esa-sceva/satcom-chunk-collection huggingface dataset:
# Measurement of the Speed of Induction Motors Based on Vibration with a Smartphone
###### Abstract  
Induction motors are key pieces of equipment in today's society, powering a variety of industrial drives and home appliances. The induction motor speed is often used to monitor the performance of all kinds of industrial drives. For example, in the industrial field, the motor speed is very often used to determine the efficiency and mechanical load of motors. In this work, a new simple, low-cost, and nonintrusive procedure is proposed for infield measurement of induction motors speed, which is based on the spectral analysis of the vibration signal of the motors. The motor vibration signal is first acquired using the accelerometers integrated into a basic phone. The acquired signal is then treated by a MATLAB-based algorithm, which can determine the motor speed by identifying the mechanical frequency of the rotor shaft from the harmonic content of the vibration signal. In this way, it is shown that the mechanical frequency corresponding to the speed of rotation of the motors can be acquired by means of the embedded accelerometers of a common smartphone, avoiding the acquisition and installation of external accelerometers. To the authors' knowledge, this could be the first time that a smartphone has been proposed as a practical means of measuring the speed of a motor by analysing its vibration. Experimental results from an extensive set of tests, including the supply of the motor from a frequency converter, show that the speed can always be measured with a relative error of less than 0.15%.  
induction motors; vibration signature analysis; motor speed measurement; noninvasive motor speed measurement; infield motor speed measurement +
Footnote †: journal: Applied Sciences
## 4 Discussion  
Table 2 presents a comprehensive comparison between the proposed THz PAA and other works. Compared to the THz PAAs proposed in [20, 21, 22, 23], the advantages of the PAA proposed in this paper are mainly reflected in 2-D beam scanning and a wider scanning range. Compared with [20, 21, 23], which achieve 1-D beam scanning with a maximum scanning angle of 90\({}^{\circ}\), can achieve 2-D beam scanning of more than 96\({}^{\circ}\) within the frequency range of 106-114 GHz. In contrast, ref. [22] can achieve 2-D beam scanning within a 60\({}^{\circ}\) range, while we can achieve 2-D beam scanning of more than 80\({}^{\circ}\) across the entire operating frequency band.
# A Novel Reliability Analysis Approach under Multiple Failure Modes Using an Adaptive MGRP Model
## 4 Case Studies
### A Numerical Series System Analysis
Especially the efficiency of the proposed method is improved by 110% and 44.4%, respectively, compared with the MRGP + H method and the MRGP + EFF method. In general, the proposed method can take into account the accuracy and efficiency of reliability at the same time, and the time-consuming can be significantly saved on the premise of ensuring the solution's accuracy.
# On Evaluating IoT Data Trust via Machine Learning
## 7 Conclusions  
Evaluating the trustworthiness of data in IoT using supervised ML is promising but also challenging to realize due to the difficulty of obtaining data annotated with trust labels. We showed that synthesizing untrustworthy data with mostly similar characteristics to the available trustworthy data is an effective way to generate untrustworthy data and create datasets that can be used to learn IoT data trust classification models. We proposed a new time-series sensor data synthesis method, called RWI, to generate untrustworthy data that closely resemble trustworthy data but are subject to subtle random deviations. To enhance the IoT data trust evaluation performance, we also proposed calculating a new set of correlation features that can compactly represent the information of both temporal and spatial correlations in the data and help distinguish between trustworthy and untrustworthy data effectively.  
Given the labeled synthetic data, we showed that when ground-truth trust labels are not available, contrary to the prior supposition, extracting labels from the data through clustering does not necessarily provide accurate or meaningful labels either for direct data trust inference or fitting an extra data trust classification model. We also demonstrated the utility of a semi-supervised ML algorithm, i.e., label propagation, for evaluating IoT data trust using datasets where only a small fraction of the data are labeled. The considered semi-supervised approach delivered high accuracy compared to those of supervised ML-based approaches. In the real world, a semi-supervised ML-based approach is likely more desirable as it requires significantly less labeled data compared with those based on supervised ML.  
**Author Contributions:** Conceptualization, V.D. and R.A.; methodology, T.T. and R.A.; software, T.T.; validation, T.T., V.D. and R.A.; formal analysis, T.T. and R.A.; investigation, T.T.; data curation, T.T.; writing--original draft preparation, T.T.; writing--review and editing, V.D. and R.A.; visualization, T.T.; supervision, V.D. and R.A.; project administration, V.D.; funding acquisition, V.D. and R.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The Python code and data used in this work are available online ([[https://github.com/[PERSON]/IoT_datatrust_RWI](https://github.com/[PERSON]/IoT_datatrust_RWI)]([https://github.com/[PERSON]/IoT_datatrust_RWI](https://github.com/[PERSON]/IoT_datatrust_RWI)), accessed on 2 August 2023).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Singular Value Decomposition Channel Estimation in STBC MIMO-OFDM System
## 4 The Proposed SVD Channel Estimation Method
The \(N\times 1\) vector \(\hat{\mathbf{H}}_{\text{LS}}^{\alpha,\beta}\) is the CFR obtained by the LS method. The superscript \(\left(\cdot\right)^{\text{H}}\) denotes the Hermitian transpose. \(\sigma_{\text{n}}^{2}\) is the variance of the AWGN in the channel. The complexity of the MMSE can be significantly reduced by averaging the transmitted pilot data. The term \(\left(\mathbf{X}_{\text{P}}\mathbf{X}_{\text{P}}^{\text{H}}\right)^{-1}\) in Equation (9) can be replaced by its expected value of \(\left(\mathbf{X}_{\text{P}}\mathbf{X}_{\text{P}}^{\text{H}}\right)^{-1}\). The expected value of \(\left(\mathbf{X}_{\text{P}}\mathbf{X}_{\text{P}}^{\text{H}}\right)^{-1}\) can be represented as:  
\[\text{E}[\left(\mathbf{X}_{\text{P}}\mathbf{X}_{\text{P}}^{\text{H}}\right)]^{-1}=\text {E}(\left|1/x_{\text{f}}\right|^{2})\mathbf{I}, \tag{10}\]  
Figure 4: Comb-type pilot pattern in two antennas: (**a**) antenna one; (**b**) antenna two. where \(x_{k}\) denotes the pilot symbols in matrix \(X_{\text{P}}\) and \(I\) is the \(N\times N\) identity matrix. Defining the SNR as \(\text{E}\big{(}\big{|}x_{k}\big{|}^{2}\big{)}/\sigma_{n}^{2}\), the simplified MMSE channel estimator can be represented as [20]:  
\[\hat{H}_{\text{MMSE}}^{\alpha,\beta}=\mathbf{R}_{\text{{H}H}}(\mathbf{R}_{\text{{H}H}}+ \frac{\varphi}{\text{SNR}}\mathbf{I})^{-1}\hat{\mathbf{H}}_{\text{LS}}^{\alpha,\beta}, \tag{11}\]  
where \(\varphi=\text{E}\big{(}\big{|}x_{k}\big{|}^{2}\big{)}\text{E}\big{(}\big{|}1/x _{k}\big{|}^{2}\big{)}\) is a constant value depending on the signal constellation. For QPSK, its value is 1; for 16 QAM, its value is 17/9; for 64 QAM, \(\varphi\) is 13/5. To further reduce the complexity of the MMSE estimator, the SVD is used to decompose the channel autocorrelation matrix. The SVD of the channel autocorrelation matrix \(\mathbf{R}_{\text{{H}H}}\) is [24]:  
\[\mathbf{R}_{\text{{H}H}}=\mathbf{U}\mathbf{\Lambda}\mathbf{V}^{\text{H}}, \tag{12}\]  
where \(\mathbf{U}\) and \(\mathbf{V}\) are \(N\times N\) unitary matrices containing the singular vectors, and \(\mathbf{\Lambda}\) is a \(N\times N\) diagonal matrix containing the singular values \(\lambda_{1},\lambda_{2},\cdots,\lambda_{N}\) on its diagonal in descending orders. The SVD method can be represented as [24]:  
\[\hat{\mathbf{H}}_{\text{SVD}}^{\alpha,\beta}=\mathbf{U}\mathbf{\Lambda}\mathbf{V}^{\text{H}} \hat{H}_{\text{LS}}^{\alpha,\beta}, \tag{13}\]  
where \(\mathbf{\Delta}\) is a \(N\times N\) diagonal matrix and the elements on its diagonal are:  
\[\delta_{k}=\left\{ \begin{array}{cc}\frac{\lambda_{k}}{\lambda_{k}+\frac{ \varphi}{\text{SNR}}},&k=1,2,\cdots,l,\\ 0,&k=l+1,l+2,\cdots,N,\end{array} \right. \tag{14}\]  
where \(l\) is the length of the CP.
# Development of Intelligent Fuzzy Controller for a Two-Axis Solar Tracking System
## 2 Hardware Framework of Sum Track System
### Stepping Motor  
The stepping motor, which is dual-axis control, is used in the precise angle positioning and control of intermittent rotation. It has the following advantages [17; 18]:  
1. The digital signal can be directly controlled by Open Loop, rather than by a complex feedback control. Thus, the system is simple.
2. The rotational speed changes with the input pulse frequency, and it is therefore easy to control the motor acceleration and deceleration.
3. It is easy to start, has a good reaction to stopping, reversing, and speed changing.
4. The rotation angle of the motor and the input pulse is proportional to the ratio. The error is minor, and is not cumulative.
5. It has a low-speed, high torque rotation, and is brushless. It has a wide range of applications and high reliability.
## 2 Data and Methods
### Semi-Analytical Model
\begin{split} P(\sigma_{s},t,\mu,\chi,h)&=\left(1-2 \bigg{(}\frac{\mu}{\gamma_{1}}^{2}+\frac{\chi}{\gamma_{2}}^{2}\bigg{)}\right) \mathbf{H}^{\mathbf{m}}_{1}[\sigma_{s},t]\\ &+8\Bigg{(}\bigg{(}\frac{\mu^{2}}{\gamma_{1}}\bigg{)}^{4}\mathbf{ H}^{\mathbf{m}}_{11}[\sigma_{s},t]+\bigg{(}\frac{\chi^{2}}{\gamma_{2}} \bigg{)}^{4}\mathbf{H}^{\mathbf{m}}_{12}[\sigma_{s},t]\Bigg{)}\\ &+\triangle h\mathbf{H}^{\mathbf{m}}_{2}[\sigma_{s},t].\end{split}  \tag{5}\]  
We compare this technique to that used by RADS [21] which retracks the LRM and pLRM data from CS2 that relies on previous conversion of the SAR waveforms to a pLRM waveform. These data can be retracked using the same retracking algorithm of [30]. This algorithm, through a series of approximations gives the flat surface impulse response (4) as a combination of scaled exponentials, that give the final return power function (1) as a series of error functions that can be rapidly calculated for any input parameters. Whilst this algorithm is computationally efficient, there are numerous simplifications, particularly for the SAR footprint impulse response of CS2. An alternate closed form of an idealised SAR response was developed as part of the SAR Altimetry Mode Studies and Applications over Ocean, Coastal Zones and Inland Water (SAMOSA) project. These equations use approximations such as an antenna point source and linearising the product of the radar beam gain over the cross section of the antenna [31]. This model of a SAR waveform is available as part of the ESA GPOD platform for all CS2 and Sentinel-3 mission SAR mode data. An alternate semi-analytical form of the echo cross product is also available from [32]. This alternate semi-analytical form gives improved accuracy over a greater range of antenna mispointing angles and relies on fewer of the simplifications discussed above. However, implementing the semi-analytical methods of [32] to create the lookup tables required for the operational retrieval of SWH is beyond the scope of this study.
## 6 Discussion
### Design for Security-Aware Provenance-Based Approach and Implications for Future  
During the exercise, the experts discussed various areas of concern (illustrated in Section 4.2). For example, why security-relevant information on system updates is necessary, how human-centric issues need attention, and how to control and monitor security updates. These concerns also indicate the significance of the security status of different parts of a system. Moreover, experts recommended establishing a dedicated healthcare setup to enhance security and trustworthiness. As a result, this study presents new design opportunities for collaboration between academia and industry, particularly in graphical user interface (GUI) and human-computer interaction (HCI) fields.  
During the exercise, one option discussed was the inclusion of metadata relevant to policies, updates, and industry or government-maintained standards. One critical aspect is how to prevent compromised IoT devices from pretending to be in good operating condition and producing misleading security evidence. Adding evidence on standards and policies used at each layer of the architecture would provide a situational security status to end-users.  
The experts recognized various security evidence as necessary metadata for system security, which currently are not used, for example, technology related to hardware-based security, such as trusted computing [40]. Moreover, the experts mentioned that if the system is designed differently (some suggestions and proposals are provided in Section 4.2), it will enhance the system's security. Therefore, this study provides experts' insights on potential security metadata to be used in future systems. We believe that this inclusion would enhance the system's security status, and the relevant evidence would enrich the system with meaningful security information.  
Finally, the findings from this study can be applied and extended to other areas beyond digital health. An example could be smart home services, such as water meter monitoring in an apartment building or smart grid environments. Furthermore, it can also help empower end-users knowledge in the context of smart office or smart city applications that can become more resilient by visualizing security-aware provenance graph information [16].
## 5 Discussion
### Future Directions  
There are several promising directions for future research in DIR. Firstly, there is room for enhancing the performance of individual weak DIR systems, especially in challenging scenarios like blurred images, skewed documents, or complex layouts. This improvement can be achieved by developing more robust feature extraction methods and key point detection techniques tailored for weak retrievals. Machine learning techniques could also be leveraged to dynamically assign weights to weak DIR systems within ensemble methods. This adaptive weighting strategy could optimize the combination of retrieval approaches based on the specific characteristics of query images and document datasets, potentially leading to more effective results.  
Furthermore, our approach can be extended to various domains beyond education. Exploring how DIR systems can benefit fields such as healthcare, legal, or archival settings where document retrieval from printed materials is prevalent presents an exciting avenue for research. Future work can focus on optimizing algorithms and data structures to maintain accuracy while efficiently handling extensive document collections. Another promising area is human interaction with DIR systems, allowing users to provide relevant feedback on retrieved documents. Incorporating user feedback can lead to more personalized and context-aware retrieval results, enhancing the overall user experience. Finally, there is a growing interest in multimodal retrieval, where textual and visual contents within document images are considered for more comprehensive retrieval. Combining OCR-based text retrieval with image content analysis techniques can improve retrieval accuracy and address the diverse information needs of users. These research directions collectively contribute to advancing the field of DIR and expanding its practical applications.
## 5 Experiments
### Results
#### 5.3.1 N-Way Matching Accuracy  
The evaluation of \(N\)-way learning at each test state was carried out for one-shot and five-shot. For \(N\)-way one-shot learning, we chose an anchor image from one class of test, and then randomly selected \(N\) classes of images to form the support set \(X=\{x_{i}\}_{i=1}^{N}\), where \(x_{1}\), \(\forall x\in X\); the selected image's class was the same as the anchor image \(\hat{x}\), and the other images in the support sets were from different classes. The similarity score between \(\hat{x}\) and other images was calculated using our model. Specifically, if the similarity score of the feature vector of \(x_{1}\), which could be represented as \(S=\{s_{i}\}_{i=1}^{N}\), was the maximum of \(S\), then the task could be labeled as a correct prediction; otherwise, it was regarded as an incorrect prediction. For \(N\)-way five-shot learning, we randomly selected \(N\) unseen classes and six instances, in which five instances of each class were randomly selected as the support set, \(X=\{x_{1},\dots,x_{i}\}_{i=5}^{N}\), and the remaining instances of each class formed the query set: its prediction procedure was the same as the test in the one-shot.  
Figure 10: Loss during training on epochs.  
Figure 9: Network Configurations.  
The matching accuracy of N-way accuracy for the one-shot and five-shot are illustrated in Figure 11. We randomly used 50 pairs of images, 25 containing positive image pairs and 25 containing negative image pairs, to test the effectiveness of our proposed model. As shown in the N-way one-shot result in Figure 11a, 19 out of 25 positive image pairs were matched correctly, while there were 6 true negatives (i.e., 2 positive pairs not matched correctly). Similarly, 23 of 25 negative pairs matched correctly, while there were 2 false positives (i.e., 2 negative pairs matched incorrectly). For the N-way five-shot results (shown in Figure 11b), the accuracy of matching was higher, as almost 22 out 25 pairs matched correctly for both positive and negative pairs, and there were 3 incorrectly matched results.  
Figure 12 shows the projection of the embeddings space, using the two-dimensional principal component analysis (PCA) technique, where each orange point dictates the distance of a positive pair, while each blue point dictates the distance of a negative pair. Figure 12a shows the embedding space before training, while Figure 12b projects the embedding space after training. After training, we could clearly see two distinct clusters--one around the distance calculated for all positive pairs, and the other for negative pairs: this confirmed that our proposed model had learned well, so as to distinguish the similarities among positive pairs and negative pairs and separate them far apart.
## 2 Materials and Methods
### Antifungal Assay  
#### 2.3.1 Petri Plate Assay on Segmented Dishes: Salicylaldehyde (SLD; Fumigant)  
Determination of antifungal activity of SLD, a fumigant benzaldehyde derivative, on the surfaces of crop seeds was performed in segmented Petri plates (100 mm \(\times\) 15 mm; VWR International Co. (Radnor, PA, USA)). These plates were divided into four sections, three of which were filled with corn or pistachio kernels (Figure S1). For the corn testing, corn seeds were soaked in conidial suspensions of _A. flavus_ NRRL 4212 (1 \(\times\) 10\({}^{4}\) CFU mL\({}^{-1}\)), overnight (16 h), and seeds were placed in segmented Petri plates (Total 10 seeds per plate; section 1: 3 seeds, section 2: 3 seeds, section 3: 4 seeds, section 4: SLD). SLD was dissolved in ethanol (60% \(v/v\)) and was applied onto a round Whatman(tm) Grade 1 qualitative paper (2.5 cm in diameter; GE Healthcare Bio-science Co. (Piscataway, NJ, USA)) (Note: Dimethyl sulfoxide can serve as an alternative solvent [50]). Total 120 \(\mu\)L of SLD or ethanol (control) was applied on Whatman(tm) paper (double layer) per each plate (SLD concentrations tested: 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0 mM). Fungal spores on the surface of seeds were exposed to the volatilized SLD.  
The plates containing SLD (or ethanol control) and seeds were then sealed with two layers of Parafilm(r) (American National Can Co. (Chicago, IL, USA)) to prevent escape of volatilized SLD. The plates were incubated at 28 \({}^{\circ}\)C (1, 2 or 3 days). The antifungal treatments, therefore, consisted of (1) control plates: no _A. flavus_ NRRL 4212 without SLD vapor (ethanol only) for determining the level of \"seedborne\" fungal contaminants, (2) control plates: _A. flavus_ without SLD vapor (ethanol only), and (3) treated plates: _A. flavus_ with SLD vapor (0.1 to 1.0 mM on Whatman(tm) paper). After exposure to SLD, corn seeds were placed on PDA recovery plates and the level of fungal survival was monitored for 5-7 days at 28 \({}^{\circ}\)C. Throughout the experiments, corn seeds were not surface-sterilized before treatments.  
A similar antifungal assay was performed using \"unprocessed\" raw pistachio samples (harvested from orchards), as described above, to determine seedborne fungal contaminants only (\"w/o\" _A. flavus_ inoculation).  
Figure 1: Natural products or structural derivatives tested in this study. (**1**) 2,3-Dihydroxybenzaldehyde (2,3-D), (**2**) 2,4-Dihydroxybenzaldehyde (2,4-D), (**3**) 2,5-Dihydroxybenzaldehyde (2,5-D), (**4**) 3,5-Dimethoxybenzaldehyde (3,5-D), (**5**) 2-Hydroxy-4-methoxybenzaldehyde (2H4M), (**6**) 2-Hydroxy-5-methoxybenzaldehyde (2H5M), (**7**) Salicylaldehyde (SLD), (**8**) Salicylic acid (SA), (**9**) 4-isopropyl-3 methylphenol (4 IBM), (**10**) Thymol (THY), (**11**) Carvacrol (CARV), (**12**) 4-Methylcinamic acid (4-MEC), (**13**) 4-Methylcinamic acid (4-MOC).