esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Cushion Compression and Offset Test Based on Short-Circuit Force Simulation
### Calculation of Winding Electromagnetic Forces  
The magnetic field is the bridge between the primary and secondary windings for energy transfer. Transformer internal and external windings are usually wound according to a certain type. Assuming that the number of turns of the primary side winding is \(N\), it can be approximated as \(N\) coaxial conducting loops arranged in sequence [28]. The resulting spatial magnetic field exhibits axial symmetry. Therefore, a cylindrical coordinate system is established with the center axis of the conducting loops as the z-axis, assuming that the direction of the current is in the same direction as the line element dl.  
According to Biot-Savart Law, for a conducting ring located at the point \((0,z_{1})\) with radius \(r_{1}\), the magnetic field \(\mathrm{d}B\) generated at any point \(A(r,z)\) in the surrounding space due to the line element dl is given by the following [29]:  
\[\mathrm{d}B=\frac{\mu_{0}Id\mathrm{I}\times\mathbf{r}(A_{1},  
Using the Lorenz force calculation method, the radial and axial short-circuit electromagnetic forces experienced by the winding during transformer short-circuit conditions can be calculated using the following equations:  
\[F_{\text{r}}=\int\limits_{V}\mathbf{J}\times\mathbf{B}_{z}dv \tag{21}\]  
\[F_{z}=\int\limits_{V}\mathbf{J}\times\mathbf{B}_{r}dv \tag{22}\]  
where \(\mathbf{J}\) denotes the winding's short-circuit current density; \(\mathbf{B}_{z}\) and \(\mathbf{B}_{r}\) denote the axial and radial magnetic flux densities of the winding, respectively; and \(F_{r}\) and \(F_{z}\) denote the magnitudes of the radial and axial short-circuit electromagnetic forces on the winding.
# Attenuating Oxidative Damage with Macelignan in Glutamate-Induced HT22 Hippocampal Cells
## 3 Results
### Macelignan Down-Regulated Glutamate-Induced MAPK Phosphorylation  
Glutamate-induced apoptosis has been revealed to be associated with MAPK activation [40]. The regulating effect of macelignan on the MAPK signaling pathway in glutamate-induced apoptosis was examined using Western blot analysis (Figure 5). The  
Figure 4: Effect of macelignan on apoptotic cell death in HT22 cells. Cells were pretreated with Mac (5 and 10 \(\upmu\)M) for 2 h, followed by exposure to glutamate (5 mM) for 12 h. (**a**) Mac reduced chromatin condensation in glutamate-treated cells with Hoechst 33342 staining using a fluorescence microscope (scale bar, 50 \(\upmu\)m); (**b**) Mac inhibited apoptosis in glutamate-treated cells with annexin V/PI staining using a Tali Image-Based Cytometer (scale bar, 5 \(\upmu\)m); (**c**) quantification of apoptotic cells from the obtained images using TaliPCApp software (version 1.0). Values represent the mean \(\pm\) S.D. (\(n\) = 3). # \(p\) < 0.05 in comparison to the untreated cells, * \(p\) < 0.05 in comparison with glutamate-treated cells (ANOVA, Tukey’s test). Mac, macelignan; CTL, control; Glu, glutamate.  
treatment of 5 mM glutamate significantly elevated the phosphorylation of ERK, p38, and JNK, as the figure shows. On the other hand, 5 and 10 mM of macelignan dramatically reversed the expression of p-ERK and p-JNK, whereas no significant change was observed in p-p38 expression. These findings imply that macelignan prevented the activation of MAPK proteins in glutamate-treated HT22 cells.
# Fast Frequency Acquisition and Phase Locking of Nonplanar Ring Oscillators
## 1 Introduction
The frequency sweeping technique based on laser crystal temperature turning was used, with a frequency acquisition time longer than 10 s [2]. For this technique, if the loop lost locking due to a small frequency step during normal operation, it might also need more than 10 s for relocking. As the link duration of inter-satellite communication is typically in the range of 100-200 s for LEO-to-LEO links [1], the acquisition time should be reduced significantly to less than a few seconds, less than 1 s ideally. A shorter acquisition time means a longer communication time and more collected data. In this work, a faster frequency acquisition was achieved via frequency discrimination and piezoelectric frequency control. In addition, smooth continuity between the frequency acquisition process and the phase locking process was achieved. The frequency acquisition range reached 164 MHz, and the acquisition and locking time is less than 0.5 s.
# Influence of Medium Frequency Light/Dark Cycles on the Cultivation of _Auxenochlorella pyrenoidosa_
## 1 Introduction
The results indicate that for the longer light period, the larger microalgae yield at light/dark cycle ranges from 28.3 s to 45 s. This discrepancy may be caused by the experimental setups. In these experimental setups, the different light/dark cycles were obtained by changing the flow rate of the suspension through light and dark zones. For these methods, the frequency of the light/dark cycle is difficult to precisely control because microalgae movement caused by turbulent fluctuation is random. The other one is the light attenuation, where the inner diameter of the tube in experimental setups of the aforementioned literatures were 9 cm [15], 0.48 cm [17], and 0.7 cm [16; 18]. In addition, according to the description of light/dark cycle by [PERSON] et al. [19], the continuous and intermittent light aforementioned experiments were in the same light intensity, and the continuous and intermittent light of equal mean irradiance over time in the medium frequency have attracted little attention. Therefore, the effects of medium frequency light/dark cycles on microalgae are still not thoroughly understood. Based on the above discussion, the previous studies aimed to reveal the relationship between the intermittent light and the productivity of the biomass. However, the limitation in the previous experiments was neglecting the additional light and dark areas brought by the light attenuation in the cultivation systems. Therefore, the hypothesis in this study is that the additional light/dark cycle will decrease the productivity of biomass. Compared to the light/dark deduced by flow through light and dark zones in previous studies, the method of controlling the light on and off allows the frequency of the light/dark cycle to be precisely adjusted. To decrease the light attenuation, an experimental system with a low cell concentration and a short light-path was conducted to keep the microalgae cells in the same light condition. By doing this in this way, the medium frequency light/dark cycle can be easily applied to the microalgae. Due to narrow band wavelength, high brightness, and fast start without delay, five light-emitting diode light (LED) sources (blue, green, red, white, and yellow) and four light intensities were conducted in this study. The effect of two kinds of intermittent light with medium frequency light/dark cycles on the growth of the microalgae _A. pyrenoidosa_ was investigated. The study will provide guidance in designing more efficient microalgae cultivation systems.
# From Symptom Tracking to Contact Tracing:
## 2 Materials and Methods  
On 3 May 2020, we identified the first 100 coronavirus-related iOS apps by searching \"COVID-19\" into the Apple store. Because some of the apps required local phone numbers, national identification codes, and research study codes, we were only able to access a total of 58 apps. On 8 June 2020, we again gathered data on the first 100 coronavirus-related iOS apps, using the same search terms and, due to the same constraints, were only able to access 69 apps. In addition, we recollected data on apps that were originally on the top 100 list in May (and were not in the top 100 in June), if they were still available in the iOS Store as on 8 June 2020.  
We collected data related to app origin, app features and tools, app functionality, privacy policies, and other qualities derived from the M-Health Index and Navigation Database (MIND) at apps.digitalpsych.org [9]. Questions for app origin, country, app functionality, and privacy policies were based on prior work developing a framework for the evaluation of mental health apps [8]. We selected questions relevant to an analysis of COVID apps, including those pertaining to app developer, origin, functionality, accessibility, and privacy and security settings. These questions can all be answered with binary or numeric responses so as to minimize subjectivity.  
Questions in regards to app features were based on an understanding of what features would be most important for users regarding the COVID-19 pandemic. For example, features such as updated statistics, a map, educational materials, and national/local news provide users updated information about confirmed cases, death rates, where potential hotspots are within their county, state, or country, and updated national and/or local protocols. Other features specific to COVID-19 such as contact tracing, telehealth services, community-engagement efforts, and symptom tracking were chosen to be included in the questions as these tools are specific to the COVID-19 pandemic and could possibly help with monitoring the spread of the virus or support the community and patients virtually [10]. We also collected data regarding the presence of mental health resources, through direct support, links to points of care, or other educational information. Ultimately, our questions were chosen to answer four primary questions: Where are COVID-19 apps being developed? What are they doing? Are they adequately protecting user data? How are they changing over time?  
Figure 1: A Framework to Assess coronavirus disease 2019 (COVID-19)-Related Apps. Five main levels of consideration, with guiding questions at each level, illustrate the various facets of evaluation.
## 3 Results and Discussion
### Results of Advanced Performance-Related Characterization Tests
#### 3.3.3 LAS Tests
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Parameter** & **NB** & **SW3** & **SQ3** & **SW4** & **SQ4** & **SW5** & **PW3** & **PQ3** & **PW4** & **PQ4** & **PW5** \\ \hline A & \(5.3\times 10^{4}\) & \(6.9\times 10^{4}\) & \(6.8\times 10^{4}\) & \(97\times 10^{4}\) & \(1.1\times 10^{6}\) & \(2.0\times 10^{5}\) & \(5.7\times 10^{4}\) & \(44.4\times 10^{4}\) & \(6.8\times 10^{4}\) & \(5.3\times 10^{4}\) & \(5.2\times 10^{4}\) \\ B & 2.122 & 2.157 & 2.162 & 2.217 & 2.180 & 2.193 & 2.175 & 2.182 & 2.224 & 2.238 & 2.229 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Fitting parameters of fatigue lines. Figure 12: Results of LAS tests—fatigue lines: (**a**) SBR-modified binders; (**b**) PBR-modified binders. effect on strain sensitivity, with only a small increase of parameter B. On the contrary, PBR polymers had a minor effect on the A parameter, while they produced an increase of parameter B, which was slightly higher than that induced by SBR modification.
# A Model and Quantitative Framework for Evaluating Iterative Steganography
## 3 The Proposed Method
### Characteristic Values  
The characteristic value, \(chIIF^{s}\), is defined as the smallest value of the \(IIF\) function at which the information extracted from the steganogram, \(s\), is sufficient to decode the message, \(m\). The level of \(chIIF^{s}\) serves as a defining feature that strongly characterizes a specific steganographic technique.  
Conversely, the characteristic value, \(chIteration^{s}\), is defined as the first iteration at which the \(IIF\) function attains the characteristic value, \(chIIF^{s}\) (refer to Equation (17)).  
\[chIIF^{s}=IIF\Big{(}s^{chIteration^{s}},\ m\Big{)} \tag{17}\]  
where  
\(s\)--the steganogram created by encoding the message, \(m\);  
\(m\)--the message that is encoded into the steganogram, \(s\);  
\(chIIF^{s}\)--the minimum value of the \(IIF\) required to successfully decode the message, \(m\), from the steganogram, \(s\),  
\(chIteration^{s}\)--the first iteration at which the \(IIF\) reaches the value of \(chIIF^{s}\).  
The characteristic value, \(chIIF^{s}\), serves as an indicator of the robustness of a given steganographic technique and the steganogram, \(s\), itself. A lower value of \(chIIF^{s}\) implies that less decoded information is required to reveal the hidden message, allowing for greater tolerance to distortions or transformations in the steganogram.  
Conversely, the characteristic value, \(chIteration^{s}\), serves as an indicator of the steganogram's capacity. A lower \(chIteration^{s}\) value indicates that the hidden message is decoded faster, requiring fewer iterations. For iterative steganography, this translates to higher capacity, reflecting the ability to encode and decode more bits of information in less time. In the context of capacity, iterative steganography differs significantly from classical, non-iterative steganography. For unitary objects, capacity can be understood as a measure of the amount of information that can be encoded in a single instance, relative to the total information capacity of the individual steganogram. In iterative steganography, where the primary paradigm involves incremental changes in information during each iteration, capacity is instead understood as the amount of information that can be obtained per unit of time or per individual iteration. This distinction highlights why \(chIteration^{s}\) serves as an effective indicator of information capacity in iterative steganography.
## 1 Introduction
Because of their powerful feature extraction and analysis capabilities, neural networks (NNs) have been applied into signal equalization and nonlinear compensation [23; 24; 25; 26]. [PERSON] experimentally demonstrated about 2 dB quality factor (Q-factor) enhancement in terms of fiber nonlinearity compensation using an artificial neural network (ANN) for 40 Gb/s 16 quadrature amplitude modulation (16-QAM) coherent optical orthogonal frequency division multiplexing (CO-OFDM) at 2000 km of uncompensated standard single-mode fiber (SSMF) transmission [27]. [PERSON] employed dynamic multi-layer perceptron networks for long haul transmission systems and demonstrated performance improvement and a significant superiority of neural network complexity over the digital back-propagation method [28]. Based on 20 GHz class optical components, [PERSON] realized the single carrier 100 Gb/s transmission using a NN [29] and also concluded that an NN learning the inner rules of pseudo-random binary sequence (PRBS) would lead to overestimation. In this paper, an improved neural network-based equalization method is proposed and experimentally demonstrated. The up-to-137 Gb/s transmission of PAM-4 signals with 25 G class 850 nm optical devices is achieved over an in-house fabricated 40 cm optical backplane. An in-depth investigation is conducted regarding the impact of delayed taps and spans on equalization performance. A performance comparison of the proposed method with the traditional MLSE and DFE is also undertaken. For the bit rate from 80 to 100 Gb/s, the proposed method achieves the adopted HD-FEC requirement at received optical powers (RoPs) of \(-9\) and \(-8\) dBm, while DFE and MLSE cannot meet the HD-FEC requirement. When the bit rate increases from 120 to 137 Gb/s, proposed equalization method still successfully maintains the acceptable system performance at RoP of \(-4\) and \(-2.5\) dBm. Furthermore, the specific bit error rate (BER) performances for a varied maximum achievable bit rate under different RoPs that apply MLSE and the proposed method are also analyzed. This provides an important reference for future system design. It is worth mentioning that, because our backplane has a large attenuation of 8.7 dB, in order to comprehensively study the performance of the system under different receiving optical powers, we used an independent 850 nm laser and an external modulator in the experiment. As the fabrication technology improves and insertion loss decreases, the proposed algorithm can also be applied to a backplane transmission system based on low cost vertical cavity surface emitting lasers (VCSELs). The remainder of the paper is organized as follows. Section 2 introduces the operation principle of the NN-based equalization scheme. In Section 3, the experimental results and discussions on the evaluation of system performance are presented.
# Evaluating Human versus Machine Learning Performance in a LegalTech Problem
## 2 Materials and Methods
### Study Design  
The legal system in Hungary is a limited precedent-based system, and the judicial practice formally distinguishes six different groups of matters, in other words, law areas: criminal law, military criminal law, administrative law, labor law, civil law, and economic law. The published court decisions counted more than 170,000 documents when the research was done. These documents are relatively long. An average text contains 3330 words. The published case law is entirely in Hungarian, due to the special agglutinative property of the Hungarian language, which makes most natural language processing tasks quite difficult [33].  
We selected 220 documents for this survey. These documents were pre-labeled and cross-checked by legal experts. We used this test set as a reference for further evaluations. It was an important point to select a similar amount of documents from the six different groups of matters by the following two different aspects. Firstly, it has only one exact solution and it can be classified easily; secondly, it has many possible categorizations, and it is very hard to find both.  
We selected a roughly similar set of documents, where only one label and another set with multiple labels could be added. Moreover, we chose an equal proportion of rare categories, where there was little training data for training the algorithm, and common categories, where there were many documents for training data for the machine learning algorithm. We did this in order to simulate the real working conditions and the effect of the monotonicity of the task, the fatigue, and the different learning patterns of the humans had on performance [5,34]. During the labeling process, the participants had to proceed in the same fixed order. During the sorting of the test sets, we put the hardly categorizable decisions after a similar, simple case.  
The participants had three hours to label as many documents as they could. There were 18 participants involved in this study with three different competence levels:  
* Laymen: Never received formal legal training in their life, so they were not a student of any law university and had not received any law-related training. They only met law in their everyday life.
* Lawyers: At least fourth-grade law students or people with law degrees.
* Legal editors: Legal database editors employed by Wolters Kluwer Hungary, whose task is to categorize legal documents and manually enrich them with other metadata.  
Every group was composed of six people, and they were divided into two subgroups. The first subgroup could use the assistance of the machine learning labeler, while the second subgroup did the labeling independently.
# Improving Structural Power Content Analysis Robustness for Satellite Navigation Applications +
## 5 Results
#### Textat  
To further evaluate the performance of the different suggested filter combinations, datasets from the Texas Spoofing Test Battery (TEXBAT) were used [11]. SPCA results on these datasets are already described in reference literature [7; 12]. In this evaluation, these results were reproduced, and an additional set of results was gathered, where an AM jammer source with a duty cycle of 50% was modulated onto the signal in the non-spoofed time frame.  
In Figure 4, the normalized SPCA test statistics for the different filter combinations on the DS7 dataset, which contains a sophisticated matched-power spoofing scenario, are shown [13]. Originally, the file contains a completely clean signal from 0 to 110 s, after which a spoofer is switched on until the end of the scenario. In this figure, it can be seen that a jammer was added from 30 to 85 s, as the SPCA test statistic increases significantly. A clear reduction in this statistic can be noted when using a DC notch filter, both during jamming and spoofing. The difference between a comb noise filter and a notch noise filter seems to be relatively small. The fact that this reduction is seen for both the jamming and spoofing period emphasizes that the application of the DC notch filter (and the notch noise filter) increases jamming resistance, with some decrease in sensitivity with regard to spoofing detection.  
Table 1 shows the truth table results corresponding to the DS7 analysis. These results show that there were next to no false alarms in the no-jamming case, but about 50% for the nominal case, which was expected as the duty cycle of the jammer was set to 50%. It is seen that when no DC notch filter was applied, the SPCA algorithm was triggered on the additive jamming energy, resulting in these false alarms. With the DC notch filter applied, this false alarm rate dropped significantly, where the notch noise filter outperforms the comb noise filter. This table also confirms the reduction in spoofing detection sensitivity, as the notch noise and DC combination, which shows the best jamming resistance, also has a slightly decreased spoofing detection performance (from 75% to 72%).  
Figure 4: The timeseries results of the comparison of filter combinations on the clean and jammed TEXBAT DS7 dataset.
## 3 Description of the Simulation Model
### Generalizations and Assumptions
Currently, there are no publicly available models or principles based on which such decision-making could be modelled accurately. * Active measures by the crew. As per the simulation model structure presented in Figure 1, the network of routes along which ships operate throughout a simulation is assumed fixed. This means that active crew measures, such as maneuverings to avoid local areas with difficult ice conditions, are not considered. As a result, particularly for sea areas with partially ice-covered waters, the simulation outcome can be assumed conservative. In principle, this limitation could be overcome, e.g., by applying a voyage optimization tool as proposed by [25]. However, this would make the approach significantly more complex.
###### Abstract  
The non-proportionally damped system is very common in practical engineering structures. The dynamic equations for these systems, in which the damping matrices are coupled, are very time consuming to solve. In this paper, a modal perturbation method is proposed, which only requires the first few lower real mode shapes of a corresponding undamped system to obtain the complex mode shapes of non-proportionally damped system. In this method, an equivalent proportionally damped system is constructed by taking the real mode shapes of a corresponding undamped system and then transforming the characteristic equation of state space into a set of nonlinear algebraic equations by using the vibration modes of an equivalent proportionally damped system. Two numerical examples are used to illustrate the validity and accuracy of the proposed modal perturbation method. The numerical results show that: (1) with the increase of vibration modes of the corresponding undamped system, the eigenvalues and eigenvectors monotonically converge to exact solutions; (2) the accuracy of the proposed method is significantly higher than the first-order perturbation method and proportional damping method. The calculation time of the proposed method is shorter than the state space method; (3) the method is particularly suitable for finding a few individual orders of frequency and mode of a system with highly non-proportional damping.  
Keywords:modal perturbation method; non-proportional damping; complex modal characteristic equations; undamped system; real modal characteristics +
Footnote †: journal: Applied Physics Letters