esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Assessment of Operating Point \(OR=0.05\)/\(Pr=0.20\)
### Axial Pressure Excitation at 600 Hz  
Based on the discussion of Figures 5 and 6, a superposition of the first two SPOD modes is used for the reconstruction of the flow field. Because temporal excitations are observed and the peaks in the energy spectrum include more than one frequency, a narrow band around the prominent frequency is analyzed to reproduce this behavior.  
For Peak\({}_{LF}\) at 600 Hz, the reconstructed pressure field for \(M1+2\) and frequencies of 500 Hz to 700 Hz is shown in Figure 7 across one period (\(T=1/600\) s). The individual plots show the pressure distribution on the valve head (Cartesian plot using lateral coordinates; normal view onto the valve head against flow direction), the diffuser wall (polar plot using the axial position as the radial coordinate; Angle consistent to the valve head) as well as a cross section of the diffuser (Cartesian plot of the cross section of the diffuser pressure field. Location of the plane indicated by the horizontal solid line on the head and diffuser wall) based on the SPOD reconstruction. At first sight, the pressure amplitude at this frequency shows an almost axisymmetric oscillation of the pressure field across the whole diffuser in axial direction. The strongest pressure amplitudes occur within the supersonic core and suggest a strong vertical oscillation of the shock.  
Figure 6: Comparison of the raw and bandpass-filtered pressure trends for sensor location Diffuser 3/3: (**a**) CFD; and (**b**) experiment.  
The comparison of the reconstructed flow field in Figure 8a supports this finding and shows a significant vertical displacement of the strong shock across half a period as marked by the dashed line. For the first time step in Figure 8a, the width of the supersonic core in the lower half is increased, and a small normal shock can be observed in the center (\(Ma<1\)). The flow condition at this time is comparable to the instantaneous flow in Figure 4b. Linked to the strong oscillation of the shock, the pressure in the recirculation zone around the core flow increases periodically and interacts with the jets at the diffuser inlet. Along with the pressure oscillation in the lower half of the diffuser, reoccurring impingements on the valve head are observed and periodic axial forces are predicted at this frequency.  
Following the temporal evolution of the pressure field, the recirculation zone around the supersonic jet up to the height of the strong shock seems to behave as a spring in axial direction, and an excitation across several periods with temporally changing flow directions near the wall (indicated by the arrows) can be observed due to the superposition of the different frequencies.
## 2 Computational Methods  
All calculations in the present study are carried out exploiting the plane-wave pseudopotential procedure within the framework of the density functional theory (DFT) using Quantum ESPRESSO software package [6]. The exchange correlation potential is contemplated by the generalized gradient functional (GGA) with the correction of Perdew-Burke-Ernzerhof approximation for solids (PBEsol) approximation [7]. An optimum cutoff energy of 90 Ry is considered in the all calculations. The integration of the Brillouin zone is carried out with \(12\times 12\times 12\) k-points using the standard Monkhorst-Pack special grids [8]. The geometry optimizations are done with the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm (for stress minimization). The total energy convergence occurs at the energies less than 1 meV/atom. For geometry relaxation, the force on atom is converged below a threshold of 0.01 eV \({}^{\circ}\)A-1. Cubic unit cell with the space group Pm\(\overline{3}\)m containing 5 atoms (Figure 1) is used for the calculations.  
The elastic constants of cubic RFeO\({}_{3}\) (R = La, Eu, Gd) structures are calculated using continuum mechanics method, which are explained in detail in our previous work [8]. Conforming to this approach, the unit cell is exposed to a series of deformations \(\eta\) based on the crystal structure. The following equation shows how to calculate the elastic energy of a deformed structure:  
\[\mathrm{E}(\eta_{\mathrm{I}})=\mathrm{E}(0)+\mathrm{V}_{0}\sum_{\mathrm{I=1}}^{ 3}\tau_{\mathrm{I}}^{(0)}\eta_{\mathrm{I}}+\frac{\mathrm{V}_{0}}{2!}\sum_{ \mathrm{I,J=1}}^{3}\mathrm{C}_{\mathrm{I}}\eta_{\mathrm{I}}\eta_{\mathrm{I}} \tag{1}\]  
where V\({}_{0}\), E(0), \(\tau\), and C are, respectively, denoting the volume, energy of the equilibrium state, Lagrangian stress tensor, and the elastic stiffness constant. To abbreviation in indexing, we utilize Voigt notation in this equation. Using Equation (1), the elastic constants of the material (C\({}_{\mathrm{ij}}\)) can be calculated as:  
\[\mathrm{C}_{\mathrm{ij}}=\frac{1}{\mathrm{V}_{0}}\frac{\partial^{2}\mathrm{E} }{\partial\eta_{\mathrm{I}}\partial\eta_{\mathrm{I}}} \tag{2}\]  
According to the high-symmetry structure of the cubic phase of RFeO\({}_{3}\), only three independent elastic constants (C\({}_{11}\),C\({}_{12}\), and C\({}_{44}\)) are calculated using Equation (2). The elastic constants for the cubic, orthorhombic, and monoclinic deformations can be obtained using the following deformation  
Figure 1: Schematic representation of crystal structures of RFeO\({}_{3}\) (R = La, Eu, Gd).  
tensors [9]. For each deformation mode, different magnitudes of deformations from \(-10\%\) to \(20\%\) were applied.  
\[\mathrm{D_{Cubic}}=\left[\begin{array}{ccc}\eta&0&0\\ 0&\eta&0\\ 0&0&\eta\end{array}\right]\mathrm{D_{Ortho}}=\left[\begin{array}{ccc}\eta&0&0 \\ 0&-\eta&0\\ 0&0&\frac{\eta^{2}}{1-\eta^{2}}\end{array}\right]\mathrm{D_{Mono}}=\left[ \begin{array}{ccc}0&\eta&0\\ \eta&0&0\\ 0&0&\frac{\eta^{2}}{1-\eta^{2}}\end{array}\right]\]
## 3 Results
### Tensile Fracture Mechanism Under Low-Current Electrical Fatigue  
Tensile tests on wires fatigued at 0.35 A and 0.37 A (Figure 12) were conducted to quantify the effects of cyclic electrothermal loading on mechanical strength. For the 0.35 A condition, the UTS remains nearly constant after 100, 200, and 300 cycles, indicating that extended low-current cycling does not further impair tensile performance. Relative to the as-received state, the UTS at 0.35 A decreased by \(\sim\)50%, reflecting a partial loss of cold-work strengthening due to Joule-heating-driven dislocation recovery. This microstructural softening, discussed in Section 4.3, is not accompanied by a significant loss of ductility. SEM images of the fracture surfaces (Figure 13a,b) reveal uniformly deformed profiles with no signs of interfacial decohesion or early cracking on the Cr\({}_{2}\)O\({}_{3}\)-rich surface oxide layer (Figure 14a).  
Figure 11: EPMA mapping shows a \(\sim\)1.4 \(\upmu\)m oxide layer in 0.35-C200 and a thicker \(\sim\)3.8 \(\upmu\)m Fe-rich scale in 0.37-C200.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline Sample & 0.35-C100 & 0.35-C200 & 0.35-C300 & 0.37-C100 & 0.37-C200 & 0.37-C300 \\ \hline FC (A) & 0.49 & 0.48 & 0.48 & 0.49 & 0.43 & X \\ \hline \hline \end{tabular}
\end{table}
Table 3: I\({}_{\rm{fuse}}\) after high-cycle electrical fatigue at 0.35 A and 0.37 A.  
Figure 12: Tensile properties of 631 SS wire: (**a**) Strength-elongation curve; (**b**) YS and UTS; (**c**) TE after 0.35 and 0.37 A electrical fatigue.  
The Cr\({}_{2}\)O\({}_{3}\)-rich surface oxide layer indicates mechanical compatibility with the underlying matrix. This behavior supports the interpretation that oxidation remains superficial and does not compromise fracture integrity at this current level [26; 27].  
In contrast, 0.37 A cycling leads to continuous declines in UTS, YS, and TE as the cycle count increases (Figure 12). Distinct changes in fracture morphology accompany the decline in ultimate tensile strength (UTS), yield strength (YS), and total elongation (TE). SEM fracture images (Figure 13b) reveal increasing crack density near the surface, which correlates with the growth of a Fe\({}_{2}\)O\({}_{3}\)-containing oxide layer and localized grain coarsening. Surface cracks frequently originate at the oxide-matrix interface and propagate inward, fragmenting the ductile zone. EDS mapping (Figure 14b) further confirms the presence of Fe\({}_{2}\)O\({}_{3}\) within surface oxide regions and reveals that crack propagation is spatially correlated with Fe-rich domains.  
Figure 13: Surface and fracture morphology after electrical fatigue: (**a**) At 0.35 A; (**b**) At 0.37 A.
Sensors_**2011**, _11_, 4401-4424; doi:10.3390/s110404401  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**A Secure Cluster-Based Multipath Routing Protocol for WMSNs**  
**[PERSON] \({}^{1,\star}\), [PERSON] \({}^{1}\) and [PERSON] \({}^{2}\)**  
\({}^{1}\) Computer Architecture Department, Technical University of Catalunya, Barcelona 08029, Spain;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) Computer Engineering Department, The Hashemite University, Zarqa 13111, Jordan;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +34-698-454-689.  
_Received: 9 February 2011; in revised form: 8 April 2011 / Accepted: 11 April 2011 / Published: 15 April 2011_  
**Abstract:** The new characteristics of Wireless Multimedia Sensor Network (WMSN) and its design issues brought by handling different traffic classes of multimedia content (video streams, audio, and still images) as well as scalar data over the network, make the proposed routing protocols for typical WSNs not directly applicable for WMSNs. Handling real-time multimedia data requires both energy efficiency and QoS assurance in order to ensure efficient utility of different capabilities of sensor resources and correct delivery of collected information. In this paper, we propose a Secure Cluster-based Multipath Routing protocol for WMSNs, SCMR, to satisfy the requirements of delivering different data types and support high data rate multimedia traffic. SCMR exploits the hierarchical structure of powerful cluster heads and the optimized multiple paths to support timeliness and reliable high data rate multimedia communication with minimum energy dissipation. Also, we present a light-weight distributed security mechanism of key management in order to secure the communication between sensor nodes and protect the network against different types of attacks. Performance evaluation from simulation results demonstrates a significant performance improvement comparing with existing protocols (which do not even provide any kind of security feature) in terms of average end-to-end delay, network throughput, packet delivery ratio, and energy consumption.  
keywords:wireless multimedia sensor networks; WMSN; WSN; routing protocol; multimedia delivery; multipath; cluster-based routing; security
# Manifolds-Based Low-Rank Dictionary Pair Learning for Efficient Set-Based Video Recognition
## 2 Related Works
### Image Set Classification
#### 2.1.1 Static Modeling Methods  
With static modeling methods, each image set is modeled with some unique, fixed methods, such as parametric distribution function, subspace, covariance matrix, multiorder statistics, and domain (or hypersphere). For instance, CDL [1] and Log-Euclidean Metric Learning (LEML) [13] algorithms use covariance matrices to represent image sets and then use logarithm kernels to map the modeling representations to the Hilbert space to easily measure the similarity between image sets. Projection Metric Learning (PML) [12] models each image set as a subspace (i.e., a point on a Grassmann manifold), and directly learns a projection matrix on a Grassmann manifold to reduce the computational cost. The MMFML [2] algorithm jointly uses multiple representation models to represent one image set, which aims to fully use the complementarity between different representation models. Discrete Metric Learning (DML) [14] also combines static and dynamic modeling methods; however, it ignores the importance of dictionary learning. Since static modeling can markedly reduce the sample, the computing time of static modeling methods is usually fast. However, most of these static modeling methods only focus on learning more discriminative feature representations after obtaining static models, ignoring the design of a powerful classifier, resulting in limited classification results.
## 1 Introduction
The current research methods for subsidence estimation predominantly rely on probabilistic integral models combined with temporal functions, and there is a lack of research regarding the inversion of static estimation parameters. In this study, we conducted synergistic monitoring of surface settlement within a mining area using InSAR and UAV technologies. The data obtained from these two methods were integrated, and the accuracy of the fused dataset was subsequently validated to be within a 10 cm margin, as corroborated by precise leveling data. This study also demonstrates that the fused data can effectively support the application of the BFGS algorithm for inverting probabilistic integral model parameters. Furthermore, the prediction results align closely with the observed surface deformation patterns. Thus, it presents a novel approach to globally automated subsidence monitoring in mining areas.
# Knowledge Distillation-Based GPS Spoofing Detection for Small UAV
## 3 System Modeling  
GPS is critical to the navigation of UAVs. The UAV with a GPS module can receive the signal, \(Sa_{i}\), from the satellite, which contains a timestamp and position information. The UAV GPS module leverages the information to calculate the localization, \(Pr_{i}\). Here, \(Pr_{i}=(x_{i},y_{i},z_{i})\). With the collections of multiple satellites' signals, \(\sum_{i=1}^{M}Sa_{i}\), the UAV can have a more accurate localization. However, hackers with software-defined radio (SDR) can simulate the signal generated from the satellite, denoted as \(Sa_{i}\), to generate a forged signal, denoted as \(Sd_{i}\), to mislead the UAV into calculating a wrong position, \(Pf_{j}\). Here, \(Pf_{j}=(x_{j},y_{j},z_{j})\). With strong signals, the hackers can generate multiple forged signals, \(\sum_{i=1}^{N}Sd_{i}\), to control and navigate the spoofed UAV into a sensitive area. In flight, the UAV connects to the ground control station (GCS) with a telemetry radio. The UAV acquires the acceleration, \(A_{k}\), and the rotation, \(R_{k}\), data from IMUs and obtains the received signal strength indicator (RSSI) in time \(l\), \(RS_{l}\). \(A_{k}=(Ax_{k},Ay_{k},Az_{k})\). \(R_{k}=(Ry_{k},Rp_{k},Rr_{k})\). Here, \(Ax_{k},Ay_{k}\), and \(Az_{k}\) represent the acceleration of time \(k\) in x, y, and z directions, respectively, and \(Ry_{k},Rp_{k}\), and \(Rr_{k}\) represent rotations of time \(k\) in yaw, pitch, and roll, respectively.
# A Multi-Machine and Multi-Modal Drift Detection (M2D2) Framework for Semiconductor Manufacturing
## 3 Methodology
### Phase 4: Interpretability  
Once drift is detected (short- or long-term), engineers need clarity about _which features_ or _maintenance records_ caused the model to flag an anomaly. Multi-modal attention can guide the explanation.  
1. Attention Visualization: In a Transformer-based system, each attention head yields weight \(\alpha_{ij}\). By visualizing \(\alpha_{ij}\), one can identify which textual or categorical tokens strongly influenced sensor-based anomaly detection.
2. SHAP/LIME: Final drift scores can be treated as a function for Shapley value decomposition orLIME-based perturbation [24]. If textual tokens, e.g., \"current fluctuations,\" appear with high contribution, the cause is more transparent.  
Root-cause analysis or drift explanation can be used to specify \"which dimension or which textual clue\" triggered the drift verdict, enabling targeted corrective actions.
# Directional Support Vector Machines
## 3 Support Vector Machine  
For an intended output \(y\in\{-1,1\}\) and a classifier score \(f(\mathbf{x}|\mathbf{w})\), the hinge loss of the prediction \(f(\mathbf{x}|\mathbf{w})\) is defined as \(\mathcal{L}(\mathbf{w}|\mathbf{x},y)=\max(1-f(\mathbf{x}|\mathbf{w})\cdot y,0)\), where \(\mathbf{x}\) is the input and \(\mathbf{w}\) is the vector of parameters of the model. The Support Vector Machine (SVM) model solves the problem:  
\[\operatorname*{arg\,min}_{\mathbf{w}}\sum_{n=1}^{N}\mathcal{L}(\mathbf{w}|\mathbf{x}_{n}, y_{n})+\lambda||\mathbf{w}||^{2}, \tag{7}\]  
where \(\lambda\geq 0\) is a regularization parameter. In standard \(\mathbb{R}^{D}\) spaces, the model \(f(\mathbf{x}|\mathbf{w})\) is set to the affine form, \(f(\mathbf{x}|\mathbf{w})=w_{0}+\sum_{i=1}^{D}w_{i}x_{i}\), and the previous equation can be equivalently written as:  
\[\operatorname*{arg\,min}_{\mathbf{w}}\sum_{n=1}^{N}\max\left(1-y_{n}\left(w_{0}+ \sum_{i=1}^{D}w_{i}x_{n,i}\right),0\right)+\lambda\sum_{i=1}^{D}w_{i}^{2}. \tag{8}\]  
In the trivial unidimensional space, the model boils down to \(f(x_{1}|w_{0},w_{1})=w_{0}+w_{1}x_{1}\), and the partition of the input space is defined by a single threshold; see Figure 1a.  
In the following, to avoid unnecessarily cluttering the presentation, we will stay in the unidimensional space, returning only in the end to the multi-dimensional problem. We will also assume the period \(2\pi\) for the directional data.
# A Review of Adsorbents for Heavy Metal Decontamination: Growing Approach to Wastewater Treatment
## 6 Factors Affecting the Adsorption Process
### Synthetic Polymer Adsorbents
Figure 11 depicts the COP-63 sorption properties based on the hard-soft acid and base theory in which heavy metal ions in water are selectively engrossed in the organic polymer's disulphide groups and dangling thiol/thione groups in the presence of competing cations. Pb\({}^{2+}\) (99%), Cu\({}^{2+}\) (97%), and Fe\({}^{3+}\) (99%) were all removed by polyvinylamine at a concentration of 0.1 wt% [229]. [PERSON] et al. reported a new chelating agent poly-ammonium dithiocarbamate (PADTC) and poly-sodium dithiocarbamate (PSDTC). For the extraction of copper, nickel, and zinc, different adsorption parameters were utilised, such as a 0.1 g adsorbent dosage, a 20-min reaction period, and a preliminary metal ion concentration of 100 mg/L [230]. Poly-ammonium dithiocarbamate had the highest adsorption capacity for Cu (II) 245.53 mg/g, Ni (II) 234.47 mg/g, and Zn (II) 226.76 mg/g. Figure 11: Selective interactions among heavy metal ions and sulphur groups in covalent organic polymer suspended in water. Reused with permission from [228].
## 1 Introduction  
In\({}_{0.53}\)Ga\({}_{0.47}\)As based-III-V compounds have attracted a great deal of attention for their advantages in high electron mobility over their Si-based counterparts. However, poor interface quality between In\({}_{0.53}\)Ga\({}_{0.47}\)As and high dielectric constant (high-k) gate dielectrics has imposed an enormous challenge for implementing inversion-type enhancement mode metal-oxide-semiconductor field-effect transistors (MOSFETs). Proper surface pre-treatment and insertion of interface passivation layer [1; 2; 3] have generally employed to achieve improved interface quality. Those approaches usually performed prior to high-k deposition, however, interface state traps created during the high-k deposition need to be passivated by a post-oxide treatment.  
It can be expected that fluorine (F) will be an effective passivation agent for In\({}_{0.53}\)Ga\({}_{0.47}\)As because F has high binding energy with In (5.25 eV), Ga (5.99 eV), and As (4.26 eV), respectively [4]. F incorporation has been demonstrated on Si, Ge, and In\({}_{0.53}\)Ga\({}_{0.47}\)As substrates. It has been found that F can passivate high-k bulk traps and interface defects at high-k/substrates (Si, Ge and In\({}_{0.53}\)Ga\({}_{0.47}\)As) interface [5; 6; 7; 8; 9]. Although previous reports showed that the insertion of a thin interface passivation layer could improve interface quality [10], those layers usually have relative lower k value [11]. This may hinder equivalent oxide thickness (EOT) scaling and as a result, hardly meet the requirement for the sub 22 nm nodes.  
In this paper, we systematically studied the effects of CF\({}_{4}\)/O\({}_{2}\) plasma power wattage and treatment time on HfO\({}_{2}\)/In\({}_{0.53}\)Ga\({}_{0.47}\)As gate stack. The condition of CF\({}_{4}\)/O\({}_{2}\) plasma has been optimized, which significantly improves the effective channel mobility (\(\mu_{\rm eff}\)), transconductance (G\({}_{\rm m}\)), drive current (I\({}_{\rm d}\)), and subthreshold swing (SS). With F incorporation, we have successfully developed excellent interface quality of HfO\({}_{2}\) directly on In\({}_{0.53}\)Ga\({}_{0.47}\)As without using interface passivation layer. Fluorinated samples exhibit low interface trap density (D\({}_{\rm it}\)) of 4.9 \(\times\) 10\({}^{11}\) cm\({}^{-2}\)eV\({}^{-1}\), which is the lowest value compared to prior reported HfO\({}_{2}\)/In\({}_{0.53}\)Ga\({}_{0.47}\)As gate stacks.
## 4 Evaluation and Results' Discussion
### Discussion  
From the analysis and comparison of the above experiments, we can conclude that our protocol offers the following advantages:  
(1) In our protocol, the number (\(t\)) of participants has almost no impact on the computation and runtime of the client. When \(t\geq 2^{4}\), the client's runtime was about \(1/(2^{log(t)})\) of the server's runtime. Therefore, our protocol is very suitable for unbalanced scenarios with a large number of participants.  
(2) Compared to the typical related protocol [28], our protocol demonstrated a significant improvement in efficiency. The sever and client runtimes were approximately \(1/6\) of [28].
## 5 FGI Challenges and NGN Solutions for Future Networks
### Identity and Addressing  
For many years, there were separate networks for different purposes. Consequently, every network used its own identifying mechanisms. Currently, customers prefer to have the same identity for all applications (e.g., the same phone number across any network).  
To respond to this challenge, the NGN implements the system of private and public identity. The private identity is used by the network e.g., for authorization. The public identity (user is allowed to possess multiple public identities) is used by other users and by applications. In this way, anybody can reach the user using his public identity independently of the currently used access network. In such a way, one can use the same number via mobile and fixed network (this notion does not make the same sense in the NGN today).