esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Characterization of the Fiber Compound-Ring Lasers
### Tunable Optical Filter  
The insertion losses (ILs) and corresponding full-width-half maximum (FWHM) linewidths within the entire tuning range (1520-1570 nm) of the tunable filter is shown in Figure 4. The insertion losses decrease as the wavelength increases. The maximum IL of 5.5 dB and minimum IL of 2.2 dB were measured at 1520 nm and 1570 nm, respectively. A similar downward trend was also noticed when the FWHM linewidths were plotted against the wavelengths as shown in Figure 4. The linewidth varies from 0.4 to 0.32 nm at 1520 and 1570 nm, respectively.  
Figure 3: Illustrates the output spectra of the fiber laser threshold for each individual SOA gain medium. (**a**) shows the lasing threshold of the SOA\({}_{1}\); (**b**) shows the lasing threshold of the SOA\({}_{2}\) gain medium.  
Figure 2: Amplified spontaneous emission (ASE) spectra of the SOA\({}_{1}\) (green solid line), and ASE of SOA\({}_{2}\) (blue broken line) of the fiber compound-ring laser with both two SOAs set at bias current \(I_{B}\) of 200 mA each.  
Due to the downward trend of the insertion losses from the tunable filter, an upward trend is also expected in the output power of the proposed fiber compound-ring laser for a constant gain setting of the SOAs. Consequently, a constant output power can be obtained over the entire wavelength tuning range by adjusting the bias current (\(I_{B}\)) of the SOAs but at the expense of signal broadening of the fiber laser source. The reflectivity of both SLMs was set at less than 0.1% so that both output ports of the fiber compound-ring lasers (i.e., OUT1 and OUT2) have the same output power. Then, by collecting both clockwise and counter-clockwise propagating light beams through the SLM\({}_{1}\) and SLM\({}_{2}\), respectively, we measured the 3-dB bandwidth at different bias current levels at the 1550-nm wavelength by using an OSA. The 3-dB bandwidth increased from 0.1985 to 0.2182 nm as the bias current was increased to the standard bias current of each of the SOAs (see Table 1).
## 3 Related Concepts
### Weighted Graph Image Filtering (WGIF)  
Filtering images using weighted graph models involves the application of discrete regularization frameworks on weighted graphs to unify image and mesh filtering. This approach leads to various nonlinear filters, such as the bilateral and double-weighted average image filters, which enable the smoothing of images while preserving their structures. Using graph-based algorithms for image segmentation and filtering involves representing an image with a graph, where the vertices are individual pixels, and the edges measure the similarity between the pixels based on user-specified criteria. The weighted graph model allows for the development of edge-aware structures, such as the segment graph, which can be used to design novel image filters for fast structure-preserving smoothing. These filters can smooth out high-contrast details and textures while preserving major image structures well. Mathematically can be expressed as follows:  
\[\mathrm{f_{j}}=\sum\
olimits_{i\in\mathrm{N(i)}}w_{\mathrm{ij}}\mathrm{f_{i}} \tag{7}\]  
where \(\mathrm{f_{j}}\), \(\mathrm{N(i)}\), \(\mathrm{f_{i}}\), and \(\mathrm{w_{ij}}\) denote the updated value of pixel i, set of neighboring pixels of pixel i, value of neighboring pixel i, and weight of the edge between pixels i, j, respectively.
###### Abstract  
This paper presents a practical method of receiving waves having orbital angular momentum (OAM) in the far field of an antenna transmitting multiple OAM modes, each carrying a separate data stream at the same radio frequency (RF). The OAM modes are made to overlap by design of the transmitting antenna structure. They are simultaneously received at a known far-field distance using a minimum of two antennas separated by a short distance tangential to the OAM conical beams' maxima and endowed with different pseudo-Doppler frequency shifts by a modulating arrangement that dynamically interpolates their phases between the two receiving antennas. Subsequently down-converted harmonics of the pseudo-Doppler shifted spectra are linearly combined by sets of weighting coefficients which effectively separate each OAM mode in the frequency domain, resulting in a higher signal-to-noise ratios (SNR) than possible using spatial-domain OAM reception techniques. Moreover, no more than two receiving antennas are necessary to separate any number of OAM modes in principle, unlike conventional MIMO (Multi-Input, Multi-Output) which requires at least K antennas to resolve K spatial modes.  
Keywords:orbital angular momentum; phase mode; twisted waves; radio frequency; receiver; pseudo-Doppler; interpolation; multi-input multi-output; MIMO; frequency-domain; time-gated frequency-shift interpolation +
Footnote †: journal: Applied Sciences
## 5 Use Cases
### Example 1: DNS  
We developed a new service definition (dnsServiceDef.js) and a simple DNS client. The LDAF client initiates a WebSocket connection for the transaction between the client and the LDAF server. The LDAF server sends a DNS query to the predefined DNS server and receives the responses. These transactions are encapsulated in the UDP messages. The service definition includes the parsing rules from the DNS response of the DNS server and forms message syntax for the LDAF client.  
The regular DNS transaction executed by the NodeJS environment results in one request and one response, as shown in Figure 5. The sizes of the messages are 72 B and 88 B, respectively. The DNS data sizes are 30 B and 46 B. The request size might depend on the particular domain name.  
Figure 6 presents the corresponding message sequence between the LDAF client and LDAF server. An HTTP request and response are required to set up the WebSocket. Once the socket is up, there is one request and one response in the transaction. The sizes of the messages are 75 B and 72 B, respectively. The WS data is 15 B and 16 B. Although the LDAF does not address low-latency requirements for IoT applications, we estimated the delay introduced by the application of LDAF server and service. For DNS without LDAF the response time was about 4 ms. The LDAF added \(\sim\)40 ms including the additional delay for TCP session setup.  
The sizes of the messages in regular DNS transactions and in LDAF are comparable. This is because DNS is not a communication-intensive protocol. It applies UDP for transport and therefore gains an advantage of 12 B per packet over WebSocket using TCP. However, if we focus only on data fields in both transactions, the difference is more evident. Binary encoding in LDAF reduces the size of the message content field of all messages to about 1/5.
# Securing IoT Data Using Steganography: A Practical Implementation Approach
## 6 Conclusions  
In this paper, we designed an audio steganography scheme to address the security issues of IoT, which takes into account the collective characteristics of IoT devices. The algorithm is lightweight, noise-resilient, and provides a high payload, which makes it suitable for the deployment of IoT systems. The proposed scheme is based on locating areas to hide data in high frequencies of the audio phase, leading to an increased payload capacity, reduced cover signal disturbance and preserved naturalness of the stego file. The scheme is resilient to noise, where the payload capacity is independent of the signal type, and of low computational complexity, making it appropriate for capability-constrained IoT devices. We utilized audio signals as covers to expand the IoT steganography application range, currently limited to images, and to allow data protection within the increased number of voice-enabled devices. The performance results show that we achieved a low detectability rate of 59.3% and a high payload capacity of 24 kbps at 32 dB \(SNR\). By varying the \(LSB\) depths, the \(SegSNR\) and \(PESQ\) scores gradually increased from 42 to 48 dB and from 4.38 to 4.41, respectively. The simulation and implementation results also indicate that our algorithm (in the presence of the stego channel) is resilient to noise and its performance is very close to the performance of the channel with \(BPSK\), \(QPSK\), \(16-QAM\) and \(64-QAM\) modulation schemes in the absence of the stego channel. In all, the proposed scheme can meet IoT steganography requirements and challenges by being able to provide for data protection, survive IoT communication channel noise, and accommodate a large number of devices and the IoT's large traffic volume.  
This research received no external funding.  
**Data Availability Statement:** No new data were created in this study. Data sharing is not applicable to this article.  
**Conflicts of Interest:** The author declares no conflict of interest.
## 3 Results and Discussion
### Field-Effect ICEO of a Janus micropillar Subjected to a Controllable Gate Voltage
#### 3.3.1 Effect of AC Gate-Voltage Bias on ICEO Mixing- and Pumping-Flow Components  
The net rotating direction of the transversal flow component \(\mathrm{u_{y}}\) for sample mixing can be converted conveniently by a reversal in the polarity of the gate voltage by simply tuning its amplitude \(\mathrm{V_{G}}\) (Figure 5A-D)). In this sense, AC field-effect control over the asymmetric ICEO circulation induced by the Janus pillars opens up new opportunities for handling nanoscale analytes in multiple flow directions in modern microfluidic systems. To be specific, as shown in Figure 5B,C, imposing a small but sufficient negative (positive) gate voltage offset with \(\beta=-0.1\) (\(\beta=0.1\)), the counterclockwise (clockwise) ICEO eddy on the top (bottom) of the central pillar begins to dominate over its clockwise (counterclockwise) counterpart on the bottom (top), in stark contrast with the two symmetric fluid rolls induced by a neutral gate voltage, which agrees well with the schematic diagram in Figure 1E,D. This new up-down asymmetry of ICEO circulation leads to the generation of a net downward (upward) rotating fluid motion \(\mathrm{u_{mix}}\) along the channel's width for a negatively (positively) polarized metal phase (Figure 5B,C), since more cations (anions) than anions (cations) are forced by the negatively (positively) biased gate terminal to accumulate within the bipolar IDL at the metal-phase/electrolyte interface (Figure 1E,D). By applying a sufficiently large negative (positive) gate-voltage bias with \(\beta=-0.9\) (\(\beta=0.9\)) to the metal part of the Janus pillar, however, the central post approaches the voltage supply of the bottom (top) DE plate with \(\mathrm{V_{G}}=0.05\mathrm{V_{D}}\) (\(\mathrm{V_{G}}=0.95\mathrm{V_{D}}\)), and a single ICEO whirlpool swirling in the counterclockwise (clockwise) direction is induced on the right side of the Janus pillar, as shown in Figure 5A,D, instead of the original two symmetric eddies in Figure 4B, free from any gate-voltage offset or asymmetric offset (Figure 5B,C) and subjected to a small gate bias. The reason behind this is that only unipolar cations (anions) exist within the IDL on the ideally polarizable metal surface of the Janus pillar when the negative (positive) gate-voltage deviation approaches its limit value (Figure 5A,D). The net transversal ICEO convection \(\mathrm{u_{y}}\) from the Janus AC-FFET may be of great value in enabling the chaotic mixing of incoming nanoparticles propelled spontaneously by the axial ICEO pump-flow component \(\mathrm{u_{x}}\), even in a biased AC powering scheme responsible for simultaneously inducing the two orthogonal electro-osmotic flow components \(\mathrm{u_{x}}\) and \(\mathrm{u_{y}}\) (Figure 5A-D)). The axial laminar streams driven by \(\mathrm{u_{x}}\) intersect vertically with the local ICEO circulation \(\mathrm{u_{y}}\) of lateral symmetry breaking, resulting in twisted streamlines rolling forward around the voltage-biased Janus pillar.
## 1 Introduction
The RUL prediction of mechanical gyros and its practical applications was illustrated. [PERSON] et al. [12] used truncated normal distribution to build a RUL prediction model of lithium-ion with measurement error based on the Wiener process. Furthermore, the heuristic algorithm was proposed to update real-time parameters. The review in [13] discussed the RUL prediction methods based on the Wiener process and the variants of which are include nonlinearity, covariates, multi-source and variability, moreover, the degradation process of two or more different stages and multivariate were also discussed. In this paper, the method in [8] of these methods is the best candidate for the RUL prediction of the PCCS because of its advantages, such as the recursive update of the degradation coefficient, the EM algorithm is adopted to update the model parameters and the exact PDF of the RUL is deduced based on first hitting time (FHT). The data in our case is not equal interval, so the method in [8] needs to be improved to solve the issue. In this paper, the wear data of the PCCS from Guangzhou Metro are analyzed in the first place. The total life cycle distribution is explored and the wear development trend is investigated. After that, the method in [8] is extended for unequal interval data. Firstly, the method on how to transfer the unequal interval data into equal interval data is investigated in detail. Furthermore, the RUL prediction methods based on Wiener linear model and the parameter estimation methods are briefly reviewed. Besides, the detailed parameter reconstruction algorithm based on UIWGLRCM is addressed. Finally, the proposed RUL prediction method considering unequal data interval is applied to the RUL prediction of the PCCS. The proposed method is also compared with other methods available in the literature. To the best knowledge of the authors, there is not any other report considering the RUL prediction of the PCCS yet. This paper provides a practical method for estimating the RUL of the PCCS. The main contribution of this paper is that the RUL prediction method in [8] is extended to the data with unequal intervals, and it is successfully applied to the RUL prediction of the PCCS. A practical solution is provided to the RUL prediction of the PCCS. The remaining sections in this paper are organized as follows. The PCCS is introduced in Section 2 before the wear data analysis is presented. The RUL prediction issue is also stated in this section. In Section 3, the RUL prediction algorithm based on the Wiener process considering unequal interval data is presented in detail. The RUL prediction case study of the PCCS is provided in Section 4. The conclusions are given in Section 5.
# Transfer Learning in Multimodal Sunflower Drought Stress Detection
## 2 Materials and Methods
### Planting Setup  
A focused selection of twelve sunflower inbred lines was undertaken at the Institute of Field and Vegetable Crops, Novi Sad, Serbia, based on prior evaluations of drought resistance, productivity, and their response to fungal diseases, such as _Marophomina phaseolina_ and _Diaporthe helianthi_. These inbred lines are noteworthy for their roles in breeding programs, particularly as the female parents in the production of sunflower hybrids [25].  
The growth chamber of the Institute of Field and Vegetable Crops, situated at Rimski Sameevi, close to Novi Sad, Serbia (45\({}^{\circ}\)19\({}^{\prime}\)44.7204\({}^{\prime\prime}\) N, 19\({}^{\circ}\)49\({}^{\prime}\)39.29916\({}^{\prime\prime}\) E), was used for the experiments. The experiment was conducted under controlled conditions to evaluate the response of sunflower inbred lines to drought conditions. In factorial experiments with a completely randomized design, inbred lines were cultivated under three distinct soil moisture statuses: well watered (70% gravimetric water content [GWC]) and drought stress (42% and 50% GWC). In a growth chamber with artificial lighting, the experiment was carried out at 23 \(\pm\) 2 \({}^{\circ}\)C, 16/8 h of light and dark, and about 70% relative humidity. The rhizotrons used in the experiment had inner dimensions of 29 \(\times\) 59 \(\times\) 1.5 cm. The majority of the roots would be visible on the transparent side of the rhizotron due to the 45\({}^{\circ}\) rhizotron placement angle. The substrate Klasmann Deilmann Potground P was placed inside each rhizotron, and its GWC was measured with the Moisture Analyzer DBS 60-3 Kern & Sohn GmbH (Balingen, Germany). Before being sown, the sunflower seeds were kept at 23 \({}^{\circ}\)C for 24 h on damp filter paper. One seed that was beginning to germinate was placed at a one-centimeter depth into a rhizotron. On the surface of the soil, a 1 cm layer of Perlit was applied to decrease water loss. One rhizotron was considered to be one replicate. At one GWC level, there were ten randomly distributed replicates of each genotype. The plants were grown in rhizotrons with a 70% GWC until the main root of each plant reached  
Figure 1: Pipeline framework of the proposed methodology for sunflower drought stress detection.  
the base of the rhizotron. The majority of young sunflower plants reached the growing stage of first true leaves in the control (70% GWC) and cotyledon stage under drought stress (50% GWC).
# Bond Fatigue of TRC with Epoxy Impregnated Carbon Textiles
## 3 Results
### Development of Deformation  
#### 3.3.1 General Remarks  
The development of deformation during the cyclic loading can be shown in cyclic creep curves. According to e.g., [24; 25], these curves are divided into three sections. In the first section, the deformation increases rapidly and non-linearly. Afterwards, in section two, the curve increases linearly and less steeply. Section three is also called the beginning of fatigue failure; it is characterized by a non-linear and rapid increase again. The end of section three and thereby also the cyclic creep curve is marked by the failure of the specimen.  
To investigate the deformation behavior of the tested specimens, their cyclic creep curves were compared. Therefore, the average deformation of the two extensometers at mean stress is shown as a function of normalized load cycles, whereby \"0\" marks the beginning of the cyclic loading. Due to the measurement at intervals, curves of specimens with a very small number of cycles to failure consist of just a few measuring points, and in most cases, their failure was not recorded. For this reason, these curves are shown just until the last measuring point during the cyclic loading.  
Exemplary, Figure 6 displays the cyclic creep curves of a failed and a non-failed specimen with the same anchorage length and load level. As one can see, the first two sections of the cyclic creep curve are clearly visible. Remarking on section three is not that simple with the present investigations, because the failure of the specimens mostly was not recorded. Assessing specimens that failed at a quite high number of load cycles, nearly none, or if any, just a low non-linear increase in deformation can be noticed before failure, which means that failure occurs quite abruptly.  
As one can see also in Figure 6, the absolute value of deformation is no indicator of impending failure, because the non-failed specimen reached higher deformations than the failed one with the same anchorage length and load level.
## 2 Materials and Methods
The tensile failure test, unlike the shear test, utilizes two tools: an upper clamp that maintains the Diameter A piece in a fixed position and a lower clamp that is attached to the machine clamp and is moved along with it. The test begins with the movement of the lower clamp, acting upon the Diameter B rod and breaking the weld with pulling force as shown in Figure 8b. This test design provides data regarding the strength of the joints made in this welding process. Once the results of the two orthogonal force tests were obtained, the average of the three values obtained for each test piece was calculated, and the tensile and shear results were combined, thereby determining the force necessary for a weld failure. The transportation and placement of the rebar at the construction site was performed using tools that subject these welds to the combination of efforts proposed. This result is depicted in Figure 9.  
After these tests and calculations were performed, the data were used to apply two filters. A filter in the lower part discarded the values of weld failure under 3430 N, as this would make it difficult to transport the parts by truck. On the other hand, those values in which the resulting (\(\Delta\)real) joint thickness was 15% lower than the theoretical value (A+B) were also discarded. The error threshold was set at 15%. This information is presented in a summarized format in Table 3, which includes the assessment of the welds according to the different parameters utilized herein. Figure 8: Shear (**a**) and tensile (**b**) failure tests. Figure 9: The force necessary for weld failure. Then, we created tables to depict the results obtained in the laboratory for each combination of thickness. To facilitate the interpretation of the results, the valid results were filtered to create summary graphs. In these graphs, the intensity in percentage with respect to the nominal (% maximum machine power) is represented along the X axis, while the tensile strength measured in Newtons (N) is represented along the Y axis. The graphs are represented grouped by the joint thickness. The legend of each graph has three lines. Each line represents three tests with the same electrode pressure (second digit) and different welding cycle times (first digit) expressed as pulses (equivalent of the percentage).
# Turbofan Engine Health Prediction Model Based on ESO-BP Neural Network
## 4 Simulation Experiments and Result Analysis
### Model Evaluation Metrics  
To evaluate the accuracy of the prediction model proposed in this study, three evaluation metrics were used. The root mean square error was used to measure the difference between the predicted and actual values. This metric indicates the average deviation between predicted and actual values and is commonly used in regression tasks. A smaller RMSE value indicates a greater accuracy, and its unit is the same as that of the original data. The \(RMSE\) is calculated using Equation (19).  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}(\hat{y}_{i}-y_{i})^{2}}{n}} \tag{19}\]  
The coefficient of determination, which represents the model's explanatory power for the dependent variable, has a range of values from 0 to 1. A higher value indicates a better fit of the model to the data. The \(R^{2}\) is calculated using Equation (20).  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(\hat{y}_{i}-y_{i})^{2}}{\sum_{i=1}^{n}(y_{i}- \overline{y})^{2}} \tag{20}\]  
It is better for the predicted health index of the turbofan engine to be slightly underestimated rather than overestimated [35]. If the predicted health index cannot reach zero before a failure occurs, it poses a serious safety hazard to the aircraft. To address this issue, this study employs a scoring function (\(Score\)) proposed by the International Conference on Prognostics and Health Management (PHM08) Data Challenge [38]. The scoring function penalizes situations where the model's predicted values are larger more severely. The score is calculated by Equations (21) and (22).  
\[S_{i}=\begin{cases}e^{-\frac{\hat{y}_{i}-y_{i}}{10}}-1,\ \ \hat{y}_{i}<y_{i}\\ e^{\frac{\hat{y}_{i}-y_{i}}{10}}-1,\ \ \hat{y}_{i}\geq y_{i}\end{cases} \tag{21}\]  
\[Score=\sum_{i=1}^{n}S_{i} \tag{22}\]  
Figure 8: Grid search for the number of hidden nodes. The search range was determined by Equation (11).  
From Equation (19) to Equation (22), \(\hat{y}_{i}\) indicates the predicted value of the \(i\)-th sample, while \(y_{i}\) denotes its real value and \(n\) is the total number of samples.