current, ambient temperature, and contact resistance values for each sample. Configuration panel (on the left side):  
* LOAD CONFIGATION: The ability to load a previously saved test configuration. * TRIGGER: Indicates the status of the measurement trigger (e.g., activation of the test under specific conditions). The three graphs visible in the top right corner show the derating test results:
* Graph 1 (Current vs. Temp): The relationship between maximum current and ambient temperature. * Graph 2 (Resistance vs. Temp): The relationship between contact resistance and temperature. \begin{table}
\begin{tabular}{c c} \hline \hline
**Correct Factor** & 0.8 \\ \hline
**max. temp. [\({}^{\circ}\)C]** & 100 \\ \hline
**nom. current [A]** & 16 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Maximum temperature, correct factor, and nominal current. Figure 3: Derating program screen. * Graph 3 (Combined data): A summary of key parameters in one graph. The graphs display measurement points and trend lines illustrating how parameters change with increasing ambient temperature or current load. The program automatically identifies threshold values, such as the maximum current for a given temperature (intersection point of the graphs), which helps determine derating values.
# Building Scalable and Secure Multicast Delivery Infrastructure in a Local Area Network
## 4 Core Based Multicast Tree
### Security Analysis
#### 4.2.1 Impersonation Attack  
There are two types of impersonation attacks possible in a network. In the first case, a malicious device (switch, router, or a host) without an active connection to the network may try to join the network using someone's identity. In the second case, a legitimate device may already join the network and pretend to be someone else.  
The first case is not possible. In order to decrypt the challenge, a node must need its private key. A node impersonating someone else will not have the knowledge of the private key of the impersonating node.  
In the latter case, if \(M\) wishes to impersonate \(L\), it needs to have the knowledge of \(L\)'s private key in order to digitally sign the messages. Thus, the second case is also impossible in our proposal.
# Infrared Multi-Scale Small-Target Detection Algorithm Based on Feature Pyramid Network
## 3 Results
### Implementation Details  
Two datasets, IRSTD-1k [34] and SIRST [25], are selected for experimental verification. IRSTD-1k is a single-frame infrared small-target dataset. The dataset contains 1000 samples with various backgrounds, distances, and types of small targets, as shown in Figure 5. SIRST contains 427 samples and 480 small-target instances, many of which are blurred and hidden in complex backgrounds such as sky or water, as shown in Figure 6. In real infrared scenes, the network's performance may be limited by differences in image sizes and the restricted amount  
In the implementation process, the optimizer of the proposed method utilizes stochastic gradient descent (SGD) with a momentum of 0.9. The initial learning rate is set to 0.05, the weight decay coefficient is 0.0004, the batch size is 8, and the number of epochs is 300. The loss function employed is soft IoU, and its value is calculated as shown in Equation (13).  
\[SoftIoU=1-IoU \tag{13}\]  
The proposed model is compared with five data-driven methods, namely, FPN [22], U-Net [23], TBC-Net [24], ALC-Net [25], and DNA-Net [26]. The parameter settings are the same as those in the original paper. Table 2 shows the parameter settings involved in various methods.
# Multi-Head TrajectoryCNN: A New Multi-Task Framework for Action Prediction
## 4 Experiments
### Ablative Study  
We will show the effectiveness of our proposed classification block (CB), the regression head (RH), and the classification head (CH). This section provides experimental results on the NTU RGB+D dataset using cross-view evaluation protocols. Detailed results are shown in Table 2. The \"Accuracy\" column denotes the performance of action semantic prediction, and the \"Average error\" column denotes the average error across all time steps for human motion prediction.  
Figure 5: Frame-wise performance of human motion prediction. “MPPE” denotes the mean per-joint position error of future poses by Equation (5), and “time steps” denotes the frames of future poses.  
Figure 6: Visualization of future poses. The dark poses denote the ground truth, the blue poses denote the predictions of TrajectoryCNN [8], and the red poses denote the predictions of our method.  
Evaluation of CB. \"w/o CB\" shows the effectiveness of the classification block by replacing the CB with RB. Compared with \"Multi-head TrajectoryCNN\", the accuracy of action semantic predictions decreases by 5.11%, and the average errors are slightly improved by a margin of 0.21. Compared to the structures between \"w/o CB\" and \"Multi-head TrajectoryCNN\", the main difference lies in the classification head. This may be the possible reason why the motion prediction is almost unchanged. The CB block is improved from RB by adding MaxPooling layers, which increases the receptive field to obtain higher semantic features for predictions. This may be the reason for the decreasing performance of action semantic predictions.  
Evaluation of regression head. \"w/o RH\" shows the effectiveness of the regression head by removing the regression head in Figure 3. In this way, the network only focuses on predicting action semantics. Therefore, the results for motion prediction are not available. Table 2 only reports the accuracy of \"w/o RH\" for action semantic predictions. Compared with \"Multi-head TrajectoryCNN\", the accuracy of \"w/o RH\" decreases by 8.44%, showing that the regression head of future motion prediction can guide accurate predictions of action semantics.  
Evaluation of classification head. \"w/o CH\" shows the effectiveness of the classification head by removing the classification head in Figure 3. In this way, the network only focuses on predicting future human motion while ignoring the prediction of action semantics. Therefore, the results for action semantics are not available. Table 2 only reports the average errors of \"w/o CH\" for human motion predictions. Compared with \"Multi-head TrajectoryCNN\", the errors of \"w/o CH\" increase by 0.3. The results show that the classification head's semantic predictions can also assist in predicting future human motion to some extent.
# Electrically Conductive Nanocomposite Fibers for Flexible and Structural Electronics
## 3 Results and Discussion  
### Composite Fibres Content  
During the research, three types of conductive filler were used to fabricate the composites. Carbon nanotubes, graphite powder, and a mix of carbon nanotubes and graphite were used as a functional phase. Additionally, different amounts of conductive filler were used to fabricate fibers with different electrical and mechanical properties. The compositions of all the tested materials are shown in Table 1, together with the symbols that will be used throughout the rest of the article. In accordance with the datasheets, the densities of the materials used in the calculation of the volumetric content in the composite were 1.08 g/cm\({}^{3}\) for SBS polymer, 2.6 g/cm\({}^{3}\) for CNT, and 2.2 g/cm\({}^{3}\) for graphite.
# A Comparative Study of Differential Quadrature Methods for METE Nanobeam Vibrations
## 3 Method of Solution
### Sinc Differential Quadrature Method (SDQM)  
The function of cardinal sine is utilized as a shape function to approximate \(v_{i}(i=1:N)\) as follows:  
\[S_{j}(x_{i},h_{x})=\frac{\sin\left[\pi(x_{i}-x_{j})/h_{x}\right]}{\pi\big{(}x _{i}-x_{j}\big{)}/h_{x}} \tag{35}\]  
The step size, denoted by \(h_{x}\), is a positive value.  
To approximate the unknown function \(v\) and its derivatives at specific discrete nodes, the cardinal sine acts as the shape function. This method expresses the approximation as a weighted combination of the nodal values \(v_{i}(i=-N:N)\)[34].  
\[v(x_{i})=\sum_{j=-N}^{N}\frac{\sin\left[\pi\big{(}x_{i}-x_{j} \big{)}/h_{x}\right]}{\pi\big{(}x_{i}-x_{j}\big{)}/h_{x}}v(x_{j}),\quad(i=-N:N),h_{x}>0 \tag{36}\] \[\left.\frac{\partial v}{\partial x}\right|_{x=x_{i}}=\sum_{j=-N}^ {N}C_{ij}^{(1)}v\big{(}x_{j}\big{)},\quad\left.\frac{\partial^{2}v}{\partial x ^{2}}\right|_{x=x_{i}}=\sum_{j=-N}^{N}C_{ij}^{(2)}v\big{(}x_{j}\big{)},\quad(i =-N:N), \tag{37}\]  
Likewise, we can approximate \(\frac{\partial^{2}v}{\partial x^{3}},\frac{\partial^{4}v}{\partial x^{4}}\) and determine \(C_{ij}^{(3)},C_{ij}^{(4)}\)  
For each variable, \(v\in\{\;\mathrm{W},\Theta,\Psi,\Phi\}\), the weighting coefficients \(C_{ij}^{(1)},C_{ij}^{(2)},C_{ij}^{(3)}\), and \(C_{ij}^{(4)}\) are obtained by differentiating Equations (35) and (36):  
\[C_{ij}^{(1)} =\left\{\begin{array}{ll}\frac{(-1)^{i-j}}{h_{X}(i-i)},&i\
eq j,\\ 0&i=j\end{array}\right., \tag{38}\] \[C_{ij}^{(2)} =\left\{\begin{array}{ll}\frac{2(-1)^{i-j+1}}{h_{X}(i-j)^{2}},& i\
eq j\\ -\frac{\pi^{2}}{3h_{X}^{2}}&i=j,\end{array}\right.\] \[C_{ij}^{(3)} =\left\{\begin{array}{ll}\frac{(-1)^{i-j}}{h_{X}^{2}(i-j)^{3}} \big{(}6-\pi^{2}(i-j)^{2}\big{)},&i\
eq j\end{array}\right.,\] \[C_{ij}^{(4)} =\left\{\begin{array}{ll}\frac{4(-1)^{i-j+1}}{h_{X}(i-j)^{4}} \big{(}6-\pi^{2}(i-j)^{2}\big{)},&i\
eq j\end{array}\right.\]
## 1 Introduction
The presence of geometric interferences between the vessel and the endoprosthesis causes failure of the operation, for which the surgeon is forced to change from an EVAR to open surgery; other problems are linked to the duration of the stent-graft and post operative problems. Custom vascular stents represent the next generation of stents, which are able to accommodate the patient's anatomical and vascular structure. In this article, the authors have focused exclusively on the innovation linked to the customization of the stent/graft in terms of the curvature and dimensions of the local diameter of the rings. As far as the design of the metallic stent is concerned, the next step will be to optimize it for a potential increase in fatigue life related to the number of loops and the central bending angle [5; 6]. Furthermore, given that such a thoughtful stent/graft requires a new production technology, in particular additive manufacturing, the design will have to be optimized also for this reason. The authors instead propose a stent-graft made for the specific morphology of the patient and therefore a total customization in terms of the shape of the endoprosthesis so that it can be inserted and correctly anchored via EVAR. The aneurysm size is the main parameter used today in clinical practice to evaluate the risk of rupture. Wall stress distribution is considered a better indicator of the AAA rupture risk in order to reduce the incident of rupture. A patient-specific study has demonstrated that the maximum wall stress was 12% more accurate and 13% more sensitive in predicting AAA rupture than the maximum diameter [7,8]. This parameter is influenced by the blood pressure, the aneurysm diameter, shape, wall thickness, wall mechanical properties, and the presence of thrombus. Several studies of AAA by means of computational models have been reported in the literature [7,9,10,11,12,13,14,15,16,17,18,19], each of which considerably simplifies the geometry of the vessel and of the aneurysm, without reporting a study carried out on a reconstruction of the aneurysl vessel from a CT scan. Over the years, Computational Fluid Dynamics (CFD) analysis and, in the last decade, fluid-structure interaction (FSI) have been used to simulate the complex cardiovascular system. FSI offers a more accurate representation of the in vivo environment and is of major benefit as both graft and aneurysm wall stresses as well as fluid forces can be determined. Models based on idealized geometries and, more recently, patient-specific models have been used to study the mechanical characteristics of the aneurysm, also evaluating the influence of size, boundary conditions, or constitutive properties. In this paper, a patient-specific case of an abdominal aortic aneurysm was studied. Computational analyses were implemented on the patient-specific model of an abdominal aortic aneurysm and its customized stent graft. Hemodynamic parameters such as flow pattern, pressure, and wall shear stresses are highlighted to evaluate the benefits of using a patient-specific stent-graft.
# 3D Liver and Tumor Segmentation with CNNs Based on Region and Distance Metrics
## 3 Experiment and Discussion
### Experimental Results
#### 3.2.2 Qualitative Evaluation  
The qualitative results are shown in Figure 4. After visual inspection of the above results, great improvements were found when employing the distance-based loss function. Especially in the case of highly imbalanced between foreground and background voxels (such as rows 2, 4, and 5), the results of the joint training models have been greatly improved. The results obtained by the model only using the regional loss function showed that many small regions (including liver or tumor) were not correctly segmented. In contrast, adding distance-based loss functions for joint training improved the segmentation results by varying degrees. Furthermore, the model adding the SDM loss function obtained better results in most cases, which was also reflected in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & **Training** & **Testing** & **Loss** & \multirow{2}{*}{**DSC**} & **HD95** & **ASD** & \multirow{2}{*}{**TNR**} & **TPR** \\  & **Dataset** & **Dataset** & & & & & & \\  & & & & & & & & \\ \hline \multirow{6}{*}{Liver} & \multirow{6}{*}{3D-IRCADb} & \multirow{6}{*}{LiTS 2017} & \(L_{R\epsilon g}\) & 0.913 & 9.8 & 2.64 & 0.879 & 0.995 \\  & & & \(L_{HD}\) & **0.924** & 8.06 & 2.25 & 0.914 & 0.996 \\  & & & \(L_{BD}\) & 0.921 & 8.41 & 2.11 & 0.922 & 0.996 \\  & & & \(L_{SDM}\) & 0.923 & **7.82** & **2.03** & **0.941** & **0.997** \\ \cline{2-10}  & & & \(L_{R\epsilon g}\) & 0.919 & 10.74 & 3.01 & 0.858 & 0.995 \\  & & & \(L_{HD}\) & 0.928 & 7.45 & 2.54 & 0.945 & 0.997 \\  & & & \(L_{BD}\) & 0.926 & 7.12 & 2.75 & 0.951 & 0.997 \\  & & & \(L_{SDM}\) & **0.934** & **6.88** & **2.31** & **0.960** & **0.998** \\ \hline \multirow{6}{*}{Tumor} & \multirow{6}{*}{3D-IRCADb} & \multirow{6}{*}{LiTS 2017} & \multirow{6}{*}{LiTS 2017} & \(L_{R\epsilon g}\) & 0.598 & 15.74 & 4.11 & 0.564 & 0.998 \\  & & & \(L_{HD}\) & 0.644 & 12.32 & 3.77 & 0.58 & 0.998 \\ \cline{1-1}  & & & \(L_{BD}\) & **0.653** & 11.72 & **3.14** & 0.644 & 0.998 \\ \cline{1-1}  & & & \(L_{SDM}\) & 0.651 & **10.41** & 3.18 & **0.682** & **0.998** \\ \cline{1-1} \cline{2-10}  & & & \(L_{R\epsilon g}\) & 0.587 & 18.22 & 4.62 & 0.526 & 0.999 \\ \cline{1-1}  & & & \(L_{HD}\) & 0.627 & 15.41 & 4.28 & 0.557 & 0.999 \\ \cline{1-1}  & & & \(L_{BD}\) & 0.631 & 13.64 & 3.62 & **0.682** & 0.999 \\ \cline{1-1}  & & & \(L_{SDM}\) & **0.634** & **13.25** & **3.24** & 0.674 & **0.999** \\ \hline \hline \end{tabular}
\end{table}
Table 3: The cross-validation experimental results of the LiTS 2017 and 3D-IRCADb datasets.
## 1 Introduction  
A cyber-physical system (CPS) enables physical gadgets with sensing abilities to interact with the internet or controllers as required [1]. The communication channels used can be short-distance communication or wireless technology to continually upgrade the physical environment condition or physical device status to a remote server or controller [2]. The recent developments in wireless communications and sensor technologies have utilized the CPS in several application fields, such as aviation and chemical industries, electronics, material manufacturing with automatic supply chain, and smart industries, which includes transport, etc. [3]. The advent of CPS applications in different fields even paves the way for novel security challenges and issues to safeguard confidential data or infrastructure against cybersecurity. The attacks include cyberattacks via internet-connected devices, and physical assaults which can lead to supply chain disruption or system failures [4]. Therefore, CPSsecurity is very challenging compared to the classical IT and network substructure [5]. Figure 1 represents the CPS and potential security threats.  
Intrusion detection systems (IDS) are highly capable of detecting and preventing data breaches by stopping and detecting intrusions [6; 7; 8]. Anomaly detection (AD) and Misuse detection are two kinds of ID. Misuse detection depends on patterns or information, whereas AD relies on behavior. Present IDS have a higher detection rate, which leads to several false alarms. In an IDS, false positives must be reduced. Several IDS were applied with the help of various ML methods as they can find valuable data from the database. Such techniques have the potential to diminish false positives. ML techniques, involving IDS, an association of rules, and GA were enforced through artificial neural networks (ANNs). Ensemble learning merges different ML techniques. The authors found that an ensemble method involving ML techniques lessens false positives.  
This study develops an Explainable Artificial Intelligence Enabled Intrusion Detection Technique for Secure Cyber-Physical Systems (XAIID-SCPS). In the XAIID-SCPS technique, a Hybrid Enhanced Glowworm Swarm Optimization (HEGSO) algorithm is applied for feature selection purposes. For intrusion detection, the Improved Elman Neural Network (IENN) method was exploited with an Enhanced Fruitly Optimization (EFFO) algorithm for parameter optimization. Moreover, the XAIID-SCPS technique integrates the XAI approach LIME for better explainability and understanding of the black-box method for the accurate classification of intrusions. The simulation values of the XAIID-SCPS technique can be tested on benchmark intrusion datasets.  
The rest of the study Is organized as follows. Section 2 elaborates on the existing IDS models in the CPS environment. Next, the proposed XAIID-SCPS technique is discussed in Section 3, and the experimental results are defined in Section 4. Finally, Section 5 concludes the work with key findings and possible future work.  
Figure 1: CPS and potential security threats.