esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on the Remaining Life Prediction Method of Rolling Bearings Based on Multi-Feature Fusion
## 4 Experimental Verification
### Parameter Configuration of Multi-Feature Fusion Networks  
In both cases, the hyperparameters of the multi-feature fusion network are the same, as detailed in Table 1. The hyperparameters were selected using cross-validation of numerous training datasets, taking into account the prediction accuracy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Module** & **Layer** & **Parameters** \\ \hline \multirow{8}{*}{Transformer} & \multirow{8}{*}{Transformer Encoder Layer} & Num\_features: 2560 \\  & & Eps: \(1\times 10^{-5}\) \\  & & Momentum: 0.1 \\  & & affine: True \\  & & Track running stats: True \\ \cline{3-3}  & & D\_model: 2 \\  & & Nhead: 2 \\ Transformer & \multirow{2}{*}{Transformer Encoder Layer} & Dim feedforward: 2048 \\  & & Dropout: 0.1 \\  & & Activation: relu \\  & & Normalize before: False \\  & & Num layers: 3 \\ \cline{3-3}  & & In features: 5120 \\  & & Out features: 1 \\  & & Bias: True \\ \hline \multirow{8}{*}{TCN} & \multirow{8}{*}{Temporal Clock} & N\_inputs: 2560 \\  & & N\_outputs: 8 \\ \cline{3-3}  & & kernel\_size: 3 \\ \cline{3-3}  & & Stride: 1 \\ \cline{3-3}  & & dilation: 1, 2, 4 \\ \cline{3-3}  & & dropout: 0.5 \\ \cline{3-3}  & & Num\_inputs: 2560 \\ \cline{3-3}  & & Num\_channels: 8, 8, 8 \\ \cline{3-3}  & & Kernel\_size: 3 \\ \cline{3-3}  & & Dropout: 0.2 \\ \hline \multirow{8}{*}{LSTM} & \multirow{8}{*}{LSTM} & Nor: 2560 \\  & & input features: 2 \\ \cline{3-3}  & & number of hidden states: 3 \\ \cline{3-3}  & & number of input features: 7680 \\ \cline{3-3}  & & Hidden Size: 3 \\ \cline{3-3}  & & Flatten \\ \hline \multirow{2}{*}{FNet} & Dim: 100 \\  & Depth: 3 \\ \cline{3-3}  & & Mlp\_dim: 100 \\ \cline{3-3}  & Dropout: 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of a multi-feature fusion network model.
# Post-Quantum Biometric Authentication Based on Homomorphic Encryption and Classic McEliece
## 3 Proposed Scheme Based on Homomorphic Encryption and Classic McEliece
### Generation and Matching of Protected Biometric Features
#### 3.2.2 Matching of Protected Biometric Features  
The _DECODE_ algorithm is applied at verification to obtain the recognition decision. We propose the following procedure:  
Figure 3: Biometric Feature Transformation: (**a**) at the enrollment phase and (**b**) at the verification phase. (**c**) shows the application of the XOR operation to the biometric features transformed in (**a**) and (**b**).  
The XOR operation of the encoded substrings is obtained, that is, \(cb_{i}^{R}\oplus cb_{i}^{V}=H\cdot eb_{i}^{R}\oplus H\cdot eb_{i}^{V}\).
2. \(eb_{i}^{R}\oplus eb_{i}^{V}\) is recovered by the \(DECODE\) algorithm. Figure 3c shows the result of the application of the XOR operation to the transformed biometric features \(eb_{i}^{R}\) and \(eb_{i}^{V}\). * By the homomorphic property of Classic McEliece, \(ENCODE\left(eb_{i}^{R}\oplus eb_{i}^{V}\right)=ENCODE\left(eb_{i}^{R} \right)\oplus ENCODE\left(eb_{i}^{V}\right)\). Hence, \(cb_{i}^{R}\oplus cb_{i}^{V}=H\cdot eb_{i}^{R}\oplus\ h\cdot eb_{i}^{V}=H\cdot \left(eb_{i}^{R}\oplus eb_{i}^{V}\right)\). * It is ensured that \(eb_{i}^{R}\oplus eb_{i}^{V}\) has Hamming weight smaller than or equal to \(t\). The same transformation is applied at the enrollment and verification phases for each individual. Therefore, the application of the \(DECODE\) algorithm to \(cb_{i}^{R}\oplus cb_{i}^{V}\) can recover \(eb_{i}^{R}\oplus eb_{i}^{V}\) because the padding bits from position 1 to \((n-k)\) and from \((n-k+z_{i}+1)\) to \(n\) are 0 s after the XOR application and the Hamming weight of the bits from \((n-k+1)\) to \((n-k+z_{i})\), which correspond to \(b_{i}^{R}\oplus\ b_{i}^{V}\), has a Hamming weight smaller than or equal to \(t\). The simplest solution is to make \(z_{i}\) equal to \(t\) because, in this way, the Hamming weight of \(b_{i}^{R}\oplus\ b_{i}^{V}\) is always smaller than or equal to \(t\). However, biometric features allow for making \(z_{i}\) greater than \(t\), ensuring the substrings \(b_{i}\) meet such a requirement. The latter solution is preferred to reduce the size of communicated and stored data. * Given that the substrings \(eb_{i}^{R}\) and \(eb_{i}^{V}\) are constructed with a Hamming weight greater than \(t\), \(DECODE\) algorithm cannot recover \(eb_{i}^{R}\) and \(eb_{i}^{V}\), from \(cb_{i}^{R}\) and \(cb_{i}^{V}\), even with the knowledge of the private key \(\Gamma\). 3. A distance measurement is applied to \(eb_{i}^{R}\oplus eb_{i}^{V}\), for example, the Hamming distance, by computing the Hamming weight. 4. The sum of the partial distances when all the substrings \(eb_{i}^{R}\oplus eb_{i}^{V}\) are decoded generates the final distance result by computing: \[HD=\sum_{i=1}^{N}Hamming\ weight(eb_{i}^{R}\oplus eb_{i}^{V})\] (1) The resulting Hamming Distance can be normalized by the number of bits of the binary string \(b\). 5. The final result is compared to a threshold value to determine the recognition decision.
# Buffer with N Policy and Active Management
## 5 Numeric Examples
### Impact of Active Management during the Energy-Saving Phase  
The intention of this part is to assess the influence of active management solely in the energy-saving phase. Therefore, passive queue management is assumed in the transmission phase, i.e., \(d_{2}(n)=d^{*}(n)\), where \(d^{*}(n)\) is given in (1). In the energy-saving phase, active management is used in the following form:  
Figure 3: Energy efficiency vs. parameter \(b\) of active management during the transmission phase. The color changes with \(b\) as in Figure 1.  
Figure 2: Average queue size vs. parameter \(b\) of active management during the transmission phase. The color changes with \(b\) as in Figure 1.  
\[d_{1}(n)=d_{1}(n,a)=\left\{\begin{array}{ll}0,&\text{if}\quad n<50,\\ \\ \frac{((n-50)/50)^{a}}{2},&\text{if}\quad 50\leq n{<}100,\\ \\ 1,&\text{if}\quad n\geq 100.\end{array}\right. \tag{50}\]  
where \(a\) is a non-negative parameter. Generally speaking, (50) is of the same type as (49), but it operates over a narrower range of queue sizes, 50-100, and is limited to 0.5, to prevent the energy-saving phase from growing indefinitely. A few shapes of \(d_{1}(n)\) are depicted in Figure 4.  
In Figure 5, the average queue length versus parameter \(a\) is depicted. As observed, the function decreases across its entire domain. Therefore, we have again the surprising phenomenon described above, now in its entirety. Apparently, the duration of the energy-saving phase decreases faster with \(a\) than the queue sizes. Consequently, the average queue size decreases with \(a\), which would not be possible in a classical system without the N policy.  
In Figure 6, the energy efficiency versus \(a\) is shown. As expected, the energy efficiency decreases with \(a\) because a smaller \(a\) prolongs the energy-saving phase, thereby increasing \(E\). Similar to the previous section, \(a\) can be utilized to achieve a desired energy efficiency. For \(a=0\), we have \(E=0.59\), while for \(a=\infty\), we have \(E=0.50\). Hence, any value of \(E\) within the range \((0.50,0.59)\) can be obtained in this manner. For example, \(E=0.55\) is achieved when \(a=0.562\).  
Figure 4: Dropping function \(d_{1}(n,a)\) for several values of parameter \(a\).  
Figure 5: Average queue size vs. parameter \(a\) of active management during the energy-saving phase. The color changes with \(a\) as in Figure 4.
## 2 Data and Methods
### LAI Data  
Observed LAI data were collected at three forest sites (_i.e._, CBS, DHS and XSBN) in 2005 and 2010 and at one cropland site (YC) during 2005-2011 from the Chinese Ecosystem Research Network ([[http://www.cerndata.ac.cn](http://www.cerndata.ac.cn)]([http://www.cerndata.ac.cn](http://www.cerndata.ac.cn))). LAI was measured by a leaf area scanner or plant canopy analyzer monthly every five years at forest sites, and several times each year according to the development stages for crops at the cropland site.
## Appendix A  
_Appendix A.1_  
Pseudocode with constructive heuristics.  
```
Input:\(S\), \(C\), \(m_{s}\), \(Q^{2}\), \(d_{i}\), \(C_{ij}\) Output:\(Routes_{s}\), \(Demand_{s}\) // Assign Clients to Satellites \(Cluster_{s}\leftarrow[[]\) for s in \(S]\) forach\(i\in C\)do \(cost_{i}\leftarrow\) inf \(s^{*}\leftarrow\) inf forach\(s\in S\)do if\(C_{ij}[i,s]\leq cost_{i}\)then \(cost_{i}\gets C_{ij}[i,s]\) \(s^{*}\leftarrow\) s else  end if  end for \(Cluster_{s}[s^{*}]\).add(i) end if // Generate Initial Solution with Insertion Method \(Routes_{s}\leftarrow[[]\) for s in \(S]\) \(Demands_{s}\leftarrow[0\) for s in \(S]\) forach\(s\in S\)do whileSize(\(Cluster_{s}[s]\)) > 0 and Size(\(Routes_{s}[s]\)) <= \(m_{s}[s]\)do \(Rout\leftarrow[\)s, s\(Weight\gets 0\) \(BestCost\leftarrow\) inf \(BestClient\leftarrow\) inf \(BestPosition\leftarrow\) inf forach\(i\in Cluster_{s}[s]\)do forposition\(\rightarrow\) 1 to Size(\(Route\)) - 1 do \(cost\) = \(C_{ij}[Route[position-1],i]+C_{ij}[i,Route[position]]-C_{ij}[Route[position-1],Route[ position]]\) if\(cost\leq BestCost\) and\(Weight+d_{i}[i]\) <= \(Q^{2}\)then  BestCost = cost  BestClient = i  BestPosition = position else  end if  end for ifBestClient!= infthen  Route.insert(BestPosition, BestClient)  Weight = Weight + d\({}_{i}[\)BestClient\(]\) \(Cluster_{s}[s]\).remove(BestClient) else \(Routes_{s}[s]\).add(Route)  Route = [s, s] \(Demands_{s}[s]=Demands_{s}[s]+Weight\)  Weight = 0  end if  end for ifSize(\(Route\)) > 2 then \(Routes_{s}[s]\).add(Route) \(Demands_{s}[s]\gets Demands_{s}[s]+Weight\) else  end if  end for  end for
```  
**Algorithm A1**In Insertion Constructive Heuristic.
# Enhancing Cybersecurity in Smart Grids: False Data Injection and Its Mitigation
## 3 Classifications of Cyber-Attacks in SGs
### Trojan Horses  
It can be defined as computer software that appears to have a useful function and contains hidden and potentially harmful functions that can bypass security mechanisms and sometimes exploit the legitimate authority of a control and communication system unit [71; 102]. Since they are confusing terms, it is useful to highlight the feature that distinguish viruses from Trojans, Worms, and Stuxnet here:  
* Trojans appear to be harmless software that do not interfere with the system. However, when a situation arises, they will come into play and exploit times for other malicious applications.
* Worms are programs on their own that can spread themselves in the net. On the contrary, a virus is not a self-sufficient program to infect. It spreads by attaching itself to other files, but if the infected file is not opened, the virus cannot spread to other environments [36; 102].
* Stuxnet is using spread USB devices and changing the Ladder logic code of PLCs [70]. This attack involves human factors as well as technology and process management.
## 5 Future Trends, Challenges, and Opportunities
### Model Deployment and Updating  
Wireless communications systems operate under naturally dynamic conditions. However, most reported ML approaches perform offline training. Given the quick variation in channel conditions, monitoring and updating ML models is crucial to ensure the model's performance under these dynamics. Hyperparameter tuning is another important issue for the effectiveness of an ML model [112]. ML benchmarks can be improved by properly adjusting parameters, such as the number of layers, the number of neurons, the choice of the optimizer, and the loss function, among others.
# A Comprehensive Evaluation of Possible RNSS Signals in the S-Band for the KPS
## 2 S-Band and Adjacent Bands Systems
### Other Communication Systems in Adjacent Bands  
The industrial, scientific, and medical (ISM) band and long-term evolution (LTE) band are located near the S-band. According to article No.5.150 in [26], the ITU allocates some frequency bands, including the 2400-2500 MHz band, for ISM applications. Since ISM bands are unlicensed bands, short-range devices (SRDs) providing low-power/short-range radiocommunication also operate in ISM bands [31]. SRD applications include wireless-fidelity (Wi-Fi), Bluetooth, and near-field communication (NFC) [32]. For the 2.4 GHz ISM band, only the band 2400-2483.5 MHz can be used for SRD applications in North America, Europe, and South Korea [33].  
The band from 2500 MHz to 2570 MHz is allocated to the LTE band 7 uplink by the ITU. In South Korea, the leading mobile operators, namely, SK Telecom and LG U+, use the band 2500-2550 MHz. Figure 1 shows the frequency allocation from 2.4 GHz to 2.6 GHz in South Korea. Most radio devices equipped with RNSS receivers also provide short-range and/or LTE radiocommunication functions. Considering that there is an in-device coexistence interference problem between Bluetooth, Wi-Fi, and LTE [34], we can anticipate in-device coexistence interference between RNSS and the adjacent bands' systems.
## 5 Application of PNA Invasion to Biological Research  
PNA invasion, which can recognize sequences in dsDNA, has been applied in various biological studies. As mentioned above, the backbone of PNA is different from those of DNA and peptide, making it highly resistant to nucleases and proteases, and this is a great advantage in studies including intracellular applications. One example of biological applications of PNA invasion is the control of enzyme functions working on DNA (Figure 15a). Enzymatic functions of restriction enzymes and methylases were successfully inhibited by double-duplex invasion [99,100]. The control (both inhibition and activation) of RNA transcription activity using invasion has also been reported [42,56,101,102]. As a genetic engineering tool, the construction of DNA cutters has been accomplished based on PNA invasion [103,104] (Figure 15b). In addition, genome editing, which is currently a key technology in molecular biology research, has been reported to be achieved by PNA invasion complex formation [38,57,58,105,106,107,108,109].  
Figure 14: (**a**) Structures of \({}^{\text{CNV}}\)K and \({}^{\text{CN}}\)U. (**b**) Scheme of photo-cross-linking reactions of \({}^{\text{CNV}}\)K and T. (**c**) The lower reactivity between \({}^{\text{CNV}}\)K and \({}^{\text{CN}}\)U suppresses the photo-cross-linking between probes.  
The above applications are based on the sequence-specific recognition of dsDNA via invasion complex formation. More interestingly, invasion is accompanied by a significant change in the local structure of the target dsDNA. This is entirely different from the simple DNA binding event and makes it possible to develop novel biotechnologies such as enzyme recognition control and genome editing.
# Personalized Privacy Protection Based on Space Grid in Mobile Crowdsensing
## 4 Personalized Privacy Protection
### Personalized Private Space Decomposition Framework Design
#### 4.1.2 Personalized Privacy Budget Allocation  
When establishing the dynamic meshing results, using the conclusions in Reference [30], we will reduce the total budget \(\varepsilon\). It is divided into two parts. The first part \(\varepsilon_{1}\) is the privacy budget required to add noise to the node when establishing the first-level grid node. The second part \(\varepsilon_{2}=\varepsilon-\varepsilon_{1}\) will be used to calculate the noisy count of the second-level grid nodes. And the calculation formula of \(\varepsilon_{1}\) is as follows:  
\[\varepsilon_{1}=\varepsilon\times\alpha \tag{6}\]  
\(\alpha\) is the percentage of the total budget divided when dividing the first-level grid (\(0<\alpha<1\)). According to the literature [30], the parameter \(\alpha\) is not critical for privacy protection results. When the range of \(\alpha\) is from 0.2 to 0.6, the noisy data are roughly similar to the original data. Therefore, in order to achieve a personalized level of privacy protection, we will set \(\alpha\) to a random number from 0.3 to 0.6.
## 3 Results
### Maintenance Event Timelines and Workloads
#### 3.2.2 Corrective Maintenance Event Timeline
By comparing the number of failures for Scenarios 1, 2, and 4 (which is based on case study historical failure data) and for Scenario 3 (based on OREDA data), it can be observed that the OREDA data is underestimating the number of failures for some components (e.g., rotor and motor), while it is overestimating the number of failures for some components (e.g., bearing and seal). That might be due to the mean issue (as the OREDA database presents the mean and upper failure rates of several failure events occurring based on several installations, where some failure events might even not be registered). By comparing the number of failures occurring in Scenarios 1, 2, and 4 with Scenario 5, it can be concluded that Scenario 5 (based on well-known deterioration curves) underestimates the number of failures. Let us take rotor as an example, the mean time \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{3}{*}{**Scenario**} & \multicolumn{5}{c}{**Maintenance Workload [h]**} \\ \cline{2-6}  & **Corrective** & \multicolumn{2}{c}{**Scheduled Maintenance**} & **Intelligent** & \multirow{2}{*}{**Total**} \\ \cline{3-3} \cline{5-6}  & **Maintenance** & **with Impact** & **without Impact** & **Maintenance** & \\ \hline
**1** & 107,700 & 1311 & 3425 & 0 & 112,436 \\ \hline
**2** & 44,055 & 2578 & 6682 & 0 & 53,316 \\ \hline
**3** & 3385 & 3237 & 8763 & 0 & 15,385 \\ \hline
**4** & 4183 & 3262 & 8736 & 0 & 16,181 \\ \hline
**5** & 3724 & 3251 & 8765 & 360 & 16,101 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Summary of the maintenance workload of the five simulated use case scenarios. Figure 17: Corrective maintenance event timeline of Scenario 5 during 20 years of operation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{3}{*}{**Component**} & \multicolumn{5}{c}{**Scenario**} \\ \cline{2-6}  & **1** & **2** & **3** & **4** & **5** \\ \hline
**Rotor** & 8 & 8 & 2 & 8 & 3 \\ \hline
**Bearing** & 13 & 14 & 26 & 13 & 5 \\ \hline
**Seal** & 19 & 35 & 45 & 29 & 22 \\ \hline
**Motor** & 24 & 27 & 7 & 26 & 26 \\ \hline
**Total** & 64 & 84 & 80 & 76 & 56 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Failures occurring during 20 years of operation based on the simulated results. between failures based on the deterioration curve is 4.4 years for rotor (see Table 8), while the mean time between failures based on case study historical failure data is 2.5 years (see Table 11). This can be justified as the available deterioration curves that authors could find only cover three out of twenty-eight failure modes considered in Scenario 1, 2, and 4.
# Principle and Applications of the Coupling of Surface Plasmons and Excitons
## 3 Recent Progress in Coupling between Surface Plasmons and Excitons
### Strong Coupling between Plasmons and Excitons on Localized Surfaces
[82] presented an experimental demonstration of the strong coupling between a surface plasmon propagating on a planar silver thin film and the lowest excited state of CdSe nanocrystals, which was characterized by a Rabi splitting energy of 112 meV at room temperature. They also studied the quantum electrodynamics in the cavity. Additionally, for the dipole moment of the transition of a single optical emitter material, e.g., perovskites [83, 84], dye molecules and excitons in semiconductors, any type of optical transition that is associated with a given oscillator strength can be used for reaching the strong coupling regime [85]. For a general single-particle surface plasmon and a single emitter-coupled composite system, the whole system cannot achieve strong coupling; therefore, a strong coupling effect with the single emitter can only be achieved using the gap mode, with its stronger local limiting effect on the light. In 2016, [PERSON] et al. [86] showed that photon emitters placed in an optical cavity experienced an environment that changed how they were coupled to the surrounding light field; this was called the nanoparticle-on-mirror (NPoM) geometry. The assembly of the so-called NPoM structure involved placement of particles on the gold film and addition of methylene blue dye to the gap between the particles and the gold film. The gap mode formed by this system is similar to the gap mode between two vertical spherical dimers. In this mode, the plasmon's binding properties to the subwavelength of the light are effectively improved, while the mode volume is reduced, and the electric field intensity in  
Figure 12: (**a**) SEM image of the prism and scattering spectrum of single nanoprism strongly coupled to J-aggregates [77]. (**b**) Structure diagram between J-aggregate excitons and single plasmonic dimers and polarized scattering spectra of plexcitonic dimer with detected polarization different angles [79]. (**c**) SEM images of the nanoparticles imaged on top of Au surface to visualize J-aggregate shell and the scattering spectrums of uncoupled and coupled system [78]. the gap is enhanced. Simultaneously, they used chemical methods to cause the orientation of the dye molecule's transition dipole moment to be parallel to the direction of the strongest electric field, and strong coupling between a single optical emitter and the surface plasmon nanoresonator was finally achieved, as illustrated in Figure 13.  
Current research tends toward enhancement of the strong coupling between local surface plasmons and excitons using the subwavelength binding characteristics of the metallic NPs and the microcavity surface plasmons [87].
# Dual Attention Fusion Enhancement Network for Lightweight Remote-Sensing Image Super-Resolution
## 4 Results
### Results on SR Benchmark Datasets  
To further validate the generalization performance of our model, we compare it with excellent SR methods on natural image datasets, including LGCNet [21], IDN [51], MADNet [74], FeNet [46], FDENet [57], DARN-S [56], IFIN-S [55], BMFNet [58], and TARN [49]. Similar to [46], we utilize four benchmark datasets: Set5 [66], Set14 [67], BSD100 [68], and Urban100 [69], which cover urban buildings, ecological environments, flora, and fauna. The models used for testing are all pre-trained models provided by the respective researchers. The test results are shown in Table 4. Our findings indicate that in terms of reconstruction accuracy for \(\times\)2, \(\times\)3, and \(\times\)4 SR tasks, DAFEN performs better or comparably to other lightweight SR networks on most test datasets. Despite IFIN-S achieving optimal results in some cases, its parameter count is higher than DAFEN. Additionally, due to the higher computational complexity of the transformer architecture used by IFIN-S, its computational load is about 1.5 times that of DAFEN. Compared with the TARN method,  
Figure 6: Visualization results of several SR methods and our proposed networks (DAFEN and DAFEN-S) on UC-Merced for \(\times\)4 SR.  
which has a significantly higher parameter count than DAFEN, our method achieves comparable performance. Moreover, our DAFEN-S greatly outperforms the FeNet baseline and LGCNet, which have a similar number of parameters. DAFEN-S also performs similarly to the FeNet method, while its parameter count and Multi-Adds are nearly halved. The results on natural image test sets demonstrate that DAFEN has strong generalization capabilities, confirming its exceptional effectiveness. To assess perceptual quality, we present three reconstruction results of some models on the BSD100 and Urban100 test sets in Figure 7. It is clear that DAFEN achieves the best visual experience in terms of overall image patch clarity and detail line textures. Specifically, in 'BSD100: 182053', only DAFEN successfully recovers the accurate edges of the bridge arch. Notably, both our DAFEN and DAFEN-S achieve correct results in 'Urban100: img073'. In contrast, the reconstruction results of methods such as SRCNN exhibit distorted lines, while those of methods like FeNet exhibit upward-curving lines.