esa-sceva/satcom-chunk-collection huggingface dataset:
# Nanosatellite Autonomous Navigation via Extreme Learning Machine Using Magnetometer Measurements
## 3 Parameter Optimization
### Neural Network Configuration  
This work used an SLFN trained via the ELM to improve the accuracy of the EKF state estimates. The SLFN had a width of \(L=11\) hidden neurons and the activation function was a Fourier series expansion up to the \((L-1)/2=5\) order of \(\mathbf{w}_{l}\cdot\mathbf{t}_{m}+b_{l}\) with \(1\leq l\leq L\) and \(1\leq m\leq M\).  
The input weights and biases of the SLFN were sampled from a uniform distribution between \(-0.0001\) and \(0.0001\). The number of hidden neurons and the amplitude of the distribution were determined empirically.  
Neural Network Training Process  
The EKF state estimate was given by the six components of the vector \(\boldsymbol{x}_{EKF}\). The six components of the EKF error vector \(\Delta\boldsymbol{x}_{EKF}\) were evaluated with respect to the state estimate given by the GPS \(\boldsymbol{x}_{GPS}\):  
\[\Delta\boldsymbol{x}_{EKF}=\boldsymbol{x}_{GPS}-\boldsymbol{x}_{EKF}, \tag{21}\]  
The EKF state vector \(\boldsymbol{x}_{EKF}\) and the magnetometer measurements \(\boldsymbol{\sigma}_{k}^{B}\) were used as the input of the ELM-trained SLFN, while \(\Delta\boldsymbol{x}_{EKF}\) was the target value. The output of the SLFN was the quantity \(\Delta\boldsymbol{x}_{ELM}\).  
The improved state estimate \(\boldsymbol{x}_{ELM}\) was  
\[\boldsymbol{x}_{ELM}=\Delta\boldsymbol{x}_{ELM}+\boldsymbol{x}_{EKF}, \tag{22}\]  
with an error given by \(\boldsymbol{x}_{GPS}-\boldsymbol{x}_{ELM}\). The training process scheme is shown in Figure 3.  
Tests showed that the addition of the measurement vector \(\boldsymbol{\sigma}^{B}\) to the inputs of the SLFN improved the stability of the prediction when the network was trained on a small portion of data. However, the SLFN exploiting only the EKF state vector as an input slightly improved the accuracy on the training set when trained on the same data.
## 3 Finite Element (FE) Investigation
### Identification of the True Stress-Strain Curves  
A successful fracture simulation requires reliable input data on true stress-strain curves up to the plastic deformation limit (the point just before fracture). Prior to necking, the true stress-strain curve of the specimen can be easily calculated by:  
\[\sigma_{t}=\sigma_{e}(1+\varepsilon_{e}) \tag{1}\]  
\[\varepsilon_{t}=\ln(1+\varepsilon_{e}) \tag{2}\]  
where \(\sigma_{t}\) is the true stress, \(\varepsilon_{t}\) is the true strain, \(\sigma_{e}\) is the engineering stress, and \(\varepsilon_{e}\) is the engineering strain.  
Equations (1) and (2) are based on the assumption that the specimen volume remains constant in a uniaxial stress state until the onset of necking, where the peak load is applied. However, the calculation results of such equations are no longer valid after the occurrence of necking as the steel material experiences the plastic instability. The post-necking material behavior could not be captured through extensometer or 2D-DIC measurements due to the variation in the cross-sectional area [19; 20]. Alternatively, empirical equations have been used in the literature. [PERSON] (1952) [21] proposed an empirical method to find the stress and strain values in the neck of a round specimen, but not applicable to sheet type specimen due to the complicated variation of the neck. Instead, the weighted-average method proposed by [PERSON] (1996) [22] has been used for the sheet-type specimen. The method assumes the empirical low and upper bounds for the post-necking material behavior.  
Figure 4: FE models: (**a**) Uniaxial tension specimen; (**b**) In-plane shear specimen; (**c**) Tension and shear specimen.  
However, the lower bound was found to overestimate the post-necking true stress for some steel materials. For this reason, the present study, therefore, used the modified weighted-average method by [PERSON] (2020) [23], which shows good accuracy in estimating the full true stress-strain curves of stainless-steel bolts.  
The post-necking material behavior can be expressed as the weighted-average of upper and lower bounds as follows:  
\[\sigma_{t}=\sigma_{t,u}\big{[}w\cdot\exp(\varepsilon-\varepsilon_{t,u})+(1-w) \cdot(\varepsilon/\varepsilon_{t,u})^{\varepsilon_{t,u}}\big{]}\ (\varepsilon> \varepsilon_{t,u},\ 0\leq w\leq 1) \tag{3}\]  
where \(\sigma_{t,u}\) and \(\varepsilon_{t,u}\) are the true stress and strain at necking, and \(w\) is the weighted-average constant. [PERSON] proposed the following equation for determining the value of \(w\) which is strongly dependent on the post-necking true strain:  
\[w=\frac{1}{1+a_{1}(\varepsilon-\varepsilon_{t,u})^{a_{2}}} \tag{4}\]  
The optimal values of two parameters, \(a_{1}\) and \(a_{2}\), were determined in the same manner as conducted by [PERSON] (2020) [23]. For the SS275 structural steel used in the present paper, \(a_{1}=6.5\) and \(a_{2}=1.5\) were optimized through an iterative process. The results of finite element analysis were fitted to test results until the replicated response was consistent with the measured one, as shown in Figure 5.
# Execution Model to Reduce the Interference of Shared Memory in ARINC 653 Compliant Multicore RTOS
## 4 Evaluations
### Measurement Environments  
Figure 11 shows the NXP T2080 QDS board (NXP, Eindhoven, Netherlands) used to measure the memory sharing interference between cores. The target board is equipped with QorIQ architecture-based quad-core PowerPC E6500 central processing unit (CPU). Each core has a 32 KB private data and instruction cache, and a 2 MB L2 cache is shared among the cores. The L2 cache is 16-way banked with 512 KB for each of the four cores. The main memory and the core transmit data through the CoreNet coherence fabric (eq. CoreNet BIU). The target board employs a 512 KB CoreNet platform cache and a pre-fetched L3 cache to improve access time to the main memory. The main memory is a DDR3 8 GB chip. The CoreNet BIU provides high concurrency by providing multiple and parallel address access. For this purpose, two coherence granules are processed per cycle.  
Figure 10: Multi-TDMA.  
Figure 9: AER execution model with pseudo-partition.  
We measured the number of L1 data cache misses, the L2 cache misses, and the CoreNet BIU requests (L3 and main memory access) to identify the memory sharing interference. As shown in Figure 11, each core shares the L2 cache and LLC. Therefore, cache eviction may occur depending on the cache access of each core. This factor can cause inter-core interference. In particular, we measured the effect of inter-core interference on the partitions' process execution times. Each cache misses count was read from the core's performance monitor register (PMR). The PMR consists of a performance monitor global control (PMGC), a performance monitor local control register (PMLC), and a performance monitor counter (PMC). We set event flags for the L1, L2 cache misses rate, and BIU requests in the PMGC and the PMLC and the read values counted in the PMC during benchmark execution. The PMGC and PMLC can only be configured in hypervisor mode, while VxWorks 653's guest (POS) operates in guest-hypervisor mode and user mode. Therefore, since the PMGC is not set up in the partition process, it is necessary to set up the event flag in the initialization of MOS. The PMC is accessible in user mode. We used the _move instruction_ of PMR (_mfpmr_) in the assembly code to access the values of the PMC. The latency of the _mfpmr_ instruction was four cycles. The execution time of the benchmark was measured with the POSIX timer library _clock_gettime ()_. We measured the corresponding cache misses only in Core 0 due to the limited the number of registers; the execution time was measured in the partitions of all four cores. All experiments were repeated a hundred times. In addition, only one process was allocated to each partition.
## 3 Intelligent Contact Tracing Based on Transmission Characteristics
### Overall Process of IntelliTrace  
Algorithm 1 represents the exposure assessment procedure in the IntelliTrace system, which determines \"close contact\" between a confirmed case (\(u\)) and a potential contact (\(v\)). Here, \(t_{confirm}\) represents the time when u was confirmed, and \(t_{infectious}\) indicates the infectious period, which is the time during which an infected individual can transmit the virus to others. The starting time (\(t\)) of the exposure assessment algorithm is initialized as the time obtained by subtracting \(t_{infectious}\) from \(t_{confirm}\). The IntelliTrace classifies the location of the confirmed patient as either indoors or outdoors based on satellite information received from the mobile phone GPS sensor, which is generally used to distinguish between indoors and outdoors in smartphones, in order to reflect different transmission characteristics of COVID-19 in indoor and outdoor spaces (\(is Indoor(u,t)\) in Algorithm 1). A strong GPS signal strength detected by the phone's GPS sensor indicates a 'GPS provider' and \(is Indoor\) is classified as outdoors. If the signal is weak, suggesting reliance on the 'Network provider', then \(is Indoor\) is classified as indoors [21]. For indoor spaces, it uses space-based indoor Wi-Fi contact tracing to confirm the co-location status for at least \(t_{minExposurePeriod}\) in the same space (\(IsExposureIndoor(u,v,t,tminExposurePeriod)\) in Algorithm 1). The implementation details of \(IsExposureIndoor\) are expanded in Section 3.2. For outdoor spaces, it employs trajectory-based outdoor GPS contact tracing to determine whether v has moved along the same path as the confirmed case for at least \(t_{minExposurePeriod}\) (\(IsExposureOutdoor(u,v,t,tminExposurePeriod)\) in Algorithm 1). The implementation details of \(IsExposureOutdoor\) are expanded in Section 3.3. Here, \(t_{minExposurePeriod}\) refers to the minimum exposure time required to classify it as a \"close contact\" when someone who is susceptible to infection has been exposed to an infectious case. The CDC defines \"close contact or exposure\" as someone who spends at least 15 min within six feet of a confirmed coronavirus case. During the loop, if exposure is not detected, the algorithm increments 't' by 1, progressing in 1-minute intervals. If exposure is not found after completing the loop, the algorithm returns false, indicating that there was no close contact between \(u\) and \(v\).
## 4 Discussion  
The results indicate that under AWGN conditions, the conventional system outperforms the proposed system when the SNR level is higher than 8 dB. However, under fading channels, the proposed semantic-aware system outperforms the conventional approach from low to high-mid SNR regions, demonstrating improved robustness against multi-path fading effects. This highlights the ability of semantic-aware transmission to preserve intelligibility and perceptual quality even in highly degraded conditions.  
It is important to note that although the proposed system shows improved PESQ and SDR scores in fading scenarios, its STOI performance does not always align with these results. While our system effectively enhances perceptual speech quality compared to the conventional system, as reflected in PESQ and SDR, it potentially introduces subtle distortions in the temporal envelope of the reconstructed speech. These distortions, though not significantly impacting overall perceptual quality, can affect the short-time temporal envelope correlation, which is the core metric assessed by STOI. Specifically, we believe that the semantic decoder, in its effort to generalize and reconstruct speech from compressed semantic features, may smooth out or alter fine-grained temporal details that are critical for STOI. One potential mitigation strategy would be using the hybrid loss function.  
Figure 8: Conventional versus proposed reconstructed speech spectrograms under the Rayleigh channel at SNR \(=\) 10 dB. The upper and lower spectrograms correspond to the conventional and proposed models, respectively.  
Incorporating a hybrid loss term that explicitly penalizes temporal envelope distortions during training could help align STOI with PESQ and SDR. This could involve using a time-domain loss function or a STOI-specific loss component.  
While we are aware of latency implications, this research places its core focus on the practical applicability of semantic communication. We posit that the semantic computations are executed on a high-performance edge device, mitigating potential latency concerns. Furthermore, the computational efficiency of the proposed model can be significantly enhanced through hardware acceleration, such as Tensor Processing Units (TPUs). Additionally, the application of established model compression techniques, including pruning [28] and quantization [29], effectively reduces the computational burden on resource-constrained IoT devices.  
These results show the trade-off between bit-level accuracy and semantic reconstruction. While conventional systems excel in ideal conditions, semantic-aware models demonstrate resilience to real-world wireless impairments, making them more suitable for low-SNR and multipath environments.
# Design and Implementation of a Contextual-Based Continuous Authentication Framework for Smart Homes
## 6 Security Analysis
### IP/MAC Address Spoofing and Data Protection  
The efficacy of MAC address spoofing can be somewhat reduced through the monitoring of network traffic and clients connected to the router. An IP address conflicts or is malformed, or any non-IEEE compliant MAC addresses could, could be other indicators of possible spoofing attempts. Hence, the use of certificate-based authentication would prevent the possibility of the Gateway being impersonated by an attacker.  
For the sake of privacy, no information related to users is stored on the end devices as they are susceptible to loss or theft. This ensures that if the device is compromised, the adversary cannot learn the user profile and simulate the user's behavior to gain system access. Furthermore, all data is verified and replicated to a backup location to avoid both data loss and the need to retrain user profiles, thus preventing a malicious user from rebuilding a user profile to match their own contexts.
## 4 Discussion  
We studied the response relationship between \(SATDA\) and \(SCA\), as well as the response relationship between \(SSTA\) and \(SCA\) in four typical sea areas (i.e. ARC, BMC, KE and GS) by using data from QSCAT, AMSR-E and ECMWF. The results indicate that \(SCA\) is more sensitive to \(SATDA\) than \(SSTA\). As shown in Figure 7, the coupling coefficient between \(SATDA\) and \(SCA\) is significantly higher than that between \(SSTA\) and \(SCA\), reaching a maximum of 0.62 in summer and decreasing to 0.2 in winter. This suggests that \(SATD\) plays a crucial role in influencing the microwave scattering coefficient. This result is consistent with previous studies on air-sea coupling, indicating that \(SATD\) affects the scattering coefficient by influencing wind speed [22; 23; 25]. We also observed seasonal variations in the values of \(k_{sstda}\) and \(k_{ssta}\). In the four sea regions, the coupling coefficients \(k_{sstda}\) and \(k_{ssta}\) reach their maximum values in summer (December and January in the Southern Hemisphere, and July and August in the Northern Hemisphere) and their minimum values in winter (July and August in the Southern Hemisphere, and December and January in the Northern Hemisphere).  
To understand the seasonal variation of the coupling coefficient \(k_{sstda}\), we analyzed the influence of the background marine environment on \(k_{sstda}\) using the data from January 2002 to June 2009. The results for the ARC sea area are shown in Figure 8. It can be observed that \(k_{sstda}\) exhibits a negative correlation with \(RMMSATD\) and \(RMMS\), while showing a positive correlation with \(RMMAT\) and \(RMMSST\). And the absolute value of the correlation coefficient between \(k_{sstda}\) and \(RMMSATD\) is the largest. The scatter plots of \(k_{sstda}\) varying with these parameters over the four regions are plotted together in Figure 9. The results show that the correlation between the \(k_{sstda}\) and \(RMMSATD\) is the strongest, with a correlation coefficient of \(-0.81\). Comparing the correlation coefficient with that in Figure 8, the value of the correlation coefficient between \(k_{sstda}\) and \(RMMSATD\) remains almost unchanged. These results show once again that \(RMMSATD\) is the key parameter affecting the coupling coefficient \(k_{sstda}\).  
In order to prove the effect of \(SATD\) on the scattering coefficient, we introduced \(SATD\) into traditional Ku-band GMF. As shown in Figure 11, it can be observed that the \(BIAS\) and the \(RMSE\) decrease by about 0.5 dB and 0.7 dB when the effect of \(SATD\) on the ECMWF wind speed is considered. This improvement is important for applications such as sea surface wind retrieval. Although the influence of \(SATD\) on the microwave scattering coefficient is discussed in this study, the influence of other marine environmental parameters on the scattering coefficient can be further explored in future studies to gain a more comprehensive understanding of the influencing factors of the scattering coefficient.
## 2 Related Study
### SECOM Semiconductor Analysis Dataset  
The semiconductor manufacturing process utilizes semiconductor wafers as substrates and processes them through a series of steps. The main steps include: cleaning the wafer, depositing the film, cleaning after film formation, exposure, development, etching, inserting impurities, generating semiconductor properties, activation, assembly, and packaging. At each stage, sensors measure relevant parameters, including film thickness, size, resistance, temperature, etc. Through data analysis and machine learning, predictive maintenance, fault detection, process monitoring, and yield improvement are performed. These methods can also extract useful information from limited event records. Large amounts of data help solve predictive maintenance issues and build fault detection and diagnosis models. Through timely fault detection and diagnosis, downtime is reduced, costs are lowered, and product quality is enhanced [25].  
Data were collected from a complex modern SECOM semiconductor manufacturing process that was under consistent surveillance through the monitoring of signals/variables collected from sensors or process measurement points. The dataset contained 1567 examples, 591 features, and 104 failures. Random forests were used to extract the 16 most important features for subsequent analysis to filter the key features because the SECOM dataset contained 591 features, as shown in Figure 6[26; 27; 28]. For the SECOM dataset, the target column \"\(-1\)\" corresponds to a pass and \"\(1\)\" corresponds to a fail.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Feature Types**} & \multicolumn{1}{c}{**Feature**} \\ \hline Statistical feature & Mean, median, min, max, peak to peak, Std, variance, RMS, Absmean, Abslogmean, Meanabsdev, \\  & Medianabsdev, coefficient of variance, midrange \\ \hline Signal factor-related feature & Shape factor, impulse factor, crest factor, clearance factor, skewness, kurtosis, entropy \\ \hline FFT, PSD-related feature & 1. Highest 3 FFT frequencies and values (6) 2. Highest 5 PSD frequencies (5) 3. Energy sum of FFT and PSD (2) \\ \hline Wavelet packet decomposition (WPD)-related feature & Variance of 3 levels of WPD coefficient: Level 1 (2) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Feature values of vibration and current features after feature extraction.  
Figure 5: Frequency spectrum from MCS for (**a**) healthy bearing, (**b**) outer ring damage, and (**c**) inner ring damage [23].
## 4 Conclusions  
Hydrothermal synthesis was used to produce \(CuO\) nanomaterials with different morphologies by varying the pH level. This approach controls the amount of intermediate \(Cu_{2}(OH)_{3}NO_{3}\), which influences the final \(CuO\) structure, including NPs, NRs, and NSs obtained at pH values of 4, 7, and 12, respectively. These \(CuO\) nanomaterials exhibit distinct characteristics like shape, electrical/electronic transport properties, light absorption capacity, and photocurrent response, all of which are crucial for their ability to detect glucose. For glucose testing applications, \(ITO/CuO\) electrodes were fabricated using synthesized \(CuO\) nanomaterials. Various characterization techniques (XRD, SEM, UV-VIS, and EIS) confirmed the improved sensor performance due to the nanomaterial properties. The photoelectrochemical (PEC) approach revealed the highest efficiency for glucose sensing with \(CuO\) NRs. \(CuO\) NPs also showed some sensing ability, while \(CuO\) NSs were the least efficient. The research successfully responded to glucose signals for detection ranging from 0 to 150 nM with two linear regions in which the detection limit was 0.61 nM for \(CuO\) NRs and 0.69 nM for \(CuO\) NPs. Additionally, the lowest solution resistance in the EIS analyzer also shows favorable \(CuO\) NRs in photoelectrochemical sensing techniques when compared to \(CuO\) NPs and NSs forms. Due to the combination of semiconducting nanomaterials with the PEC technique, this achievement demonstrates the capability of the PEC sensor using \(CuO\) nanomaterials to detect glucose down to the nanomolar level, as supported by the EIS and PEC measurements. These results are promising for broader applications in scientific research and life sciences.  
Figure 11: Mechanism of non–enzymatic glucose sensing on the \(ITO/CuO\) electrode.  
**Author Contributions:** Conceptualization, T.T.T., M.H.H.T. and V.C.T.; methodology, V.C.T., T.T.N. and A.D.N.; validation, M.H.H.T., V.C.T. and T.T.N.; formal analysis, T.T.T. and A.H.H.H.V.; investigation, T.T.N. and A.D.N.; resources, T.T.T., V.C.T. and T.T.N.; data curation, V.C.T. and T.T.N.; writing--original draft preparation, T.T.T., T.T.N. and A.H.H.H.V.; writing--review and editing, V.C.T. and T.T.T.; visualization, T.T.T., T.N.T. and A.D.N.; supervision, V.C.T. and T.N.T.; project administration, T.N.T.; funding acquisition, T.N.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Vietnam National University Ho Chi Minh City (VNU-HCM) grant number B2022-20-01/HD-KHCN.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available upon request from the corresponding author.  
**Acknowledgments:** We acknowledge Ho Chi Minh City University of Technology (HCMUT), and VNU-HCM for supporting this study.  
**Conflicts of Interest:** The authors declare no conflicts of interest.