esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Spectroradiometric Measurements on the Ground  
Spectroradiometric ground data were acquired using an ASD FieldSpec III spectroradiometer (ASD Inc., Boulder, CO, USA) which measures radiances with 3 nm full-width-half-maximum (FWHM) over the wavelength range from 350 nm to 1050 nm. The instrument has a spectral sampling interval of 1.4 nm and a signal-to-noise ratio (SNR) of 4000. The integration time of the sensor was adjusted manually to the incident light conditions to avoid signal saturation and enable sufficient SNR. The field of view of the fiber optic is about 25\({}^{\circ}\).  
Spectroradiometric measurements were performed from 07:00 to 16:00 UTC on each observation day. The fiber optic was installed on a robotic arm of 60 cm length at approximately 1.50 m above the canopy. Vegetation height depended on field and measurement date and was 43 cm on DOY 127, 77 cm on DOY 176, 47 cm on DOY 183, and 63 cm on DOY 253. Consecutive scans of four different areas of the field were performed, interrupted by measurements of a SpectralonTM white reference panel (WR) (25 cm \(\times\) 25 cm) (Labsphere, North Sutton, NH, USA), similar to the protocol described in [20]. One measurement cycle took roughly six minutes. For each position, 10 spectra were recorded, each representing an average of 25 individual spectra automatically integrated by the spectroradiometer. For the subsequent analyses, only measurements not affected by any kinds of errors and changes in atmospheric conditions were used. Vegetation indices were calculated from the reflectance of the observed surface given by the ratio of the radiance spectra above the vegetation surface and the radiance above the WR panel.
## 3 Results
### Point Measurements  
Point measurements were also performed in both soils to check and compare the precision of the sensors. For this kind of measurements, the results of both capacitive and optical sensors were compared with gravimetric measurements.  
Soil samples of \(\sim\)3 L were prepared for the test. The measuring initial point is with soil totally dried by an oven to get a \(\sim\)0% humidity. At each sample, 300 mL of water were added to the soil until saturation was achieved. For the gravimetric measurements at each sample, just after performing the measures with both capacitive and optical sensors, a sample of soil was taken in a cylinder with a volume of 100.14 cm\({}^{3}\), weighed, dried in the oven, and weighed again to check the real weight of water (difference in weight before and after the drying process) and as a consequence the humidity contained in the sample. Results that are shown in Figures 8 and 9 illustrate the regression slopes for both experiments showing R\({}^{2}\) = 0.988 for the capacitive sensor and R\({}^{2}\) = 0.978 for the optical sensor in Soil1 and R\({}^{2}\) = 0.956 and R\({}^{2}\) = 0.949 respectively in Soil2.  
Each value was measured five times with a lapse time of 30 s between measurements and the mean value is represented in Figures 8 and 9. The maximum error of the measured samples was 9% and the minimum 2%, being the mean error of point measurements of 4%.  
An important aspect to comment is the difficulty to perform accurate gravimetric measurements. The operation principle of this technique is to take a known volumetric sample of wet soil, weigh it, dry the sample in the oven and weigh again to analyze the water weight evaporated. To perform accurate measurements the volume and compaction of the soil sample are critical. Although the volume is not a problem, to maintain the same compaction during the drying process of the soil is not obvious and it requires complex techniques and devices. This difficulty led to small discrepancies, as can be seen in Figures 8 and 9.
# M-Emu: A Platform for Multicast Emulation
## 3 Implementation
### Multicast-Emulation Framework  
Many IP multicast solutions need to maintain multicast trees, such as PIM-SM, PIM-SSM and DVMRP. Therefore, the construction and maintenance mechanism of multicast trees is important for multicast emulation. According to the type of multicast tree established, multicast routing protocols can be categorized as a Source-Based Tree (SBT) or Shared Tree (ST)-based [32; 33]. Both SBT and ST use the shortest paths between the source or core and receivers to form the multicast tree. In order to enable M-Emu to build SBT and ST, we designed the Multicast-Emulation Framework based on ILDM. This framework enables M-Emu to not only construct ST and SBT but also to flexibly adjust the position of multicast tree root points and grafting points, thus supporting more complex multicast tree structures. Therefore, the Multicast-Emulation Framework makes M-Emu highly versatile.
# Hybrid Particle Swarm Optimization for High-Dimensional Latin Hypercube Design Problem
## 3 Proposed Method  
The PSO can rapidly find the optimal solution when dealing with optimization problems with lower dimensions. However, it is prone to appear premature convergence when faced with higher dimension problems. It falls into the predicament of the local optimal solution [31; 40].  
In this section, we proposed an optimization framework based on the following procedures:  
(1) The ROV rule is firstly used [49] to transform continuous space into discrete space in the PSO evolution.  
(2) The MPD is adopted to solve the oscillation problem.  
(3) Four local search strategies are used to make the global best particle _gBest_ go to a better position.  
(4) An improved hybrid method is adopted based on the PSO to preserve the diversity of the population and solve the problem of premature convergence.  
(5) The boundary reflection strategy is adopted to address the problem of exceeding the boundary in the evolution of high-dimensional particle swarms.  
The pseudo-codewords of the IHPSO algorithm are shown in Algorithm 1.  
```
1: Initialize particle position and velocity
2: Evaluate the function value of different particles
3: Initialize the _pBest_
4: Initialize the _gBest_ data
5: while \(t<\) MI (Maximum Iterations)
6: if (flag \(<m\))
7: Use Equation (1) for speed update
8: else
9: Use Equation (2) for speed update
10: Update the position of each particle
11: Discretize particle position data using ROV rules
12: Update the personal best particle _pBest_
13: Optimize _gBest_ using the local search algorithm; if it is not better than _gBest_, randomly replace a current particle
14: Update the global optimal particle _gBest_
15: \(t=t+1\)
16: end
17: return _gBest_
18: end
```  
**Algorithm 1** Pseudo-codewords of proposed IHPSO algorithm
# Investigation of High-Sensitivity pH Sensor Based on Au-Gated AlGaN/GaN Heterostructure
## 4 Conclusions  
We designed and fabricated the Au-gated AlGaN/GaN heterostructure-based pH sensor. A 10 nm thick Au nanolayer was used as the sensing membrane of the pH sensor, which differs from the conventional oxidation method to improve sensitivity. The sensor demonstrates very high potential sensitivity and current sensitivity to microliter solutions. It is noteworthy that the current sensitivity of the Au-gated sensor can reach 3.9 times that of the SiO\({}_{2}\)-gated sensor. Dynamic experimental results show that the Au-gated pH sensor can quickly respond to the pH variations of microliter solutions. The fabricated Au-gated AlGaN/GaN pH sensor exhibits higher sensitivity than some previous reports. Furthermore, an in-depth analysis is provided, elaborating on the reasons why the Au-gated sensing membrane can achieve higher sensitivity, focusing on the aspects of surface-active sites and adsorption energy. In conclusion, our study confirms the viability of the Au-gated AlGaN/GaN pH sensors and provides a beneficial path for researching high-sensitivity pH sensors.  
Conceptualization, M.Y. and J.S.; methodology, M.Y. and J.S.; software, Y.Z. and J.W.; validation, M.Y. and J.S.; formal analysis, M.Y. and J.S.; investigation, M.Y. and J.W.; resources, [PERSON] and T.Z.; data curation, M.Y. and J.S.; writing--original draft preparation, M.Y.; writing--review and editing, R.S., S.C. and Z.L.; visualization, Y.Z. and J.W.; supervision, S.C. and Z.L.; project administration, J.S. and T.Z.; funding acquisition, S.C. and Z.L. All authors have read and agreed to the published version of the manuscript. [PERSON] and [PERSON] contributed equally to this study.  
This research was funded by the National Key Research and Development Program, grant number 2022 YFB3604702.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors would like to thank the staff of the Institute of Semiconductors, Chinese Academy of Sciences, for their assistance in device fabrication.  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### Low Condensation Temperature with Branch Stream  
In this scheme, which is shown in Figure 13, the exhaust steam at the expander outlet directly exchanges heat with the cooling water. In the wash water stream, a branch is divided to heat the ethane column reboiler and the low-pressure propane column reboiler. At this time, the propylene tower reboiler is simultaneously responsible for two heat source streams. At this time, new control conditions need to be added so that the two heat streams still have sufficient energy and temperature to meet the energy supply of the propylene tower after passing through the ORC system. From Figure 14 we could find in this scheme the R227 EA working fluid also obtain the maximum output work and the maximum exergy efficiency compared with other working fluids and mixed working fluids.  
Figure 11: ORC system at low condensation temperature.  
Figure 12: Maximum output power and exergy efficiency of different working fluids at low condensation temperature: (**a**) Pure working fluids; (**b**) mixed working fluids.  
Table 3 shows the comparison between different schemes. From the table, we can see that the scheme 4 gives out the maximum output power and maximum exergy efficiency, and the best working fluid is pure R227 EA. The maximum output power is 4.95 MW, and the maximum efficiency is 36.2%. Besides, for all working conditions, the ORC system obtains the maximum output power and maximum efficiency when the working fluid is R227 EA, which also shows that the working fluid can obtain better results at this temperature level.  
Figure 14: Maximum output power and exergy efficiency of different working fluids at low condensation temperature with branch stream: (**a**) Pure working fluids; (**b**) mixed working fluids.  
Figure 13: ORC system at low condensation temperature with branch stream.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Conclusion Temperature (°C)**} & \multicolumn{3}{c}{**Pure Working Fluid**} & \multicolumn{3}{c}{**Mixed Working Fluid**} \\ \cline{2-7}  & **Wmax (MW)** & **Emax** & **Working Fluid** & **Wmax (MW)** & **Emax** & **Working Fluid** \\ \hline
57 & 3.52 & 30.50\% & R227 EA & 3.42 & 30.10\% & R227 EA/R1234 ZE \\
51 & 3.09 & 28.80\% & R227 EA & 2.9 & 28.10\%
# On-Orbit Experimental Result of a Non-Deployable 430-MHz-Band Antenna Using a 1U CubeSat Structure
## 5 Testing on the Ground
### Link Budget  
Table 2 shows the link budget of HNT mission. The Loop Hentenna has a low gain, and the transmitter has a low output power. However, the ground station has a high-gain antenna with 22 dBi. Therefore, the link margin is positive when the satellite elevation is higher than 30 deg even if the link budget is calculated with conservative assumptions.
## 2 Dark Current Measurement and Noise Correction Method Based on Focal-Plane Temperature
### QWIP Dark Current Model  
The dark current \(I_{dark}\) of the QWIP device can be expressed as:  
\[I_{dark}=n^{*}evA, \tag{3}\]  
where \(n^{*}\) is the number of electrons of the dark current transmitted continuously in the thermally excited state, \(e\) is the electronic charge, \(v\) is the transmission velocity, and \(A\) is the area of the photosensitive pixel.  
The number of electrons \(n^{*}\) in the dark current can be expressed as [17]:  
\[n^{*}=\left(\frac{m^{*}}{\pi\hbar^{2}L_{p}}\right)\int_{E_{1}}^{\infty}f(E)T( E,V)dE, \tag{4}\]  
Figure 2: LWIR QWIP detection system response model.  
where \(m^{*}\) is the effective mass of the electron, \(\hbar\) is the Planck constant, \(L_{p}\) is the quantum well period number, \(E_{1}\) is the ground state energy, \(f(E)\) is the Fermi factor, \(V\) is the bias voltage of the QWIP, and \(T(E,V)\) is the individual barrier current transport factor. When the thermal excitation effect is dominant in the dark current, \(T(E,V)=1\); when the phonon-assisted attempted penetration effect is dominant, \(T(E,V)=0\). The expression of the Fermi factor \(f(E)\) is:  
\[f(E)=[1+exp(E-E_{1}-E_{F})/kT]^{-1} \tag{5}\]  
where \(E_{F}\) is the Fermi energy level, \(T\) is the focal-plane temperature, and \(k\) is the Boltzmann constant.  
From Equations (3)-(5), it can be concluded that the magnitude of the dark current \(I_{dark}\) is directly related to the temperature \(T\) and the bias voltage V, which are the most easily adjustable operating conditions after the detector is packaged. Under ordinary bias (0\(\sim\)3 V), the thermal excitation effect is discussed as the dark current dominant mechanism by adding the condition \(T(E,V)=1\) (E \(>E_{b}\)) to Equation (4), where \(E_{b}\) is the energy required for the leap. Equation (4) can be simplified to Equation (6):  
\[n^{*}=\Big{(}m^{*}kT/\pi\hbar^{2}L_{p}\Big{)}e^{-(E_{c}-E_{F})/kT}, \tag{6}\]  
where \(E_{c}\) and \(E_{F}\) are the conduction band and valence band energies. The conduction band is the difference between the spectral cutoff energy and the ground state energy:  
\[E_{c}=E_{b}-E_{1}, \tag{7}\]  
Important dark current versus temperature relationships were obtained for:  
\[I_{dark}/T\propto e^{-(E_{c}-E_{F})/kT}, \tag{8}\]  
The above is the dark current divided by the temperature, with e as the exponential function of the base and temperature as the variable, and the two are proportional to each other. The energy between the conduction band and valence band of the same QWIP device is fixed, so \(E_{c}-E_{F}\) is constant. There are sufficient experiments to prove the correctness of Equation (8)'s conclusion in [PERSON]'s study, wherein two QWIP devices with different spectral detection ranges were prepared, the dark currents were measured by changing several temperature points (ranging from 25 K to 120 K), and the relationship between dark currents and temperature was obtained. The theoretical model of Equation (8) plays an important role in this study and is described in detail in the subsequent sections.
## 2 Materials and Methods
### Drug Release  
Glass samples of 10 mg were immersed in 10 mL of phosphate-buffered saline (PBS) at pH 7.4, which is physiological pH, in plastic tubes. The tubes were incubated in a shaker at 37 \({}^{\circ}\)C with continuous stirring to mimic the physiological condition until drug release reached 100%. Samples of 2 mL were collected at 30 min intervals and then 1-day intervals, and analyzed using UV-visible spectroscopy (Thermo Fisher Scientific, Madison, WI, USA) to determine the released drug concentration. The removed volume was replenished with an equal volume of fresh PBS solution to maintain sink conditions.  
BG-V drug release data were fitted to various mathematical models [20; 21] and the to determine the predominant release kinetics. The models applied in this study include the following:  
Zero-order model: This model suggests a uniform drug release rate independent of concentration. The following equation is used to calculate zero-order fit:  
\[\mathrm{C_{t}=C_{0}+kt}\]  
where \(\mathrm{C_{t}}\) and \(\mathrm{C_{0}}\) are the amount of drug dissolved at time t, and the initial amount of drug in solution, respectively. k is the zero-order rate constant.  
First-order model: Where the drug release rate is directly proportional to the remaining concentration, showing a linear concentration-dependent relationship. It is calculated using this equation:  
\[\mathrm{\frac{dC}{dt}=-kC}\]  
Which can also be expressed as follows:  
\[\mathrm{log\,C=log\,C_{0}-\frac{Kt}{2.303}}\]  
where \(\mathrm{C_{0}}\) is the initial drug concentration and k describes the first-order rate constant.  
Higuchi model: This describes the drug release from matrix systems based on diffusion. The following equation is used to calculate the fit:  
\[\mathrm{f_{t}=Q=A\sqrt{D(2C-C_{s})C_{s}t}}\]  
Q refers to the amount of drug released at time t. A is the area, the drug initial concentration is C, and \(\mathrm{C_{s}}\) describes the drug solubility. D refers to the diffusivity of the drug [22].  
The Higuchi model can be simplified to describe drug release from matric and polymeric systems as follows:  
\[\mathrm{\frac{M_{t}}{M_{so}}=k\sqrt{t}}\]where M\({}_{\rm t}\)/M\({}_{\infty}\) represents the cumulative amount of drug released at time t, and k is the Higuchi constant.  
Korsmeyer-Peppas: This is a model that combines diffusion and erosion effects and can be calculated using the following equation:  
\[\frac{\rm M_{t}}{\rm M_{\infty}}=\rm k^{\prime}t^{n}\]  
where M\({}_{\rm t}\)/M\({}_{\infty}\) is the cumulative amount of drug released at time t, k' is the kinetic constant, and \(n\) is usually used to describe a specific diffusion mechanism. In order to describe the diffusion mechanism of the system determined by the exponent, \"n\" is used. If n = 0.5, it represents Fickian diffusion. If 0.45 \(<\) n = 0.89, then the release is controlled by non-Fickian transport. The release is considered zero-order, (case II) transport, if n= 0.89. If n is higher than 0.89, then the diffusion mechanism is a super case II transport [22].
## 7 Conclusions  
This paper introduces a reliability analysis methodology employing surrogates enriched by a hybrid learning process. The hybrid learning focuses on regions near limit-state approximation candidate samples farthest from existing training points, maximizing the probability of misclassifications. The resulting Kriging model is constructed based on identified DoE points. The proposed method proves accurate and efficient through successful applications to diverse reliability problems, including highly nonlinear limit-state functions and complex structures. When compared to traditional learning functions, the HAK scheme exhibits reliable performance. Notably, as reliability analysis problems scale up, the computational costs of both structural and reliability analyses become substantial. Traditional simulation-based approaches show a significant increase in computational time for larger problems, while the proposed Kriging approach, coupled with subset simulations and the hybrid active learning process, demonstrates notable computational efficiency. This efficiency facilitates exploration of diverse scenarios, including those with random  
Figure 16: (**a**) Distribution of material random variables within the discretized beam domain, (**b**) failure probabilities corresponding to a specific number of material random variables obtained by FORM, SORM, AK-MCS, and the proposed method (HAK).  
material property distributions and real-world complexities, highlighting the versatility and applicability of the proposed method.  
For future research directions, the current study primarily addressed component failure probabilities with a single limit-state function. However, in practical scenarios, system reliability often takes precedence as it can describe realistic and practical failure modes and conditions. Nevertheless, system reliability analysis generally entails higher computational costs and more complex steps to consider the statistical dependence of random variables on the performance of limit-state functions, identify the system failure domain, and estimate system failure probabilities. Thus, future research will be dedicated to exploring system reliability analysis using Gaussian process modeling. Additionally, investigating reliability-based design and topology optimization employing Kriging is identified as another promising avenue for future research.  
This research was funded by the Innovative and Interdisciplinary Research Program of Syracuse University (Grant no. II-50-2021).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The author declares no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Negation Detection on Mexican Spanish Tweets:
## 3 Corpus of Negations in Mexican Spanish: The T-MexNeg
### Annotation Protocol  
Our first step towards negation annotation is the classification of tweets, whether there is a negation present at all to analyze or not. As explained above, we sampled a collection of tweets and carried out a manual binary classification; 4895 tweets were found to have some sort of negation in their content. This preliminary step allowed us to carry out a much more detailed analysis of the negations within the previously detected tweets. To achieve this, an independent three-way tagging was carried out to identify each particular syntactic negation as well as their respective components.  
In Spanish, there are three basics levels of negation: lexical, morphological, and syntactic. This work only approaches the study of syntactic negation. According to RAE [46], negative sentences express false states or the nonexistence of the action that is in the sentence. Syntax negation is a syntax operator word that affects the whole sentence or a section of it. This syntax operator is called negation cue. They can be adverbs, prepositions, indefinite pronouns, and conjunctions. Usually, in Spanish, negation cues precede the verb, but they can also appear postponed.  
The section affected by the negation cue is called scope. The words that are specifically reached by it, which can be verbs, nouns, or phrases, are referred to as event [47]. Therefore, the basic requirements to create a negative sentence are the negation cue, the scope, and the event. Figure 1 presents three sentences with negation cue annotated in red, negation event annotated in purple, and negation scope in blue. In the first sentence \"@072 CDMX Esperemos que le den solucion lleva mds de 24 horas y no la atienden\" (@072 CDMX Let's hope they give you a solution it takes more than 24 h and they don't attend her) we can observe that the negation cue is the word \"_no_\" (don't), the negation event is \"_la atienden_\" (attend her), and the negation scope is \"_no la atienden_\" (don't attend her).  
Our annotation protocol follows the criteria suggested by [PERSON] et al. [47], but includes some modifications caused by the specific features of a Twitter-based corpus. In general, we identify three main negation components: Negation Cue, Event, and Scope. We also differentiate among three types of negation cues: Simple Negation (Neg_exp), Related Negation (Neg_rel), and False Negation (No_neg). In the next sections, we explain the details of each category. In addition, we simplified the categorization because the tweets are shorter than the reviews analyzed by the followed criteria. For example, we use the tag Neg_rel when two negation cues appear in the same sentence but there is only one event negated.