esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Analysis of the Fructose, Glucose, Sucrose, Maltose, and Ethanol Concentrations in Apple Products  
To determine the sugar concentration, 2-3 mL of sample was diluted with -70 mL of distilled/deionized water, heated to 60 \({}^{\circ}\)C in a water bath for 15 min, clarified with 2.5 mL of Carrez I (85 mM K\({}_{4}\)[Fe(CN)\({}_{6}\)] \(\times\) 3H\({}_{2}\)O) (Sigma-Aldrich, Darmstadt, Germany) and 2.5 mL of Carrez II (250 mM ZnSO\({}_{4}\)\(\times\) 7H\({}_{2}\)O) (Sigma-Aldrich, Darmstadt, Germany) solutions, and made up to 100 mL with distilled/deionized water. After 15 min, the samples were filtered through a filter paper and a 0.22 \(\upmu\)m nylon syringe filter (Sigma-Aldrich, Darmstadt, Germany) before analysis. A standard solution of a sugar's mixture was prepared by dissolving 0.2 g each of fructose (Sigma-Aldrich, Darmstadt, Germany), glucose (Sigma-Aldrich, Darmstadt, Germany), sucrose (Sigma-Aldrich, Darmstadt, Germany), and maltose (Sigma-Aldrich, Darmstadt, Germany) in 100 mL of distilled/deionized water. A 2 mg mL\({}^{-1}\) standard solution of sugar mixture was prepared following dilution with distilled/deionized water. Chromatographic conditions were as follows: the eluent was a mixture of 75 parts by volume of acetonitrile and 25 parts by volume of water; the flow rate was 1.2 mL min\({}^{-1}\); 20 \(\upmu\)L was injected. A YMC-Pack Polyamine II column (250 \(\times\) 4.6 mm, 5 \(\upmu\)m; YMC Co., Ltd., Kyoto, Japan) was used. The column temperature was set at 28 \({}^{\circ}\)C. Detection was performed using an ELSDLIII Evaporative Light Scattering Detector (Shimadzu Corp., Tokyo, Japan).  
Ethanol concentrations were determined enzymatically using a Megazyme Ethanol Assay Kit (Megazyme, Bray, Ireland). The quantification of ethanol requires two enzyme reactions; in the first reaction catalyzed by alcohol dehydrogenase (ADH), ethanol is oxidized to acetaldehyde by nicotinamide-adenine dinucleotide (NAD+). However, since the equilibrium of reaction lies in favor of ethanol and NAD+, a further reaction is required to 'trap' the products. This is achieved by the quantitative oxidation of acetaldehyde to acetic acid in the presence of aldehyde dehydrogenase (Al-DH) and NAD+. The amount of NADH formed in this reaction pathway is stoichiometric with twice the amount of ethanol. It is the NADH which is measured by the increase in absorbance at 340 nm.
# Secure Indoor Localization Based on Extracting Trusted Fingerprint
## 6 Conclusion  
We researched the instability of the RSSI and analyzed the factors that caused the RSSI instability. Over time, physical device performance may deteriorate, equipment may be impaired and the localization environment may change; these factors can cause large changes in the RSSI. In this article, we mainly analyzed the influence of the changing environment, impaired APs, broken APs and newly-introduced APs on the localization system. We found that the instability of the RSSI is mainly the problem of fingerprint matching in the online phase and the offline phase. When the RSSI is unstable  
Figure 13: Localization accuracy analysis.  
or new APs are added and the fingerprint database is unchanged, the difference in RSSI between the online phase and the offline phase is larger, resulting in an increase in localization error. Therefore, the choice of stable and credible fingerprints can effectively improve the localization accuracy.  
We analyzed the changing characteristics of RSSI when the environment was disturbed and the physical equipment was impaired. We found a significant difference between the standard deviation of RSSI, the size of RSSI change and the affected range of RSSI. In addition, when collecting RSSI, the location and direction of the collection point will affect the size of RSSI. We proposed a new fingerprint database update method. The method uses the fixed reference anchor node for fingerprint database updating, which can reduce the interference factor when collecting training data. We proposed the voting mechanism, which can well identify the impact of different factors on RSSI and then extract the trust factor of the fingerprint and locate it by using safe and trusted location fingerprints.  
According to the experimental verification, the proposed algorithm can identify the impaired APs well. In further work, a variety of studies will be possible by detecting impaired APs; for example, calculating the safety index of the area by detecting the impaired frequency of APs and making the hazard warning through the impaired area of APs to avoid unnecessary disasters. In addition, APs deployed in this article adopt an irregularly-shaped deployment. In future work, we can discuss how to deploy APs reasonably so that we receive the most APs in the location area.  
This work was supported by the Key scientific and technological research and development plan of Hunan Province (2017 GK2030), and the National Natural Science Foundation of China (61672220), and Electronic Information and Control of Fujian University Engineering Research CenterMinjiang University.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed reagents/materials/analysis tools; [PERSON] wrote the paper; [PERSON] proposed the architecture of localization system and the model, and prove the experiments.  
The authors declare no conflict of interest.
## 3 The Friendly-Sharing Approach
### Buffer Management
#### 3.2.2 Message Dropping  
Message dropping refers to the order followed when making room for a new arriving message. For this, we need to determine which message or messages have to be dropped in order to make room for the incoming message. This operation is called only when the buffer is full. Figure 2b shows the approaches we considered. More specifically:  
1. _Random:_ The message or messages to be removed from the buffer are selected randomly.
2. _FIFO (First-In, First-Out)_: The first message to be removed from the buffer is the oldest message arriving at the mobile device, taking into account the reception time. From the figure: message \(M3\) that arrived at time \(t_{n}\) will be removed first, and message \(M1\) with arrival time \(t_{n+1}\) will be the next to be deleted.
3. _Oldest:_ With this approach, the message with lower TTL value \(t_{n}\) will be removed from the buffer. In the example in the Figure, \(M3\) will be the first, the next message to be dropped will be \(M1\) by having TTL value \(t_{n+1}\), etc.
4. _Largest:_ This approach removes the largest messages. In the figure, we can see that the dropping queue is organised according to the message size, i.e., \(M3\) with size \(S_{n}\) will be deleted first than message \(M1\) with a message size of \(S_{n+1}\).  
Since only the FIFO method was implemented in the ONE simulator, we implemented the three other policies.
# Design and Analysis of a Hybrid MPPT Method for PV Systems Under Partial Shading Conditions
## 3 Results and Discussion
### Comparison of the Proposed Method with the Other Methods
\begin{table}
\begin{tabular}{l l} \hline \hline
**MPPT** & **Energy Production at MPP** \\ \hline Perturb \& Observe MPPT & 8.825 kWh \\ Perturb \& Observe and Kalman Filter Hybrid MPPT & 8.812 kWh \\ Proposed Perturb \& Observe and Particle Swarm Optimization & 8.803 kWh \\ Hybrid MPPT & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Electric power production of MPPT methods. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**MPPT Algorithm** & **Undershoot Voltage** & **Convergence Time** & **Tracking Accuracy** \\ \hline Perturb \& Observe MPPT & 3 V & 8 ms & Medium \\ Perturb \& Observe and Kalman Filter Hybrid MPPT & 2 V & 6 ms & Medium \\ Proposed Perturb \& Observe and Particle Swarm & 1.5 V & 5 ms & High \\ Optimization Hybrid MPPT & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of MPPT methods based on simulation results. Figure 22: Continuation of the reference voltages graph produced by different algorithms in a case where the irradiance value changes. compared to the others. It is understood that fast tracking is carried out at the moments of irradiance value change. If the limitations of the method proposed in this study are mentioned in terms of benefiting future studies and literature, there may be a possibility of steady-state oscillation for the P&O method under various conditions in the system. In addition, powerful hardware may be needed due to the computational load of the PSO algorithm. Finally, it is useful to take into account that if the parameters of the PSO algorithm are not chosen well, the algorithm may get stuck in local minima.
## 2 Stiffness Calculation
### Mesh Stiffness Calculation of Healthy Gears
#### 2.1.3 Calculation of Comprehensive Mesh Stiffness  
For the above two cases, since the gear is not a rigid body absolutely, the flexible deformation of the gear matrix should be considered when calculating the comprehensive stiffness, and the corresponding flexible stiffness can be expressed as [4]:  
\[\frac{1}{k_{f}}=\frac{\cos^{2}\alpha}{EL}\left\{L^{*}\left(\frac{\mu_{f}}{S_{f }}\right)^{2}+M^{*}\left(\frac{\mu_{f}}{S_{f}}\right)+P^{*}\left(1+Q^{*}\tan^{ 2}\alpha\right)\right\} \tag{30}\]  
where \(L\) is the gear tooth width, \(\mu_{f}\) and \(S_{f}\) are shown in Figure 5. The coefficients \(L^{*}\), \(M^{*}\), \(P^{*}\) and \(Q^{*}\) can be calculated from the following polynomials.  
\[X_{i}^{*}\left(h_{fi},\theta_{f}\right)=A_{i}/\theta_{f}^{2}+B_{i}h_{fi}^{2}+ C_{i}h_{fi}/\theta_{f}+D_{i}/\theta_{f}+E_{i}h_{fi}+F_{i} \tag{31}\]  
where, \(X_{i}^{*}\) can be the coefficient \(L^{*}\), \(M^{*}\), \(P^{*}\) and \(Q^{*}\). \(h_{fi}=r_{f}/r_{\rm int}\). The definition of \(\theta_{f}\) is given in Figure 5. The parameter values in the Formula (31) are shown in reference [25].  
After considering four kinds of potential energy and flexible deformation of gear matrix, the comprehensive mesh stiffness of a pair of gear tooth can be expressed as follows:  
\[k=1/\left(\frac{1}{k_{h}}+\frac{1}{k_{b1}}+\frac{1}{k_{a1}}+\frac{1}{k_{s1}}+ \frac{1}{k_{f1}}+\frac{1}{k_{b2}}+\frac{1}{k_{a2}}+\frac{1}{k_{s2}}+\frac{1}{k _{f2}}\right) \tag{32}\]  
Figure 5: The instructions of \(\mu_{f}\) and \(S_{f}\).  
The total potential energy of a pair of mesh gears can be expressed as follows:  
\[\begin{array}{ll}U&=\frac{F^{2}}{2\xi}=U_{h}+U_{b1}+U_{a1}+U_{s1}+U_{b2}+U_{a2}+U _{s2}\\ &=\frac{F^{2}}{2}\Big{(}\frac{1}{k_{h}}+\frac{1}{k_{h1}}+\frac{1}{k_{a1}}+\frac {1}{k_{a1}}+\frac{1}{k_{b2}}+\frac{1}{k_{a2}}+\frac{1}{k_{a2}}\Big{)}\end{array} \tag{33}\]  
where the subscripts 1 and 2 represent the driving gear and driven gear, respectively. For example, \(k_{b1}\) indicates the bending stiffness of the driving gear, and other symbols follow similar naming rules.
# Experimental and Numerical Analysis of an Innovative Combined String-Cable Bridge
## 2 Experimental Program
### Arrangement of Measuring Points and Loading Scheme  
Two types of measuring tools were selected for this model test (Figures 4 and 5): displacement meters and strain gauges. A Novotechnik TYP:TR-0100, ART No 023264, F.NR 119988/A data logger was used to record displacement data. The vertical displacements of the model were measured at 16 points (see Figure 4). The stresses of the string and cable were measured by stress gauges with a base of 20 mm (resistance of R = 202.3 \(\Omega\)). All strain gauges (28 pcs, see Figure 5) were connected to the Ahlborn ALMEMO 5990-2 device, which was used to obtain all the data. The ALMEMO(r) 5990-2 data logger has max. 90 electrically isolated measuring inputs with up to 100 measuring channels for more than 65 measuring ranges, a real time clock, and a 500 kB memory for approximately 100,000 measured values. Memory cards up to 32 MB can be attached. The device can be operated by means of the LCD graphic display, a softkey keypad, and a control key. User menus can be configured to adapt the display for any application. Two output sockets allow for connecting any ALMEMO(r) output modules, for example, the analogue output, digital interface, trigger input or alarm contacts. Several devices can be networked by simply connecting them with network cables.  
The numbers for the displacements and strain gauges are based on the data and inputs of the Ahlborn ALMEMO 5990-2 device. In Figure 5, sensors from 70-79 are placed in the right part of the model, sensors 80-89 in the left part of the model, and sensors 90-99 in the central part of the model. The tensions of the lower cable are measured by 4 sensors (83, 88, 94, 73, see Figure 5), and those of the string by 24 sensors. This proportion of strain gauge placement was chosen due to the capabilities of the measuring equipment and the maximum number of sensors. Since the focus is on the string that takes the load directly, most of the strain gauges were installed on it.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Bar No. (mm)** & **Modulus of Elasticity (N/mm2)** & **Tensile Strength Rm (MPa)** & **Offset Yield Strength RP0.2 (MPa)** \\ \hline
1 & 200.11 & 857 & 808 \\
2 & 203.98 & 859 & 804 \\
3 & 201.26 & 859 & 810 \\
4 & 205.87 & 853 & 793 \\ Average: & 202.81 & 857 & 803.75 \\ \hline \end{tabular}
\end{table}
Table 1: Mechanical properties of bar elements (6 mm diameter).  
Figure 4: Scheme of displacement sensors. Where: (1, 2, 3...) numbers of sensors.
## 7 Mode-Locking Techniques
### Hard/Soft Aperture Mode-Locking  
In order to prefer ML operation, one can exploit the variation of \(w_{0}^{(ML)}\) compared to \(w_{0}^{(CW)}\) (Figure 4) in combination with an aperture to introduce an intensity-dependent loss mechanism into the cavity. Usually, the working point for mode-locking is near a stability limit, where the laser mode is tightly focused into the crystal, maximizing the intensity-dependent Kerr effect. At a given \(\delta\) close to a stability limit (Equation (5)), the crystal position can be translated along the beam propagation axis to affect the Kerr strength, defined by:  
\[\gamma=\frac{P_{c}}{w_{cw}}\left(\frac{dw}{dP}\right)_{P=0} \tag{14}\]  
where \(P\) is the pulse peak power and \(w\) is the mode radius at one of the end mirrors. The Kerr strength of Equation (14) represents the change of the mode size due to a small increase in \(P\) (normalized to the CW mode size), which is a measure for the effect of the Kerr lens on the cavity. Investigating the variation of \(\gamma\) as a function of the crystal position, one finds two possible mechanisms to inflict loss on the CW mode [12]: hard aperture, which is applicable when the ML mode is smaller than the CW mode, as in Figure 4a; and soft aperture, which is applicable in the opposite scenario when the ML mode is larger than the CW mode, as in Figure 4b.
# Overview on Different Types of Solar Cells: An Update
## 3 Perovskite Solar Cells
### Current Progress of Tandem Solar Cells  
A steep absorption edge, exceptionally low sub-gap absorption, tunable bandgap (by changing halide composition), and desirable V\({}_{\text{oc}}\) of 1.15 V corroborate the hybrid perovskite as a suitable candidate for top wide-bandgap cells in the tandem configuration. Furthermore, the low-temperature fabrication of perovskite helps to monolithically integrate it as a top cell without damaging the bottom cell. A high power-conversion efficiency (PCE) of 32.5% and 29.8% has been achieved in monolithic 2T and 4T perovskite/silicontandem solar cells, where perovskite was used as the top sub-cell and silicon as the bottom cells, respectively. Other than the above, various perovskite-based tandem cells such as perovskite/CZTSSe (PCE-14.2%), perovskite/CIGS (PCE-26.8%), perovskite-DSSC (PCE-17.5%), and perovskite-perovskite (PCE-25.9%) have been widely developed in recent years and will be ready for commercialization within a couple of years.
# Walking Gait Phase Detection Based on Acceleration Signals Using LSTM-DNN Algorithm
## 4 Experimental Results  
From Table 2, it can be found that the SVM model has the highest accuracy and F-score value when the RBF kernel function is taken at the step speed of 0.78 m/s, 1.0 m/s, and 1.25 m/s. Thus, it can be determined that the kernel function of the SVM is an RBF kernel function. When the kernel function of SVM selects RBF, the recognition accuracy of the model under three kinds of asynchronous speeds is 77%, 71%, and 75%, respectively, and the F-score value of the model also reaches 78%, 72.8% and 75.9%, respectively. From Table 3, it can be found that the KNN model has the highest accuracy and F-score value when the pace is 0.78 m/s and 1.0 m/s. When K takes 2, the accuracy and F-score value are the highest. Thus, it can be determined that the K value of the KNN is 5, 5, 2, respectively, at a step speed of 0.78 m/s, 1.0 m/s, and 1.25 m/s. KNN's accuracy is 76%, 69%, and 73% at three different paces, and its F-score is 77%, 70%, and 75% at three different paces.  
Table 4, Table 5, and Table 6 respectively classify the performance for each training function in terms of stance and swing phases under three kinds of sync speed. According to Table 4, Table 5, and Table 6, it can be observed that the LSTM-DNN algorithm has more than 91% and 92% gait phase accuracy and F-score at three different paces. In addition, we can see that LSTM-DNN and LSTM have a high recognition rate of gait phase, with both accuracy rates and F-scores over 86%. However, both KNN and SVM algorithms have a low recognition rate of gait phase, with accuracy and F-scores less than 86%.
###### Abstract  
A simple, stable and inexpensive dual-output port widely tunable semiconductor optical amplifier (SOA)-based fiber compound-ring laser structure is demonstrated. This unique nested ring cavity enables high optical power to split into different branches where amplification and wavelength selection are achieved by using low-power SOAs and a tunable filter. Furthermore, two Sagnac loop mirrors, which are spliced at the two ends of the compound-ring cavity not only serve as variable reflectors but also channel the optical energy back to the same port without using any high optical power combiner. We propose and discuss how the demonstrated fiber compound-ring laser structure can be extended in order to achieve a high power fiber laser source by using low power optical components, such as N \(\times\) N couplers and (N \(>\) 1) number of SOAs. A coherent beam-combining efficiency of over 98% for two parallel nested fiber ring resonators is achieved over the C-band tuning range of 30 nm. Optical signal-to-noise ratio (OSNR) of +45 dB, and optical power fluctuation of less than \(\pm\)0.02 dB are measured over three hours at room temperature.  
semiconductor optical amplifier; beam combining; power scalability; Sagnac interferometer; laser tuning; ring laser +
Footnote †: journal: Applied Physics Letters B
# Remaining Useful Life Prediction of Broken Rotor Bar Based on Data-Driven and Degradation Model
## 5 Synthetic Data Manipulation
### Feature Extraction
The performance of the time-domain features is more significant than that in the frequency domain. However, selecting the most meaningful features using one of the selection techniques for an accurate RUL prediction is required.
## Data
### Remotely Sensed Data  
MODIS 250m 16-day composite NDVI data acquired by the TERRA platform (Product MOD13Q1) were downloaded from the Earth Observation System (EOS) data gateway. MODIS NDVI temporal profiles (NDVI\({}_{\text{MODIS}}\)) were affected by errors related to the presence of cloudy sky conditions over the compositing period. In order to reduce this influence and to reconstruct high-quality vegetation index time series, the original MODIS NDVI time series were processed following the method proposed by [36], which is based on the recursive application of a Savitzky-Golay filter [37]. The smoothed MODIS NDVI time series of the studied poplar plantation were then extracted as the average of the three pixels within the experimental field.
## Appendix A Gradient: Divergence, and Curl in Cylindrical Coordinates
### Coordinate Transformation  
The transformations between cylindrical and Cartesian coordinates are:  
\[\mathcal{C}artesian\rightarrow\mathcal{C}ylindrical:\left\{\begin{matrix} \rho=\sqrt{x^{2}+y^{2}}\\ \phi=\arctan(y,x)\\ z=z\end{matrix}\right\} \tag{4}\]  
\[\mathcal{C}ylindrical\rightarrow\mathcal{C}artesian:\left\{\begin{matrix}x= \rho\cos\phi\\ y=\rho\sin\phi\\ z=z\end{matrix}\right\} \tag{5}\]  
The unit vector in the cylindrical coordinates are the functions of position. They point to radius, tangential, and vertical directions, respectively. The forward and inverse transformations of unit vectors between Cartesian and cylindrical coordinates are:  
\[\mathcal{C}artesian\rightarrow\mathcal{C}ylindrical:\left\{\begin{matrix} \hat{\rho}=\hat{i}\cos\phi+\hat{j}\sin\phi\\ \hat{\phi}=-\hat{i}\sin\phi+\hat{j}\cos\phi\\ \hat{z}=\hat{k}\end{matrix}\right\} \tag{6}\]\[Cylindrical\to Cartesian:\begin{Bmatrix}\hat{\hat{z}}=\hat{r}\cos\phi-\hat{\phi} \sin\phi\\ \hat{j}=\hat{r}\sin\phi+\hat{\phi}\cos\phi\\ \hat{k}=\hat{z}\end{Bmatrix} \tag{10}\]  
If we calculate the derivatives of \(\hat{\rho}\), \(\hat{\phi}\), and \(\hat{z}\) with respect to \(\rho\), \(\phi\), and \(z\), the only non-zeros terms are:  
\[\frac{\partial\hat{\rho}}{\partial\phi}=-\hat{i}\sin\phi+\hat{j}\cos\phi=\hat{\phi} \tag{11}\]  
\[\frac{\partial\hat{\phi}}{\partial\phi}=-\hat{i}\cos\phi-\hat{j}\sin\phi=-\hat {\rho} \tag{12}\]  
These useful terms can simplify the derivation of Divergence and Curl under cylindrical coordinates.
###### Abstract  
This paper presents an approach to identifying political fake news in Spanish using Transformer architectures. Current methodologies often overlook political news due to the lack of quality datasets, especially in Spanish. To address this, we created a synthetic dataset of 57,231 Spanish political news articles, gathered via automated web scraping and enhanced with generative large language models. This dataset is used for fine-tuning and benchmarking Transformer models like BERT and RoBERTa for fake news detection. Our fine-tuned models showed outstanding performance on this dataset, with accuracy ranging from 97.4% to 98.6%. However, testing with a smaller, independent hand-curated dataset, including statements from political leaders during Spain's July 2023 electoral debates, revealed a performance drop to 71%. Although this suggests that the model needs additional refinements to handle the complexity and variability of real-world political discourse, achieving over 70% accuracy seems a promising result in the under-explored domain of Spanish political fake news detection.  
fake news; Spanish; curated synthetic dataset; fine-tuning; Transformer-based models; BERT; RoBERTa Article