esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Quality of Deep Representations for Kepler Light Curves Using Variational Auto-Encoders
## 3 Experimental Setup and Results
### Dataset
#### 3.1.1 Data Representation  
For the detrended flux light curve \(\vec{f}^{(i)}\), we used the global-folded representation proposed by [PERSON] [6]. First, it produces \(\vec{p}^{(i)}\), a vector with the same number of points by folding the detrended light curves on the period (\(P\) with the event centered (fold step). Then, a median window was applied every \(P/T\) times over the folded light curve \(\vec{p}^{(i)}\) (global step) producing \(\vec{x}^{(i)}\), a vector with \(T\) points (empty values masked with 0) such that each light curve has the same length with the width interval depending on the period \(P\). The parameter \(T\) controls the trade-off between a detailed representation and having enough points on each window for the median to be meaningful.  
Figure 4 shows examples of folded and global-folded representations of some light curves (based on Kepler). The second example illustrates a general disadvantage of this method, where long-period KOIs may end up with very narrow transits that fall entirely within a small number of bins. On the other hand, the third example shows how the global-folded representation helps obtain a cleaner version of the light curve when the planets are small.  
As explained in Section 2.2, the delta times (\(\vec{\delta}^{(i)}\)) of \(\vec{x}^{(i)}\) are considered as an additional input for the learning models. Furthermore, please recall that the measurements of the global-folded light curve \(\vec{x}^{(i)}\) were previously scaled through \(\vec{x}^{(i)}_{5}=\frac{\vec{x}^{(i)}}{s^{(i)}}\) for VRAE\({}_{t}\), while the proposed S-VRAE\({}_{t}\) model directly uses the un-scaled global-folded light curve \(\vec{x}^{(i)}\).
# LED Current Balance Using a Variable Voltage Regulator with Low Dropout \(v_{DS}\) Control
###### Abstract  
A cost-effective light-emitting diode (LED) current balance strategy using a variable voltage regulator (VVR) with low dropout \(v_{DS}\) control is proposed. This can regulate the multiple metal-oxide-semiconductor field-effect transistors (MOSFETs) of the linear current regulators (LCR), maintaining low dropout \(v_{DS}\) on the flat \(v_{GS}\)-characteristic curves and making all drain currents almost the same. Simple group LCRs respectively loaded with a string LED are employed to implement the theme. The voltage \(VV_{dc}\) from a VVR is synthesized by a string LED voltage \(Nv_{D}\), source voltage \(v_{R}\), and a specified low dropout \(v_{DS}=V_{Q}\). The \(VV_{dc}\) updates instantly, through the control loop of the master LCR, which means that all slave MOSFETs have almost the same biases on their flat \(v_{GS}\)-characteristic curves. This leads to all of the string LED currents being equal to each other, producing an almost even luminance. An experimental setup with microchip control is built to verify the estimations. Experimental results show that the luminance of all of the string LEDs are almost equal to one another, with a maximum deviation below 1% during a wide dimming range, while keeping all \(v_{DS}\) of the MOSFETs at a low dropout voltage, as expected.  
Keywords:linear current regulator; variable voltage regulator; LED; low dropout voltage +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Real-Time qRT-PCR of Terpene Synthesis-Related Genes  
Pine needles of _P. thunbergii_ with different levels of resistance after inoculation with PWNs for 0 and 24 h were selected as experimental materials (_P. thunbergii_ uninoculated with PWNs was the CK. According to the manufacturer's instructions, total RNA was extracted from _P. thunbergii_ using TRIzol Reagent (Invitrogen, Carlsbad, CA, USA). The RNA concentration and purity were analyzed by a Nano Drop ND-2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). cDNA was synthesized using 1 ug of total RNA by HiScript II Q RT SuperMix for qPCR according to the manufacturer's instructions (Vazyme, Nanjing, China).  
RT-qPCR specific primers were designed according to the coding sequence of pine trees. By the use the EF1a-F gene of _P. thunbergii_ as an internal reference, RT-qPCR of geranylgeranyl diphosphate synthase (_GGPPS_) and 3-hydroxy-3-methylglutaryl-coenzyme A reductase 1 (_HMDH1_), two kinds of Alpha-farnesene synthase (_PT_), were performed by real-time fluorescence quantitative PCR (RT-qPCR) (American Applied Biosystems Company, Foster City, CA, USA). The primers for qPCR were designed on the NCBI website. Data were calculated and analyzed via 7500 Software V2.3 (American Applied Biosystems Company, Foster City, CA, USA). The sequences of the primers used are shown in Table S1. Each RT-qPCR procedure was replicated 3 times.
# Evaluating Packaging Design Relative Feature Importance Using an Artificial Neural Network (ANN)
## 3 Results
### Case Study 1: Relative Feature Importance of the Synthetic Dataset
#### 3.1.2 Relative Feature Importance Analysis of the Synthetic Dataset  
The BCS feature importance in this dataset was evaluated using the four methods mentioned earlier. Each method provided the relative importance of the four BCS features (ECT, thickness of the corrugated board, box perimeter, and box depth). To ensure comparability across methods, the BCS feature importances were normalized so that the sum of the importances for all four features equaled 1 within each method, as illustrated in Figure 8.  
The ranking of BCS feature importance consistently identified by the four methods is ECT > perimeter > thickness > depth. However, the connection weights method shows an unusually high importance for the depth feature, which deviates significantly from the expected value of zero given the synthetic dataset's design (recall that this synthetic dataset was generated using the simplified McKee formula, which does not account for depth. Therefore, the influence of depth on BCS is theoretically zero for this dataset). This discrepancy renders the results from the connection weights method unreliable. Therefore, the feature importance results of the other three approaches were averaged to provide a comprehensive estimate of BCS feature importance. The average BCS feature importance from these three methods shows that ECT has a weight of 0.525, a perimeter of 0.295, a thickness of 0.180, and a depth of 0.004, as illustrated in Figure 9 (left). When compared to the theoretical BCS feature importance ranking calculated using the simplified McKee formula (depicted in Figure 9 (right)), the results from the three reliable methods are closely aligned. This highlights that the ANN approach is a highly promising and effective tool for evaluating the relative importance of packaging design features, offering significant potential to revolutionize the optimization process in packaging design.  
Figure 8: ANN-evaluated BCS feature importance of the synthetic dataset generated using the simplified McKee formula.
## 3 Existing Method
When receiving a Bid Invitations Query message from a mobile host, the server selects data requests that will be included in bid invitations sent with a Bid Invitations message as follows. The server selects currently effective requests for data that are likely to be obtained by the mobile host based on the location of the mobile host. One possible way of selecting such requests is by comparing the distance between the POI and the current location of the mobile host with a threshold distance. In Section 5, we compare the existing method with the proposed method by simulation. In the simulation model, we assume that a mobile host obtains requested data on its planned trajectory, and a server predicts whether a mobile host obtains the requested data as follows. The server uses the location of a mobile host included in a Bid Invitations Query message to update a database of the latest locations of mobile hosts. Then, for each data request, the server calculates the minimum value of \(R\), the radius of circle \(C\) with the POI of the data request as the center, so that the expected number of hosts in circle \(C\) can be greater than or equal to a configuration parameter \(\theta_{n}\) (Figure 4). If the calculated value of \(R\) is larger than the configuration parameter \(R_{\max}\), then \(R\) is set to \(R_{\max}\). The server determines that a mobile host is likely to provide data for the data request if the location of the mobile host recorded in the host location database is within circle \(C\). The current model does not take the planned trajectory of mobile hosts into consideration. The extension of the model to consider the planned trajectory of mobile hosts is for further study. Note that a bid invitation and a bid are protected by a message authentication method, such as an electronic signature, to assure sender authentication and prevent message modification. We assume that some identity information of a mobile host is used by the server for message authentication. This can lead to privacy issues. Privacy issues are for further study. Figure 4: How to select mobile hosts that can obtain data for a data request (existing method, for \(\theta_{n}=5\)). Figure 3: Example of the bidding sequence of the existing method.
## 3 Numerical Simulation Analysis
### Variable Velocity Motion (VV)  
To better align with the communication process under actual underwater complex motion, we incorporated variable velocity motion conditions into the simulation, during which the Doppler factor varies within the duration of the signal, as shown in Figure 6a, while Figure 6b illustrates the variations in acceleration of the relative motion between the transmitter and receiver. The Doppler factor \(\alpha(i)\) for the \(i\)-th symbol is given by the following:  
\[\alpha(i)=\frac{v_{0}\pm v_{0}(1-cos(\frac{\pi i}{n}))}{c},i=1,2,3...n \tag{22}\]  
Here, \(n\) denotes the number of symbols in the transmitted signal, \(c\) represents the speed of sound, and \(v_{0}\) denotes the initial velocity. Figure 5c,d presents the BER curves for variable speed motion under two simulated channels, and it is evident that CCF-MSS outperforms the other methods significantly. When the speed varies in the low-speed range (\(-1.14\) m/s, \(1.14\) m/s), when the BER curve converges to \(10^{-3}\), the performance loss of FDE-MSS compared to CCF-MSS is approximately \(1\) dB. When the speed varies in the high-speed range (\(1.28\) m/s, \(3.84\) m/s) when the BER curve converges to \(10^{-3}\), the performance loss of FDE-MSS compared to CCF-MSS is approximately \(2\) dB. The simulation results under variable speed motion conditions demonstrate that CCF-MSS exhibits superior resilience to  
Figure 6: (**a**) illustrates the variation in Doppler factors during variable velocity motion under two types of channels; (**b**) depicts the changes in acceleration during variable velocity motion under both channels (with identical acceleration variations).  
speed variations compared to FDE-MSS, and when acceleration is constant while speed varies over a larger range, the performance gap becomes even more pronounced.
# Performance Tradeoff Analysis of Hybrid Signaling SWIPT Systems with Nonlinear Power Amplifiers
###### Abstract  
Simultaneous wireless information and power transfer (SWIPT) is a promising technology to achieve wide-area energy transfer by sharing the same radio frequency (RF) signal and infrastructure of legacy wireless communication systems. To enlarge the effective range of energy transfer in practice, it is desirable to have a hybrid signaling SWIPT scheme, which combines a high-power multitone energy signal with a low-power broadband information signal. This paper presents a systematic study on the performance of hybrid signaling SWIPT systems with memoryless nonlinear transmitter power amplifiers (PAs). Using PA efficiency and signal-to-noise-and-distortion ratio (SNDR) as the metrics to measure the efficiency of energy transfer and information transmission, respectively, we derive the tradeoff between these two metrics for two PA classes, two nonlinear PA models, and two SNDR definitions. Our results reveal insights into the fundamental performance tradeoff inherent in SWIPT systems using hybrid signaling schemes.  
SWIPT; SNDR; energy conversion efficiency; nonlinear PA Article
## 4 Methods
### Data Leveling Methods
#### 4.2.3 MSC Method  
Since both 1:200,000 and 1:50,000 scale geochemical data are available, we can use the 1:200,000 data as a standard and adjust the data from the northern and southern regions to the same level. Based on this concept, this study proposes an MSC method. The method primarily uses small-scale data as the calibration standard, adjusting large-scale data (which contains systematic errors) within the small-scale range to the corresponding level.  
The first step is to sparsify the 1:50,000 data. The principle is as follows: for each 1:200,000 data point, we select the nearest 1:50,000 data point as the matching point. In some cases, a 1:200,000 data point may have multiple 1:50,000 data points nearby. In this case, the minimum distance principle is applied, selecting the 1:50,000 data point with the smallest distance for matching.  
The second step is to fit the linear relationship between the sparsified 1:50,000 data and the 1:200,000 data within the corresponding region. To ensure the reliability of the linear relationship, this part, like the BL method, uses the element percentiles and performs line fitting on the scatter plot to obtain the fitting parameters.  
The third step is to apply the fitting parameters obtained in the previous stage to the 1:50,000 data of the northern and southern regions separately to complete the data leveling.
# Development of a Pre-Evaluation and Health Monitoring System for FAST Cable-Net Structure
## 3 Establishment of the FAST MST Model
### Update Process of MST Model
The strain of the main cable can be modified and updated in the MST model accordingly with Equation (4):  
\[\Delta\varepsilon_{j}=\frac{L_{j1}-L_{j2}}{L_{j2}} \tag{4}\]  
Here, \(\Delta\varepsilon_{j}\) is the strain-correction value of the \(j\)th main cable; \(L_{j1}\) is the measurement length of the \(j\)th main cable; and \(L_{j2}\) indicates the theoretical length, which can be calculated by the following equation:  
\[L_{j2}=L_{ch}+L_{cb}+(B_{1}+B_{2})/2 \tag{5}\]  
Here, \(L_{ch}\) represents the length of the cable head of the main cable; \(L_{cb}\) is the length of the cable body of the main cable; \(B_{1}\) and \(B_{2}\) are the outsourcing diameter of the pinhole centre of connectors at both ends, respectively, of the main cable from Table 1.  
Through the above iterative method, the model can be continuously updated to narrow the gap between theoretical and measured values. For FAST, the error of the cross node is not allowed to be greater than 5 mm. When all cross nodes meet this requirement, the model after the last iteration is regarded as usable. We habitually use the APDL language to compile the finite-element program so that the MST model can be automatically updated. A flow chart of the updating process is shown in Figure 5.
# The Effect of Selected Herbal Extracts on Lactic Acid Bacteria Activity
## 3 Results  
### Determination of the Effect of Plant Extracts on Lactic Acid Fermentation  
In the first stage of the research, changes in the pH of milk samples enriched with portions of plant extracts were analyzed. The obtained results are presented in Figure 1. The change in pH during the fermentation process indicated that none of the added plant extracts inhibited fermentation. Statistical analysis showed that only the milk samples added with the first three doses of the extracts (from 0 to 1.0%) constituted one homogeneous group (\(p\) = 0.001), where the pH values were higher by on average 0.2-0.3 than the samples added with higher doses of the tested plant extracts. The milk samples with 1.0% or higher portion of the plant extracts constituted separate homogeneous groups (\(p\) = 0.001) for each analyzed extract. It should be noted that the pH did not reduce below 4.5 for the addition of any extract above 2.0% concentration. For selected additives (sage and cistus), a pH of even 4.1 was achieved for the lowest dose, and 4.8 for 5.0% dose. For the remaining additives, the differences in pH observed between their lowest and the highest portion were approximately 0.4. This difference is significant and confirms that a large amount of additives can inhibit the biochemical activity (i.e., acidifying activity) of LAB present in the yogurt starter culture. In all cases of milk samples fermented in the presence of the tested plant extracts, the pH was no longer statistically significantly reduced after 3.5 h of fermentation.  
Figure 1: The effect of plant extracts on lactic acid fermentation (average values and standard deviations of three replicates): (**a**) valerian (_Valeriana officinalis_ L.); (**b**) sage (_Salvia officinalis_ L.); (**c**) chamomile (_Matricaria chamomilla_ L.); (**d**) cistus (_Cistus_ L.); (**e**) linden blossom (_Tilia_ L.); (**f**) ribiwort plantain (_Plantago laccolata_ L.); and (**g**) marshallow (_Althaea_ L.).
## 4 Discussion
### Role of Temporal Sampling and Evaluation Criterion  
In terms of TAE the scenario 20, a scenario using six measurement periods with 7 days was ranked highest, followed by scenarios 8 and 9. The latter two were scenarios with only four measurement periods, which, however, achieved only moderate model errors. Except for scenarios 1 and 13 which were highly ranked in terms of TAE but not in terms of model error, there was no scenario with only 1 day of measurements per period among the best performing scenarios. This was similar to the findings for the projection of ammonia emissions in the before mentioned study [21]. The ammonia study reported acceptable results with scenarios 25 and 22 in terms of TAE but not in terms of model error. In our current study on methane, however, these two scenarios were rather low ranked in terms of TAE and model error. Moreover, while the study for ammonia reported no scenario with only three measurement periods among the best ranked ones, here we found that scenario 1 and 2 reached acceptable results in term of the TAE, but low quality in terms of model error. In the preceding study on ammonia only the scenario 12 reached good performance in terms of both TAE and model error. In the current study on methane this scenario, however, was ranked only on an intermediate place.  
All of this indicates that while the recommendation of six times 7 days of measurements seems to be rather general, in specific situations and setups also smaller training datasets can achieve good modeling results for gaseous emissions from cattle barns with regularly scraped solid concrete floor. The latter is in accordance with the recommendations of the VERA (i.e., Verification of Environmental Technologies for Agricultural Production) protocol for housing systems which also recommends a minimum of six measurement periods distributed over the year with at least 1 day of measurements [17].  
Moreover, in accordance with the study on ammonia, we found that most of the best performing methods focused on training data from transition periods and measurement periods with a duration of at least 7 days. Among the best performing scenarios winter measurements were particularly underrepresented while transition periods should be preferred. This is likely related to the fact that the emission dynamics during transition times represent conditions close to the annual average while during winter and summer more extreme emission value can be expected. Our results further support the previous findings that scenarios with 7 days and 14 days result in a comparable model performance. In consequence, measurement protocols with 7 days can be considered sufficient, particularly in setups with six measurement periods distributed over the year with a preference for transition periods with moderate temperatures.
# Comparison of Multivariate Analysis Methods as Applied to English Speech
## 2 Methods
### Procedure  
Following the [PERSON] theorem [15] and the 12,000-Hz sampling frequency of the English speech samples, 19 critical-band filters were constructed to cover the frequency range of 50-5300 Hz. Their center frequencies ranged from 75-4800 Hz. The frequency bands were adopted from [PERSON] and [PERSON] (1980) [16]. The frequency range below 50 Hz was not utilized because it should have been less relevant to the speech signal.  
The factor analyses were performed as follows. [PERSON] et al. (2017) calculated the powers of the filter outputs by squaring and moving-averaging them with a Gaussian window of \(\sigma\) = 5 ms. Thus, they obtained a smoothed power fluctuation for each filter, and the powers for the 19 filters were sampled at every 1 ms as 19 variates for factor analysis. In comparison with that method, here we followed [PERSON] et al. (2016) [12], where the speech signals were sampled every 1 ms with a 30-ms-long Hamming window. [PERSON] et al. then transferred every 30-ms-long segment to a power spectrum by Fast Fourier Transform (FFT) [12]. Following this, the power spectrum was smoothed with a 5-ms short-pass filter by cepstral analysis [17]. The origin of the factors, which are represented as orthogonal vectors in the 19-dimensional variate space, was shifted from the gravity center of the data points to the silent point, at which all variates (powers) are zero. In this origin-shifted factor analysis, a silent part in the speech signal is represented always at the silent point. This method thus should be suitable for observing the natures of relatively weak sounds, which are closer to the silent point. This was not the case in [PERSON] et al. (2017) [8], in which a normal factor analysis was used.  
Together, we here performed four different factor analyses: with and without cepstral liftering, and with and without the origin shift. We obtained the factor loadings and took up the factor scores of the central midpoints of time for the labeled phonemes. We then analyzed the distribution of the phonemes as represented in the three-dimensional factor space. The three-dimensional axes derived from the four factor analyses were rotated by varimax rotation [18], resulting in spectral factors.