esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Lessons Learned
### Information Sharing  
Once a high-profile malware incident is detected, the most important tasks are (a) to contain the threat and recover from the incident locally, and (b) to disseminate the intelligence information to mitigate the global effects of the malware. However, the problem is that once the victim of a malware attack has recovered from the incident, it has no incentive anymore to share information and forensics data. On the contrary, it prefers avoiding information sharing in order to preserve its reputation and the privacy of its sensitive data. Thus, again, the problem is related to misaligned incentives. Anecdotal evidence suggests that security vendors are often unable to obtain forensics information even if their own product detected the infection.  
This lack of information sharing has negative consequences, as it practically hinders forensic analysis and efficient global response. For example, in case of Flame and Gauss, no dropper component has ever been made public, and it is possible that no dropper was identified at all, due to the lack of sharing forensic information by the victims. Consequently, the vulnerabilities exploited by the dropper of these pieces of malware are still unknown, and hence, they cannot be patched, letting the attackers continue using them in future attacks.  
In contrast to this, in case of Duqu, our laboratory emerged as a trusted mediator between the victim company and Symantec, and we managed to convince the company to share information with our help with Symantec. Ultimately, this collaboration among these three parties led to the discovery of the dropper file, and the zero-day Windows kernel exploit that it used. We could then notify Microsoft, which proposed an immediate workaround, and released a patch for Windows [8] a few weeks later in December 2011. Microsoft went further and diligently checked its entire code base for the presence of the same vulnerability, which resulted in the release of further patches [30] in May 2012. Thus, in case of Duqu, information sharing by the victim helped to protect millions of users around the entire world.  
Clearly, the solution we followed does not scale. According to our experience, very few end-user firms are able to produce sanitized forensic information that can be shared for global incident response. While security vendors possess the knowledge to produce sanitized forensic material, the process is demanding and highly personalized at the moment. This implies again that trust needs to be established between end-users and security experts who are able to prepare forensics evidence while protecting the firm's identity and interests at the same time. Furthermore, to ease the load on these experts, we need to seek semi-automatic production of anonymized forensics evidence, which is a key challenge.
# Service-Aware Clustering: An Energy-Efficient Model for the Internet-of-Things
## 2 The Energy-Efficient Internet-of-Things Model
### The SAC Algorithm
#### 2.4.5 Step 5: TDMA Communication  
In this paper, we assumed that each sensor node senses data at a fixed rate and always has data to send to its cluster head during its allocated time slot. Then, the node switches into sleep mode until the next transmission slot. Once data is received from cluster members, it is aggregated by the cluster head and then forwarded to the next cluster head along the path to the base station. The HSNs use the 915-MHz channel periodically to interrogate sensor tags within their range, while the 2.4-GHz channel is used for intra-cluster communication. The RFID reader operates differently, that is once the information is collected, it is routed to the next hop along the HSN-to-HSN path.
# HCTC: Hybrid Convolutional Transformer Classifier for Automatic Modulation Recognition
## 4 Experimental Results and Discussion
### Numbers of Parameters and FLOPs and Accuracy at 0 dB and the Average Accuracy from 0 to 18 dB
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Name** & **Accuracy at a 0 SNR** & \
\begin{tabular}{c} **Average Accuracy** \\ **from 0 dB to 18 dB** \\ \end{tabular} &
\begin{tabular}{c} **Number of Trainable** \\ **Parameters** \\ \end{tabular} & **Number of FLOPs** \\ \hline Proposed HCTC & 87.03 & 90.45 & 361,611 & 364,944 \\
1 DCNN-PF & 79.68 & 83.57 & 174,923 & 173,840 \\ CGDNet & 77.32 & 80.76 & 124,933 & 139,221 \\ CLDNN & 79.18 & 82.84 & 164,433 & 183,620 \\ CLDNN2 & 82.95 & 84.59 & 517,643 & 536,439 \\ CNN2 & 82.27 & 83.51 & 858,123 & 857,478 \\ DenseNet & 79.36 & 81.91 & 3,282,603 & 3,281,798 \\ GRU & 84.27 & 88.65 & 151,179 & 346,243 \\ IC-AMCNet & 80.77 & 84.05 & 1,264,011 & 1,263,494 \\ MCLDNN & 85.55 & 90.06 & 406,199 & 665,738 \\ MCNET & 78.64 & 81.73 & 121,611 & 120,285 \\ PET-CGDNN & 85.36 & 89.40 & 71,871 & 169,300 \\ ResNet & 74.77 & 82.47 & 3,098,283 & 3,097,478 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Numbers of parameters and FLOPs and accuracy at 0 dB and the average accuracy from 0 to 18 dB for the RML2016a dataset. Figure 8: Accuracies of various models when evaluated at an SNR of 0 dB with the RML2016a dataset. Figure 10 shows the number of trainable parameters for each model, demonstrating insights into the learning capacity. Models such as DenseNet and ResNet [34] possessed significantly higher numbers of trainable parameters, whereas models such as PET-CGDNN [37], CGDNet [36], and MCNET [30] had fewer parameters, which indicates reduced computational requirements. The proposed HCTC model indicates a competitive number of parameters but shows the highest accuracy at an SNR of 0 dB and over an average SNR range from 0 dB to 18 dB. These results are pivotal for understanding the tradeoffs between model complexity and efficiency, which helps to balance performance with real-time deployment constraints. Figure 11 shows the number of FLOPs required for each model, highlighting the computational complexity involved in running them. Models such as DenseNet and ResNet [34] required high numbers of FLOPs, indicating that they were computationally intensive. By contrast, models such as CGDNet [36] and MCNET [30] had fewer FLOPs, suggesting that they were more efficient in terms of the computational cost. Figure 10: Numbers of trainable parameters of various models with the RML2016a dataset. Figure 9: Average accuracy of various models when evaluated at an SNR of 0 to 18 dB with the RML2016a dataset.
# A Stackelberg Game Approach to Model Reference Adaptive Control for Spacecraft Pursuit-Evasion
###### Abstract  
A Stackelberg equilibrium-based Model Reference Adaptive Control (MSE) method is proposed for spacecraft Pursuit-Evasion (PE) games with incomplete information and sequential decision making under a non-zero-sum framework. First, the spacecraft PE dynamics under \(l_{2}\) perturbation are mapped to a dynamic Stackelberg game model. Next, the Riccati equation solves the equilibrium problem, deriving the evader's optimal control strategy. Finally, a model reference adaptive algorithm enables the pursuer to dynamically adjust its control gains. Simulations show that the MSE strategy outperforms Nash Equilibrium (NE) and Single-step Prediction Stackelberg Equilibrium (SSE) methods, achieving 25.46% faster convergence than SSE and 39.11% lower computational cost than NE.  
spacecraft systems; PE game control; Stackelberg game; model reference; adaptive control Article
# Pharmacological and Cosmeceutical Potential of Seaweed Beach-Casts of Macaronesia
## 3 Results and Discussion
### Collagen Inhibition  
Eight out of 30 extracts presented 50% inhibition or more at 250 ug/mL, one reaching 81.7% (5.E).  
The IC\({}_{50}\) values of these extracts were calculated (Figure 8, Table S4, Supplementary Material), six of them ranging from 163.06 ug/mL to 248.76 ug/mL (1.E, 1.M, 8.M, 11.E, 11.M and 12.M), while the other two (5.E and 5.M) exhibited potent collagenase inhibitory activity (IC\({}_{50}\) values of 41.04 ug/mL and 59.34 ug/mL, respectively). These two extracts are, respectively, better and similar collagenase inhibitors than the positive control EDTA (IC\({}_{50}\) = 59.26 ug/mL).  
Unfortunately, the fractionation of the most active extracts resulted in fractions with an IC\({}_{50}\) higher than 250 ug/mL (Table S4, Supplementary Material), which were considered inactive.  
Surprisingly, extract 12.E which exhibited low anticollagenase activity but was fractionated due to its antityrosinase activity, gave rise to one very active fraction (12.E.4) (IC\({}_{50}\) = 40.11 \(\pm\) 0.88 ug/mL),  
Figure 8: Collagen inhibition activity (IC\({}_{50}\) values) by extracts. EDTA was used as reference.  
more active than EDTA. Considering the solvent used to obtain this fraction (methanol: water (1:1, v/v)), it is expected that in its chemical composition it will have mostly polar compounds such as phlorotannins and fragments of polysaccharides.  
There are several reports of collagenase inhibition by seaweed extracts and/or compounds extracted thereof. [PERSON] et al. [80] obtained complete inhibition of bacterial collagenase-1 activity at 20 \(\upmu\)g/mL of _Ecklonia cava_ (Ochrophyta, Phaeophyceae) phlorotannins extract using a gelatin digestion assay. [PERSON] et al. [81] report 54.5% inhibition of the enzyme by 50 \(\upmu\)g/mL ethanol: water extracts of _Sargassum plagiophyllloides_ (Phaeophyceae), a value which the authors attributed to the high concentration of extracted phloroglucinols. That level of activity is comparable to the results reported herein, namely of extracts 5.M, 5.E and fraction 12.E.4 (50% of inhibition by 40.11-59.34 \(\upmu\)g/mL). Ethanol extracts from _Turbinaria decurens_ brown alga, and a fucoxanthin-enriched fraction of this extract with an amount of fucoxanthin threefold higher than the found in the extract, almost completely inhibited collagenase activity at 40 \(\upmu\)g/mL and 80 \(\upmu\)g/mL [71].  
Other authors reported results which were much less significant than the one obtained in the present work. For example, [PERSON] et al. [82] reported a 24.5% inhibition of this enzyme by _Fucus vesicusus_ aqueous extracts at a concentration of approximately 166.7 \(\upmu\)g/mL; and [PERSON] et al. [83] reported 29.6% inhibition at 1 mg/mL of _Myagropsis myagroides_ (Phaeophyceae) extract. Compound Sargachromoal I, isolated from _M. myagroides_ brown alga, exhibit an IC\({}_{50}\) of 54.01 \(\upmu\)g/mL [83], a value of the same order as the IC\({}_{50}\) values reported here for extracts 5.M, 5.E and fraction 12.E.4, which are mixtures containing a wide array of compounds.
# Manifolds-Based Low-Rank Dictionary Pair Learning for Efficient Set-Based Video Recognition
## 4 Experimental Results and Analysis
### Experiments on Set-Based Video Face Recognition Task
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Honda**} & \multicolumn{3}{c}{**YTC**} \\ \cline{2-7}  & **Accuracy** & **Tra. Time** & **Tes. Time** & **Accuracy** & **Tra. Time** & **Tes. Time** \\ \hline ISCRC & 96.41 \(\pm\) 2.24 & N\(/\)A & 9.36 & 69.31 \(\pm\) 2.02 & N\(/\)A & 1171 \\ RNP & 96.41 \(\pm\) 2.16 & N\(/\)A & 2.49 & 70.35 \(\pm\) 2.44 & N\(/\)A & 47.12 \\ DLRC & 34.36 \(\pm\) 2.15 & N\(/\)A & 10.15 & 38.37 \(\pm\) 6.70 & N\(/\)A & 183.2 \\ PLRC & 67.53 \(\pm\) 6.64 & N\(/\)A & 33.84 & 49.26 \(\pm\) 2.24 & N\(/\)A & 3102 \\ DRA & 70.12 \(\pm\) 9.22 & 41.23 & 38.33 & 30.19 \(\pm\) 0.35 & 2238 & 2482 \\ JMLC & 100.0 \(\pm\) 0.00 & N\(/\)A & 1.81 & 71.89 \(\pm\) 3.13 & N\(/\)A & 986 \\ CDL & 100.0 \(\pm\) 0.00 & 3.56 & 8.58 & 69.18 \(\pm\) 2.65 & 12.58 & 15.69 \\ PML & 96.67 \(\pm\) 2.01 & 5.55 & 3.51 & 66.13 \(\pm\) 3.16 & 65.58 & 18.37 \\ LEML & 97.18 \(\pm\) 3.32 & 22.34 & 3.90 & 50.60 \(\pm\) 3.01 & 40.66 & 39.96 \\ MMFML & 100.0 \(\pm\) 0.00 & 2.53 & 0.02 & 71.32 \(\pm\) 4.36 & 18.32 & 0.56 \\ DML & – & – & – & 70.89 \(\pm\) 9.75 & 10.38 & **0.30** \\ \hline MbLRDPL-SPD & 100.0 \(\pm\) 0.00 & 0.89 & 0.003 & 72.16 \(\pm\) 2.44 & 9.73 & 0.47 \\ MbLRDPL-GM & 100.0 \(\pm\) 0.00 & 0.78 & 0.002 & 71.85 \(\pm\) 2.53 & 9.16 & 0.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification accuracy (%), training time (Tra. Time), and testing time (Tes. Time) (seconds) of different methods on Honda and YTC datasets. Figure 3: Some examples of three datasets: (**a**) Honda (all images come from one video), (**b**) YTC (all images come from different videos), and (**c**) ETH-80 (the images in the first row come from different classes, whereas the images in the second row come from a same class but different image sets). Since our MbLRDPL uses the dictionary learning scheme, Table 3 provides a comparison of the proposed model's performance with that of some dictionary learning methods: 59.2% for D-KSVD [20], 62.3% for ADDL [21], 60.1% for DDL [22], 61.9% for SLatDPL [23], and 66.31% for CEBSR [5] on the YTC dataset. These results were extracted from the literature [5]. This table shows that our MbLRDPL method is substantially better than these dictionary learning methods (providing and improvement of 5.85% over the best result achieved by CDBSR), likely because the static modeling used in MbLRDPL can effectively capture the discriminative information of each image set.
# Bloom Filter Approach for Autonomous Data Acquisition in the Edge-Based MCS Scenario +
## 3 Materials and Methods
### Decentralized Edge-Based MCS Environment
Similarly, the first time a new worker enters the system, the coordinator looks for an ME host in the geographical region where the worker is currently situated. If there were no users in this area before, the coordinator dynamically deploys a new ME host in the area. Each ME host is responsible for one square-shaped geographical area with a side length of 10 km which has a unique MGRS identifier. Afterward, the worker announces his/her presence and capabilities (i.e., type of sensed data) to the corresponding host, so an MCS service operating on this ME host is aware of all possible workers in its deployment area and their capabilities. As a result, the ME host will assign active tasks to the new worker only if he/she can potentially complete them, thus preventing the worker's device overload. Then, the new worker can start the sensing process and submit data to the MCS service running on the corresponding ME host. ME hosts have substantial computational power compared to mobile devices, so they can perform data aggregation and processing of all data produced by workers under their responsibility, disseminate results and control the data acquisition by managing all workers in the area. Each ME host sends the aggregated data for its deployment area to the cloud coordinator, who performs different analytics and correlates received data to extract knowledge about the sensed phenomena for a larger area.
# A Performance-Consumption Balanced Scheme of Multi-Hop Quantum Networks for Teleportation
## 2 Preliminary  
### Entangled State  
The Bell state is the most commonly used entangled state in quantum communication. The basic state in this paper is  
\[\ket{0}=\binom{1}{0},\ket{1}=\binom{0}{1}. \tag{1}\]  
Then, the Bell states can be expressed as  
\[\ket{\Phi^{+}}=\frac{1}{\sqrt{2}}(\ket{00}+\ket{11}),\ket{\Phi^{-}}=\frac{1}{ \sqrt{2}}(\ket{00}-\ket{11}) \tag{2}\] \[\ket{\Psi^{+}}=\frac{1}{\sqrt{2}}(\ket{01}+\ket{10}),\ket{\Psi^{-} }=\frac{1}{\sqrt{2}}(\ket{01}-\ket{10}).\]  
If applying a unitary operation such as the Pauli matrix on Bell states, we obtain  
\[\ket{\Psi^{+}}=(X\cdot\ket{0})\otimes\ket{0}+(X\cdot\ket{1})\otimes \ket{1}=X\cdot\ket{\Phi^{+}} \tag{3}\] \[\ket{\Phi^{-}}=(Z\cdot\ket{0})\otimes\ket{0}+(Z\cdot\ket{1})\otimes \ket{1}=Z\cdot\ket{\Phi^{+}}\] \[\ket{\Psi^{-}}=(Y\cdot\ket{0})\otimes\ket{0}+(Y\cdot\ket{1})\otimes \ket{1}=Y\cdot\ket{\Phi^{+}},\]  
where  
\[X=\left(\begin{array}{cc}0&1\\ 1&0\end{array}\right),Y=\left(\begin{array}{cc}0&-i\\ i&0\end{array}\right),Z=\left(\begin{array}{cc}1&0\\ 0&-1\end{array}\right).\]Thus, we choose \(|\Phi^{+}\rangle\) to share in the network. Another entangled state commonly used in quantum communication is the GHZ state. Since the fidelity of GHZ states decreases with the increasing number of qubits [25], we select 3-qubit GHZ states to use.  
\[\begin{split}|\Psi_{1}\rangle&=\frac{1}{\sqrt{2}}(|00 0\rangle+|111\rangle),|\Psi_{2}\rangle=\frac{1}{\sqrt{2}}(|001\rangle+|110 \rangle)\\ |\Psi_{3}\rangle&=\frac{1}{\sqrt{2}}(|010\rangle+|10 \rangle),|\Psi_{4}\rangle=\frac{1}{\sqrt{2}}(|011\rangle+|100\rangle)\\ |\Psi_{5}\rangle&=\frac{1}{\sqrt{2}}(|000\rangle-|11 1\rangle),|\Psi_{6}\rangle=\frac{1}{\sqrt{2}}(|001\rangle-|110\rangle)\\ |\Psi_{7}\rangle&=\frac{1}{\sqrt{2}}(|010\rangle-|1 01\rangle),|\Psi_{8}\rangle=\frac{1}{\sqrt{2}}(|011\rangle-|100\rangle).\end{split} \tag{4}\]  
For the same reason, we choose \(|\Psi_{1}\rangle\) to share in the quantum network.
# An Enhanced System Architecture for Optimized Demand Side Management in Smart Grid
## 3 Proposed CHEMA and its Implementation
### Pricing Scheme  
There are various pricing strategies in dynamic energy management modeling such as ToU, RTP, CPP, _etc._ In CHEMA, the ToU pricing scheme is used and rates are taken as Pakistani Rupees (PKR) 10.0/kWh (0.094 USD) for off-peak hours, PKR 12.0/kWh (0.113 USD) for mid peak and PKR 15.0/kWh (0.141 USD) for peak hours. The scheme is implemented in Simulink with clock and embedded code as shown in Figure 3. Rates of this module are used at various places to compute the energy cost.
## 2 Theoretical Framework
### Propagation through Cloud  
Apart from the rain, cloud is another key atmospheric parameter that attenuate the amplitudes of propagated microwave via reflection, absorption, scattering, refraction, and spreading phenomena. It is possible to reliably articulate or quantify attenuation due to a cloud utilizing its liquid contents. Mathematically, it can be conveyed by [57] in Equation (12):  
\[\gamma_{aa}(c)=\gamma_{C}\left[\frac{LWC}{\sin(\theta)}\right] \tag{12}\]where \(LWC\) indicates the liquid water content and \(\gamma_{c}\) is given by Equation (13), and its parameters are defined in Equations (14)-(18) as follows.  
\[\gamma_{C}=\frac{0.819f_{c}}{e^{\prime\prime}\left[1-\left(\frac{2\frac{2\mu^{ \prime}}{e^{\prime\prime}}}{\varepsilon^{\prime\prime}}\right)^{2}\right]} \tag{13}\]  
where:  
\[\varepsilon^{\prime}=\frac{\varepsilon_{0}-\varepsilon_{1}}{1+\left(\frac{f_ {c}}{f\gamma_{pri}}\right)^{2}}+\frac{\varepsilon_{1}-\varepsilon_{2}}{\left( \frac{f_{c}}{f\gamma_{\text{sec}}}\right)^{2}}+\varepsilon_{2} \tag{14}\]  
\[\varepsilon^{\prime\prime}=\frac{f_{c}(\varepsilon_{0}-\varepsilon_{1})}{fr_{ pri}\left[1+\left(\frac{f_{c}}{f\gamma_{pri}}\right)^{2}\right]}+\frac{f_{c}( \varepsilon_{1}-\varepsilon_{2})}{fr_{\text{sec}}\left[1+\left(\frac{f_{c}}{ f\gamma_{\text{sec}}}\right)^{2}\right]}+\varepsilon_{2} \tag{15}\]  
\[\varepsilon_{0}=77.6+103.3(\Lambda-1) \tag{16}\]  
\[fr_{pri}=20.09+142(\Lambda-1)+294(\Lambda-1)^{2} \tag{17}\]  
\[fr_{\text{sec}}=590-1500(\Lambda-1) \tag{18}\]  
where \(\Lambda=330/T\), \(\varepsilon_{1}=5.48\) and \(\varepsilon_{2}=3.51\).
# Design, Modeling, and Simulation of a Wing Sail Land Yacht
## 2 Materials and Methods  
This work applies the principles of land sailing physics to the design, modeling, and simulation of a shore monitoring land yacht. The next subsections cover these different stages, detailing the relevant steps required to reproduce the results.  
A rigid-wing sail land yacht uses an airfoil as a sail instead of a conventional fabric sail [12]. The wing sail generates aerodynamic forces, such as lift and drag, and is more efficient, robust and easier to control than conventional fabric sails as, generally, these fabric sails require more than one actuator to be controlled and a wing sail only requires one [12]. Even though wind propelled vehicles, when compared with their fuel or electricity based counterparts, display greater power autonomy, it can be further increased with the adoption of a free rotating wing sail equipped with a wing flap.  
By default, a free rotating wing sail automatically aligns to the wind direction, defining an angle between the cord of the wing and the oncoming wind flow, called angle of attack. This angle of attack can be controlled by changing the direction of the wing flap. Specifically, when the angle between the wing flap and the apparent wind changes, it generates a linear force that induces torque on the wing sail, defining a new angle of attack.
# Dual-Band Single-Layered Frequency Selective Surface Filter for LTE Band with Angular Stability
## 3 Optimization and Analysis of Proposed FSS
### Equivalent Circuit Model  
For a deeper understanding of the design's operating principle, an equivalent circuit model (ECM) is employed as shown in Figure 6. Furthermore, the comparison of the simulated results of \(S_{11}\) and \(S_{21}\) obtained from HFSS and ECM shows an appropriate response with the simulated results as shown in Figure 7.  
Figure 5: Effect of cross-structure (**a**) length L1 and (**b**) width W1 on reflection and transmission coefficients of C-FSS and effect of rectangular monopole (**c**) length L2 and (**d**) width W2 on reflection and transmission coefficients of C-FSS.  
It is evident from the available literature that the modified cross structure can be represented by L-C series/parallel networks [36]. The copper part makes an inductor while the gaping forms capacitance. The ECM lumped components are optimized by matching the ECM response with the response obtained from the Advanced Design System and using the built-in Finite-Element Method (FEM) in the HFSS simulator. The optimized values of each lumped component are as follows: L1 = 938.16 nH, L2 = 541.80 pH with R1 = 0.01 ohm, L3 = 91.19 pH, L4 = 1.48 nH with R2 = 0.3 ohm, C1 = 2.13 \(\times\) 10\({}^{-11}\), C2 = 31.33 pF, C3 = 2.51 \(\times\) 10\({}^{-11}\), and C4 = 68.54 pF. These values can be calculated using the formulas given in [37; 38].  
Analyzing the E-field and surface current distributions under TE polarization allows one to gain a thorough knowledge of the proposed C-FSS through the development of the ECM [39]. However, the suggested shape affects the dual-resonance transmission/reflection phenomenon. An analysis of the surface current [40] and electric field distribution [41] is conducted at the dual frequency of 1.9 and 2.1 GHz, as illustrated in Figure 6. It can be observed from Figure 8a, which shows the electric field distribution at 1.9 GHz, and Figure 8c, which shows the electric field distribution at 2.1 GHz, that the maximum electric field distribution occurs along the arms of the cross structure, with the highest concentration at the center of the cross structure for 2.1 GHz resonance. Consequently, the width and  
Figure 6: ECM model of proposed Cruciform FSS.  
Figure 7: Comparative analysis of C-FSS with ECM model.  
distance between the center of the cross structure and the rectangular monopole will affect the resonance of the structure.  
Additionally, the strong surface current distribution is located at the corner of the rectangular monopole, as shown in Figure 8b for 1.9 GHz and Figure 8d for 2.1 GHz. The three-square stubs exhibit more limited current density than the rectangular monopole where the square stub is absent. As a result, it affects the resonance and results in dual-band resonance.