esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Background
### Trust Management in IoV
#### 3.1.5 Trust Computation  
When trust has been established, it has to be managed throughout the duration of the target node interaction. Some commonly considered modules in trust computation are:  
* Trust Propagation: This module assists in establishing the trustworthiness of various communication system nodes based on previously established worthiness values while collaborating. It combines features from both a centralized approach, where trust is granted to entities through a single, trusted node or mechanism, and a decentralized system, where no one entity acts as a central point of control. The module's main features are trust transitivity and trust fusion. Instead of determining each individual entity's trust, resource computation costs in this module can be decreased by measuring trust value in a propagating network [34].
* Trust Aggregation: Multiple network pathways can be used to disseminate different versions of a node's trust value. When it receives diverse trust values for the node, this module aims to define a singular value based on the sum of data received. The Bayesian model, weighted sum approaches, and fuzzy logic are the most commonly used aggregation strategies. The primary principle for composing trust from the path of trust for various received values is trust aggregation.
* Trust Update: This refers to the process of bringing trust values up to date, and it can be divided into three schemas:
* Event-driven Trust: this is where node trust is adjusted after an event or during the occurrence of a transaction.
* Time-driven trust: this is where the aggregation scheme is used to adjust the node trust value within a determined time period.
* Continuous trust update: this is mostly used to protect integrity and is used to regulate one single node task.
* Trust Prediction: the purpose of this module is to predict trust connections between entities by utilizing selected criteria. This module predicts whether trust can develop between trusted nodes.
* Trust Evaluation: This module often contains sections on experience, suggestions, and global knowledge. Requesting node neighbors provides experience, which is continually updated in the table of trust, from which it is communicated as a recommendation trust node. The assessed trust value is linked to the global knowledge component.
* Trust Formation: This is the module where the trust formula is defined. To define how trust can be computed, it is necessary to establish the set of trust qualities and metrics considered by the trust formula. For the formation of the trust formula, the two trust categories of multitrust and single trust must be defined.
## 6 Results
### Wireless Interfaces  
* The Z-Wave interface demonstrated a range of up to 30 m in an open space environment, with successful transmission of motion sensor data to the Z-Wave controller at distances up to 25 m.
* The ZigBee interface demonstrated a range of up to 50 m in an open space environment, with successful transmission of temperature sensor data to the ZigBee coordinator at distances up to 40 m.
* The Wi-Fi and 4G-LTE interfaces showed high reliability, with a packet delivery success rate of 99% for both interfaces.
* The IR interface demonstrated reliable transmission and reception of commands between the remote control and IR receiver within a range of 10 m.
# Interference Mitigation for GNSS Receivers Using FFT Excision Filtering Implemented on an FPGA
## 3 Proposed Methodology
### Mitigation Algorithm (FFT Excision)  
The proposed solution for interference mitigation is the FFT excision algorithm. In its most simple form, FFT excision is the process of converting the signal into the frequency domain via FFT, selecting the frequency bins to be removed, and then performing an inverse FFT to return the signal to the time domain for processing by the receiver's acquisition and tracking routines. However, as we demonstrate, this process can result in the removal of large amounts of the received spectrum for certain jammer frequencies. The frequency bin resolution is an important term for determining the location of the centre of interference in the spectrum. The frequency resolution is the difference in the frequency between each bin, and thus sets a limit on how precise the results can be. The frequency bin resolution (FBN) is calculated as in Equation (3):  
\[Frequency\;bin\;resolution\;(FBN)=\frac{\text{Fs}}{FFT\;length} \tag{3}\]  
where (\(fs\)) is the sampling frequency. Therefore, for a given front-end bandwidth, the resolution improves with the length of FFT. From this formula, increasing the FFT size results in the frequency resolution decreasing (better resolution). As a result, the FFT results will have more points and represent the spectrum of the signal more precisely, since the distance between these bins (in terms of the frequency resolution) is smaller. If a narrowband (bandwidth less than one bin) jamming signal is not periodic within the length of FFT, its frequency spectrum will not necessarily be confined to a single bin of FFT. Instead, the discontinuities at the boundaries of the data block taken for FFT will broaden the frequency response. In Figure 7a,b, an FFT of a 6.5 MHz CW interferer and a 6.5033 MHz CW interferer, respectively, is shown when sampling at 26 MHz and (\(J/No\)) equal to 110 dB-Hz. In the case of 6.5 MHz, CW repeats every four samples and hence is periodic in FFT. Its spectral energy is contained within a single FFT bin. The receiver's front-end filter shape is shown in Figure 7a. While in Figure 7b, the 6.5033 MHz CW is not periodic, and its frequency response is much broader. One can obviously that see more bins of FFT would need to be removed in the non-periodic jammer case. Additionally, at high amplitudes, the sidelobes can raise the effective noise floor of the receiver. In practice, a jammer is seldom perfectly synchronized with the receiver's sampling frequency and therefore a different approach must be taken.  
Figure 6: Sampled signal characteristics (raw collected GNSS receiver data).
# A High-Resolution Magnetic Field Imaging System Based on the Unpackaged Hall Element Array
## 3 Experiments and Analysis
### Measuring the Wireless Charging Coils inside the Apple Phone  
Apple has developed a magnetic wireless charging technology. The phone and charger are drawn together by magnets inside the phone and charger for fast and stable charging. If one of the magnets on the coil provides a smaller magnetic force, the weak suction between the phone and the charger at that position tends to shift the phone in the direction of that magnet, affecting the charging efficiency of the phone. Therefore, there is a requirement to measure and screen the magnets.  
The magnetic field distribution arising from the array of magnets inside the Apple phone appears in Figure 14a. For measuring the wireless charging coil sample, we reacquired a coil from the interior of an Apple phone, as shown in Figure 14b. Figure 14c,d plots the measurements of the unpackaged Hall element array for this sample. The high  
Figure 12: Defective stainless steel plate used for magnetic leakage detection.  
Figure 13: Two Hall arrays detected and imaged the MFL signal caused by the defective steel plate: (**a**) the detection result of the Hall sensor array; (**b**) the detection result of the unpackaged Hall element array; (**c**) 2D image of the MFL signal from (**a**); (**d**) 2D image of the MFL signal from (**b**).  
resolution system provides a very clear visualization of the magnetic field distribution of the coil. The magnetic field peak values from each magnet range from 43 mT to 51 mT in absolute terms. From the imaging results, no magnet with magnetic anomalies is detected; the entire magnet array exhibits a symmetrical field distribution and provides a tight suction to the other coils.  
In addition, we also obtained the magnet array on a non-genuine wireless charging coil, as shown in Figure 15a. The gap between two adjacent magnets is 470 \(\upmu\)m. Figure 15b presents the images measured by an unpackaged Hall element array. From the test results, the array successfully detected and visualized these gaps. Meanwhile, the magnetic field peaks produced by these magnets range from 51 mT to 72 mT in absolute value, with a relatively asymmetric distribution of the magnetic field. As to further improving the spatial resolution of the system using a special movement of the X-Y axis motorized rail, the results are shown in Figure 15c. The spacing between adjacent Hall elements reduces to approximately 127.5 \(\upmu\)m, increasing the resolution of the system to 199.22 ppi. These gaps become easier for the system to recognize, and the magnet gaps in the black dashed area appear more distinctly. This indicates that the system with high spatial resolution captures more detailed information about the magnetic field and visualizes the magnetic characteristics of the sample more realistically.
## 3 Results and Discussion
### Testing Candidates
Various computational aspects are taken into account in order to analyze the ECG parameters for the detection of atrial fibrillation episodes for a specific group of individuals in this paper. This study elucidates various factors contributing to the functionality of PMLD matrices. For example, the arrangement of ECG parameters within the matrix structure, the degree of freedom available for the balancing of its elements, and the order of the matrix play crucial roles in the proposed classification technique. In general, increasing the matrix order has been shown to enhance the PMLD's sensitivity. However, an important observation is made about the sensitivity of the fourth-order PMLD. In few of the cases, it is observed that the sensitivity of the fourth-order matrix was slightly lower than that of the third order. It other words, the second, third and fifth order of the PMLD have shown a reliable increase in the sensitivity whereas the same increase cannot be achieved by the fourth-order PMLD matrix. Again, as previously mentioned, there are several aspects that influence the intended results. For instance, many contemporary methods for the detection of atrial fibrillation are focused on finding the impact of certain diseases or medication on atrial fibrillation. On the other hand, major techniques used for the detection of atrial fibrillation are based on the analysis of relatively long ECG records (sometimes persons are required to wear ECG monitors for more than 24 h). Our approach is completely different. Firstly, we are designing an algorithm capable of detecting atrial fibrillation episodes from short ECG records (no longer than 07 min). Secondly, the participants in the unhealthy group do not take any medications, so the impact of diseases and medications completely falls out of the scope of this paper. Therefore, a straightforward comparison with other techniques and methods is simply not possible. Nonetheless, the PMLD's overall effectiveness, sensitivity, and variability in ECG signal analysis are clear and unquestionable. It is important to mention that the proposed method is computationally efficient, making it suitable for real-time applications. The time complexity of the PMLD matrix approach is manageable, even with higher-order matrices as shown already. Furthermore, the PMLD matrix approach demonstrates its efficacy even with limited datasets. While this serves as a strong point to its robustness, it also poses a limitation to the study. Specifically, the self-training nature of the decision support system implies that its  
Figure 5: Third test candidate under the classification criteria for the third and fifth order PMLD matrices. efficiency and accuracy are contingent upon the size of the data fed into it. Hence, the study highlights the critical interplay between dataset size and the system's predictive capabilities. In essence, the study underscores the multifaceted nature of PMLD in ECG signal analysis, emphasizing its significance over the MA1 and MA2 matrix architectures.
## 4 UWB Indoor Positioning System
### UWB Ranging Algorithms
Overall, RSSI-based algorithms can provide a low-cost solution for indoor localization, but their accuracy can be impacted by various environmental factors. In addition, these algorithms may not be suitable for applications that require high precision, such as industrial automation or autonomous vehicle navigation. The theoretical relationship between received signal strength and distance is as follows: \[\tau_{ij}=\frac{T_{round_{i}}-T_{reply_{j}}}{2}.\]5 (9) where \(d_{0}\) is the reference distance, \(P_{i}\) and \(PL(d_{0})\) are transmitted power and pass loss at the reference point, and \(x_{\sigma}\) is a Gaussian random variable with zero mean that represents shadow fading and the path loss exponent. * **Comparison of Positioning Algorithms:** Table 3 summarizes the advantages and disadvantages of the mentioned positioning algorithms. These positioning algorithms are compared in terms of accuracy, efficiency and cost in \[\tau_{ij}=\frac{T_{round_{i}}-T_{reply_{j}}}{2}.\]6 From the figure, it can be observed that TDoA, TWR-ToA, and ToA have the highest accuracy. RSSI followed by ToA and AoA have the lowest implementation cost, while ToA followed by TDoA and TWR-ToA have the highest efficiency. From the figure, it can be concluded  
\[\tau_{ij}=\frac{T_{round_{i}}-T_{reply_{j}}}{2}.\]7 AoA positioning algorithm.
## 5 Seismic Reservoir Characterization Results  
Pre-stack inversion resulted in 3D volumes of acoustic impedance (Zp), shear impedance (Zs), and _VpVs_ ratio results. An inversion cross-section shows good reservoir continuity between wells Well-A and Well-B (Figure 9). Seismic interpretation indicates that the deposit has a pinch-out to the north and south and partially to the east. According to the results of the seismic inversion, the reservoir has reduced collector properties toward the east. Influence of shallower gas-bearing channels on impedance image visible as a pull-down effect is recognized on some parts of pre-stack inversion sections.  
In this study, we introduce variations of porosity and thickness, which resulted in 78 pseudo-wells. This high number of reservoir property combinations exceeds reservoir property scenarios using only input well data. Porosity log prediction is validated using 30% of overall wells, and validation correlation is 89.9% using 8 attributes. Figure 10 presents the porosity prediction of seven chosen pseudo-wells compared with the input porosity log from the synthetic-well catalog.  
In the final step, synthetic AVO seismic and synthetic impedance were replaced with real seismic angle stacks and impedance inversion results. The control blind well, Well-A, was not used in this process. The total porosity map (Figure 11a) showed good reservoir continuity with clear reservoir delineation. The influence of the shallower gas-bearing channels on the results was minimized. The total porosity map (Figure 11b) based only on data from two input wells resulted in sparse porosity distribution without emphasized reservoir boundaries.  
Figure 9: Acoustic impedance seismic profiles as a result of seismic inversion on actual seismic data.
# Mobility Prediction of Mobile Wireless Nodes
## 6 Results and Evaluation
### Evaluation of the RSSGM Mobility Model  
The RSSGM mobility model was the last model considered. This model is used to simulate user movement that has a self-similarity routine. RSSGM achieved very high prediction accuracy in all scenarios and with all classifiers. The LR algorithm had the  
Figure 5: Average accuracy with a different number of APs using the RW mobility model.  
Figure 6: Average accuracy with a different number of APs using the Gauss–Markov mobility model.  
lowest accuracy (80% on average) when compared with other classifiers and, as noted in LR, the number of APs affected the accuracy in inverse proportion. When five APs were used, the accuracy value was 91%; when twenty APs were used, the accuracy decreased to 67%. Generally, all classifiers had the same behavior of an inverse relationship between accuracy and the number of APs. However, SVM, LDA, and KNN presented a good prediction accuracy (i.e., an average of 92%). The DT classifier was the most accurate, with 98% on average. Such a result indicates that the DT classifier is the best classifier to use with the RSSGM model. Classifier accuracy is shown in Figures 7 and 8, illustrating the behavior of classifiers when the numbers of APs and centroids are changed.
## 3 Results and Discussion
### Time Dependence of Photoresponse  
The time-dependent photoresponse was also measured to investigate the dynamic photoresponse of the device. A mechanical chopper and an oscilloscope (Agilent 86100A) were used to analyze the time-dependent photoresponse. Figure 6a,b present the time-dependent photoresponse of the device, which was measured using an optical chopper with a frequency of 1/30 Hz, incident power of 164 nW, at V\({}_{\rm{G}}\) = \(-\)10 to 10 V with a step of 5 V and V\({}_{\rm{DS}}\) = 5 V, respectively. The photocurrent of our device decreases as the time increases under positive gate voltage and increases as the time increases under negative gate voltage, which means our device can work in both bright and dark backgrounds. Figure 6c presents response time as a function of V\({}_{\rm{G}}\). Rise time decreases as V\({}_{\rm{G}}\) increases. The occupied trap states in WSe\({}_{2}\) increase as V\({}_{\rm{G}}\), leading to more photoelectrons participating in channel conductance.
# Sequence-to-Sequence Video Prediction by Learning Hierarchical Representations
###### Abstract  
Video prediction which maps a sequence of past video frames into realistic future video frames is a challenging task because it is difficult to generate realistic frames and model the coherent relationship between consecutive video frames. In this paper, we propose a hierarchical sequence-to-sequence prediction approach to address this challenge. We present an end-to-end trainable architecture in which the frame generator automatically encodes input frames into different levels of latent Convolutional Neural Network (CNN) features, and then recursively generates future frames conditioned on the estimated hierarchical CNN features and previous prediction. Our design is intended to automatically learn hierarchical representations of video and their temporal dynamics. Convolutional Long Short-Term Memory (ConvLSTM) is used in combination with skip connections so as to separately capture the sequential structures of multiple levels of hierarchy of features. We adopt Scheduled Sampling for training our recurrent network in order to facilitate convergence and to produce high-quality sequence predictions. We evaluate our method on the Bouncing Balls, Moving MNIST, and KTH human action dataset, and report favorable results as compared to existing methods.  
Keywords:convolutional neural network; hierarchical features; long short-term memory; recurrent neural network; video prediction +
Footnote †: journal: Applied Sciences
# Reliability Analysis of Hybrid Laser INS Under Multi-Mode Failure Conditions
## 2 Mechanical Simulation Analysis of Hybrid Laser INS Structure
### Mechanical Simulation Analysis of the Hybrid Laser INS Structure
#### 2.2.3 Mechanical Simulation Analysis of the Hybrid INS Structure
At the same time, the safety factor corresponding to the maximum stress of SiCp/Al composite material is significantly higher than that of ZL107 material. This indicates that using high-strength, high-modulus materials for the inner and outer frames notably improves the mechanical performance of the hybrid INS under high overload and dynamic conditions.
## 3 Method
### Integrity Authentication Process  
The subject-sensitive hash sequence of an HRRS image needs to be stored together with the corresponding image. When the content integrity of the image needs to be authenticated, the subject-sensitive hash sequence of the image is recalculated and compared with the previous hash sequence to determine whether the HRRS image has been tampered with. Similar to existing subject-sensitive hashing, our algorithm also employed the normalized Hamming distance [42] to compare differences between hash sequences.  
Figure 4: Flow of the proposed subject-sensitive hash algorithm.  
We denoted the hash sequences of the original image and the image to be authenticated as \(SH\) and \(SH^{\prime}\), respectively. Then, the normalized hamming distance was as follows:  
\[Dis=\left(\sum_{i=1}^{Length}\left|SH(i)-SH^{\prime}(i)\right|\right)/Length \tag{2}\]  
where _Length_ represents the length of the hash sequence. Obviously, the value of normalized hamming distance is a decimal in the range of 0 to 1. The larger the value of \(Dis\), the greater the difference between the two hash sequences; conversely, the smaller the value, the smaller the difference between the two hash sequences. If \(Dis\) is 0, it means the two hash sequences are completely consistent.
###### Abstract  
The utilisation of medicinal plants has gained importance due to emerging drug resistance in diseases, including type 2 diabetes mellitus (T2 DM). _Vaccinium myrtillus_ (VM) and _Vaccinium vitis-idae_ (VV) are particularly noteworthy as their leaves and aerial parts (no flowering leaves with stems) are rich in polyphenols and antioxidants with potential positive impacts on blood sugar levels. The aim of this study was to compare the chemical composition, antiradical, and \(\alpha\)-amylase inhibitory activities of the leaves and aerial parts of the Latvian VM and VV. Two samples of VM and two samples of VV were collected from two different locations. Dried plants were used to prepare 70% ethanol extracts and freeze-dried samples. The total tannin and phenolic contents were determined, and the phytochemical compounds were characterised by LC-MS. The \(\alpha\)-amylase inhibition activity and the antiradical activity in the extracts were measured. The VV ethanol extracts had the highest polyphenol content at 632.80 mg/100 g, followed by the freeze-dried extracts at 968.52 mg/100 g. The highest polyphenol content in the VV ethanol extracts was 632.80 mg/100 g and 968.52 mg/100 g in the freeze-dried extracts. The ethanol extracts of VM (1.34%) and VV (2.85%) had the highest content of tannin, whereas the freeze-dried extracts showed significantly lower tannin content in VM (0.21%) and VV (0.18%). A high correlation was observed between DPPH (2,2-diphenyl-l-1-picrylhydrazyl) and \(\alpha\)-amylase inhibition activity (R = 0.86, \(p\) = 0.015). This information can be used for additional control of sugar levels in T2 DM patients. Comparing both plants with each other and different types of samples (ethanol extracts, freeze-dried samples), it was concluded that the difference exists in both variants. The VM samples showed higher results (\(\alpha\)-amylase inhibition activity, DPPH radical scavenging activity) than the VV samples, and the freeze-dried samples had higher results than the ethanol extracts.  
\(\alpha\)-amylase; antioxidants; DPPH (2,2-diphenyl-l-1-picrylhydrazyl); liquid chromatography-mass spectrometry (LC-MS); phenolic compounds; bilberry; lingonberry 2024