esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Amplification and Sequencing of Bacterial 165 rRNA  
The hypervariable 4 (V4) region of the bacterial 165 rRNA coding region was PCR amplified using barcoded, 1-step sequencing primers for each sample [15]. PCR reactions were performed using varying concentrations (5 ng/\(\mu\)L-10 ng/\(\mu\)L) of total sample DNA, along with 0.5 \(\mu\)L of each primer (forward and reverse), 12.5 \(\mu\)L of 2X Hot Start Ready Mix (KAPA Biosystems, Wilmington, MA, USA) and nuclease-free water (Integrated DNA Technologies (IDT), Coralville, IA, USA) to a total volume of 25 \(\mu\)L. PCR reactions were conducted using the following cycling conditions: initial denaturation of 95 \({}^{\circ}\)C for 3 min; 25 cycles of 95 \({}^{\circ}\)C for 30 s, 55 \({}^{\circ}\)C for 30 s and 72 \({}^{\circ}\)C for 30 s; followed by a final extension at 72 \({}^{\circ}\)C for 5 min (Thermocycler; C1000 Touch Thermal Cycler, BioRad, Hercules, CA, USA). PCR products were then assessed by gel electrophoresis in a 1.0% low-melt agarose gel (National Diagnostics, Atlanta, GA, USA). Bright bands at \(\sim\)380 bp were cut out of the gel, placed on a 96-well collection plate and purified using a Zymoclean Gel DNA Recovery Kit (Zymo Research, Irvine, CA, USA). Purified DNA was quantified using a Qubit(r) 2.0 fluorometer, equimolarly pooled and sequenced on an Illumina MiSeq using a v2 kit (2 \(\times\) 250 bp) (Illumina, San Diego, CA, USA) with custom sequencing primers as described by [PERSON] et al. and [PERSON] et al. [13; 15]. A total of 34 samples were sequenced, including 15 experimental (with _L. monocytogenes_), 15 controls (without _L. monocytogenes_) and 4 negative controls (for both DNA extraction and PCR amplification).
# Explainable Artificial Intelligence Approach for Improving Head-Mounted Fault Display Systems
## 3 Results and Discussion
### Customized HMFD Application  
The final stage in our methodology is the integration of the insights presented above into the VR application. The application functions similarly on both the computer and the headset versions, ensuring accessibility even if no functional headset is available. The primary difference between the two versions lies in the movement and interaction controls. Figure 13 illustrates this interaction and the user's placement within a 3D model representation of a wind turbine field. Each turbine is accompanied by a panel displaying readings from various common turbine sensors and a set of buttons allowing the user to navigate different locations within the wind turbine. Interaction relies on controllers emitting rays, like the red one shown in Figure 13, with the weather data display following the position of the left controller.  
The HMFD interaction method, demonstrated in Figure 13, uses controllers emitting rays, while the computer version uses a simple cursor, represented by the red circle in Figure 13. Inside a wind turbine, users can view models of central units, including generators and transformers, providing access to data linked to specific components.  
Figure 14 illustrates the detection of anomalies on a wind turbine component. The potentially faulty component is highlighted with a particle effect. When one wants to display the data associated with sensors whose behavior is abnormal, one highlights them by changing the writing color. If the user is outside a wind turbine, detected anomalies are displayed as shown in Figure 14,c. In this scenario, a generator anomaly is indicated by the red highlighting of the button granting access to it. While comparing Figure 14,c, two levels of anomaly detection become apparent. Figure 14 represents a typical warning, while Figure 14 indicates a critical anomaly, escalating from a warning due to frequent occurrences. In this example, critical errors are defined as anomalies that occur 7 times in the last 30 days.  
Using this information, maintenance operators can promptly decide whether to investigate further on site or replace a part, thus saving time.  
Figure 13: Windfarm monitoring clients’ views: (**a**) HMFD interface, (**b**) PC interface.
# Compositional Analysis and Aroma Evaluation of Feijoa Essential Oils from New Zealand
## 2 Results and Discussion
### Aroma Active Compounds in Feijoa Essential Oil
Compound labels are in accordance to their listing orders in each chemical group in Table 2.  
compounds can be regarded as major contributors to the aroma of feijoa essential oils. Moreover, the PC-2 scale has further separated the four feijoa cultivars into different groups, with Opal Star and Unique in the upper side while Wiki Tu and Apollo in the lower side. This indicates that the Opal Star and Unique group can be better characterized by the 14 compounds in the upper side and the Wiki Tu and Apollo group had closer correlations to the eight compounds in the lower side. More specifically, \(\delta\)-cadinene, 2-undecanone, germacrene D, \(\alpha\)-cubebene, \(\alpha\)-terpineol, \((E)\)-geraniol, \((Z)\)-3-hexenyl butanoate, and the unknown compound (RI of 1367) contributed greatly to the aroma of the essential oils from Opal Star and Unique cultivars. On the other hand, the Apollo and Wiki Tu essential oils were strongly associated with linalool, methyl benzoate, ethyl benzoate, \(\beta\)-myrrene, and the unknown compound (RI of 1153). It is also noted that ethyl hexanoate (ester 1) and 3-octanone (ketone 2) were indifferent compounds in the aroma of the tested feijoa essential oils, as they are positioned on the boundary of PC-2. It is well known that different cultivars of a fruit could have a significant impact on the aroma and flavor of the fruit [43; 44]. Essential oils, containing concentrated aroma compounds, may further magnify the cultivar differences. Therefore, in the potential application of feijoa essential oils in the flavor and fragrance industries, cultivar selection may be of importance to maintain a standardized aroma experience.
# A Hybrid Robust-Learning Architecture for Medical Image Segmentation with Noisy Labels
## 5 Conclusions  
We concentrate on the problem of 3D medical image segmentation with noisy labels. We present a novel noise-robust hybrid architecture to combat noisy labels. Specifically, we propose a slice-level label-quality awareness module, which is jointly trained with a conventional segmentation module to predict label quality scores for each slice. We further introduce regularization loss by distance transform maps to boost the segmentation performance. With the calculated quality scores, we apply a re-weighting strategy on the total loss to distill effective supervision information from relatively clean samples. Our architecture can be trained without knowing the prior noise distribution or the availability of an extra trusted subset. Experimental results on the publicly available medical datasets (CT for liver segmentation and MRI-T2 SPIR for abdomen multi-organ segmentation) demonstrate the effectiveness of our hybrid noise-robust architecture. In the future, we will investigate the relationships of multiple organs and hard sample mining for learning with noisy labels.  
**Author Contributions:** Formal analysis, [PERSON]; Funding acquisition, [PERSON]; Investigation, [PERSON]; Methodology, [PERSON]; Project administration, [PERSON]; Supervision, C.G. and J.W.; Validation, [PERSON]; Writing--original draft, [PERSON]; Writing--review & editing, J.S. and C.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Beijing Municipal Natural Science Foundation (No. L192026) and Tsinghua-Foshan Innovation Special Fund (TFISF) (No. 2020 THFS0111).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Verification Measurements
### Detection Accuracy  
Using the measured data and the given a priori information of the different measurement scenarios, the accuracies indicating the probability of the cross-range direction of the target were calculated. For this purpose, the neural network was trained first, and its classification accuracy was then compared with the accuracy of the unsupervised classification method. For the training of the neural network 1188 data sets were used, which were measured in a predefined test environment. Furthermore, 297 datasets of the measurement scenario #0 were used for the validation of the training. The comparison of the training process and the corresponding classification accuracy of the validation data is shown in Figure 18.  
Figure 16: Measured radar diagram of scenario #0 with indicated time gates.  
Figure 17: Extracted transfer function of the time gated targets with indicated cross-range features.  
As can be seen, the training process reaches 100 percent accuracy after approximately 85 iterations and achieves a very good classification probability of the cross-range direction with 100% validation accuracy. Based on these results, the fundamental proof of the suitability of the concept is provided. With the help of additional training data in different measurement scenarios, the precision of the method could be further improved at this point. The subsequent classification results of the unsupervised and supervised methods for scenarios I-IV can be found in Table 3. For the calculation of the detection accuracies, the classified directions were used and compared to the true direction. Based on the correctly classified directions the mean value (in %) was determined. The training and the calculation of the classes was carried out with the help of MATLAB using an _Intel(R) Core(TM) i7-9700 CPU_. With this setup the training required 3 minutes and the calculation time of the neural network classification required 0.6 seconds. The calculation of the unsupervised method required 0.9 seconds and was therefore slightly slower than the supervised method. However, we expect that the unsupervised method will slow down for an increased number of directions, because of its unparallelizability structure.  
Table 3 shows that the supervised classification has higher accuracy than the unsupervised classification. This is due to the fact that the extraction of the features is more robust towards calibration errors of the radar system. These calibration errors are already taken into account during the training of the network since the training data also contains these errors.  
Figure 18: Plot of the LSTM-training progress using validation data of the measured scenario #0.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scenario No.** & **Accuracy (Unsupervised Method)** & **Accuracy (Supervised Method)** \\ \hline I & 38.89\% & 85.5\% \\ II & 22.73\% & 100\% \\ III & 0\% & 0\% \\ IV & 0\% & 60.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Table of detection accuracies.
## 2 Mathematical Model
### Dynamic Model of Water Surface Take-Off in Inertial Coordinate System  
In this section, the dynamic model established in the body coordinate system \(\mathcal{F}^{b}\) is transformed into the water-air integrated inertial coordinate system \(\mathcal{F}^{I}\) for the convergence of controller design. Let \(R_{\Theta}\) be a transformation matrix from \(\mathcal{F}^{b}\) to \(\mathcal{F}^{I}\), and the dynamic equation of the HAUQ in the coordinate system can be written as  
\[\begin{bmatrix}V^{b}\\ \omega^{b}\end{bmatrix}=\begin{bmatrix}R_{\varphi}^{T}&0_{3\times 3}\\ 0_{3\times 3}&R_{\Theta}^{-1}\end{bmatrix}\begin{bmatrix}\dot{P}\\ \dot{\Theta}\end{bmatrix} \tag{4}\]  
So, there is  
\[(M+M_{\Delta}(t))\dot{V}^{b}= (M+M_{\Delta}(t))\Big{(}\dot{R}_{\varphi}^{T}\dot{P}+R_{\varphi} ^{T}\dot{P}\Big{)}\] \[= (M+M_{\Delta}(t))\Big{(}R_{\varphi}^{T}\dot{P}-S(\omega^{b})V^{b} \Big{)} \tag{5}\]  
Then,  
\[(M+M_{\Delta}(t))R_{\varphi}^{T}\ddot{P}=(M+M_{\Delta}(t))\Big{(} \dot{V}^{b}+S(\omega^{b})V^{b}\Big{)}\] \[(M+M_{\Delta}(t))\Big{(}R_{\varphi}^{T}\Big{)}^{-1}R_{\varphi}^{ T}\ddot{P}=(M+M_{\Delta}(t))\Big{(}R_{\varphi}^{T}\Big{)}^{-1}\dot{V}^{b}+(M+M_{ \Delta}(t))\Big{(}R_{\varphi}^{T}\Big{)}^{-1}S(\omega^{b})V^{b} \tag{6}\]  
According to the property \(R_{\varphi}^{T}=R_{\varphi}^{-1}\) of the orthogonal matrix, we have  
\[(M+M_{\Delta}(t))\ddot{P} =(M+M_{\Delta}(t))\Big{(}R_{\varphi}V^{b}+R_{\varphi}S(\omega^{b} )V^{b}\Big{)}\] \[=R_{\varphi}\big{(}-(M+M_{\Delta}(t))S(\omega^{b})V^{b}+NF^{b}+A_ {\varphi}(V^{b})+R_{\varphi}^{T}(G+G_{\Delta}(t))g+\Delta_{1}\big{)}+(M+M_{ \Delta}(t))R_{\varphi}S(\omega^{b})V^{b}\] \[=R_{\varphi}A_{\varphi}(V^{b})+(G+G_{\Delta}(t))g+R_{\varphi}NF^{b }+R_{\varphi}\Delta_{1} \tag{7}\]  
In addition, \(\dot{\omega}^{b}\) is given by  
\[\dot{\omega}^{b}=\dot{R}_{\Theta}^{-1}\dot{\Theta}+R_{\Theta}^{-1}\dot{\Theta} \tag{8}\]Thus,  
\[(J+J_{\Delta}(t))R_{\Theta}^{-1}\dot{\Theta}= (J+J_{\Delta}(t))\dot{\omega}^{b}-(J+J_{\Delta}(t))\dot{R}_{\Theta}^ {-1}\dot{\Theta}\] \[= -S(\omega^{b})(J+J_{\Delta}(t))\omega^{b}+B_{\omega}(\omega^{b})-( J+J_{\Delta}(t))\dot{R}_{\Theta}^{-1}\dot{\Theta}+(L+L_{\Delta}(t))F^{b}+\Delta_{2} \tag{9}\]  
In summary, the dynamic equation of the HAUQ in the water-air integrated inertial coordinate system is  
\[\begin{cases}(M+M_{\Delta}(t))\ddot{P}=R_{\wp}A_{\wp}(V^{b})+R_{\wp}NF^{b}+(G+G _{\Delta}(t))g+R_{\wp}\Delta_{1}\\ (J+J_{\Delta}(t))R_{\Theta}^{-1}\dot{\Theta}=-S(\omega^{b})(J+J_{\Delta}(t)) \omega^{b}-(J+J_{\Delta}(t))\dot{R}_{\Theta}^{-1}\dot{\Theta}+(L+L_{\Delta}(t ))F^{b}+B_{\omega}(\omega^{b})+\Delta_{2}\end{cases} \tag{10}\]  
It can seen from Equation (10) that the HAUQ also has four independent inputs \(F_{i},\ i=1,...,4\) compared with the traditional quadrotor, but it has six degrees of freedom, \(x(t),y(t),z(t),\)\(\phi(t),\)\(\theta(t),\) and \(\psi(t)\). There exists complex coupling relations between states. The sudden change in the medium causes the complex forces and the additional mass which change quickly in a short time.
# Optical Realization of Wave-Based Analog Computing with Metamaterials
## 2 Computational Metamaterials  
In 2014, [PERSON] et al. [102] proposed a concept of \"computational metamaterials\" by locally tailoring the electromagnetic parameters of the metamaterials, the elaborately designed meta-structures can reshape the spatial profile of the input signal to perform mathematical operations including spatial differentiation, integration and convolution. The basic idea of computational metamaterials is schematically illustrated in Figure 1a as an example. The stacked multi-layered structure is functional as a first-order differentiator, for arbitrary input wavefronts\(f_{1}(y)\) and \(f_{2}(y)\), the corresponding output profiles are proportional to \(df_{1}(y)/dy\) and \(df_{2}(y)/dy\), respectively. Compared with conventional analog signal processors and Fourier optics system, metamaterial-based approach provides more flexible mechanism for manipulation, more integrable volume and subwavelength thickness.  
In general, there are two major protocols to realize metamaterial-based analog computing: metasurface (MS) approach and Green's function (GF) approach. Similar to the classical 4\(f\) systems, the MS approach is based on spatial Fourier transformation, with a single-layered metasurface or multi-layered meta-transmit(reflect)-array to realize the desired transfer function, sandwiched between two subblocks performing the Fourier and inverse Fourier transform (see Figure 1b). In the GF approach, by optimizing the transmitted (reflected) response of the metamaterial slabs it can also act as certain mathematical operations without involving additional Fourier lenses. The former has the advantage of implementation simplicity and the latter has a more compact size. In the following sections, we will briefly review the design principle and recent progress pioneered by these two approaches.
# Spectrogram Classification Using Dissimilarity Space
## 4 Clustering
### K-Medoids  
K-medoids is a clustering technique very similar to k-means. It partitions a set of observations into \(k\) clusters by minimizing the sum of distances between a pattern and the center of that pattern's cluster. The main difference between k-means and k-medoids is that, in the first case, the center of a cluster is its centroid, or mean, whereas, in the latter case, the center is a member, or medoid, of the cluster. A medoid is an observation in a cluster whose sum of distances from the other observations within the cluster is minimal. The basic algorithm for K-medoids loops through the following three steps:  
1. Build-step: each \(k\) cluster is associated with a potential medoid. The first assignment can be performed in various ways; the standard MATLAB's implementations uses the k-means++ heuristic.
2. Swap-step: within each cluster, each point is tested as a potential medoid by checking whether the sum of the within-cluster distances gets smaller using that particular point as the medoid. If so, the point is defined as a new medoid. Every point is then assigned to the cluster with the closest medoid.
3. Repeat steps 1-4 until medoids no longer swap (i.e., until the algorithm converges) or until the maximum number of iterations is reached.
## 4 Discussion
### Limitations  
This pilot study provided an interactive and interesting exercise program for stroke patients aimed at improving their posture and balance. However, there were some limitations to the study. First, the primary limitations were the small sample size and the lack of a control group. The compliance of the subjects may also have affected our recruitment efforts. Second, the study did not use any assessment tools more accurate that the mRS to evaluate the risk of falls. Third, the study did not follow-up on the effects, if any, of the exercise program after the completion of the 12-week training program itself.  
This was a pilot study, and the small number of subjects was a major limitation. In order to ensure patient safety, and because of limitations related to research funding and scheduling, the study had to be implemented in a manner approved by the IRB, such that we could not recruit a larger number of participants.  
In addition, as indicated in the criteria listed in Table 1, we excluded patients at a high risk of falling as indicated by the BBS and mRS scores, as well as patients with certain diseases, such that none of the participating patients was at increased risk for falls. As such, the results of this study cannot be generalized to patients with poorer balance or patients with more severe strokes and certain other diseases.  
Therefore, in future studies, researchers may want to consider recruiting stroke patients who take part in the original rehabilitation protocol without adopting the MITs in order to detect the differences, if any, between the training programs with and without the MITs. For randomized controlled studies, control subjects with the same conditions can be recruited to make sure that the participants who participate in the 12-week exercise program achieve a better ability to control their postural stability. We also recommend study designs including a follow-up period to look into the sustained effects of the investigated exercise program.  
In addition, considering the ceiling effect in BBS, we suggest adding more challenge scales (e.g., the Four Step Square Test) for future studies, so as to ensure the validity and reliability of the results. We also recommend that future studies use the Fall Risk Assessment Score (FRAS) to evaluate the risk of falls before and after the intervention period, look into the sustained effects of the investigated exercise program, and recruit a control group to more effectively investigate the effects of the exercise program.
## 3 Simulation
To avoid simulation errors, the experimental results were averaged over 10 time points. Due to the reduced spectral leakage, adding window functions significantly improves the incoherent accumulation compared to the RW. The five-term MSD and second-order HSCW windows offer the highest SNR gain, followed by the Blackman window and first-order HSCW. The second-order HSCW's incoherent accumulation SNR is 3.28 dB higher than that of the RW. Figure 4: Comparison of different FFT point sizes: (**a**) 128-point FFT; (**b**) 256-point FFT. Figure 5: Comparison of spectral accumulation results under N times of non-coherent accumulation: (**a**) N = 1; (**b**) N = 250; (**c**) N = 500; (**d**) N = 1000; (**e**) N = 2000; (**f**) N = 5000. In order to evaluate the incoherent accumulation results of different window functions, this paper uses the energy center of gravity method to conduct a refined analysis of the frequency spectrum. The number of spectral lines within the main lobe of the window function is \(4K/M\) (\(K\) represents the number of FFT points, and \(M\) represents the number of data points). It follows that the RW, Blackman window, and five-term MSD intra-major flap spectral lines are \(2K/M\), \(6K/M\), and \(10K/M\). The content shown in Figure 7 shows the spectral RMSE obtained after adding different window functions to the noise containing signals of different frequencies, and after accumulating 5000 times through the spectral incoherence, and then correcting by using the energy center of the gravity algorithm. The horizontal coordinate in the figure represents the frequency of the input signal, and the vertical coordinate represents the computed RMSE. Figure 6: SNR gain curve. Figure 7: Comparison of frequency estimation errors. From the figure, it can be seen that due to the wide width of the main lobe of the five-term MSD window, the energy is released too dispersed in the spectrum, and the results obtained by using the energy center method of correction present a large error. With the increase in the order, the RMSE of the frequencies after processing the data with different orders of HSCW decreases with the increase in the order, and the corresponding average RMSE values of the 1 st- to 3 rd-order HSCW are 164.2 kHz, 116.7 kHz, and 101.9 kHz, respectively. The average RMSE values obtained by adding the RW and Blackman's window are 254.9 kHz and 174.3 kHz.