esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
The main contribution of this paper is the substitution of sensors, whereby high-bandwidth, small-sized and cost-efficient MEMS accelerometers are used to replace traditional fiber-optic gyroscopes. The proposed method combines the DOB and the TLC only based on MEMS accelerometers and CCD to improve the DSA in FTOS. The DOB method is mainly used to enhance the DSA in medium frequency, and the composited velocity loop is to exchange parts of medium-frequency performance to low-frequency DSA, which can be applied in other occassions. What's more, we combine the feedback control, DOB disturbance feedforward control and sensor fusion to fulfil the sensor's potential. This fusion idea can also be transplanted to other sensors in further studies. Experiments verify that the proposed method has the best comprehensive performance and the MEMS accelerometers could totally substitute the FOG's role in FTOS.  
As the proposed method to correct the drift of the velocity integrated by accelerometers would bring the CCD's delay to the velocity loop, the DSA in medium frequency is not perfect. Our future work will focus on further improving the medium-frequency performance. It will be a meaningful task to correct the drift and cut down the time delay influence of CCD.  
**Acknowledgments:** Comments and suggestions from [PERSON] and [PERSON] are very much appreciated during this study.  
**Author Contributions:** [PERSON] is the head of the research group that conducted this study. He contributed to research with his original idea, performing the experiment, and writing this paper. [PERSON] joined analyzing the theory and writing the paper. [PERSON] and [PERSON] guided the experiment and put forward suggestions. [PERSON] and [PERSON] joined the experiment.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 10: Disturbance attenuation of the four methods.
# Improved Adaptive Federated Kalman Filtering for INS/GNSS/VNS Integrated Navigation Algorithm
###### Abstract  
To address the issue of low positioning accuracy in unmanned vehicles navigating in obstructed spaces due to easily contaminated navigation measurement information, an improved adaptive federated Kalman filtering INS/GNSS/VNS integrated navigation algorithm is proposed. In this algorithm, an inertial navigation system (INS) serves as the common reference system, and, together with the global navigation satellite system (GNSS) and visual navigation system (VNS), they form the subsystems that together make up the main system. In the event of faulty measurement values in the subsystems, a combination of the residual chi-square and sliding-window averaging methods are used for fault detection to improve the fault tolerance of the integrated navigation algorithm. Additionally, an adaptive sharing factor is proposed to adjust the accuracy of the integrated navigation algorithm based on the accuracy of the sub-filters. Simulation experiments demonstrated that, compared with classic federated Kalman filtering, the proposed algorithm reduced the root mean square errors (RMSEs) of the three-dimensional position by 56.4%, 54.8%, and 43.4% and the root mean square errors of the three-dimensional velocity by 71.0%, 72.1%, and 28.4% in the event of sub-filter faults, effectively solving the problem of low positioning accuracy for unmanned vehicles in obstructed spaces while ensuring the real-time performance of the system.  
Keywords:integrated navigation; improved adaptive federal Kalman filter; residual chi-square; sliding-window averaging methods; adaptive information sharing factor +
Footnote †: journal: Applied
## 4 Experiment and Discussion
### Quantitative Analysis
#### 4.4.2 Instruction Space Compression Analysis  
From Section 4.4.1, it is clear that the total undocumented instruction mining time shrunk as the instruction search space decreased. Table 4 shows the original instruction search space of the four DSPs and the instruction space after the three steps of our method. According to Table 4, we can conclude that the instruction search space was reduced from \(10^{9}\) to \(10^{3}\) after the first step of our method. Moreover, after the disassembly method, the number of instructions to be tested was further reduced by 50%. This meant that the instruction generation method based on valid instruction information bits compressed the instruction space by a factor of \(10^{6}\), which was also the key to the fast search speed.
# Research on Fault Diagnosis Method for Rotating Machinery Based on Edge Computing
## 4 Fault Diagnosis Method Based on Edge Computing
### Construction of the Dual-Channel CNN Decision-Level Fusion Model
#### 4.3.1 Envelope Spectrum  
In numerous existing studies, the envelope spectrum has been proven to be highly effective for the frequency-domain features of fault diagnosis [29]. Theoretically, obtaining the envelope spectrum of an input signal requires two steps: first, the signal envelope is obtained and then a frequency transformation is performed. In this study, the Hilbert transform is used to capture the signal envelope. First, the input is decomposed into a finite set of components, known as Intrinsic Mode Functions (IMFs), using Empirical Mode Decomposition (EMD). The IMFs form a complete and orthogonal basis of the initial signal [32; 33]. The formula is shown below:  
\[x(t)=\sum_{i=1}^{n}c_{i}(t)+r_{n}(t) \tag{15}\]  
where \(x(t)\) is the original signal, \(c_{i}(t)\) is the i-th IMF component, \(r_{n}(t)\) is the final residual component, and \(n\) is the number of IMFs.  
After obtaining the IMF components, each IMF component is subjected to a Hilbert transform to obtain an analytical signal, which is expressed as follows [33]:  
\[z_{i}(t)=c_{i}(t)+jH[c_{i}(t)] \tag{16}\]  
where \(H[c_{i}(t)]\) is the Hilbert transform of \(c_{i}(t)\) and \(j\) is the imaginary unit.  
The purpose of the Hilbert transform is to convert a real signal into a complex signal in order to compute the instantaneous amplitude and instantaneous phase. By calculating the absolute value of the analytical signal \(z_{i}(t)\), the envelope of the signal can be obtained [33].  
\[ Envelope(t)=\left|z_{i}(t)\right|=\sqrt{c_{i}(t)^{2}+H[c_{i}(t)]^{2}} \tag{17}\]  
Figure 6: CNN-DS structure.  
In addition to envelope calculation, this study also employs EMD for denoising, as described in Section 3.1. After filtering out high-frequency noise and capturing the signal envelope, a Fourier transform is performed. Figure 7 shows the results of the Fourier transform, where (a) is the original vibration signal without any signal processing and (b) is the envelope signal obtained through EMD and Hilbert transform. As shown in the figure, the signal obtained after EMD and Hilbert transform contains fewer high-frequency components than the original. This indicates that EMD significantly reduces the high-frequency components of the faulty bearing while preserving its characteristic frequencies. The spectrum in Figure 7b, known as the envelope spectrum, will be used as the input for the vibration data channel of the CNN [32].
## 2 Data and Materials
### ALS Data  
The ALS data acquisition was accomplished from 24 March to 7 April 2015 using a _Riegl Q560_[37] under leaf off conditions. The mean flying height above ground was 600 m, and resulted in a footprint diameter of about 0.3 m and an average pulse density of 11.2 pulses per m\({}^{2}\). The ALS datasets were  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Attribute** & **Minimum** & **1 th Quartile** & **Median** & **Mean** & **3 th Quartile** & **Maximum** \\ \hline DBH (cm) & 7.0 & 23.8 & 36.2 & 36.73 & 47.9 & 112.5 \\ Tree height (m) & 5.2 & 19.8 & 24.8 & 24.65 & 29.9 & 50.6 \\ Stems per hectare & 48 & 202 & 412 & 742.4 & 839 & 6014 \\ Number of recorded trees per plot & 2 & 5 & 7 & 7.6 & 10 & 16 \\ Maximum radius (m) & 0.3 & 3.0 & 5.25 & 5.9 & 8.1 & 20.8 \\ Limiting circle radius (m) & 2.6 & 8.9 & 12.6 & 12.4 & 15.7 & 28.1 \\ HDOP & 0.8 & 1.1 & 1.2 & 1.32 & 1.5 & 3.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic statistics of the 133 investigated BWI plots. DBH: diameter at breast height; HDOP: horizontal dilution of precision.  
Figure 1: Study area and German National Forest Inventory (_Bundeswaldinventur_, BWI) sampling design. Background: Web Map Service (WMS) of forest types provided by Copernicus [35].  
provided by the state forest service of Rhineland-Palatinate in the form of pre-classified (ground vs. other classes) LAS-files. For each of the investigated BWI plots, we selected a circular subset with a radius of 38 m around the plot center.
## 6 Results and Analysis  
### Importance of Landslide Conditioning Factors Using the ORAE Method  
The results of the factor selection based on the ORAE method is shown in Figure 4. The most important factors for landslide occuurence were determined based on the average merit (AM) metric of this method. The results indicated that, although all 15 conditioning factors had an effect (AM > 0) on landslide incidence, road density (AM = 72.701) had the highest predictive capability because most of the observed landslides in the Mu Cang Chai District were located near roads. It was followed by elevation (AM = 63.793), distance to road (AM = 63.218), aspect (AM = 60.344), land use (AM = 58.046), river density (AM = 57.758), distance to river (AM = 56.609), lithology (AM = 53.735), curvature (AM = 52.586), fault density (AM = 49.137), slope (AM = 49.137), profile curvature (AM = 48.850), plan curvature (AM = 48.850), distance to fault (AM = 48.563), and rainfall (AM = 45.114) (Figure 4).
# Uniform Linear Antenna Array Beamsteering Based on Phase-Locked Loops
## 1 Introduction  
Antenna selection is critical to any wireless system design [1]. Some of the design criteria for an antenna are its frequency, bandwidth, physical size, and directivity [2]. The application of the wireless system defines these criteria, for applications with long range or direction-finding (e.g., satellite communications, power transfer, radar, localization), a highly directive antenna allows for increased range and angular accuracy [3]. Antenna arrays allow a directional antenna to be created from individual elements which can be omni-directional [4]. Antenna arrays are composed of several antenna elements (often identical) arranged in a particular geometric configuration (linear or planar) [5].  
This paper deals with Uniform Linear Arrays. Phased arrays can be steered electronically by use of controllable phase shifters. These phase shifters can be analogue or digital. This means that a linear array can electronically scan its beam quicker than antennas which have to be mechanically rotated. Removing the need for mechanical rotation can simplify the overall system design as well as reducing size and weight, therefore opening up more applications. In this paper, we explore the theory behind ULA antenna design, particularly in terms of how its physical size and directivity can be designed. We show how to create an ULA demonstrator that uses PLLs as independent sources for each element, we also explain how to measure the antenna radiation pattern and compare it to the theoretical results. A design example is used to illustrate the theory and practical techniques are discussed. This paper is organized as follows. In Section 2, we discuss the theory and the design aspects of ULAs using numerical examples and also give the specification of our example problem and use these criteria to discuss the basic definitions of antenna parameters with respect toarrays. In Section 3, we discuss electronic beam-scanning techniques as applied to ULAs. In Section 4, we implement these techniques on a fabricated antenna array and present the hardware required for demonstration on a 5-element linear array. In Section 5, we present basic requirements of the set-up for measurement and calibration along with the measurement results. Finally, we conclude in Section 6.
## 5 Conclusions  
In the present work, we propose an extension of the DPH index to predict failure during damage processes in quasi-brittle materials. To the best of the authors' knowledge, this is the first application example of that index with experimental data. The experiment-based analysis was also backed up by simulation results. The main conclusions from such an endeavor are as follows:  
* While the original version of the DPH index was used only as a predictor of total structural failure, our proposed alternative could also identify intermediate local failure events. Its performance as a failure predictor was consistent in comparison with the widely used \(b\)-value during the experimental test. However, this could be a peculiar characteristic of the test carried out in the paper, and before generalizing its advantages, the persistence of this tendency must be verified by new tests to verify the index performance in other representative specimens of different materials and dimensions.
* Although the simulated results could not confirm the usefulness of the proposed DPH index conclusively, they showed that the behavior of the actual elastic energy within the system (which is necessary to calculate the original index) could be successfully approximated through the product between the applied displacement and the reaction from the structure. Since the direct measurement of such energy within the structure is unattainable, such an alternative method is of significant practical importance because it enables using a hitherto strictly theoretical tool in the experimental study of damage processes.  
As a final remark, as the first application instance of the DPH index to an experimental setup as far as the authors are aware, this work's findings still require further confirmation before its conclusiveness can be ascertained. Future work will concentrate on widening the proposed index's application spectrum to other types of structures and damage processes to seek such confirmation.  
Conceptualization, B.N.R.T., I.I. and G.L.; methodology, B.N.R.T., I.I. and G.L.; software, B.N.R.T.; validation, B.N.R.T.; formal analysis, B.N.R.T. and I.I.; investigation, B.N.R.T., I.I. and M.S.; resources, I.I. and G.L.; data curation, I.I. and G.L.; writing--original draft preparation, B.N.R.T., M.S. and I.I.; writing--review and editing, B.N.R.T., M.S., I.I. and G.L.; visualization, B.N.R.T.; supervision, G.L. and I.L.; project administration, I.I.; funding acquisition, I.I. and G.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by CNPq and CAPES (Brazil), and the sponsorship guaranteed with basic research funds was provided by Politecnico di Torino.  
Not applicable.  
The authors acknowledge the financial support received from the Brazilian National Council for Scientific and Technological Development (CNPq, Brazil) and from the Coordination for the Improvement of Higher Education Personnel (CAPES-Brazil). The sponsorship guaranteed with basic research funds provided by Politecnico di Torino, Italy, for its financial aid in this work is also acknowledged.
## 3 Review of the Scheme of [PERSON] et al. [19]
### Initial V2I Authentication  
Following registration, \(V_{i}\) begins authentication with \(RSU_{j}\). The initial V2I authentication process is as follows:  
1. \(V_{i}\) enters \(ID_{i}\) and \(PW_{i}\) into \(SC_{i}\), triggering several computations. \(SC_{i}\) calculates \(HPW_{i}=h(ID_{i}||PW_{i})\), \(r_{i}=A_{i}\oplus HPW_{i}\), \(x_{i}=B_{i}\oplus h(r_{i})\), \(RPW_{i}=HPW_{i}\oplus h(x_{i})\), and \(RID_{i}=h(ID_{i}||RPW_{i}||r_{i})\). Then, it verifies whether \(C_{i}\stackrel{{?}}{{=}}h(h(RID_{i}||x_{i})\bmod l)\).
2. \(V_{i}\) selects a random \(b_{i}\) and computes \(P_{ij}=(r_{i}\cdot b_{i})\cdot P_{j}\), \(P_{i}=(r_{i}\cdot b_{i})\cdot P\), \(M_{1}=RID_{i}\oplus h(ID_{j}||P_{ij}||T_{1})\), and \(M_{2}=h(RID_{i}||P_{ij}||T_{1})\). It sends the computed values \((M_{1},M_{2},P_{i},T_{1})\) to \(RSU_{j}\).
3. \(RSU_{j}\) checks the timestamp \(T_{1}\), calculates \(P_{ij}=s_{j}\cdot P_{i}\), and derives \(RID_{i}=M_{1}\oplus h(ID_{j}||P_{ij}||T_{1})\). It confirms the presence of \(h(RID_{i}||k)\) in the blockchain and verifies \(M_{2}\stackrel{{?}}{{=}}h(RID_{i}||P_{ij}||T_{1})\), authenticating \(V_{i}\).
4. \(RSU_{j}\) generates random values \(b_{j}\) and \(n_{j}\) and a timestamp \(T_{2}\). The RSU calculates \(Q_{ij}=b_{j}\cdot P_{i}\), \(Q_{j}=b_{j}\cdot P\), \(TID_{i}=RID_{i}\oplus h(n_{i}||ID_{j}||k)\), \(M_{3}=TID_{i}\oplus h(Q_{ij}||RID_{i})\), \(SK_{ij}=h(Q_{ij}||TID_{i}||RID_{i})\), and \(M_{4}=h(SK_{ij}||T_{2})\). These values \((M_{3},M_{4},Q_{j},T_{2})\) are sent to \(V_{i}\).
5. Upon receipt, \(V_{i}\) calculates \(Q_{ij}=(r_{i}\cdot b_{i})\cdot Q_{j}\), determines \(TID_{i}\) as \(M_{3}\oplus h(Q_{ij}||RID_{i})\), and derives \(SK_{ij}\) as \(h(Q_{ij}||TID_{i}||RID_{i})\). The RSU checks if \(M_{4}\) equals \(h(SK_{ij}||T_{2})\). If so, \(RSU_{j}\) uploads \((TID_{i},n_{j},ID_{j},Sig_{j}(h(TID_{i}||RID_{i}||n_{j})))\) to the blockchain.
# Efficient Bloom Filter-Based Routing Protocol for Scalable Mobile Networks +
## 4 Conclusions  
Improved network scalability, decreased resource consumption, efficient content retrieval, and adaptive routing are just some of the concrete advantages that result from using the suggested Hybrid Routing Algorithm in real-world mobile networks. There are benefits and difficulties associated with all different kinds of routing protocols. However, non-geographic protocols may have trouble scaling and may not be as efficient in retrieving material. Geographic protocols exploit location information but might be sensitive to movement. Content-centric protocols have scalability issues but favour efficient content retrieval. As a solution to these issues, the suggested Hybrid Routing Algorithm merges the content-centricity of NDN with the memory efficiency of Bloom Filters in order to strike a good balance between scalability, content retrieval speed, and resource utilisation, especially in real-world mobile networks. To maximise both content-centric gains from NDN and memory efficiency from Bloom Filters, the Hybrid Routing Algorithm employs an aggregation mechanism. When it comes to storing and disseminating network topology data, the Bloom Filters are indispensable.  
Conceptualization, P.S. and M.M.; methodology, J.B.; software.; validation and writing--original draft preparation, B.J.; supervision, G.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data can be obtained from the corresponding author on request.  
We acknowledge the institutional management for their support to carryout this work.  
The authors declare no conflict of interest.
## 3 Results
### Antimicrobial Susceptibility of the MRSA Isolates
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Antibiotic** & **MRSA 1** & **MRSA 2** & **MRSA 3** & **MRSA 4** & **MRSA 5** & **MRSA 6** \\ \hline Augmentin & \(\leq\)2 & \(\leq\)2 & \(\leq\)2 & \(\leq\)2 & \(\leq\)2 & \(\leq\)2 \\ \hline Benzyl penicillin & \(\leq\)0.25 & \(\leq\)0.25 & \(\leq\)0.25 & \(\leq\)0.25 & \(\leq\)0.25 & \(\leq\)0.25 \\ \hline Oxacillin & \(\geq\)4 & \(\geq\)8 & \(\geq\)8 & \(\geq\)4 & \(\geq\)8 & \(\geq\)4 \\ \hline Cefuroxime & \(\leq\)1 & 8 & 2 & 8 & \(\leq\)1 & \(\leq\)1 \\ \hline Cefuroxime/Axetil & 8 & \(\leq\)1 & \(\geq\)64 & 8 & \(\geq\)64 & 2 \\ \hline Clindamycin & \(\leq\)0.5 & 2 & \(\leq\)0.5 & \(\geq\)4 & \(\geq\)4 & \(\geq\)4 \\ \hline Amikacin & \(\geq\)16 & 8 & \(\leq\)4 & \(\geq\)16 & \(\geq\)16 & \(\geq\)16 \\ \hline Imipenem & \(\leq\)1 & \(\leq\)1 & \(\leq\)1 & \(\leq\)1 & \(\leq\)1 & \(\leq\)1 \\ \hline Ciprofloxacinacin & \(\geq\)8 & \(\geq\)8 & \(\geq\)8 & \(\geq\)8 & \(\geq\)8 & \(\geq\)8 \\ \hline Levofloxacin & \(\geq\)8 & \(\geq\)8 & 4 & \(\geq\)4 & \(\geq\)8 & \(\geq\)8 \\ \hline Erythromycin & \(\leq\)0.25 & \(\geq\)8 & \(\geq\)8 & \(\geq\)8 & 1 & \(\geq\)8 \\ \hline Sulfamethoxazole/trimethoprim & 8 & 16 & 16 & 8 & 16 & 16 \\ \hline Tigecycline & \(\leq\)0.12 & \(\leq\)0.12 & \(\leq\)0.12 & \(\leq\)0.12 & \(\leq\)0.12 & 256 \\ \hline Tetracycline & \(\geq\)32 & \(\geq\)32 & \(\geq\)32 & \(\geq\)32 & \(\geq\)32 & \(\geq\)320 \\ \hline Rifampicin & \(\leq\)1 & \(\leq\)0.5 & \(\geq\)4 & 2 & \(\geq\)4 & \(\geq\)4 \\ \hline Gentamicin & \(\geq\)16 & \(\leq\)4 & \(\leq\)4 & \(\leq\)4 & \(\leq\)4 & 8 \\ \hline \end{tabular}
\end{table} 
Table 1: Minimum inhibitory concentration of the MRSA isolates against tested antibiotics. Figure 2: Showing the zones of mean \(\pm\) SD inhibition of methicillin-resistant _Staphylococcus aureus_ (MRSA), MRSA 1–6 bacterial isolates against _Nigella sativa_ (NS) dilutions. Comparison of 10 \(\upmu\)g/mL and other concentrations. * \(p\)-value: 0.034; ** \(p\)-value: 0.0001. MRSA 1 was the isolate that was most inhibited by the 5.0 \(\mathrm{\SIUnitSymbolMicro g}\)/mL NS dilution. As with the higher NS concentrations, differences in mean zones of inhibition were significant when compared with those of the 1.0 and 0.1 \(\mathrm{\SIUnitSymbolMicro g}\)/mL NS, with \(p\)-values of 0.045 and 0.0002, respectively. For the 2.5 \(\mathrm{\SIUnitSymbolMicro g}\)/mL NS concentration, statistical differences in inhibition zones were significant when compared to the lower NS dilutions (Figure 2).
# Deep-Learning-Based Framework for PET Image Reconstruction from Sinogram Domain
## 2 Methodology  
### Problem Definition  
In PET imaging, photons emitted by radioactive tracers follow a random Poisson process. Therefore, it is assumed that the imaging model of the PET system obeys a Poisson distribution. The measured PET data \(y\), referred to as \"projection data\", equal the sum of the coincidence events captured by each detector and are usually stored in sinogram mode,  
\[\mathbf{y}\ \sim\ \text{Poisson}\{\overline{y}\} \tag{1}\]  
where \(y\in\mathbb{R}^{I}\) is a collection of detected events, \(I\) is the number of lines of response, and \(\overline{y}\) is the mean of the Poisson distribution. In addition, the projection data \(y\) can be described as a projective transform about the unknown activity image \(x\in\mathbb{R}^{I}\).  
\[\mathbf{y}=\mathbf{G}\mathbf{x}+\mathbf{s}+\mathbf{r}, \tag{2}\]  
where \(G\in\mathbb{R}^{I\times I}\) is the system matrix, and \(J\) is the number of pixels in the image space. In reality, \(y\) includes not only the true coincidences, but also scattered coincidences s and accidental coincidences \(r\). In the specific acquisition process of PET, the method of the verbal coincidence window is usually used to correct the accidental coincidence, which can effectively remove accidental events in the data. Therefore, one can generally describe the PET measurement model as follows:  
\[\mathbf{y}=\mathbf{G}\mathbf{x}+\mathbf{e}. \tag{3}\]  
The task can then be defined as a process of generating an activity map x from sinogram data \(y\). However, this task is an ill-posed optimization problem in theory. Traditional solutions primarily involve iterative methods.