esa-sceva/satcom-chunk-collection huggingface dataset:
# Observations on the Computation of Eigenvalue and Eigenvector Jacobians +
## 4 Compact Expressions for Eigenvalue and Eigenvector Jacobians  
Using the insights from [39], it is now possible to arrive at a simpler and more efficient solution. Beginning again with Equation (21), left multiply by \(\mathbf{v}_{0}^{H}\) in order to form a new equation:  
\[\mathbf{v}_{0}^{H}\mathbf{A}\mathbf{v}=\lambda\mathbf{v}_{0}^{H}\mathbf{v}. \tag{34}\]Recall that, while it is common to set \(\mathbf{v}_{0}\equiv\mathbf{v}\), \(\mathbf{v}_{0}\) is not a function of \(\mathbf{A}\). Taking the derivative of Equation (34) leads to  
\[\mathbf{v}_{0}^{H}\mathbf{A}\frac{\partial\mathbf{v}}{\partial\mathbf{A}_{ \text{vec}}}+\mathbf{v}^{T}\otimes\mathbf{v}_{0}^{H}=\mathbf{v}_{0}^{H} \mathbf{v}\frac{\partial\lambda}{\partial\mathbf{A}_{\text{vec}}}+\lambda \mathbf{v}_{0}^{H}\frac{\partial\mathbf{v}}{\partial\mathbf{A}_{\text{vec}}} \tag{35}\]  
through simple application of the chain rule and identities pertaining to the vectorization of a matrix. Note again that a superscript of \(T\) indicates a standard transpose while a superscript of \(H\) indicates the Hermitian (or conjugate) transpose. Now, recalling Equation (31), the right most term of Equation (35) vanishes. Thus, after incorporating Equation (30) and a few simple rearrangements, one finds  
\[\frac{\partial\lambda}{\partial\mathbf{A}_{\text{vec}}}=\frac{\mathbf{v}_{0}^ {H}\mathbf{A}\frac{\partial\mathbf{v}}{\partial\mathbf{A}_{\text{vec}}}+ \mathbf{v}^{T}\otimes\mathbf{v}_{0}^{H}}{\alpha}, \tag{36}\]  
which expresses the eigenvalue Jacobian as a function of \(\mathbf{A}\), \(\mathbf{v}\), \(\mathbf{v}_{0}\), \(\partial\mathbf{v}/\partial\mathbf{A}_{\text{vec}}\), and \(\alpha\).  
We now turn our attention to finding a compact expression for the eigenvector Jacobian. Observe that Equations (22) and (36) create a system of two equations with two unknowns. Therefore, substituting Equation (36) into Equation (22),  
\[\left(\mathbf{A}-\lambda\mathbf{I}\right)\frac{\partial\mathbf{v}}{\partial \mathbf{A}_{\text{vec}}}=\frac{\mathbf{v}\left[\mathbf{v}_{0}^{H}\mathbf{A} \frac{\partial\mathbf{v}}{\partial\mathbf{A}_{\text{vec}}}+\mathbf{v}^{T} \otimes\mathbf{v}_{0}^{H}\right]}{\alpha}-\mathbf{v}^{T}\otimes\mathbf{I}, \tag{37}\]  
which can be arranged to give  
\[\left[\mathbf{A}-\lambda\mathbf{I}-\frac{\mathbf{v}\mathbf{v}_{0}^{H}\mathbf{ A}}{\alpha}\right]\frac{\partial\mathbf{v}}{\partial\mathbf{A}_{\text{vec}}}= \frac{\mathbf{v}(\mathbf{v}^{T}\otimes\mathbf{v}_{0}^{H})}{\alpha}-\mathbf{v }^{T}\otimes\mathbf{I} \tag{38}\]  
as an equation that isolates the eigenvector derivative. This expression can be simplified to  
\[\left[\mathbf{A}-\lambda\mathbf{I}-\frac{\mathbf{v}\mathbf{v}_{0}^{H}\mathbf{ A}}{\alpha}\right]\frac{\partial\mathbf{v}}{\partial\mathbf{A}_{\text{vec}}}= \mathbf{v}^{T}\otimes\left(\frac{\mathbf{v}\mathbf{v}_{0}^{H}}{\alpha}- \mathbf{I}\right) \tag{39}\]  
by manipulating the Kronecker products.
# Mobility-Multihoming Duality
## 3 Identifier Locator Network Protocol (ILNP)
### Security and Privacy  
As a basic enhancement to security compared to IPv6, all ILNP packets carry a pseudo-random Nonce value [41] in each new transport flow. This provides protection against off-path attackers, including remote traffic based Denial-of-Service (DoS) attacks and spoofed/forged packets. If the threat model is greater, then an IPsec [42] can be used with an ILNP by using NID values in place of IP addresses for the IPsec security association (SA) [43].  
Figure 2: An example of a Locator Update (LU) handshake for a Mobile Node (MN). The MN discovers a new L64 value via an IPv6 Router Advertisement (RA) message. It updates its local ILNP Communication Cache (ILCC) and sends an LU message to the Correspondent Node (CN). The CN updates its own ILCC and sends a LU-ack (acknowledgement) to the MN.  
For NID values, ILNP can use any of the privacy mechanisms used to generate IID values for IPv6 [44; 45]. However, ILNP has the additional benefit in that it can generate and use ephemeral NID values [46]. A new NID value can be used for each transport layer flow, further improving security by making spoof/forging attacks harder. This also enhances privacy by preventing flow correlation attacks [47]. Furthermore, by using network layer multihoming and multipath communication, it is possible to perturb traffic monitoring and analysis attacks, even for encrypted traffic [48].
# A Hierarchical Sparse Discriminant Autoencoder for Bearing Fault Diagnosis
## 4 Experimental Verification
### Experimental Test 1
#### 4.1.3 Comparison Results of Different Methods  
In order to analyze the performance of the HSDAE method, the proposed method was compared with the methods in the different literature for performance testing. The comparison methods mainly include the following four methods: LGSSAE [36], FDSAE [37], DisAE [38] and BNAE [39]. By considering the contingency of a single experiment, the following experimental results are the average results of 20 experiments.  
It can be summarized from Table 3 that in the four data sets, the HSDAE method has the highest average diagnostic accuracy compared with other methods, and the highest average test accuracy can reach 99.39%. Because the load and speed of the four data sets are different, the comparison method shows different diagnostic performances on the four data sets, and the average test accuracy fluctuates more than 1%. The proposed method has a more stable performance on the four data sets, and the average test accuracy fluctuates less than 0.3%, indicating that it has a certain degree of robustness. Among the comparison methods, BNAE performed the worst, with an average accuracy of about 80%; LGSSAE and FDSAE achieved good results in data set A, but they did not perform well on data sets B, C and D; DisAE has achieved high test accuracy on multiple data sets, but the stability is not as good as HSDAE. In addition, from the standard deviation of the 20 experimental results, the standard deviation of the HSDAE method is the smallest, and its maximum standard deviation is only 0.0040, which shows strong stability compared with other methods.  
To further illustrate the stability of the proposed method, 20 experimental results of data set D are selected, and a box plot is drawn, as shown in Figure 10. Moreover, the smaller the length of the blue box, the smaller the data fluctuation. Therefore, the LGSSAE, DisAE and HSDAE methods are more stable. However, the red \"+\" in the figure indicates the abnormal value in the data. Thus, the LGSSAE method and the DisAE method occasionally lose their diagnostic level. In general, the proposed method shows the best stability among all methods.  
Figure 9: Comparison of t-SNE diagrams of experimental results on data set C. (**a**) The t-SNE graph of data set C; (**b**) the t-SNE diagram of HSDAE features without discriminant distance; (**c**) the t-SNE diagram of HSDAE features.
###### Abstract  
Solid-phase microextraction (SPME) was coupled to gas chromatography mass spectrometry (GC-MS) and a method optimized to quantitatively and qualitatively measure a large array of volatile metabolites in alfalfa glandular trichomes isolated from stems, trichome-free stems, and leaves as part of a non-targeted metabolomics approach. Major SPME extraction parameters optimized included SPME fiber composition, extraction temperature, and extraction time. The optimized SPME method provided the most chemically diverse coverage of alfalfa volatile and semi-volatile metabolites using a DVB/CAR/PDMS fiber, extraction temperature of 60 \({}^{\circ}\)C, and an extraction time of 20 min. Alfalfa SPME-GC-MS profiles were processed using automated peak deconvolution and identification (AMDIS) and quantitative data extraction software (MET-IDEA). A total of 87 trichome, 59 stem, and 99 leaf volatile metabolites were detected after background subtraction which removed contaminants present in ambient air and associated with the fibers and NaOH/EDTA buffer solution containing CaCl\({}_{2}\). Thirty-seven volatile metabolites were detected in all samples, while 15 volatile metabolites were uniquely detected only in glandular trichomes, 9 only in stems, and 33 specifically in leaves as tissue specific volatile metabolites. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) of glandular trichomes, stems, and leaves showed that the volatile metabolic profiles obtained from the optimized SPME-GC-MS method clearly differentiated the three tissues (glandular trichomes, stems, and leaves), and the biochemical basis for this differentiation is discussed. Although optimized using plant tissues, the method can be applied to other types of samples including fruits and other foods.  
Medicago sativa; alfalfa; SPME; GC-MS; volatiles +
Footnote †: journal: Applied Physics Letters  
1 The Samuel Roberts Noble Foundation, 2510 Sam Noble Parkway, Ardmore, OK 73401, USA; 1 [EMAIL_ADDRESS]  
2 Metabolomics Center, Department of Biochemistry, University of Missouri, Columbia, MO 65211, USA; 2 [EMAIL_ADDRESS]  
3 Bayer CropScience, 700 Chesterfield Parkway, West Chesterfield, MO 63017, USA; 3 [EMAIL_ADDRESS]  
4 Correspondence: [EMAIL_ADDRESS]  
5 Current address: Samsung Particulate Matter Research Institute, Samsung Advanced Institute of Technology, 130 Samsung-ro, Yeongtong-gu, Suwon-si 16678, Gyeonggi-do, Korea.  
6
## Data Acquisition and Model Construction
### Characterization and Modelling Extension to Inductor Series  
This characterization and modelling methods have been applied to three Coilcraft inductor series named MSS1210, MSS1260 and XGL6060. The curves in Figure 10 show the characterization results for a set of inductor values in each series. Red dashed lines are presenting the modelled curves with the extraction of parameters listed in Table 2 hereafter. The parameters \(K_{lr}\) and \(P_{lr}\) are equivalent and comparable to the logic shown in [11] as the \(K_{lr}\) (\(k_{i}\) in [11]) is high for material that accepts low frequency as N87 and lower for high-frequency materials. As well, \(P_{lr}\) (\(\alpha\) in [11]) is close to two for high frequency material. These factors show that HF materials will brutally lose their interest at a given frequency (when \(R_{lr}\) became predominant in front of \(R_{hr}\)).  
The produced models are now ready to be used in converter design optimization. It is important to notice that this model offers the possibility to identify the origins of losses. By taking into account \(R_{dc}\) the part of the losses due to RMS currents is identified. Moreover, with \(R_{lr}\) and \(R_{hr}\) the losses due to the high-frequent ripple current are extracted. This will allow comparing and optimising inductor value, switching frequency, and ripple amplitude into converters to minimize total losses.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Inductor Series** & **Factor** & **MSS1210** & **MSS1260** & **XGL6060** \\ \hline Volume (mm\({}^{3}\)) & 1 & 1353 & 777 & 274 \\ \hline \multirow{2}{*}{\(R_{DC}=K_{l}*L^{P_{l}}\)} & \(K_{l}\) & 1 & 430 & 700 & 710 \\ \cline{2-6}  & \(P_{l}\) & 1 & 0.915 & 0.915 & 0.915 \\ \hline \multirow{2}{*}{\(R_{lr}=L*K_{lr}*f^{P_{lr}}\)} & \(K_{lr}\) & \(1\times 10^{-3}\) & 210 & 88 & 0.3 \\ \cline{2-6}  & \(P_{lr}\) & 1 & 1.5 & 1.55 & 1.9 \\ \hline \multirow{2}{*}{\(R_{hr}=L*K_{hr}*f^{P_{lr}}\)} & \(K_{hr}\) & 1 & 67 & 45 & 134 \\ \cline{2-6}  & \(P_{hr}\) & 1 & 1.049 & 1.089 & 1.028 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Series resistance model parameters for the three manufacturer index series.  
Figure 10: Evolution of the losses produced by three inductors under test. Validation of the \(R_{dc}+R_{lr}+R_{hr}\) model.
# Development of Parameters towards Voice Bifurcations
## 2 Materials and Methods
### Case Study Samples and Study Outcomes  
Four cases are chosen to illustrate the parameters' response to various voice types and mid-phonation bifurcations, as shown in Table 4. All cases are of female voice. The three cases with voice disorders (one case of unilateral vocal fold paralysis (UVFP) and  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Parameter** & **Value** \\ \hline Sampling rate & \(f_{s}\) & 2000 \\ Window Size & \(N\) & 100 (50 milliseconds) \\ Window Offset & \(N_{\text{offset}}\) & 20 (10 milliseconds) \\ Window Function & & Hamming \\ Number of PSD Samples & \(N_{\text{fft}}\) & 1024 \\ \(\mathcal{F}_{1}\) Relative Threshold & \(\rho\) & 1/4 (0.25) \\ Minimum \(f_{2}\) frequency & \(f_{g}\) & 25 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experiment Parameter Setup.  
two cases of polyp) exhibit mid-phonon bifurcations; Case 3 appears to have more than 10 observable bifurcations in 0.948 s. These pathological cases, as a result, have multiple signal types: all exhibit Types I and II; Case 4, in addition, exhibits Type-III behavior. For each case, the following short-term measurement outcomes are plotted: spectrogram, frequencies of the selected and considered peaks (\(f_{0}\), \(f_{1}\), \(\mathcal{F}_{1}\)), HDF, and BI.  
Next, two additional experiments are conducted to observe the performance of the spectral peak selection algorithm as its parameters are varied, namely, the \(\mathcal{F}_{1}\) threshold \(\rho\) and the window size \(N\). They are both tested with the Case 3 signal. The following four threshold values are considered: \(\rho\in\{1/2,\ 1/4,\ 1/8,\ 1/16\}\). In decibels, they correspond to \(-3,\ -6,\ -9\), and \(-12\) dB below the second strongest spectral peak power. A more aggressive (lower) threshold setting is expected to increase the incorrect \(f_{1}\) selection in the Type-I window. Additionally, four window sizes are considered: \(N=200\) (100 ms), 150 (75 ms), 100 (50 ms), and 50 (25 ms). While a short window size is desirable for bifurcation detection, too short of a window causes the mainlobes of harmonic peaks to be too wide to distinguish them apart.
# Occlusion-Robust Multi-Target Tracking and Segmentation Framework with Mask Enhancement
## 3 Methodology
### Mask-Enhanced Multi-Object Tracking and Segmentation Framework
#### 3.3.1 Mask-Integrated Multi-Hypothesis Tracking  
The mask-integrated MHT method redefines traditional hypothesis management by incorporating mask shape and occlusion dynamics. The core of the method operates with a short tracklet \(\mathcal{T}\), where each node in the hypothesis tree represents a sequence of mask-annotated detections rather than an isolated bounding box.The hypothesis generation process is governed by geometric validation criteria that evaluate the spatial-temporal compatibility of candidate tracklets. For any given pair of tracklets \(\mathcal{T}_{p}\) and \(\mathcal{T}_{q}\), their associated validity is determined by a mask crossing constraint:  
\[\text{valid}(\mathcal{T}_{p},\mathcal{T}q)=\begin{cases}1,&\text{if }\frac{1}{ \Gamma}\sum_{k=1}^{l}\text{maskIoU}(M_{k}^{p},M_{k}^{q})\geq\tau_{g}\\ 0,&\text{otherwise}\end{cases} \tag{23}\]  
where \(\tau_{g}\) is the threshold that ensures physical plausible associations. This constraint filters out conflicting hypotheses and allows legitimate occlusions, which is an improvement over  
Figure 3: The components in the Mask-Enhanced MOTS-based Framework.  
bounding box based methods that often fail to distinguish overlapping targets. Temporal coherence is modeled by an exponential smoothing mechanism. Each hypothetical branch maintains a coherence score \(\psi_{t}\), which is calculated as follows:  
\[\psi_{t}=\gamma\psi_{t-1}+(1-\gamma)\left(\frac{1}{L}\sum_{k=t-L+1}^{t}|f_{k}-f_{ t}|_{2}\right) \tag{24}\]  
Scores are adaptively weighted by the deviation (\(f_{k}\)) of recent features from the moving average \(f_{t}\), where the smoothing rate is controlled by \(\gamma\). Branches that show sudden coherence drops are truncated as potential tracking failures. To handle dynamic occlusions, the framework integrates the occlusion state classification \(\sigma_{t}^{A}\) from Section 3.2. The hypothesis scoring function \(\mathcal{S}(H_{i}^{j})\) is augmented with an occlusion-aware term:  
\[\mathcal{S}(H_{i}^{j})=\mathcal{S}_{base}+\lambda(\sigma_{t}^{A})\cdot \mathcal{S}_{occ} \tag{25}\]  
The \(\mathcal{S}_{occ}\) term incorporates mask continuity metrics for occluded targets (\(\sigma_{t}^{A}=1\)), motion consistency for occluded targets (\(\sigma_{t}^{A}=2\)), and scene context similarity for scene occluded targets (\(\sigma_{t}^{A}=3\)). This adaptive scoring ensures that the hypothesis is consistent with the observed occlusion patterns.
## 3 Results  
LUMA was able to be implemented based on the previous considerations. Firstly, generating the canonical form of a UCUM unit and extracting the base unit representation was of major importance when establishing the mapping between LOINC and UCUM. Initially, choosing an appropriate unit to generate the representation for all units that would later fit a _Property_ is rather arbitrary, but there is a caveat in the form that the mapping between LOINC and UCUM is unidirectional in nature, seeing as there are multiple _Properties_ that share the same base unit representation. Secondly, a ConceptMap was generated to formalize the associations between LOINC's property axis and the UCUM base unit representations.  
Users interact with LUMA via a graphical user interface that consists of two components. In the first step, users establish mappings between their proprietary units and UCUM by inputting a LOINC code and finding an appropriate unit match from a subset of eligible choices sourced from a list containing roughly over 1300 entries in total. In the second step, users may then optionally perform unit conversions involving their proprietary units by making use of the previously established mappings to UCUM. The LUMA back-end consists of a FHIR terminology server providing the LOINC data set, FHIR CodeSystem, ValueSet and ConceptMap resources, as well as a RESTful UCUM service used for generating human-readable display names, converting units and verifying their base unit representation to match against LOINC's _Properties_. Both tools are coordinated via a Controller implemented independently from the details of the graphical user interface, making it flexible enough to accommodate different usage paradigms.  
Of the 176 quantitative _Properties_, 159 were able to be mapped with confidence. Based on data gathered from three important lists of commonly used LOINC codes, the bulk of all observations and testing actually gathers around a very small subset that is entirely covered by our results. A user evaluation happened with five participants of the LABR laboratory network ([[https://www.ladr.de/](https://www.ladr.de/)]([https://www.ladr.de/](https://www.ladr.de/)) (accessed on 29 April 2022)) in Germany.  
Figure 2: This figure outlines the rough interplay between the components. Specific implementation details have been abstracted.
# Improvement of the Sensor Capability of the NAO Robot by the Integration of a Laser Rangefinder
## 3 Results
### Experimental Setup  
The first tests performed on the robot aimed to verify its behavior's fittingness, in terms of mechanical stability, when the helmet was worn. At the same time, the problems at the different joints and relative servo motors were investigated due to the greater weight placed on the robot's head. Successively, the performance of the rangefinder sensor was tested in comparison with the ultrasound sensor in some operative scenarios.  
Regarding the former issue, several different tests have been performed by carrying out a series of movements, starting with simple movements of the head only and, later, letting the robot walk. In these tests, any stability loss in the robot's movement was monitored, together with any possible critical alteration in the current absorption of the servo motors and the relative operating temperatures. As for the second issue, to verify the improvements obtained by integrating the rangefinder laser sensor, the robot that wore the manufactured helmet was initially tested to compare the performances of the rangefinder sensor with those obtained by the sonar sensor embedded in the NAO robot (test #1). The latter test (test #2) evaluated three scenarios characterized by the presence of both fixed and moving objects. In these scenarios, the NAO robot was stationary or moving, acquiring information about the position of the obstacles in the surrounding environment, both with the camera and the rangefinder sensors. All the tests were performed in a domestic-like environment. In particular, in test #1, the robot was placed in front of a wall in a rest position, and the measure of the distance from the wall was carried out by using the rangefinder sensor over the robot's head and the ultrasound sensor embedded in the robot's trunk. Figure 8a depicts this experimental setup.  
The three scenarios employed for test #2 are as follows:  
* Scenario #1: The robot NAO is moving in an environment of fixed objects. It is advancing towards an obstacle that consists of a large box placed at an initial distance of 200 cm (Figure 8b).
* Scenario #2: The robot NAO is standing in an environment with the presence of moving objects (i.e., the Pepper Robot [54]). In particular, it is standing in front of an open door at a distance of 250 cm while the Pepper robot is moving towards it, passing through the door (see Figure 8c).
* Scenario #3: The NAO robot is advancing in an environment where other objects are also in movement (i.e., the Pepper Robot--see Figure 8d). In particular, it was initially placed in a wide corridor at a distance from the second robot of more than 500 cm (maximum detectable range for the sensor). The acquisition of data began when both robots started to move toward each other.
## 3 Results
### Windows Type and Length Selection  
For the 10 control subjects and the 10 pathological subjects, the Autocorrelation and HNR were extracted using the developed algorithm for nine speech samples (three vowels \(\times\) three tones) with the three windows (Hamming, Hanning, and Blackman) and, for each window, four lengths (3, 6, 12, and 24 glottal periods). Praat software also determined the Autocorrelation and HNR for the same speech samples. The difference in HNR and Autocorrelation between the algorithm and Praat was determined for each speech sample. Finally, the differences were averaged for all 90 patient samples (average over 90 values) for each window and length.  
Table 1 presents the mean of absolute differences and standard deviation for each window/length. The goal is to find the window type and length similar to the Praat reference values.  
From Table 1, it can be observed that for the HNR for the patients, the smallest difference (in bold) occurs with a Hanning window with six cycles (0.26 dB). The smallest  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{3}{*}{**Window**} & \multirow{3}{*}{
\begin{tabular}{c} **Window Length (No.** \\ **of Glottal Cycles)** \\ \end{tabular} } & \multicolumn{4}{c}{**Average of Absolute Differences**} \\ \cline{3-6}  & & \multicolumn{2}{c}{**HNR (dB)**} & \multicolumn{2}{c}{**Autocorrelation**} \\ \cline{3-6}  & & **Patients** & **Control** & **Patients** & **Control** \\ \hline \multirow{3}{*}{Hamming} & 3 & 2.47 (\(\sigma\) = 1.43) & 3.60 (\(\sigma\) = 2.08) & 0.005 (\(\sigma\) = 0.003) & 0.001 (\(\sigma\) = 0.001) \\  & 6 & 0.42 (\(\sigma\) = 0.29) & 0.50 (\(\sigma\) = 0.31) &  
difference for the control subjects is obtained with the Hanning and Blackman windows with six glottal cycles (0.42 and 0.40 dB). Since only one of the windows can be chosen, the Hanning window is the best one, considering controls and patient subjects.  
For the Autocorrelation, the best results are obtained with the three windows types for six glottal cycles (in bold). The smallest difference is 0.001 for the control group and 0.004 for the pathological group.  
In conclusion, for the determination of the Autocorrelation using six glottal cycles with one of the Hanning, Hamming, or Blackman windows gave the closest match to the results obtained using the default settings in Praat.