esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning Based Inversion of Locally Anisotropic Weld Properties from Ultrasonic Array Data
## 5 Conclusions  
A deep learning based framework for the real-time prediction of the spatially varying crystallographic orientations in a weld, from ultrasonic time-of-flight data, is presented. A series of deep neural networks (DNNs) are trained, using 10,000 modelled data pairs, to accurately and rapidly reconstruct grain orientation maps from ultrasonic array data collected using both through-transmission and pulse-echo transducer array configurations. Prior knowledge of the expected microstructures is used to constrain the DNN inversions and we use random perturbations to the Ogilvy weld model to generate the training data. In the case where the test data arises from commercial FE simulations, the trained DNNs predict grain orientations to within 3\({}^{\circ}\) and take approximately 0.04 s on a standard desktop computer. The improvement in defect detection and characterisation capabilities facilitated by access to these estimated grain orientation maps was demonstrated by incorporating the crystallographic orientation estimates into the delay law calculations on which the total focusing method algorithm is based. An improvement of up to 5.3 dB in signal-to-noise ratio was achieved and a strong reflection by a defect not previously detected was observed.  
Overall, these results show significant potential for the use of DNNs to improve NDE capabilities. These methods provide fast and accurate characterisations of the weld structure which can be used to improve subsequent imaging results. Furthermore, this near real-time approach offers potential for extension to NDE of dynamic manufacturing processes, such as those present in the additive manufacturing field.  
Conceptualization, J.S., K.T., A.M. and C.M.; methodology, J.S. and K.T.; software, J.S.; validation, J.S.; formal analysis, J.S. and K.T.; investigation, J.S. and K.T.; resources, C.M.; data curation, J.S.; writing--original draft preparation, J.S. and K.T.; writing--review and editing, A.M. and C.M.; visualization, J.S.; supervision, K.T., A.M. and C.M.; project administration, A.M. and C.M.; funding acquisition, K.T., A.M. and C.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was funded by the Engineering and Physical Sciences Research Council (UK): grant numbers EP/P005268/1 and EP/S001174/1 and the UK Research Centre in Non Destructive Evaluation (RCNDE): AC-for-IPI.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data and Python scripts required to reproduce the findings in this publication are made available at: [[https://github.com/jonnyrsingh/DeepLearningWeldInversion](https://github.com/jonnyrsingh/DeepLearningWeldInversion)]([https://github.com/jonnyrsingh/DeepLearningWeldInversion](https://github.com/jonnyrsingh/DeepLearningWeldInversion)) accessed on 19 November 2021.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Method for Classification
### CDBN Feature Extraction  
Convolutional restricted Boltzmann machines (CRBMs) [39; 40] are the building block of CDBN and CRBM is an extension of RBM [41] to a convolutional setting. CDBN is an unsupervised hierarchical generative model for feature extraction from unlabeled visual data [42] or acoustic signal [43] using layer-wise greedy bottom-up approach. Inspired by these studies, we construct a five-layer CDBN architecture, as shown in Figure 7. The spectral whitening in frequency domain is performed on fixed sized mel spectrogram before feeding into CRBM and the extracted features from each layer are the input to corresponding higher layer of the network. The filters of various sizes, namely [3 \(\times\) 7], [3 \(\times\) 3], [3 \(\times\) 3], and [3 \(\times\) 3] with a fixed pooling size of [2 \(\times\) 2] are used for the consecutive five layers of CDBN, respectively, where the size is denoted as [frequency bins \(\times\) frames].  
The CDBN is solely trained on cat sound data, and the features from each layer are saved for further classification. The feature map of each layer is divided for FDAP and then the feature vectors are concatenated to feed into classifiers. The CDBN features are divided in frequency range with overlapping. The first three layers is divided into four equal bands, and the fourth and fifth layer features are divided into three and two bands respectively. The concatenation of 50 features from each band of the layers results in 1 \(\times\) 850-dimensional feature vector. This feature vector is the input to five classifiers, and the prediction results are ensembled with equal priority to get the final predictions by voting. The learned features of CDBN are illustrated in Table 1.  
Figure 6: Transfer learning of CNN and the classification by extracted features. From layer one to five, the number of segments for frequency division average pooling (FDAP) in each layer is 1, 1, 4, 4, and 3, respectively. Each layer parameters are represented as (_time \(\times\) frequency \(\times\) channel_). Each segment produces a 1 \(\times\) 32-dimensional feature vector, and the concatenated feature vector is fed into various classifiers and voting for the predicted probability of the final ensemble result.
## 3 Experiments
### Entire Network Training  
The pretrained leaf segmentation module was adopted to train the LSA-Net and AE-YOLO networks. First, in the case of LSA-Net, the last loss layer of the leaf segmentation module is removed, and the spot feature encoding module is added to the back of the segmentation module, to construct the ROI-aware FES. Subsequently, the ROI-aware FES is connected with the SAC-SubNet through early and later ROI-aware feature fusions, thereby forming the entire network, as illustrated in Figure 3. Second, the proposed AE-YOLO network also requires a pretrained leaf segmentation module, as illustrated in Figure 4. Similar to the LSA-Net, the pretrained leaf segmentation module without the loss layer is added to the spot feature encoding module to form the ROI-aware FES. Subsequently, the entire network is constructed by incorporating the ROI-aware FES into the YOLO framework. The two entire networks were trained in an end-to-end manner. Therefore, the leaf segmentation module changes its pretrained parameters to be adopted for a new task. In other words, the predicted segmentation map is adjusted during the entire network training, for more accurate leaf disease identification and detection.  
It is worthwhile to check the output feature map of the leaf segmentation module. Figure 7 compares the ground truth segmentation maps and the predicted segmentation feature maps. The predicted segmentation feature maps were extracted from the trained LSA-Net. In Figure 7, it is noted that the predicted segmentation feature maps still possess discriminative features for different areas, that is, background, leaf area, and spot area. In particular, it is observed that the spot areas in the leaves have blue colors, which are clearly distinguishable from the background's red colors, and the leaves' green colors. This indicates that the leaf segmentation module can provide discriminative features. There are a few segmentation errors in the predicted segmentation feature maps. However, the ultimate objective of this study is not accurate segmentation, but leaf disease identification and detection. This implies that the leaf segmentation module is sufficient to serve as a guide to achieve more accurate leaf disease identification and detection. For reference, the mean accuracy of the leaf segmentation module, defined as the ratio of correctly classified pixels to total pixels for each class, is approximately 86%, and the mean intersection over union (IoU), also known as the Jaccard similarity coefficient, is approximately 69% [46].  
Figure 6: Example of apple leaf images: normal leaf images (**first row**), diseased leaf images with marssonina blotch (**second row**), and diseased leaf images with alternaria leaf spot (**third row**).
# IEC-61108-7 SBAS Standard for Shipborne Receivers: Preliminary Testing Validation Activities +
## 3 Sample Results
### TC-004: Dynamic Accuracy Test--B  
This test is similar to TC-003, with the difference being that the vessel is set to travel at 24 knots with varying travel directions (COG--course over ground). The receiver is expected to keep delivering nominal position accuracy under these conditions, as shown in Figure 5. The performance is very similar to that of the previous case for all the considered parameters; the number of tracked and used satellites is the same, and the horizontal errors are also very similar.  
Figure 4: Example results for dynamic accuracy test A (TC-002).  
Figure 3: Example results for the static accuracy test (TC-001).
## 4 Methodology and Implementation
### Client-Server Configuration  
In Figure 3, a client application transmits the encrypted message 'Hello secret world!' one character at a time, and, as each character arrives at the server, it is decrypted. The server processes each character to rebuild the initial message. This process is facilitated by the SMP-OTF4 IOT protocol (Algorithm 1), implemented in Python v.3.12.  
Figure 2: Diagram depictingOn-the-Fly Key Agreement and encryption between \(P_{1}\) and \(P_{2}\) using \(P_{3}\) (in blue) as an auxiliary unit in the SMPC protocol. Data owned by the client, server, and Dummy party are represented in red, green, and blue, respectively.  
The realization of this process requires the simultaneous functioning of three programs: (1) smpc_dummy.py, which acts as an intermediary for generating random numbers; (2) smpc_server.py, responsible for establishing communication with the \"dummy\" service where synchronization begins; and, finally, (3) smpc_client.py, which initializes communication coordination and initiates the sending of encrypted data to the server, synchronously generating keys in real time to transmit each character. Each of these programs uses ZeroC-Ice for network data transfer.  
The complete implementation is available for perusal in the GitLab repository at [[https://gitlab.com/smpc-ufro/smpc](https://gitlab.com/smpc-ufro/smpc)]([https://gitlab.com/smpc-ufro/smpc](https://gitlab.com/smpc-ufro/smpc)).
# Hyperspectral Imaging System with Rotation Platform for Investigation of Jujube Skin Defects
## 3 Results and Discussion
### Spectral Characteristics  
Every pixel of HSI data set used in this study has spectral information. The curves of mean reflectance spectra of different types of jujube skin conditions were shown in Figure 5. The spectral characteristics of normal, decay, cracks and rusty spots were relatively similar in the wavelength range of 680-950 nm. The rapid change in reflectance of vegetation, also known as red edge, in the range of 680-730 nm could be clearly observed. The reflectance of the spectral of glare was higher than those of all other skin types in the whole wavelength range. Moreover, the curve shape of spectral of glare is similar to that of normal surface. The possible reason could be most of the glare pixels in the data set of this study were selected from the glossy normal surface. Spectral characteristics of white and black fungus were similar, and were different from those of other skin conditions. The reflectance curves of the spectral of black and white fungi were lower than those of other skin types. Additionally, there was no rapid change in reflectance in the range of 680-730 nm. This could be due to the spectral signatures of pathogenic fungi of jujubes being different from those of green plant.  
Figure 4: (**a**) Left: The photo of a typical jujube specimen. Right: The directions of faces for linear scan and the axes of rotation for rotation scan. (**b**) The pseudo color images of typical jujube specimen obtained by linear scan. From left to right: front, right, back, left, bottom and top views. (**c**) The pseudo color images of typical jujube specimen obtained by rotation scan. Left and right: rotation about axis 6 and axis 2.  
It was found that there was no reflectance of single band in the wavelength region between 468 nm and 700 nm could be used to segment defect surface from normal using simple thresholding. It was also noticed that the reflectance of the spectra of normal skin was higher than those of other skin types in the region between 710 nm and 840 nm. However, the spectra shown in Figure 5 did not account for the non-uniform light intensity distribution on the spherical jujube surface. The reflectance of the spectra, in the above mentioned wavelength range, of normal surface near the edge could be lower than those of defect surface near the center due to intensity distortion. Thus, using simple thresholding for a single band image in this region might be inadequate for discriminating defects from normal skin. It was further noted that the slope of the curve of reflectance of normal surface spectral was negative, and those of defect surface were positive in the range of 550-630 nm. Therefore, the two-band ratio image based on wavelength images at 550 nm and 630 nm, or the first derivative of the curve of reflectance in the range of 550-630 nm could be considered for developing the detection algorithm to segment the skin defects from normal surface of jujubes.
# Validity of the Enode Sensor and My Jump 3 App for Assessing Countermovement Jump Performance
## 2 Materials and Methods
### Data Collection and Analysis Procedures  
This study employs a cross-sectional observational design to compare measurements obtained from the Enode Sensor and My Jump 3 App against those obtained from a Force Platform, which serves as the gold standard. Cross-sectional studies are well suited for comparing different measurement tools within a single time frame, enabling efficient data collection and analysis [38]. A power analysis was conducted to ensure an adequate sample size for detecting a large effect [39; 40], following [PERSON]'s criteria [41], which determined the appropriate number of participants required for this study. Thus, a sample size of 29 participants is deemed as sufficient for validity studies in cross-sectional designs that compare measurement tools [41].  
Countermovement jump assessments were conducted in a controlled team setting using laboratory-grade equipment within a S&C environment in a single session from 15:00 to 16:30, on a stable and level floor surface under consistent artificial lighting, with the laboratory temperature maintained at 21 \(\pm\) 2 \({}^{\circ}\)C. The participants completed an 8-min treadmill warmup, followed by a 4-min dynamic stretching session that included quadriceps stretches, front stretch kicks, forward lungs, bodyweight squats, and low-intensity countermovement jumps. Clear instructions were provided on performing the countermovement jump without an arm swing. Given that prior experience with countermovement jump assessments was an inclusion criterion, all the participants were already familiar with the protocol. The participants stood with their hands positioned at their hips, performed a countermovement to a depth of their choice, and then jumped as high as they could when ready. Each participant completed three maximum-effort CM]s, with a 2-min rest between attempts, and the highest jump was selected for analysis, as in similar studies [31]. Jump heights were recorded simultaneously on all the devices. The participants performed the jumps on a Force Platform while wearing an Enode Hip Belt with the Enode Sensor secured inside, and each jump was video-recorded using a smartphone for analysis in the My Jump 3 Application.
## 2 Materials and Methods
### Fundamentals of Linear Superposition Method and Parameter Selection Method
Since Equation (4) extends the function over the entire time domain, any point outside \(0\)-\(t_{0}\) where the amplitude of the vibration wave exists will have a value of \(0\):  
\[f(t)=\left\{ \begin{array}{rl}0&t\leq 0\\ g(t)&0<t\leq t_{0}\,,\\ 0&t>t_{0}\end{array} \right. \tag{4}\]  
where \(f(t)\) is the time-domain expansion function of the single-hole blast vibration waveform. On this basis, Equation (5) replaces Equation (1)--a continuous function replaces the discrete one. It avoids the problem that the waves cannot be superposed because the detonator's initiation time \(\delta t_{\text{m}}\) is not equal to any sampling time \(\{t_{n}\}\) of the vibration data:  
\[S^{\prime\prime}(t)=\sum_{\alpha=1}^{M}f(t-\delta t_{m}), \tag{5}\]  
where \(S^{\prime\prime}(t)\) is the predicted waveform function, \(f(t)\) is the single-hole waveform, \(\delta t_{\text{m}}\) is the initiation time of the \(m\)th delay, and \(M\) is the total number of the detonator delays involved in the superposition. \(S^{\prime\prime}(t)\) shows a time-amplitude curve with repeated oscillations; the absolute value of amplitude has the maximum value \(S^{\prime\prime}(t)\)max. Waveform prediction serves in the selection of blasting parameters. Maximizing the charge per hole to increase the advance per round without exceeding the control threshold \([v]\) of vibration velocity is better. Therefore, several single-shot tests collected vibration data \(\{(t_{n},g(t_{n}))\}\) for all possible masses (\(Q\)) of the explosive charged in a single hole. According to the flow described above, the maximum value \(S^{\prime\prime}(t)\)max is obtained. If \(S^{\prime\prime}(t)\)max is less than the preset threshold \([v]\), this charge \(Q\) is available. Then, we took the maximum value of all available charges \(Q\) as the final used charge per hole, which could meet the construction needs of maximizing the advance per round. Figure 1 shows the flow to select the charge amount per hole according to waveform superposition.
## 3 Physical Layer
### Spectrum Sensing Techniques
#### 3.1.1 Matched Filter  
For the detection of known signals, the matched filter technique provides arguably the most efficient and accurate option of spectrum sensing [21, 22, 23]. By identifying and demodulating the target signal, spectrum sensing based on matched filter is able to deliver maximized received signal-to-noise ratio [14] whilst minimizing the effect of other noises. However, the matched filter entirely relies on the availability of _a priori_ knowledge of the target signal, which cannot always be guaranteed. Moreover, the complexity of this approach can be considerable, both logically and physically: on one hand, maintaining the coherency with the target signal required for the demodulation introduces computational overhead, _i.e._, equipment cost [14]; on the other hand, it also tends to rise if multiple target signals are to be dealt with since individual signals have distinct characteristics and therefore require dedicated receivers [15].
## 3 Methodology
### Entrollment and Reconstruction Procedure  
In this section, we present the process of enrollment and reconstruction in Figure 2. In the enrollment stage, the generated random binary code \(K\) is firstly assigned to each user. At the same time, the biometric images \(X\) as input data and random binary code \(K\) as label data are used to train the DNN model. Then, PV is obtained by \(RNG\), and the binary code \(K\) is shuffled to produce the permuted binary code \(K_{R}\) according to PV. Next, the binary codes \(K_{R}\) and \(K\) are encoded into AD by using the fuzzy commitment encoder module. Finally, the generated PV and AD are stored in the database. In our method, only AD and PV are registered to the database instead of biometric data, which can effectively prevent information leakage about biometrics from AD and PV.  
In the bio-key reconstruction stage, we adopt the trained DNN model to generate binary code \(K^{*}\) from the query image. Then, the permuted binary code \(K_{R}^{*}\) is obtained from \(K^{*}\) by random permutation according to PV. Next, the bio-key can be correctly decoded from \(K_{R}^{*}\) and AD by utilizing the fuzzy commitment decoder module when \(K_{R}^{*}\) is close to \(K_{R}\) for genuine queries. Therefore, the final bio-key \(K_{R}\) is recovered.
## 3 Results
### Characterization of the Coatings
However, Cu-TiO\({}_{2}\) coatings exhibit reduced corrosion current density (j\({}_{\text{corr}}\)) when measured against the bare substrate. The protection efficacy (P.E.) of the coatings increased from \(46.5\) to \(73.3\%\) for the \(-25\) and \(-50\) V biased samples and then decreased to above \(60\%\) with a further increase in bias. Moreover, the \(-50\) V biased coating exhibits a marginally lower j\({}_{\text{corr}}\) and higher polarization resistance value than the rest of the coated specimens. The oxide coatings have superior corrosion resistance thanks to the high stability of TiO\({}_{2}\) generated during the sputtering process. Titanium oxide should theoretically protect the substrate from corrosion even if copper ions are eluting [40], thus effectively increasing the anti-corrosion properties of the Ti6 Al4V substrate. Figure 8: Water contact angle of the etched (**a**) and flat (**b**) substrates and coated samples at different bias values. n = 3, * \(p\) < 0.01 compared to control.