esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Subspace Clustering with Block Diagonal Constraint
## 2 Deep Subspace Clustering Model with Block Diagonal Constraint
### Model Optimization
\begin{cases}\max\{0,S_{ij}\}&\text{if }\ S_{ ij}\leq C_{max};\\ C_{max}&\text{otherwise}.\end{cases} \]  
To obtain a stable result, it is necessary to add a small positive number \(\epsilon\) to the denominator term, i.e., \(\alpha_{2}(D_{ij}+D_{ji})+\epsilon\). The proof of updating rule is provided in Appendix A. **Update**\(W\): Fixing all the other parameters, \(W\) can be solved from the following problem. Fix all other variables \(Z\), \(\Theta\), \(D\) and \(W\) to update \(C\). The corresponding sub-problem for \(C\) is then given by:  
\[\min_{W} \langle L_{C},W\rangle\] (19) s. t. \[W\in\mathcal{W}.\]  
Following [PERSON] et al. [20], the optimal solution is defined as  
\[W=VV^{T}, \tag{20}\]  
where \(V=\{v_{1},v_{2},\ldots,v_{k}\}\) and \(v_{i}\) is an eigenvector associated with the \(i\)th smallest eigenvalue of \(L_{C}\). Based on the above analysis, we summarize the optimization procedure for our model in Algorithm 1. ```
0:\(X,\alpha_{1},\alpha_{2},\alpha_{3}\), \(C_{max}\), \(D_{min}\). 0:\(Z\), \(C\), \(D\). 1:\(D_{ij}\gets D_{min}\);
2:\(W\gets I\);
3:\(C\gets 0\);
4:while not converged do
5:\(iter\gets iter+1\);
6:\(Z_{old}\gets Z\);
7:\(D_{old}\gets D\);
8:\(C_{old}\gets C\);
9:\(W_{old}\gets W\);
10: Update \(\mathbf{Z}\) with (10);
11: Update \(\mathbf{D}\) with (14);
12: Update \(\mathbf{C}\) with (17);
13: Update \(\mathbf{W}\) with (20). 14:endwhile
```  
**Algorithm 1** Optimization algorithm for DSC-BDCAccording to the previous sub-problems, we give the complexity analysis as follows: For updating \(Z\), the computational complexity in network is \(O(n^{2})\) at a time. Obviously, for updating \(C\) and \(D\), one needs \(O(n^{2})\) operations. For updating \(W\), one needs \(O(n^{3})\) operations to perform the eigenvalue decomposition. Thus, it is easy to obtain the total complexity of the proposed algorithm at each iteration by summing all the steps. We conclude that the computational complexity of our proposed method is then \(\mathcal{O}(n^{3})\).
# Local Batch Normalization-Aided CNN Model for RSSI-Based Fingerprint Indoor Positioning
## 4 Numerical Results
### Experimental Results
The positioning accuracy of the proposed LBN was further assessed using the cumulative distribution function (CDF) of location error within a specified distance, as illustrated in Figure 9. When comparing the performance of the CNN baseline (represented by the blue line), CNN + BN (the yellow line), CNN + LN (the green line), and CNN + LBN (the red line), it is evident that all four methods achieve a probability exceeding 85% within a 2-m error range. Across the entire error range, CNN + LBN consistently outperforms the CNN baseline, demonstrating its superior localization accuracy. Furthermore, the results indicate that for a 1-m error threshold, CNN + LBN achieves a CDF value of 78%, whereas CNN baseline only reaches 71%. This significant difference highlights the enhanced positioning accuracy brought about by LBN. \begin{table}
\begin{tabular}{c c c c} \hline  & **Accuracy (\%)** & **Accuracy (\%)** & **Accuracy (\%)** \\  & **(0 RP Margin)** & **(1 RP Margin)** & **(2 RP Margin)** \\ \hline CNN + LBN & 39.46 & 78.11 & 92.97 \\ CNN + LN & 34.59 & 74.32 & 89.73 \\ CNN + BN & 34.86 & 77.84 & 91.89 \\ CNN & 33.51 & 71.08 & 87.84 \\ \hline \end{tabular}
\end{table} 
Table 6: Localization accuracy across margins. \begin{table}
\begin{tabular}{c c c c} \hline  & **Mean (m)** & **Variation** & **Standard Deviation** \\ \hline CNN + LBN & 1.27 & 3.97 & 1.99 \\ CNN + LN & 2.07 & 7.61 & 2.75 \\ CNN + BN & 1.48 & 4.3 & 2.07 \\ CNN & 2.21 & 12 & 3.46 \\ \hline \end{tabular}
\end{table} 
Table 7: Comparison of mean error, variance, and standard deviation across CNN, CNN + BN, CNN + LN, and CNN + LBN models for WiFi fingerprint-based positioning.
# An Energy Efficient Sink Location Service for Continuous Objects in Wireless Sensor Networks
## 4 Performance Evaluation
### Distribution of Energy Consumption  
The distribution of energy consumption indicates the operational efficiency and existence of hot spot problem. Therefore, we can tangibly estimate overall energy efficiency and network lifetime. We divide the 1000 m \(\times\) 1000 m network into 20 \(\times\) 20 cells and observe the average used energy of the nodes in each cell. Figure 17 shows what the color in each cell means. On all of the simulations, the node, which transmitted the largest number of messages, consumed the energy less than 10 W. Therefore, we postulate 10 W for a maximum value of the total energy consumption at each sensor node. According to Figure 17, white cell consumed 0 W and black cell consumed 10 W averagely.  
Figure 18 left shows the distribution of energy consumption in TTDD. In Figure 18 left, most cells are gray even the cells which have no relationship with tracking process, such as area A. It means that most nodes wasted energy regardless of tracking process and it is because of the individual grid construction of each source. Moreover, the area B is almost black which is located between the origin point of the continuous object and the mobile sink's moving path. It is because a lot of data reports are concentrated into the area B thereby shortening the network lifetime.  
Figure 16: Comparison of total energy consumption according to the diffusion speed of the continuous object (**left**) and the frequency of location updates (**right**).  
Figure 17: The meaning of the depth of color.  
Figure 18 center shows the distribution of energy consumption in LBDD in which the most cells are white because there is no operations being performed in the area. However, the unnecessary energy consumption is occurred at area A because the inline-nodes share location update messages of the sink. In addition, the area B is deep gray because the inline nodes within same group share data from sources. In other words, the closest group to the sink is share a lot of data from the sources. In addition, the inline node, which receives a location update message from the mobile sink, reports data to the sink directly. Namely, the responsibilities for sharing and reporting data are concentrated at area B.  
Figure 18 right shows the distribution of energy consumption in OCOT in which the most cells are closely white. The cells that are not related sensing the continuous object and data report consumes energy only for grid construction and clustering in contrast with TTDD and LBDD. In addition, even the area A that is between the continuous object and the moving path of the sink is light gray. The integrated location update and direct data report of OCOT disperse messages to other area which result in prolonging network lifetime.
## 4 Algorithm
### General Description  
The proposed memetic algorithm for the JSSP-VME-MT problem is a hybrid, population-based optimization approach that effectively combines the exploration abilities of evolutionary algorithms with the intensification capabilities of local search methods. The steps of this algorithm are detailed in Algorithm 1. It starts by generating an initial  
Figure 1: Example of machine’s decaying efficiency.  
population of candidate solutions, each defined by a job sequence and a corresponding maintenance array.  
Then, in each algorithm iteration, a rank-based selection process chooses a set of parent solutions based on their fitness value. These parents then generate new offspring solutions through a crossover operator.  
Subsequently, a variable neighborhood descent (VND) is applied to each offspring as a local search method, exploring different neighborhoods to improve the quality of the solutions. This combination of evolutionary search and local optimization allows the algorithm to balance exploration and exploitation throughout the search process.  
Finally, a survivor selection procedure (combined with an elitist-based system) determines which solutions from the combined set of parents and offspring will survive into the next generation. This cycle repeats for a predetermined number of iterations. Next, we present a pseudocode of the algorithm.  
```
1. Initialize the parameters of the algorithm
2. Create the initial population
3. population\(\leftarrow\)initial population
4. For\(i=1\) to max. iterations
5. Extract the elitist population (best individuals from the current population)
6. Select parents from the population
7. Create offspring (using PMX crossover operator)
8. Execute local search (VND) on the offspring
9. Combine current population and offspring in a global population
10. Sort global population according to the objective function
11. Update the best overall solution
12. population\(\leftarrow\)elitist population + best individuals from global population
13. End For
14. Return best solution
```  
**Algorithm 1**Memetic algorithm for the JSSP-VME-MT.
## 3 Results and Discussion
### Assessment of Free Amino Acid Content  
Table 2 illustrates the free amino acid (FAA) composition of bicultured Junzao jujube pures. In comparison to the control (184.03 \(\pm\) 1.16 mg/100 g), the total free amino acids exhibited variability, with the highest content observed in JLhAp (157.17 \(\pm\) 1.12 mg/100 g), intermediate levels in JLpAp (153.11 \(\pm\) 1.80 mg/100 g), and the lowest in JLcAp (139.62 \(\pm\) 1.15 mg/100 g). Amino acids play a pivotal role in protein synthesis and contribute to the flavor profile of food [33]. While previous studies [8; 23] reported high protein amounts in jujube fruits, the lower quantities in the bicultured Junzao purees may be attributed to molecular changes--such as protein cross-linking [34; 35] facilitated by various enzymes, like tyrosinases, during drying [36]--and partially utilized by the LABs and AAB during biotransformation owing to their fastidious nature. Among the detected FAAs, L-aspartic acid and proline emerged as the most abundant, averaging 16.35% and 56.28%, respectively, of the total free amino acids in the bicultured and control pures. The initially high content of L-proline in the unfermented samples (111.70 \(\pm\) 0.10 mg/100 g), constituting around 60.7% of the total FAAs in the control, diminished after fermentation. This reduction may be attributed to significant molecular modifications initiated by LABs and AAB through their hydrolytic enzymes, including (poly)-phenol oxidases [17], esterases [13], and \(\beta\)-glucosidaseases [37]. These enzymes depolymerized macromolecular phytochemicals, such as isoflavone \(\beta\)-glycosides, into simpler forms (aglycones), leading to structural degradation in L-proline. Distinctively, JLpAp demonstrated the synthesis of  
L-methionineine (0.85 \(\pm\) 0.09 mg/100 g), a free amino acid not found in the control (JCON), JLcAp, or JLhAp. This suggests that _Lactiplantiobacillus plantarum_ Lp 28 possesses a methionine synthetase system, endowing JLpAp with eight essential free amino acids (histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, and valine), while the control, JLcAp, and JLhAp remained methionine limiting. Furthermore, glutamic acid was found at markedly high concentrations in the bicultured pures (\(\sim\)5.49%) compared to the reference sample (3.77%), possibly contributing significantly to volatile hydroxy acids, including acetic acid, hexanoic acid, and octanoic acid (Tables 4 and S2), largely under the influence of aminotransferase [9]. In conclusion, the free amino acids in bicultured Junzao jujuube purees were significantly impacted by the intricate reciprocal action of lactic and acetic bacteria in one excellent food matrix--jujuube puree.
# Recent Developments in Modulation Spectroscopy for Methane Detection Based on Tunable Diode Laser
## 2 Methane Absorption Lines  
The CH\({}_{4}\) molecule has a spherical top, and belongs to the tetrahedral point family. It exhibits four fundamental vibration modes: \(v_{1}\) = 2913 cm\({}^{-1}\), \(v_{2}\) = 1533.3 cm\({}^{-1}\), \(v_{3}\) = 3018.9 cm\({}^{-1}\) and\(\upsilon_{4}\) = 1305.9 cm\({}^{-1}\)[27]. Of these, the two bending vibrations are \(\upsilon_{2}\) (asymmetric) and \(\upsilon_{4}\) (symmetric), while \(\upsilon_{1}\) (symmetric) and \(\upsilon_{3}\) (asymmetric) are the two stretching vibrations [28]. The successive resonance spacing is about 1500 cm\({}^{-1}\)[29]. In the near-infrared region (1100-1800 nm), the \(2\upsilon_{3}\) band near 1670 nm and the \(\upsilon_{2}+2\upsilon_{3}\) band near 1300 nm are primary overtone rotational-vibrational combination bands.  
Based on the Beer-Lambert law (Equation (1)) [10; 11], many laser absorption spectroscopy techniques are applied to measure gas concentration, including WMS, FMS, and TTFMS.  
\[\tau(v)=\frac{I_{t}(v)}{I_{o}(v)}=e^{-\sigma(v)xL}=e^{-\alpha(v)} \tag{1}\]  
where, \(\tau(v)\) represents the transmittance of light, \(I_{o}(v)\) and \(I_{t}(v)\) represent the incident and transmitted intensities of a certain wavelength, respectively, \(\sigma(v)\) represents the cross-section of gas absorption at a certain wavelength, \(x\) represents the gas concentration, and \(L\) represents the length of the path that the light travels through absorbing media, \(\alpha(v)\) represents the transmission coefficient. In absorption spectroscopy, the detection sensitivity is related to the length of the absorption path and the absorption line intensity of molecule, respectively [2].  
According to differences in measurement area and monitoring technique, different frequency bands involving methane transitions from near infrared to mid infrared have been applied [30]. With the development of near-infrared light sources and fiber technology, the corresponding test system in the near infrared region is more mature [31; 32; 33]. [PERSON] et al. [34] investigated that the absorption strengths of the \(2\upsilon_{3}\) band were suitable for providing high sensitivity for ground-based high-resolution spectrometry in the near-infrared spectrum. The absorption intensity of CH\({}_{4}\) in the \(2\upsilon_{3}\) band is more than four orders of magnitude stronger than that of H\({}_{2}\)O and CO\({}_{2}\), which can be safely neglected [35]. The related spectroscopic parameters of \(2\upsilon_{3}\) band, including line positions, line intensities, line widths, line shifts, and line couplings are certain, as described in reference [36; 37; 38; 39; 40; 41].
# Aerodynamic Sensitivity Analysis for a Wind Turbine Airfoil in an Air-Particle Two-Phase Flow
## 3 Validation of Numerical Method
### Gas-Solid Two-Phase Flow Simulation for a Circular Cylinder  
The present numerical method is further applied to simulate the unsteady gas-solid two-phase flow over a circular cylinder. The Stokes (\(St\)) number is a critical parameter for quantifying the resistance of the particles to changes in their translational and rotational velocities due to the surrounding fluid flow, which is defined as the ratio of the particle relaxation time \(\tau_{P}\) to the fluid relaxation time \(\tau\)  
\[St=\frac{\tau_{P}}{\tau},\quad\tau_{P}=\frac{\rho_{P}D_{P}^{2}}{18\mu},\quad \tau=\frac{D}{V_{\infty}} \tag{7}\]  
where \(D\) denotes the diameter of the circular cylinder.  
In the experiment [24], \(D=10\) mm, the \(Re\) number based on \(D\) and \(V_{\infty}\) equals 2125, \(\rho_{P}=2500\) kg/m\({}^{3}\), and three \(St\) numbers of 0.98, 6.5 and 19.7 are investigated by using different particle diameters at a fixed particle concentration \(m=10\%\), where \(m\) is defined as the mass flux percentage of particle phase to air phase. The same parameters are considered in the numerical simulations. Figure 5 presents the particle dispersion pictures at three \(St\) numbers. As can be observed, the computed flow patterns are similar to the experimental results; with increasing \(St\), the interaction between the particles and the vortices decreases.
###### Abstract  
This work proposes a methodology to estimate the statistical distribution of the probability that a 6T bit-cell starts up to a given logic value in SRAM memories for PUF applications. First, the distribution is obtained experimentally in a 65-nm CMOS device. As this distribution cannot be reproduced by electrical simulation, we explore the use of an alternative parameter defined as the distance between the origin and the separatrix in the bit-cell state space to quantify the mismatch of the cell. The resulting distribution of this parameter obtained from Monte Carlo simulations is then related to the start-up probability distribution using a two-component logistic function. The reported results show that the proposed imbalance factor is a good predictor for PUF-related reliability estimation with the advantage that can be applied at the early design stages.  
SRAM; memory; PUF; cell mismatch; SRAM-PUF Article
## 2 Safe and Efficient UAV Route Construction Method over Wireless Multi-Hop Network
### Method for Constructing Optimal Routes in Terms of Distance and Safety Based on OLSR
#### 2.2.2 Dijkstra Method  
The Dijkstra method is a popular shortest route algorithm [40]. Each node can determine the minimum cost route between any nodes by inputting the cost of all links into the Dijkstra method. Because OLSR evaluates the routes by the number of hops, it cannot see the actual distance which is important for UAV delivery. The optimal route construction method for UAV delivery uses the Dijkstra method to construct a route that considers the UAV's travel distance. In addition, by weighting the danger level with the distance of each link, this method can construct the safe and short-distance routes.
## 6 Conclusions  
For the security problems, such as single point of failure and lack of reliable authentication mechanism faced by UAV swarm, a blockchain-based UAV identity management model is proposed, and a distributed identity authentication scheme based on DID information is established under this model. Moreover, to ensure the safe transmission of UAV communication data, a secure communication architecture based on blockchain and a set of secure transmission protocols are designed combined with cryptography. Compared with the traditional UAV management scheme, the proposed scheme has obvious advantages in UAV identity management, UAV identity authentication, scalability, and secure transmission of communication data. When the number of drones requesting the public key at the same time is 50, the processing time of identity authentication is about 1.5 s, which also shows that when the number of drones is within a certain range, the time required to process identity authentication is acceptable. Moreover, through various security analyses, it is proved that the proposed scheme is secure and reliable. Therefore, the proposed scheme is feasible in practical applications and has broad application prospects. However, as the number of consensus nodes increases, the PBFT algorithm adopted by the blockchain will cause communication bottlenecks, so in future research, the lightweight consensus algorithm will be the focus of the research.  
Conceptualization, P.H. and A.S.; methodology, P.H. and J.W.; investigation, P.H.; writing--original draft preparation, P.H.; writing--review and editing, A.S. and J.W. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Traffic Analysis Methods  
Protocol Classification is a term typically used to describe the process by which network traffic is classified into different categories or classes based on the characteristics of the communication. Classification can be made based on factors such as ports, addresses, packet headers, or traffic patterns. Classification aims to understand network traffic better and allow different levels of network management policy to manage this traffic as needed.  
Protocol Recognition is a term usually used to describe the process by which the characteristics of network communications are analyzed to identify the protocols in use. This process can be automated using a variety of techniques, including in-depth examination of network traffic and pattern matching against a database of known protocols. The goal is to identify what protocols are used within a given communication.  
Protocol Identification is a term often used as a synonym for protocol recognition, but it can also be used in a more specific sense when referring to the process of determining specific attributes or properties of a protocol that are observed in a given network traffic. Protocol identification can be important for a number of purposes, including security analysis, network optimization, and performance tuning.
# Improved Control of Forest Microgrids with Hybrid Complementary Energy Storage
## 2 Operation Mode and Coordination Control of Microgrid in Forest Area
### Operation Mode of the Forest Microgrid
#### 2.2.3 Power Limiting Mode  
When the wind power is greater than the aggregate demand of the load and the energy storage systems, maintaining maximum power point tracking of wind power unit will lead to a sharp rise in DC bus voltage to above 1.05 pu. Therefore, inertia control of the wind turbine will be conducted, which temporarily stores the excess power in the form of kinetic energy of the wind turbine. If the working condition continues, power-limiting control of the wind power unit should be carried out to maintain stability of the system.
## 9 Performance Evaluation
### Delay  
In SGAT, sensors transmit/forward the data at the end of the interval, which would cause a huge delay considering the volume of messages that each sensor needs to transmit during the network real-time operation. In addition, the messages traverse through multiple hops until it gets to the BS, which makes the accumulated delays significant. The other alternative scheme is having the sensors select one of the following subintervals (\(\omega\)) randomly to forward the message. This also will cause some unnecessary delays although it could help in hiding the temporal behavior of the sensors. ECAT scheme divides (\(\overline{\omega}\)) into subintervals (\(\omega\)), so the transmission will happen at the first available subinterval when the message is ready. We have simulated a smaller network to the one described in Section 8.3 for the transmission delays. It includes 48 SNs only with \(\omega\) distribution as presented in Figure 15. We have three simulations using SGAT, ECAT, and random delays. Figure 21 shows that delay per one-transmission increases throughout the network as the number of transmitted messages increases which could cause unjustifiable delays especially in the real time applications. Figure 22 also shows the average delays for the three schemes. It shows that using ECAT has improved delays by 64% compared to SGAT. The total delay for one message from a source to a destination (BS) is calculated according to expression Equation (37). It is a function of the distance from the BS (hc) which we technically have no control over after sensors deployment. In addition, it is a function of the chosen (\(\overline{\omega}\)) and (\(\omega\)) values for the system. The larger the (\(\omega\)), the more delays accumulated. We have simulated the same network using ECAT for the total delay as exhibited in Figure 23. It shows that the delay rises as the _hc_ increases and as the size of the intervals widens. We conclude of these simulations that the performance of ECAT is better than SGAT while it continues to provide a good temporal privacy. Using a fixed delay will reduces the delays slightly but it provides a very week temporal privacy.  
Figure 21: Total accumulated delay per one node increases as number of messages increases in the WSN. ECAT sends the message only one subinterval after the message arrival. SGAT sends the message at the end of the big interval \(\overline{\omega}\). In between, the approach of selecting one of the following subinterval randomly to send the message. Clearer image?