esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing CT Segmentation Security against Adversarial Attack: Most Activated Filter Approach
## 3 Experiments
### Limitations  
This research makes a notable stride in detecting adversarial attacks on medical imaging; however, it has certain limitations worth highlighting:  
* Modality-specific limitations: This study is focused on a grayscale CT dataset, which may limit its applicability to other types of medical imaging such as of skin lesions or retinal veins. For example, the visualization of features to distinguish between genuine and adversarial samples in gastrointestinal polyp images is shown to be challenging.
* Coverage of adversarial techniques: While this approach targets three attacks based on perturbations, it does not comprehensively address pixel-wise methods like DeepFool [27], One-Pixel Attack [28], or ASMA [29] which can be used to attack specific sub-regions of the input image.  
These limitations not only highlight areas of potential improvement for this work but also provide avenues for future research in the domain of medical imaging security.
## 5 Discussion  
In this paper, based on the cantilever beam model, an improved mesh stiffness solution method was first proposed, and two factors of the transition curve and the deformation of the gear body is considered in improved mesh stiffness solution method. Next, the effect of gear tooth cracks on the stiffness was studied. Then, a pure torsion dynamic model was established based on the lumped parameter modeling method. The influence of the fault on the vibration response of the system is analyzed. The main conclusions are as follows:  
(1) When cracks appear in the sun gear, the time-varying mesh stiffness of the gear decreases periodically. With the continuous expansion of crack size, the mesh stiffness decreases further.  
(2) When the sun gear cracks, the vibration response of the system will change. In the time domain, when the sun gear cracks, a large number of side-band will appear near the mesh frequency and its multiple frequencies. In the low-frequency band, the corresponding sun gear fault characteristic frequency 15.63 Hz (\(f_{s}-f_{H}\)) and its multiple frequencies \(N(f_{s}-f_{H})(N=1,2,3,\dots\) ) can also be found clearly. The interval between sidebands is \(f_{H}\), that is, the rotation frequency of the planet carrier.  
(3) Since the fault characteristic frequency of each gear in the planetary gearbox is different, the source of the fault can be located according to the fault characteristic frequency.  
[PERSON] conceived and designed the experiments, analyzed the experimental results and wrote the first draft of the manuscript. [PERSON], [PERSON] and [PERSON] analyzed the experimental results, critically revised the manuscript and approved the final version of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Key Research and Development Program of China (No. 2018 YFB1702401), and the National Natural Science Foundation of China (Grant Nos. 51975576, 51475463).  
The authors declare no conflict of interest.
## 8 Conclusions  
The popularity of sensor networks (and in a broader sense Internet of Things) has increased significantly over the last ten years. Integration of these embedded devices into the Internet is challenging, since they have characteristics that differ strongly from traditional internet devices, such as very limited energy, memory and processing capabilities. Initially, research focused on developing proprietary solutions that were typically vendor-specific and did not allow end-to-end connectivity between client devices and sensor devices. However, the use of standardized protocolsenables the integration of constrained devices in the IPv6 Internet, both at the network level and at the service level.  
In this paper, a high-level overview was given of the ongoing IETF standardization work that focuses on enabling direct connectivity between clients and sensor devices. To this end, different IETF groups are currently active. The IETF groups 6 LoWPAN and ROLL focus on the IPv6 addressability and routing, whereas the IETF CoRE group focuses on realizing an embedded counterpart for RESTful web services. By combining these protocols, an embedded protocol stack can be created that has similar characteristics to traditional internet protocol stacks. In fact, the IETF protocols are designed to enable easy translation from internet protocols to sensor protocols and vice-versa.  
The paper describes how the combination of IETF protocols enables flexible, direct interaction between internet clients and embedded Internet of Things devices. However, the paper also shows that the advent of standardized protocols is not an end point, but only a starting point for exploring additional open issues that should be solved to realize an all-encompassing Internet of Things. Several open challenges remain such as resource representation, security, dealing with sleeping nodes, energy efficiency, integration with existing web service technologies and tools, linking with Cloud services, use of semantics, easy creation of applications, scalability, interoperability with other wireless standards, maintainability, _etc_.  
Anyone involved in Internet of Things research (whether dealing with network layer aspects or service layer aspects) will, sooner or later, be confronted with the IETF protocols. This paper merely touches the surface of this broad domain and tries to encourage others to further explore the world of Internet-connected objects and tackle the mentioned remaining open issues and challenges.  
The research leading to these results has received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement no 258885 (SPITFIRE project), from the iMinds ICON projects GreenWeCan and O'CareCloudS, a FWO postdoc grant for [PERSON] and a VLIR PhD scholarship to [PERSON].  
The authors declare no conflict of interest.
# Strengthening of RC Beams with CFC Panels for Improving Impact Resistance
## 3 Experimental Results  
### Failure Mode and Crack Propagation  
Figure 6 shows the distribution of cracks in each specimen for each number of impacts. This figure is an enlarged view of the central part of the beam span. First, the RC beam specimen, which serves as the reference, was severely flexurally fractured in the first impact and reached the end. On the other hand, in the case of RC_FB_CFC, whose bottom was reinforced with a CFC panel, cracks occurred during the first impact but did not reach the end, and the second impact resulted in peeling failure. Similarly, in the case of RC_F3S_CFC reinforced on three sides, there was almost no damage with the first impact, but cracks with small widths were observed on the sides of the CFC panel after the second impact, and the specimen reached its final state after the third impact. In other words, it was confirmed that the reinforcement effect on the impact resistance of the CFC panel is sufficient; however, as for the RC_P3S_CFC specimen, it reached the end after the first impact, which is presumed to be due to the problem of adhesiveness.  
Each type of specimen underwent between one and three impacts until failure occurred. As shown in Figure 7, flexural cracks were observed as the final crack pattern, and the impact contact area was crushed in all the specimens. A similar phenomenon was observed in Refs. [8; 9; 35]. The crack propagation of CFC panel-strengthened RC beams seems to spread widely in comparison with conventional RC beams, which demonstrates that the crack reduction of the midspan was achieved by the CFC panel-strengthened RC beams. Among the strengthened specimens, separation of the sandwiched structure of the CFC panel was not observed, and there was only interfacial debonding between the CFC panel and concrete.
## 4 Proposed Detection Method  
In this section, a robust IFO and SI estimation method is proposed for the LTE-D2D system. By using the conjugate property between two PSSS signals and symmetric feature of the SSSS, the IFO can be solely detected independent of the synchronization signals, facilitating joint estimation of the IFO and SI to be detected sequentially. As a performance measure, the probability of detection failure of the proposed detection scheme is theoretically calculated.
# Displacement-Dependent Damping Inerter System for Seismic Response Control
## 2 Theoretical Analysis of Displacement-Dependent Damping Inerter System (DDIS)
### Stochastic Dynamic Analysis and Stochastic Linearization Method
\begin{array}{l}-\frac{1}{2}\left[1+ erf\left(\frac{u_{d}}{\sqrt{2}\sigma_{z_{h}}}\right)\right]\\ +\frac{1}{\sigma_{z_{h}}\sqrt{2}\pi}\int_{u_{d}/(\sqrt{2}\sigma_{z_{h}})}^{ \infty}erf\left(\frac{\rho_{\dot{u}_{d}\dot{z_{h}}}\dot{z_{h}}/\left(\sqrt{2} \sigma_{z_{h}}\right)}{\sqrt{1-\rho_{\dot{u}_{d}\dot{z_{h}}}}}\right)\cdot \exp\left[-\frac{z_{h}^{2}}{2\sigma_{z_{h}}^{2}}\right]dz_{h}\\ k_{\alpha q}=\left\{\begin{array}{l}\frac{\rho_{\dot{u}_{d}\dot{z_{h}}}\dot{ z_{h}}\dot{z_{h}}\dot{z_{h}}}{\sqrt{2}\sigma_{z_{h}}^{2}}\sigma_{z_{h}}\exp\left[- \frac{u_{d}^{2}}{2\sigma_{z_{h}}^{2}}\right]\left(1+erf\left(\frac{\rho_{\dot {u}_{d}\dot{z_{h}}}\dot{z_{h}}\dot{z_{h}}}{\sqrt{2}\left(1-\rho_{\dot{u}_{d} \dot{z_{h}}}^{2}\right)}\right)\right]\\ +\frac{\sqrt{2\left(1-\rho_{\dot{u}_{d}\dot{z_{h}}}^{2}\right)}}{\pi}\frac{ \sigma_{z_{d}}}{\sigma_{z_{h}}}\exp\left[-\frac{u_{d}^{2}}{2\sigma_{z_{h}}^{2} }\right]\exp\left[\frac{\rho_{\dot{u}_{d}\dot{z_{h}}}^{2}\dot{z_{h}}}{\sigma_{ z_{h}}^{2}\left(1-\rho_{\dot{u}_{d}\dot{z_{h}}}^{2}\right)}\right]\end{array}\right\}\end{array}  \tag{10}\]  
where \(\sigma_{z_{h}}\) and \(\sigma_{\dot{u}_{d}}\) are respectively the RMS of hysteretic deformation \(z_{h}\) and the RMS of the DDE velocity \(\dot{u}_{d}\); \(\dot{p}_{\dot{u}_{d}\dot{z_{h}}}\) is the correlation ratio of \(\dot{u}_{d}\) and \(z_{h}\), and \(erf\) is the error function given by  
\[erf(y)=\frac{2}{\sqrt{\pi}}\int_{0}^{y}e^{-z^{2}}ds \tag{11}\]  
Using the SLM, the expected stochastic response can be approximated as a zero-mean Gaussian process under Gaussian excitation. Based on the Laplace transformation, the governing equation in Equation (7) can be rewritten as:  
\[\left\{ \begin{array}{l}\left(-\omega^{2}\mathbf{M}+\mathrm{i}\omega\mathbf{ C}+\mathbf{K}\right)\mathbf{U}+\mathbf{R}=\mathbf{EM}A_{g}(\mathrm{i}\omega)\\ Z_{h}(\mathrm{i}\omega)\mathrm{i}\omega+c_{\alpha q}U_{d}(\mathrm{i}\omega) \mathrm{i}\omega+k_{\alpha q}Z_{h}(\mathrm{i}\omega)=0\end{array} \right. \tag{12}\]  
where \(\mathbf{U}=\{U,U_{d}\}^{T}\) and \(\mathbf{R}=\left[0,(1-\alpha)k_{d}Z_{h}\right]^{T}\) are the Laplace transformations of \(\mathbf{u}\) and \(\mathbf{r}\), respectively; \(U_{d}\), \(A_{g}\), and \(Z_{h}\) are the Laplace transformations of \(u_{d}\), \(\ddot{u}_{g}\), and \(z_{h}\), respectively. An inspection of Equations (10) and (12) reveals that the determination of \(c_{\alpha q}\) and \(k_{\alpha q}\) is not straightforward, since they implicitly depend upon the unknowns \(\sigma_{z_{h}}\), \(\sigma_{\dot{u}_{d}}\), and \(\rho_{\dot{u}_{d}\dot{z_{h}}}\). Therefore, the values of \(c_{\alpha q}\) and \(k_{\alpha q}\) are determined by an interactive process, wherein Equation (12) is solved in an iterative manner given an initial guess.
## 3 Results and Discussions
### Thermal Conductivity Variations  
The thermal insulation properties of the samples were determined by thermal conductivity tests. Based on the measurement results of the samples, it may be suggested that exc lay aggregate concretes are used in buildings as panel walls, bricks, concrete briquettes, internal and external plastering and concrete partitioning components for the building's thermal comfort. Thermal conductivity values versus exclay ratios are presented in Figure 6.  
Figure 4: Scanning electron microscopy (SEM) views of a sample.  
Figure 5: Densities with respect to exclay ratios: (a) 2–4 mm, (b) 4–6 mm.  
The thermal conductivities decrease with increasing exclay ratio and resin ratio since the inner parts of micro-structured pores are full of air. As the grain diameter increases, porosity increases, and the thermal conductivity decreases. Minimum thermal conductivity is measured as 0.207 W/mK (Sample 12--without resin) and as 0.171 W/mK (Sample 36--with resin) in the 50% exclay sample with a grain size of 4-6 mm.  
Thermal conductivity decreases further, and insulation characteristics of the material improve as a result of an increase in total porosity due to the formation of artificial pores in the resin-added samples. These decreased ratios were 3.98%\(-\)17.72% and 8.40%\(-\)17.39% according to the grain diameter.
# Quantum Photonic Simulation of Spin-Magnetic Field Coupling and Atom-Optical Field Interaction
## 4 Quantum Geometric Phases
### Geometric Phases with Other Quantum States  
On the other hand, the eigenstates given by Equation (31) can be written as single-photon states excited in rotated optical modes, that is \(e_{+}(x,y)=\cos\frac{\alpha^{\prime}}{2}e_{0}(x,y)+\sin\frac{\alpha^{\prime}}{2} e_{1}(x,y)\) and \(e_{-}(x,y)=-\sin\frac{\alpha^{\prime}}{2}e_{0}(x,y)+\cos\frac{\alpha^{\prime}}{2 }e_{1}(x,y)\); therefore:  
\[|l_{+}(0)\rangle=\cos\frac{\alpha^{\prime}}{2}|1_{0}\rangle+\sin\frac{\alpha^{ \prime}}{2}|1_{1}\rangle=|1_{+}\rangle,\ \ \ |l_{-}(0)\rangle=-\sin\frac{\alpha^{\prime}}{2}|1_{0}\rangle+\cos\frac{\alpha^{ \prime}}{2}|1_{1}\rangle=|1_{-}\rangle \tag{52}\]  
The elimination of the dynamical phase means that these eigenstates have undergone the transformation \(e^{\pm i\Phi_{g}}|1_{\pm}\rangle\); therefore, the following formal relationships can be written:  
\[e^{i\Phi_{g}}|1_{+}\rangle=e^{i\Phi_{g}}\hat{a}_{+}^{\dagger}|0_{\pm}\rangle, \ \ \ e^{-i\[PERSON]_{g}}|1_{-}\rangle=e^{-i\[PERSON]_{g}}\hat{a}_{-}^{\dagger}|0_{\pm}\rangle \tag{53}\]  
with \(\[PERSON]_{g}=-2\
u\pi(1-\cos\alpha^{\prime})\). Accordingly, the following transformations, induced by geometric phases, for the absorption operators are obtained:  
\[\hat{a}_{\pm}(z=n\Lambda)=e^{\pm i\[PERSON]_{g}}\hat{a}_{\pm}(0) \tag{54}\]  
This result can be used for obtaining geometric phases of other quantum light states. As an example, we present two states, that is the number photon state or Fock state \(|n_{+}\rangle\) and the coherent state \(|\alpha_{+}\rangle\), where \(\text{subindex}+\text{indicates}\) that the state is excited in the optical mode \(e_{+}(x,y)\). The Fock state under propagation becomes:  
\[|n_{+}(0)\rangle=\frac{1}{\sqrt{n_{+}!}}\big{(}\hat{a}_{+}(0)^{\dagger}\big{)} ^{n_{+}}\,|0\rangle\longrightarrow\frac{1}{\sqrt{n_{+}!}}e^{in_{+}\Phi_{g}} \big{(}\hat{a}_{+}^{\dagger}\big{)}^{n_{+}}\,|0\rangle=e^{in_{+}\Phi_{g}}|n_{+}\rangle \tag{55}\]  
Therefore, the quantum state has acquired a geometric phase \(n_{+}\Phi_{g}\). Likewise, the coherent state can be rewritten by using the complex displacement operator, that is,  
\[|\alpha_{+}(0)\rangle=e^{(\alpha_{+}\hat{a}_{+}^{\dagger}(0)-c.h)}|0\rangle \longrightarrow e^{(\alpha_{+}e^{i\Phi_{g}}\hat{a}_{+}^{\dagger}-c.h)}|0 \rangle=|e^{i\Phi_{g}}\alpha_{+}\rangle \tag{56}\]  
Therefore, the geometric phase is \(\Phi_{g}\), that is the same as the one acquired by a single photon. The same procedure can be applied to any other quantum light state excited in the integrated photonic grating.  
Figure 5: Elimination of the dynamical phase by using two consecutive integrated optical gratings. The second grating has an initial phase \(\pi\). Likewise, a prism is used to make a projective measure of states \(|1_{0}\rangle\) and \(|1_{1}\rangle\) for obtaining the probabilities \(P_{0}\) and \(P_{1}\) and, therefore, the geometric phase \(\Phi_{g}\).
# Measurement of Large-Sized-Pipe Diameter Based on Stereo Vision
## 4 Conclusions  
To address existing practical problems in today's large-sized-pipe-measurement methods, this paper proposes a unique solution and builds an experimental system to verify the proposed solution. The experimental results show that compared with the data measured by CMM, the relative error of the proposed measurement system is within \(\pm 0.570\%\) and the maximum absolute error is \(-1.71\) mm. In replicated tests, the maximum repeatability standard deviation of the three sizes of steel pipes (285 mm, 299 mm, and 325 mm) is 0.551 mm. While guaranteeing high precision, the proposed measurement algorithm provides a stable running cycle of about 1.5 s, which meets the needs for pipe measurement in actual production applications. In conclusion, this method enables fast in situ measurement of large-sized pipes at high precision, and therefore has broad application prospects.  
Conceptualization, P.L. and L.Z.; methodology, P.L. and L.Z.; software, P.L. and M.W.; validation, P.L., L.Z. and M.W.; formal analysis, P.L. and M.W.; investigation, P.L. and M.W.; resources, L.Z.; data curation, P.L; writing--original draft preparation, P.L. and L.Z.; writing--review and editing, P.L. and M.W.; visualization, P.L. and L.Z.; supervision, M.W.; project administration, L.Z.; funding acquisition, L.Z. and M.W. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (Grant No. 61905220), the Natural Science Foundation of Zhejiang Province (Grant No. LGF21F050004), and the Zhejiang Provincial Department of Education Scientific Research Project (Grant No. 20020059-F). The data that support the findings of this study are available from the corresponding authors upon reasonable request. The authors thank the editors and anonymous reviewers for providing helpful suggestions for improving the quality of this manuscript. The authors declare no conflict of interest.
## 5 The Proposed Hybrid Scheme
### Propagation Model  
In our proposed scheme, the FANET system consists of \(n\) UAVs, where \(n\geq 2\), and each UAV is equipped with both the radios i.e., 802.15.4 and 802.11 with dual-band capability. Two techniques, path loss and fast fading, can be used to model a wireless link between two UAVs. Attenuation of the radiated power affects path loss. On the other hand, fading results due to the multipath effect of low altitude UAV propagation. Path loss can be expressed mathematically as follows between the transmitter and receiver [48]:  
\[P_{l}(dB)=\left\{\begin{array}{ll}20\ log_{10}\left(\frac{4\pi d}{\lambda} \right)&if\ \ d\leq do\\ 20\ log_{10}\left(\frac{4\pi d}{\lambda}\right)+40\ log_{10}\left(\frac{d}{do} \right)&if\ \ d>do\end{array}\right. \tag{1}\]  
where \(\lambda\) is the wavelength of the transmitted signal and can be represented as the ratio of the speed of light c to the carrier signal f: \(\lambda\) = _cf_, while d is the distance between transmitting and receiving UAVs, and d\({}_{0}\), i.e., 8 m, is the length of the LOS. However, we employ a free space propagation model in our proposed scheme. The reason for selecting this model is that we assume a clear LOS between sender and receiver UAVs. The well-known Friis equation [49] is given, as it can express this model mathematically:  
\[P_{r}=\ \frac{P_{l}G_{t}(\theta_{t},\phi_{t})G_{r}(\theta_{r},\phi_{r})\lambda^{ 2}}{\left(4\pi d\right)^{2}} \tag{2}\]  
\[P_{r}=P_{l}G_{t}(\theta t,\phi t)Gr(\theta r,\phi r)\left(\frac{\lambda}{4\pi d }\ \right)^{2} \tag{3}\]  
where the alphabet \(P\) represents power and \(G\) denotes gain; the subscripts \(t\) and \(r\) stand for \"transmitted\" and \"received,\" respectively. Moreover, the symbols \(\theta_{t}\) and \(\phi_{t}\) are used for elevation angle (range: 0-180\({}^{\circ}\)) and azimuth angle (range: 0-360\({}^{\circ}\)), respectively. Further, \(\lambda\) is the wavelength of the transmitted signal, and d equates to the distance between the transmitting and receiving antenna. We set the receiving antenna gain \(G_{r}\) (\(\theta_{r}\), \(\phi_{r}\)) = 1 in Equation (3), and we can write received power as  
\[P_{r}=P_{l}G_{t}(\theta t,\phi t)\ \bigg{(}\frac{\lambda}{4\pi d}\ \bigg{)}^{2}. \tag{4}\]  
Taking logarithm on both sides of Equation (4), we have  
\[10 log10\ (P_{r})=\ 10\ log_{10}(P_{t})+10 log_{10}(G_{t}(\theta t,\phi t))-20 log_{10}\bigg{(}\frac{4\pi d}{\lambda}\bigg{)} \tag{5}\]  
Figure 4: Mobility models: (**a**) nomadic community (NC) with three nodes, where the nodes have a maximum distance \(r_{\max}\) to move away from the reference point and the reference point moves through the simulation area a random distance d following a random waypoint (RWP) model; (**b**) mobility pattern of one group (i.e., three mobile nodes) using reference point group mobility (RPGM).  
\[P_{r}\left(dB_{w}\right)=P_{t}\left(dB_{w}\right)+G_{t}\left(dB_{i}\right)-P_{t} \left(dB\right) \tag{6}\]  
where \(P_{t}\left(dB\right)\) represents the free-space path loss and is given by  
\[Pl(dB)=20 log_{10}\left(\frac{4\pi d}{\lambda}\right).\]
# Phase Risk Analysis of Overhead Lines Under Complex Icing Conditions
## 2 Analysis of Safety Distances for Overhead Line Conductors Under Ice-Covered Conditions  
### Transmission Line Space Structure  
Phase-to-ground discharge faults in overhead lines due to ice accumulation are significantly influenced by conductor and ground wire spatial configurations. Currently, the most common spatial arrangements for overhead transmission lines are the triangular and longitudinal layouts for their stability and efficiency under various environmental conditions. The triangular arrangement enhances stability by reducing oscillations during high winds, increases transmission capacity through optimized conductor spacing, and improves space efficiency by minimizing the required corridor width. However, attention must be given to the spacing between the ground wire and adjacent conductors during strong wind, heavy ice accumulation, and de-icing.  
The longitudinal arrangement of the conductor-ground line reduces conductor-to-ground capacitance and enhances lightning resistance. However, during de-icing, the conductor's release of elastic potential energy causes it to spring upward, decreasing the distance to the nearest ground line and increasing the risk of discharge.