esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Application Example with SmartSync
### MBSE/MBSA  
To compare the SysML model and the AltaRica 3.0 model, we first translated the AltaRica 3.0 model to S2 ML. This process was not complicated because the structural part of the AltaRica 3.0 languages is S2 ML. Blocks were translated to blocks, variables to ports, and assertions to connections. An example of this translation can be found in Figure 14. In this example, it can be seen that some attributes giving more information on the AltaRica model were included in the S2 ML model (e.g., \"type = \"Boolean\"\"); these attributes are not currently used in the SmartSync comparison.  
Once the translation was done, the models were given to the SmartSync tool as inputs. The tool then took an element of each model that it knew to correspond to one another. This was the main element of the models for the first step, i.e., the blocks representing the whole system. The tool then asked the user to align children of these two elements from both models. The user can align elements or give them the forget attribute--meaning the element is not supposed to have a counterpart in the other model--or do neither. The tool  
Figure 14: A part of the AltaRica 3.0 model (**b**) and its translation in the S2 ML model (**a**).  
Figure 13: A part of the SysML model (**b**) and its translation in the S2 ML model (**a**).  
then iterated on each couple of aligned elements until all elements were either aligned or forgotten. When no element could be aligned anymore, the remaining elements with no counterpart were considered inconsistent.  
In this case, the comparison between the architecture and AltaRica 3.0 models was done through 4 comparison steps. It shows that the MBSA model lacks some elements of the system. The missing elements are the redundant ailerons and the rudder airfoil. This is due to a communication issue when the safety model was designed based on an outdated version of the architecture model. Correction of these inconsistencies was conducted by adding the missing elements to the AltaRica 3.0 model.  
Note that all safety artifacts were ignored in this comparison, such as state variables, events, etc. The comparison of the models also has shown that interactions with the environment such as gravity, 4G network, etc. have not been taken into consideration in the MBSA model. We deemed this normal in this work, but it could be argued that it should be added; therefore, consistency assessment would have resulted in adding these interactions to the MBSA model. Such decisions are non-trivial and should be undertaken by the system engineer and safety analyst; this is why the consistency assessment cannot be fully automatized. Of course, after modification of the model, a new comparison should be operated to assert that the correction did not introduce new inconsistencies.
## 4 Experiments
### Experiments of Keypoint Detection with Boundary Removal
#### 4.1.1 Dataset  
To conduct these experiments, we initially employed partial point clouds of Armadillo, Bunny, and Buddha et al. from the Stanford 3D Scanning Repository [35] for our research. For Armadillo, we included 11 partial point clouds in our collection, numbered from ArmadilloBack_0 to ArmadilloBack_330 with 60-degree intervals between them. For Bunny, we provided six partial point clouds in our dataset, labeled Bun000, Bun045, Bun090, Bun180, Bun270, and Bun315. For Happy Buddha, we chose 15 partial point clouds from our dataset and called them HappyStandRight_0 to HappyStandRight_336 with 24-degree intervals between them. Samples of point clouds from our dataset are shown in Figure 4.
## 3 Results and Discussions  
### Ultrasound-Assisted Extraction (UAE) of Total Alkaloids Content (TALC) in Sourspo Samples  
The experimental design and results obtained at the TALC are shown in Table 1. The UAE treatments and raw material had significant effects as the TALC of the pulp and by-products (peel, seeds, and columnella) depended on the experimental conditions in the UAE (\(p<0.05\)). The highest TALC (7.48, 2.31, and 0.60 mg/g, respectively) was found in the peel, seed, and columnella under the same conditions of time (5 min) and sonication amplitude (70%) with 1-s pulse-cycle for peel and columnella, and 0.4-s pulse-cycle for seeds. Likewise, the highest TALC in the pulp (1.16 mg/g) was found using 5 min of extraction, 0.7 s of pulse-cycles, and 100% amplitude. These differences in TALC could be attributed to different factors. First, the effects of UAE conditions and structural composition of raw materials. The extraction of bioactive compounds by ultrasound is achieved by the vibration of the ultrasonic waves, which cause the acceleration of the particles and allows the solute to pass quickly to the solvent. This increases its diffusivity [14]. Some authors argue that UAE produces mechanical effects that modify the structure of the cell wall and it becomes more porous when facilitating extraction [25]. The differences in the conditions of UAE for extracting these bioactive compounds depended on the physical-mechanical phenomenon that each UAE combination generates in each raw material influencing their structural composition. The longer treatment time, the highest ultrasonic amplitude or density of acoustic energy, and a constant acoustic irradiation cause a high cavitation, which produces to greater cellular lysis [14]. However, it has also been shown that extreme ultrasound conditions can decrease the content of bioactive compounds by the phenomenon called sonolysis, which causes the formation of hydroxyl radicals that cause oxidation [26]. This may indicate that the TALC extracted from each _A. muricata_ raw material depends on the greater or lesser susceptibility to cell disruption and the stability of the ALK at the higher UAE extraction conditions of 100% amplitude, 1-s pulse cycle (permanent acoustic irradiation), and 15 min. Second, the difference in the ALKs content depended
# OFF-The-Hook: A Tool to Detect Zero-Font and Traditional Phishing Attacks in Real Time
## 2 Background
### Phishing Techniques and Attack Vectors
#### 2.2.3.  
Clone Phishing Attacks  
Clone phishing is a sophisticated attack in which a legitimate email previously delivered is replicated and altered to include malicious attachments or links [11]. The spoofed message is then sent from an address that closely mimics the original sender, increasing its perceived authenticity. The objective is to deceive the recipient into disclosing sensitive information or installing malware.  
These attacks have been used against both individuals and organizations. A notable campaign aimed at users of Google Docs and Microsoft Office 365 [12]. The phishing emails closely resembled official service notifications and directed recipients to fake login pages under the pretense of a password change. Once both old and new credentials were entered, the attackers gained unauthorized access. This example underscores the need for greater user vigilance, stronger authentication methods, and broader cybersecurity education to defend against clone phishing.
# Precise Channel Estimation Approach for a mmWave MIMO System
## 4 Proposed Extended ADMM (Ex-ADMM)-Based Channel Estimation Scheme
### Procedure for Updating \(H^{(l+1)}\)  
In order to obtain the closed-form solution for H, reformulate \(\mathcal{L}_{\mathrm{A}}\) to \(\mathcal{L}_{1}\) and consider all terms with respect to H in Equation (9). Keeping only the terms that are related to it,  
\[\begin{split}\mathcal{L}_{1}\triangleq\underset{\mathrm{H}}{ \mathrm{argmin}}&\Gamma_{\mathrm{H}}\|\mathrm{H}\|_{\mathrm{L}}+ \mathrm{tr}\Big{(}\mathrm{P}_{1}^{\mathrm{H}}(\mathrm{H}-\mathrm{I})\Big{)}+ \frac{\beta}{2}\|\mathrm{H}-\mathrm{I}\|_{\mathrm{F}}^{2}\\ &=\Gamma_{\mathrm{H}}\|\mathrm{H}\|_{\mathrm{L}}+\frac{\beta}{2} \|\mathrm{H}-\Big{(}\mathrm{I}^{(1)}-\frac{1}{\beta}\mathrm{P}_{1}^{(1)} \Big{)}\|_{\mathrm{F}}^{2}\end{split} \tag{22}\]  
Here, Equation (22) is the solution of Equation (10). To get the closed-form solution, a singular value thresholding (SVT) operator [43] is implemented on Equation (22):  
\[\mathrm{H}^{(l+1)}=\mathrm{Udiag}(\left\{\mathrm{sign}(\mathrm{h}_{\mathrm{i}} )\mathrm{max}(\mathrm{h}_{\mathrm{i}},0)\right\}_{1\leq\mathrm{i}\leq\mathrm{ r}})\mathrm{V}^{\mathrm{H}} \tag{23}\]  
Here, \(\mathrm{U}\in\mathbb{C}^{\mathrm{N}_{\mathrm{r}}\times\mathrm{r}}\) and \(\mathrm{V}\in\mathbb{C}^{\mathrm{N}_{\mathrm{r}}\times\mathrm{r}}\) are the side singular vector matrices of the matrices \((\mathrm{I}^{(1)}-\frac{1}{\beta}\mathrm{P}_{1}^{(1)})\) and \(\mathrm{h}_{\mathrm{i}}\triangleq\mu_{\mathrm{i}}-\frac{\Gamma}{\beta}\), respectively, where \(\Gamma\) is the SVT threshold operator and \(\mu_{\mathrm{i}}\) denotes the r singular values.
# Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols
## 5 Clustering Process Optimization
### Cluster Head Selection Phase  
Cluster head selection is the first phase of most clustering schemes, as the CHs are the gateway between the SNs and the BS. The role of the CH is to mediate communication between the SNs and the BS; hence, CH selection is a significant process for the subsequent clustering procedures to improve energy efficiency and lifespan of the network. Many studies have considered optimizing the CH selection process by implementing different techniques. These techniques are classified into self-organized schemes (distributed control) and assisted schemes (centralized control). Each SN in the self-organized scheme can execute its algorithm and decide on becoming the CH. By comparison, in the assisted scheme, the nodes are grouped by centralized authorities before selecting the CH for each cluster. Furthermore, self-organized schemes can be grouped into probability and non-probability-based schemes, while assisted schemes can be grouped into BS-based and CH-assisted schemes. The entire CH selection process is represented in Figure 6.
# Stable Evaluation of 3D Zernike Moments for Surface Meshes
## 2 Moments from 3D Shapes
### Basis Function: The Zernike Polynomials  
The spherical harmonics are defined on the sphere. [PERSON] [15] in 2D and later [PERSON] [21] in 3D have shown that they can be expanded to account for the whole ball by using the Zernike polynomials. Let \(\mathbf{x}\) be a point inside the unit ball \(\mathbb{B}\) with spherical coordinates \((r,\theta,\phi)\). The value of Zernike polynomial \(Z_{nl}^{m}\) at \(\mathbf{x}\) is given by:  
\[Z_{nl}^{m}(\mathbf{x})=\sqrt{2n+3}R_{nl}(r)Y_{l}^{m}(\theta,\phi), \tag{8}\]  
where \(Y_{l}^{m}\) are the spherical harmonics define above, and \(R_{nl}\) are polynomials in the radial coordinate \(r\):  
\[R_{nl}(r)=\sum_{\
u=0}^{k}Q_{nl\
u}r^{2\
u+l}, \tag{9}\]  
where  
\[Q_{nl\
u}=(-1)^{k+\
u}\frac{(2k)}{k}{k\choose\
u}\frac{(2k+l+\
u)+1}{2k}{4k \choose k}. \tag{10}\]  
The non negative integer \(n\) is the order the of Zernike polynomial, \(l\) is an integer that is restricted so that \(0\leq l\leq n\) and \((n-l)\) be an even number, \(k=(n-l)/2\), and \(-l\leq m\leq l\). The coefficients in \(R_{nl}\) were chosen to guarantee that the Zernike polynomials are orthonormal, a property expressed in the following equation:  
\[\int_{\mathbb{B}}Z_{nl}^{m}(\mathbf{x})\overline{Z_{nl^{\prime}l^{\prime}}^{m^{ \prime}}(\mathbf{x})}\mathrm{d}\mathbf{x}=\delta_{nn^{\prime}}\delta_{ll^{\prime}} \delta_{mm^{\prime}}. \tag{11}\]  
The Zernike moments \(c_{nl}^{m}\) of a shape \(S\) whose density inside the unit ball is defined by the square integrable function \(f\) are then defined as  
\[c_{nl}^{m}=\int_{\mathbb{B}}f(\mathbf{x})\overline{Z_{nl}^{m}(\mathbf{x})}\mathrm{d} \mathbf{x}. \tag{12}\]  
Note that \(m\) can be positive or negative, as it belongs to \([-l,l]\). It is enough, however, to compute the moments for \(m\) non negative as we have (see for example [22]):  
\[c_{nl}^{-m}=(-1)^{m}\overline{c_{nl}^{m}}. \tag{13}\]Reconstruction. The Zernike polynomial form a complete basis of \(L^{2}(\mathbb{B})\), we hence have  
\[f(\mathbf{x})=\sum_{n}^{\infty}\sum_{l}\sum_{m=-l}^{l}c_{nl}^{m}Z_{nl}^{m}(\mathbf{x}), \tag{14}\]  
where \(l\) is an integer that is restricted so that \(0\leq l\leq n\) and \((n-l)\) be an even number. In practice, we can use a finite number of terms to approximate \(f\).
## 4 Discussion
* Influence of glide path and pre-flaring procedures [76; 77; 15; 78; 76; 78]. * The irrigation protocol [79; 80; 81; 82; 83; 84]. * Number of files per system [85; 86]. The results of these studies cannot definitively determine the primary factor for extruding a larger amount of debris apically, or whether it is the combination of many factors [87]. Apical extrusion is a phenomenon determined by many factors, and from the studies included in this review, it is not possible to identify when the variation in apical extrusion was determined by the type of kinematic of the instrument or by other intervening factors. Retreatment of a root canal system may be more challenging and time-consuming than the initial treatment [9; 88; 89; 90]. Because of this, instrument systems have been developed specifically for retreatment procedures, and these instruments are used in continuous rotation [9; 72]. It is unclear whether the design of an instrument specifically created for endodontic retreatment may influence the amount of extruded debris. Some of the studies included in this systematic review have used instruments that the manufacturers declare as \"specifically designed for endodontic non-surgical pretreatment\", such as R-Endo, ProTaper Retreatment, M-Two Retreatment, and D-Race Retreatment, while others have used endodontic instruments not specifically designed for retreatment cases [44; 45; 47; 49; 50; 52; 56; 57]. It must also be considered that all the research included in this systematic literature review carries out a quantitative measurement of extruded debris, and not a qualitative evaluation. Further research with a qualitative analysis could lead to greater clarity on how to reduce this phenomenon [91].
# Improvement of the Zienkiewicz-Zhu Error Recovery Technique Using a Patch Configuration
## 3 Element Patch Displacement Error Recovery Technique  
The recovery of the field variable (displacement) is obtained by the least-squares fit of the computed nodal field variable (\(u\)) using a higher order polynomial over an element neighborhood patch that consists of the union of the elements surrounding an element (Figure 1). To perform least-squares fitting, the following function is minimized:  
\[\pi_{f}(a)=\frac{1}{2}\sum_{i=1}^{np}\left[u_{i}^{h}(x_{i},\,y_{i})-u(x_{i}, \,y_{i})\right]^{2}, \tag{6}\]  
where \(u_{i}\ (x_{i},\ y_{i})=P_{i}\ (x_{i},  
The finite element solution errors may be quantified in appropriate norms. The energy norm (E) giving integral measure of the error in energy may be defined as follows:  
\[\left|\left|e\right|\right|_{E}=\left[\int_{\Omega}e_{\sigma}^{*T}De_{\sigma}^{* }d\Omega\right]^{\frac{1}{2}}, \tag{13}\]  
where \(\Omega\) is problem domain and \(D\) is the elasticity matrix of linear isotropic materials.  
The effectivity (\(\theta\)) of the evaluated error is defined as the ratio of evaluated error and true error. An error estimator is asymptotically exact for a particular problem if the problem global and local (element) effectivity (\(\theta\)) converge to one when the mesh size approaches zero [1]. The accuracy (\(\eta\)) of a finite element solution may be defined as follows:  
\[\eta=\frac{||e^{*}||}{||\sigma^{*}||}\, \tag{14}\]  
\[||\sigma^{*}||^{2}=||\sigma^{h}||^{2}+||e||_{E}^{2} \tag{15}\]  
The solution is acceptable if accuracy (\(\eta\)) is less than the predefined accuracy(\(\eta_{allow}\)). If the solution accuracy is more than the predefined accuracy, the mesh may be improved in an adaptive way in guidance of error estimator. The permissible global error is given as follows:  
\[||e||_{allowable(i)}=\eta_{allowable}||e|| \tag{16}\]  
The allowable error in the ith element is calculated using the following relation:  
\[||e||_{allowable(i)}=\frac{\eta_{allowable}||e||}{\sqrt{N}}, \tag{17}\]  
The so-called element refinement parameter \(\xi_{i}\) given below, guides the refinement:  
\[\xi_{i}=\frac{||e||_{allowable}}{||e||_{allowable(i)}}, \tag{18}\]  
If \(\xi_{i}>1\), improvement of mesh is needed. The new element size (\(l_{new}\)) is found with the help of the following relation:  
\[h_{new}=\frac{h_{new}}{\xi_{i}^{1/p}}, \tag{19}\]
# On Self-Interference Cancellation and Non-Idealities Suppression in Full-Duplex Radio Transceivers
## 2 Proposed System Model and Techniques
### Transceiver's Components Linearization  
The signal that is transmitted after digital to analog conversion (represented by \(x_{I}(t)\)_and_\(x_{Q}(t)\)) and amplification (represented by \(x_{PA}(t)\)) is the baseband signal \(x_{n}\) (represented by its two components \(x_{n,I}\)_and_\(x_{n,Q}\)). DAC and low pass filter (LPF) are considered ideal, and variable gain amplifier (VGA) is omitted from calculations for the sake of simplicity. However, IQ, PA, and LNA nonlinearity is considered. The IQ imbalance problem arises because of a gain and amplitude mismatch between the in-phase and quadrature branches of the IQ mixer. Phase noise and frequency offset also result from IQ mixer's LO IQ mixer mismatch in Tx, and the Rx chain is considered with the help of the image rejection ratio (IRR), as defined by the 3 rd generation partnership project (3 GPP) long-term evolution-advance (LTE-A) specification, i.e., 25 dB for both Tx and Rx chain. 3 GPP LTE-A system specification limits the minimum image attenuation to 25 dB or 28 dB [47]. 25 dB attenuation is sufficient for the Tx chain.  
However, the Rx chain IQ imbalance adds additional image attenuation. IQ imbalance in the Rx chain affects the residual SI signal cancellation. As a widely linear model is used for analysis, so both direct and image components are taken. Image rejection ratio (IRR) determines the quality of mismatch between I and Q branches. PA nonlinearity is modeled as the Hammerstein memory model. Signal after amplification from PA gets coupled to its receiver, and this coupled signal is called an SI signal. The channel between Tx and Rx is considered a multi-path coupling channel. SI signal is 60-100 dB stronger than our desired signal (the signal which we wish to receive), so it superimposes the desired signal of interest and results in degradation and distortion of the desired signal. SI also causes the saturation of the receiver chain. Hence, it is crucial to mitigate the SI below the sensitivity of the receiver chain before it enters the LNA. Now, what we obtain at the receiver antenna is the signal \(y_{ch}(t)\), as shown in Equation (1).  
\[y_{ch}(t)=h_{d}(t)*x_{d}(t)+h_{ch}(t)*x_{PA}(t)+n_{th}(t), \tag{1}\]  
Here \(x_{PA}(t)\) denotes PA output, \(x_{d}(t)\) denotes the desired signal, \(h_{d}(t)\) denotes desired signal channel, \(h_{ch}(t)\) denotes multi-path coupling channel and \(n_{th}(t)\) denotes thermal noise.  
Figure 1: Widely linear direct conversion (DC) full-duplex transceiver.
# Optimization of DLTS Hinges for the Assembly of the Solar Arrays of a Communication CubeSat
###### Abstract
Furthermore, an integral flexible hinge was introduced and navigated by the researcher [PERSON] for solar arrays, where issues were detected in the stowed and deployment configuration [15]. In the stowed position, the integral flexible hinge experienced difficulty in folding due to the large moments, while in full deployment, high-stress concentrations caused repeatability constraints [16]. It contradicted the required criteria and restraints of the solar arrays' assembly, and consequently, the tape spring hinge was determined to be ideal, as an even stress distribution is maintained and the moments can be corrected by adding more layers of tape springs [16; 17]. Multi-layer tape springs are implemented to adjust and improve the moment; however, the material contact process is required for its stowed and deployment mechanism [18]. Thus, optimized models such as response surface methodology/RSM and Taguchi are depicted and conducted by non-linear finite element analysis (FEA) to iterate the model faster, validate the numerical model, and obtain the modified optimal configuration. This research paper will outline the analytical and numerical methodologies for the acquisition of the optimal geometric configuration of the DLTS hinge. A static finite element analysis (FEA) will be conducted by modifying and controlling the geometric parameters \(R\), \(t\), and \(\theta\) and, as a result, exploring the \(\sigma_{\text{v}}\) and steady-state moment variation tendencies. Two optimization models, Taguchi and RSM, will be set for the validation and optimal attainment of the three examined geometric parameters in order to ensure durability, non-permanent deformation, and stability throughout the deployment. The following section establishes the design constraints that need to be satisfied for the hinge mechanism. It showcases a systematic design framework from the thickness accommodation technique to the finalized design of the coupling. Section 3 concerns the model set-up and mesh sensitivity study before undertaking the static simulations. Section 4 analyzes the data obtained by the simulations and constructs two optimized models, Taguchi and RSM, to verify and acquire the ideal geometric configuration of the coupling mechanism. Finally, conclusions are made on the final design choices identified.
# A Review of Hydrogen Sensors for ECLSS: Fundamentals, Recent Advances, and Challenges
## 4 Semiconductor Hydrogen Sensors  
When the sensor is exposed to hydrogen, the adsorption and permeation of hydrogen change the resistance of the hydrogen-sensitive material in the sensor, and the resistance of the hydrogen-sensitive material changes again when the hydrogen is removed from the hydrogen-sensitive material. There are two main types of semiconductor-type hydrogen sensors: metal oxide- and non-resistive-type sensors (i.e., metal or alloy type).
# Advancements in Machine Learning-Based Intrusion Detection in IoT: Research Trends and Challenges
## 6 ML Performance Review in IDS
### Multiclass Classification  
#### 6.3.1 Observations  
Similar to binary classification, many studies also achieved high accuracy in multiclass classification. Models based on the RF technique dominate in the BoT-lot dataset. All of the studies using this dataset for multiclass classification exceeded 99%.  
In the case of the TON_IoT dataset, the techniques used are more diverse. RF (including GADAD-RF) achieved perfect scores of 100% [38; 55]. Decision Trees and LSTM were just behind the RFs, with scores of 99.9 [61] and 99 [58], respectively. However, those using KNN and voting performed worse (F1 scores: 97.4 [53] and 88.63 [75]). These results imply that the more complex the models used, the worse the predictions.  
Unfortunately, the studies covering NN models did not provide testing time; thus, we were unable to benchmark them against tree-based models for multiclass classifiers. However, Table 10 shows that [52] performed \(\approx\)45 and \(\approx\)19 times slower than the two tree-based equivalents [67].