esa-sceva/satcom-chunk-collection huggingface dataset:
# Sigma Routing Metric for RPL Protocol
## 6 Results
### Packet Delivery Ratio  
The packet delivery average is calculated with the equation PDR = (Total Packets Received/Total Packets Sent) \(\times\) 100. In Figure 13, the packet delivery ratio of SIGMA-ETX is slightly better than PH-ETX, and much greater than OF0 and MRHOF, demonstrating the validity of SIGMA-ETX. Putting 15 th hop results in context, the difference against SIGMA-ETX regarding packet delivery is 47.44% with OF0, 57.69% with MRHOF, and 87.18%. with PH-ETX.  
Figure 14 shows that with 200 packets sent, ETX, MRHOF, PH-ETX and SIGMA-ETX have similar packet loss in both the 20 nodes and 40 nodes scenarios. However, for 50 and 100 nodes, we can see that PH-ETX has a lower packet loss, delivering 80% of the sent packets in the entire network.  
Figure 12: Latency with different nodes.  
Figure 13: Packet delivery ratio.  
As shown in Figure 14, while the tendency of all metrics is to increase the packet loss in denser scenarios, SIGMA has a 10% increase in packet delivery ratio with respect to PH-ETX, and a much higher margin compared to the other metrics.
###### Abstract  
Current orbit uncertainty propagation (OUP) and orbit determination (OD) methods suffer from drawbacks related to high computational burden, limiting their applications in deep space missions. To this end, this paper proposes a multivariate attention-based method for efficient OUP and OD of Earth-Jupiter transfer. First, a neural network-based OD framework is utilized, in which the orbit propagation process in a traditional unscented transform (UT) and unscented Kalman filter (UKF) is replaced by the neural network. Then, the sample structure of training the neural network for the Earth-Jupiter transfer is discussed and designed. In addition, a method for efficiently generating a large number of samples for the Earth-Jupiter transfer is presented. Next, a multivariate attention-based neural network (MANN) is designed for orbit propagation, which shows better capacity in terms of accuracy and generalization than the deep neural network. Finally, the proposed method is successfully applied to solve the OD problem in an Earth-Jupiter transfer. Simulations show that the proposed method can obtain a similar estimation to the UKF while saving more than 90% of the computational cost.  
orbit uncertainty propagation; orbit determination; deep space; multivariate attention; Earth-Jupiter transfer; UKF +
Footnote †: journal: Applied Sciences  
**Citation:** [PERSON]; [PERSON]; [PERSON] Multivariate Attention-Based Orbit Uncertainty Propagation and Orbit Determination Method for Earth-Jupiter Transfer. _Appl. Sci._**2024**, 14, 4263. [[https://doi.org/10.3390/appl410263](https://doi.org/10.3390/appl410263)]([https://doi.org/10.3390/appl410263](https://doi.org/10.3390/appl410263))
## 2 Analysis of the FPLG System Working Process
### Analysis of Linear Motor Output Characteristics  
Based on the modeling and analysis of the linear motor, a mathematical model of the permanent magnet synchronous linear motor was developed in Matlab/Simulink (2022b). This model uses the position and velocity of the motor mover as inputs. Combining data on the mover's velocity, position, and the peak value of flux linkage, it calculates the three-phase induced electromotive force in the motor. After passing through the motor's coils, this induced electromotive force is output as corresponding phase voltages and currents.  
The system's input signals are shown in Figure 5. The mover has a stroke of 25 mm and operates at a frequency of 50 Hz. Its velocity and displacement changes over a single cycle are depicted in Figure 6. The three-phase voltage and current curves output by the linear motor under these conditions are shown in Figure 7. While the voltage shows periodic changes, it differs from the traditional sine curve. When the motor reaches the top and bottom dead centers and changes direction, the phase sequence of the generated voltage alters, and all three phases converge at zero. This is because at the phase--changing points, the mover doesn't cut the magnetic flux, resulting in zero induced electromotive force in the circuit. When the motor isn't at the dead centers and the mover moves in one direction, its motion resembles that of a rotary motor, producing a waveform similar to a sine curve. The electrical angles of each phase differ by 120\({}^{\circ}\), except when the linear motor's velocity is zero. At any moment, the phase voltages are symmetrical. The electromagnetic thrust of the motor under these conditions is shown in Figure 8, with a peak value of 2000 N-m.  
Figure 5: Input signal of linear motor.  
Figure 6: Single-cycle linear motor input signal.
## 3 Numerical Modeling
### Support Conditions
The anchor structure were simulated by the cable elements; the action of the anchor structure with the surrounding rock is defined by geometric parameters, material parameters, and cement slurry characteristics, which are mainly taken from the Shuikou-Chongzuo-Aidian Highway (Chongzuo to Aidian Crossing Section) Two-Stage Construction Drawing Design. The anchor rod calculation parameters are based on a 25 mm-diameter and 5.5 mm-thickness hollow grouting; the length of the anchor rod for the arch and side wall are 4 and 6 m, respectively. The anchor rod elastic modulus is 200 GPa, density is 7850 Kg/m\({}^{3}\), and tensile strength 0.2 MN; the installation hole diameter of the anchor rod \(D_{c}\) is 42 mm and thickness of slurry ring \(t\) = 8.5 mm, so the cross-sectional area of the anchor rod is 490.9 mm\({}^{2}\), and perimeter of the cement slurry outer ring is 0.139 m. M20 cement mortar is selected; its shear modulus is 9.5 GPa, friction angle is 25\({}^{\circ}\), shear strength peak \(\tau_{\text{pank}}\) = 0.86 MPa, and the unit length mortar stiffness and Mortar bonding force were calculated by Equations (8) and (9) [28]:  
\[k_{g}=\frac{2\pi G}{[10\ln(1+\frac{2t}{D_{c}})]} \tag{8}\]  
\[c_{g}=\pi(D_{c}+2t)\tau_{peak} \tag{9}\]  
where \(D_{c}\), \(t\), and \(G\) are the anchor rod installation aperture, the slurry ring thickness, and the shear strength of the grouting body, respectively. The parameters of the bolt are listed in Table 3.
## 5 Temperature Online Estimation
### Online Temperature Estimation Based on Kalman Filter Algorithm
The covariance matrix of the process noise can be expressed as follows:  
\[Q=\left[ \begin{array}{ccc}\text{Cov}(W_{S},W_{S})&\text{Cov}(W_{S},W_{R})& \text{Cov}(W_{S},W_{E})\\ \text{Cov}(W_{R},W_{S})&\text{Cov}(W_{R},W_{R})&\text{Cov}(W_{R},W_{E})\\ \text{Cov}(W_{E},W_{S})&\text{Cov}(W_{E},W_{R})&\text{Cov}(W_{E},W_{E})\\ \end{array} \right], \tag{28}\]  
where, \(W\)\(=[W_{S},W_{R},W_{E}]^{T}\) is the three-dimensional random variable matric in accordance with the Gaussian distribution, which represents the process noise of the stator, rotor, and end cap, respectively. Since these three random variables, \(W_{S},W_{R},W_{E}\), are independent of each other, the covariance of each other is equal to 0, so the covariance matrix of the process noise can be simplified as follows:  
\[Q=\left[ \begin{array}{ccc}\text{Cov}(W_{S},W_{S})&0&0\\ 0&\text{Cov}(W_{R},W_{R})&0\\ 0&0&\text{Cov}(W_{E},W_{E})\\ \end{array} \right]. \tag{29}\]  
The covariance of the process noise can be indicated as follows:  
\[ \begin{split}\text{Cov}(W_{S},W_{S})&=E[\left|W_{S}-E(W_{S}) \right|^{2})=\frac{\Sigma_{k=1}^{n}\left(w_{S}(k)-\overline{W}_{S}\right)^{2} }{n-1}\\ \text{Cov}(W_{R},W_{R})&=E[\left|W_{R}-E(W_{R})\right|^{2}]=\frac{ \Sigma_{k=1}^{n}\left(w_{R}(k)-\overline{W}_{E}\right)^{2}}{n-1},\\ \text{Cov}(W_{E},W_{E})&=E[\left|W_{E}-E(W_{E})\right|^{2}]=\frac{ \Sigma_{k=1}^{n}\left(w_{E}(k)-\overline{W}_{E}\right)^{2}}{n-1}\\ \end{split}  \tag{30}\]  
where \(w_{S}(k),w_{R}(k),w_{E}(k)\) represent the error between the measured temperature and the estimated temperature from the state equation of the stator, rotor, and end cap at time k, which can be obtained according to the previous experimental data in Section 5.1. Besides, an estimation of the value, \(Q\), is made according to the analog to digital converter(ADC) sampling error. In each cycle, according to the state equation and measurement equation of the system, the temperature of each part of the motor can be estimated online by applying the Kalman filter algorithm.
# Fingerprint Classification Based on Multilayer Extreme Learning Machines
## 5 Results and Discussion  
This section addresses three key aspects: hyperparameter optimization, evaluation and comparison with the state of the art, and complexity analysis. In hyperparameter optimization, hidden neurons and additional layers in the ELM models were adjusted to improve generalization and robustness. In the comparison with the state of the art, the performance of the ELM-M3 model was evaluated against the benchmarking models in fingerprint classification tasks. Finally, the complexity analysis shows that, while ELM-M3 is computationally more expensive than its original version, it is more efficient than CNN-based approaches, achieving a balance between simplicity and performance.
## 5 Materials and Methods
### Generating the Problem Instance  
To evaluate the performance of the genetic algorithm (GA) in optimizing real-world sales routes, we generated a TSP-based problem instance using actual locations from the NYC taxi dataset. The process involved two main steps:  
1. Generating Locations with Coordinates: We created a dataset containing key locations by extracting the LocationID, borough, zone, and their corresponding latitude and longitude coordinates from the NYC taxi dataset. This new dataset, Taxi Locations, serves as the reference for all problem instances.
2. Generating a Problem Instance: A subset of \(N\) random locations was selected from the Taxi Locations dataset to define a specific routing problem. To enhance computational efficiency, we precomputed the Distance Matrix (in kilometers) and the Travel Time Matrix (in minutes) for every pair of locations.  
To analyze the scalability and robustness of the proposed algorithm, we defined two problem instances using different values of \(N\): 15 and 25. The 15-location scenario simulates a small-scale delivery context, representative of local distribution routes in urban environments. In contrast, the 25-location problem reflects a more complex, medium-scale routing challenge, commonly encountered in regional logistics or metropolitan coverage. This distinction allows for a comparative evaluation of how each algorithm variant adapts to increased problem complexity, both in terms of solution quality and computational performance.  
Precomputing Distances and Travel Times  
Since the GA evaluates thousands of potential solutions in each generation, it is crucial to avoid the unnecessary recomputation of distances and travel times. If these values wererecalculated for every solution, it would introduce a significant computational overhead, drastically slowing down the optimization process.  
To mitigate this, we adopted the following strategy:  
* Precomputed Distance Matrix: The Haversine formula was used to calculate the great-circle distance between every pair of locations in the problem instance. These values were stored in Problem instance dataset for fast lookup.
* Precomputed Travel Time Matrix: The LSTM-based travel time prediction model was used to estimate the travel duration between each location pair, considering real-world conditions such as traffic and time of day. These values were stored in the Problem instance dataset for fast lookup.
* Optimization Benefit: By precomputing these values, the GA can retrieve distances and travel times directly from the Problem instance dataset instead of recalculating them in every evaluation. This drastically reduces computation time, allowing the algorithm to focus on optimizing the route.
## Nomenclature  
### Abbreviations:  
ADC Analog to digital converter  
CMA Civil, mechanical, and aerospace  
CAGR Compound annual growth rate  
DC Direct current  
EM Electromagnetic  
FBG Fiber Bragg grating  
FO Fiber optical  
LCR Inductance, capacitance, and resistance  
LED Light-emitting diode  
LHM Left hand metamaterials  
LSP Localized surface plasmon  
MFC Macro fiber composite
# Secure ECDSA SRAM-PUF Based on Universal Single/Double Scalar Multiplication Architecture
### Motivation  
RM codes are considered highly secure in the existing literature. The security of RM codes for SRAM-PUF is contingent upon the absolute security of SRAM and the parameter ROM [20; 21]. In practical scenarios, additional protective measures can be implemented to prevent attackers from reading the power-on values of SRAMs, and secure ROM can be employed to prevent attackers from reading and modifying the parameter ROM. However, these approaches entail extra hardware resources and specific electronic components. In most system-on-chip (SoC) designs, as they are modules that are directly connected to the central processing unit (CPU), conventional SRAM and ROM can be easily read and modified by attackers through software. Then, the attacker merely needs to modify \(P_{error}\) in ROM based on the power-on values of SRAMs to \"clone\" the results of SRAM-PUF. Therefore, current research in building secure SRAM-PUFs based on conventional SRAM and ROM is still lacking, which motivates the research presented in this paper.
# Model for Quantifying the Quality of Secure Service
## Notes  
* Analysis of the dynamic aspects of mobile networks or tunable security mechanisms is left for a subsequent paper, as are changing environmental conditions and temporal adversarial intrusions.
* Multi-hop architectures are outside the scope of this paper but are a straightforward extension of the model that will be addressed in a subsequent paper.
* Wired and wireless networks have different characteristic values based on the specific technologies and protocols used.
* This value reflects the quality of the encryption used, be it no encryption, a simple ROT13 algorithm, or a sophisticated encryption algorithm.
* This value highlights the differences in binary strings, and if the adversary has the ability to recognize those differences. For example, an adversary with a.mp3 file who mistakenly believes it is a.txt file, will not be able to derive useful information from that particular file.
* To clarify, \(P(s)\) is the active jamming by an adversary as quantified at the receiver, whereas availability is quantified by the transmitter's capabilities.
* As the adversarial intent of suppression is counter to that of injection, it is unlikely, although not impossible, to have high \(P(s)\) and high \(P(inj)\). This would be akin to an adversary steering a receiving channel to a compromised channel by jamming the intended channel. Neither of these speaks directly to the intent of an adversary but rather to the requirements and built-in capabilities of the transmitter and receiver.
* Signaling System Number 7 (SS7) was developed in 1975 as a set of protocols used to set up and tear down public switched telephone network (PSTN) communication connections.
* The user's identity is verified by using a combination of two or more factors: something they know, something they have, or something they are.
* In the general case, \(A_{L}\) or \(A_{D}\) may be subsets of or intersect with each other; i.e., \(A_{L}\subseteq A_{D}\), or \(A_{D}\subseteq A_{L}\) or \(A_{L}\cap A_{D}\).
* One potential implication is that each channel may carry both a portion of the data and be used as a method to check for errors on the others channels
* In a real communication architecture, all three channels would likely have more similar characteristics.
* As in the Three-Channel example, a real communication system would likely have channels with similar characteristics.
* Note the number of channels with respect to Equation (8) for this multi-channel space. For the eight-channel network, \(n=8\) even though seven channels would be sufficient based on \(\sigma=3\) listeners, \(\rho=3\) disruptors, and Equation (8).
# A New Integrated Multi-Criteria Decision-Making Model for Resilient Supplier Selection
## 2 Literature
The degrees of importance of the criteria were determined using the Grey DEMATEL technique for the two dimensions, while the scores of suppliers were determined using the Grey Simple Additive Weighting (GSAW) technique. The findings showed that customization was the most important enhancer criterion whereas the most significant reducer was the capacity limit of suppliers. Finally, the prioritized resilient suppliers were identified. [PERSON] et al. [15] presented a new integrated efficiency measurement model for resilient supplier analysis using the Principal Components Analysis (PCA) to reduce the criteria dimensions. Furthermore, DEA was employed to determine the criteria weights and suppliers' ranks. The weights of criteria were simultaneously determined by using the DEA method, entropy index, and judgments of decision-makers. A resilient-sustainable framework based on the supplier selection indicators was proposed by [PERSON] [16]. A fuzzy set theory has been applied to deal with the uncertainties in supplier selection. Furthermore, a modular FIS was designed to determine the suppliers' affinity indices to the resiliency and sustainability issues. It provided a comprehensive supplier selection model with any number of indicators and suppliers. The outcomes were analyzed using the Assurance Region DEA method (AR-DEA) in determining the indicators' weights and suppliers' ranks. Some other studies that have applied MCDM methods in resilient supplier selection are summarized in Table 1.  
As presented in Table 1, the fuzzy theory was commonly applied rather than the grey theory to deal with uncertainties in supplier selection. It is evident that AHP and TOPSIS were the most common MCDM methods in resilient supplier selection. Among the industries covered by the previous studies above, the food manufacturing industry has not been considered.
## 3 Results
### Exhaustive Descriptions of Included Studies
#### 3.2.1 Group 1: Neural Network (NN)
Additionally, the results confirmed that the multivariate approach had better performances compared to the univariate approach. _Air Quality Forecasting using Neural Networks_[35]: [PERSON] et al. suggested to apply extreme learning machine-based approach to forecast air quality. The case study was Helsinki, and the data included hourly air quality data (nitric oxide (NO), O\({}_{3}\), PM\({}_{10}\), PM\({}_{2.5}\)) and meteorological data (relative humidity, pressure, temperature, and wind). Taking into account the challenges related to big data analysis, the authors applied forward selection in order to select most correlated variables, later by applying Principal Component Analysis (PCA) they reduced the dimensionality. In general, the proposed method provided good results; however, for the future work, the authors suggested an ensemble extreme learning machine to enhance the accuracy of the prediction. _Predicting minority class for suspended particulate matters level by extreme learning machine_[36]: taking into consideration the problem related to the imbalance dataset which can affect on the prediction result, [PERSON] et al. applied ELM and SVM methods to predict PM\({}_{10}\) by handling the problem mentioned above. They also applied prior duplication strategy, which also aims to improve the output of the prediction. The data were provided by Macau government meteorological center (SMG) [37], including air quality (PM\({}_{10}\), NO\({}_{2}\), SO\({}_{2}\), O\({}_{3}\)) and meteorological data (atmospheric pressure, temperature, mean relative humidity, wind speed, rainfall, sunshine hour, wind direction) from 2003 to 2010. The results showed that ELM with or without prior duplication predicted minority classes better than SVM, also in terms of the training time and the memory ELM outperformed SVM model. _Three improved neural network models for air quality forecasting_[38]: taking into account the drawbacks of the neural network (computationally expensive training, local minima, overfitting, etc. ), [PERSON] et al. suggested to apply Adaptive Radial Basis Function (ARBF) network with and without PCA, and improved SVM to predict air quality. The dataset consisted of air quality (Respiable Suspend Particles (RSP), SO\({}_{2}\), NO\({}_{\lambda}\), NO\({}_{\lambda}\), NO\({}_{2}\), CO) and meteorological (wind speed, wind direction, outdoor and indoor temperature, solar radiation) data of the city of Hong Kong during 2000. MAE, RMSE and [PERSON]'s index of agreement (WIA) were used to evaluate the methods. The results confirmed the advantages of each proposed method (ARBM automatically defined the network architecture and had fast learning speed, ARBF/PCA was an improved version of ARBF by simplifying the latter method, and, finally, SVM had higher accuracy).
# Impact of Patient Size and Positioning on Radiation Dose in Multiphase Liver CT Examinations
###### Abstract  
Investigating the correlation between patient dimensions (Body Mass Index (BMI), weight, height) and radiation dose, the study also assesses the impact of the isocenter on dosage and X-ray tube. This retrospective study analyzed 258 consecutive three-phase liver CT exams (135 women, 123 men) performed on a Siemens SOMATOM Definition Edge scanner between January 3 and December 15, 2023. BMI, weight, height, maximum abdominal width (from topograms), and vertical isocenter status were extracted using the Dose&Care system and Horos software. BMI strongly correlated with total DLP in abdomen-plus-pelvis scans (r = 0.70) and moderately in abdomen-only scans (r = 0.54). Liver-phase DLP correlations were weaker (r = 0.44 and r = 0.28, respectively). Abdominal width showed similar associations with total DLP (r = 0.67 and r = 0.64) and liver-phase DLP (r = 0.41 and 0.37). Vertical mis-centering did not significantly affect total DLP (\(p\) = 0.174 and \(p\) = 0.705) or tube load. Patient size--not minor deviations from the isocenter--is the principal driver of radiation dose in multiphase liver CT. Automated, size-adapted protocols and restriction of scan range to clinically essential regions can preserve image quality while minimizing unnecessary radiation in routine practice.  
Keywords:ionizing radiation; medical imaging; dose optimization; isocenter; abdominal imaging +
Footnote †: journal: Applied sciences