esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Flying Ad Hoc Networks: Key Characteristics, Applications, and Wireless Technologies
## 6 UAV Mobility Models  
This section describes the different UAV mobility models supported in FANETs. Typical mobility models used in FANETs include pure randomized mobility models (the random way (RW), random waypoint (RWP), random direct ion (RD), and Manhattan grid (MG) models), time-dependent mobility models (the Gauss Markov (GM), bound-less simulation area (BSA), and smooth turn (ST) models), path-based mobility models (the semi-random circular movement mobility model (STCM), and paparazzi (PPRZM) models), group mobility models (the reference point group (RPGM), exponential correlated (ECR), column (CLMN), nomadic community (NC), pursue (PRS), and particle swarm (PS) models), and topology control mobility models (the pheromone base (PB), distributed pheromone repel (DPR)-based, mission plan-based (MBP), and self-deployable point coverage (SDPC) models [66; 73; 79; 88]. The chosen model relies on the network to keep the entire system working as a unity. There are several parameters, including the altitude, path and directing of flying, and atmospheric conditions, all of which impact UAV mobility. In addition to these parameters, other important aspects should be considered when select a mobility model for UAVs in different scenarios. These aspects include avoidance of connections that control the distance between UAVs, controlling sudden changes in the UAVs' direction, and the safety standards and deployment that need to be considered in order to prevent collisions between UAVs [100].
## 5 Conclusions  
Construction accidents generate a large amount of unstructured or semi-structured text. The effective utilization of this data has the potential to provide managers with valuable insights that can enhance the safety management of on-site construction operations. The automatic classification of accident types and extraction of key information from accident narratives are the challenges addressed in this study. Therefore, we proposed combining deep learning and text mining to analyze construction accident texts. The main conclusions of the paper are as follows:  
1. We proposed a text classification model based on CNN that can automatically classify texts related to five types of accidents: electrocution, falling, object strikes, collapse of objects, and mechanical injury. The overall accuracy of the model reaches 84%. Compared to other shallow machine learning methods, the CNN model demonstrates higher accuracy in classifying construction accident narrative texts, outperforming the other three shallow machine learning models. This indicates that our model exhibits accuracy in handling construction accident narratives.
2. Using the categorized fall accident text as an illustration, we employed the TF-IDF algorithm for text mining, extracting the foremost 20 weighted accident features that stand as representatives. Delving into these features illuminates eight pivotal accident zones and highlights three operations particularly susceptible to accidents. The presentation of this crucial information through the visualized format of a word cloud serves as a lucid guide for on-site safety management by managers, facilitating the prevention of analogous accidents.
3. Our innovative approach, combining deep learning and text mining, swiftly identified diverse accident types and extracted essential insights from accident-related Chinese texts. This research can help managers analyze and understand accident narratives, offering robust direction for post-accident emergency response and prevention measures. Moreover, it introduces fresh perspectives and methodologies into construction safety management.  
**Author Contributions:** Conceptualization, C.W.; methodology, C.W.; software, C.W.; validation, [PERSON] and J.L.; formal analysis, C.W.; investigation, C.W.; resources, C.W.; data curation, C.W. and J.L.; writing--original draft preparation, C.W.; writing--review and editing, C.W. and J.L.; visualization, C.W. and J.L.; supervision, J.L.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Natural Science Foundation of Human Province, China (Grant No. 2021J30744) and the Research Foundation of Education Bureau of Hunan Province, China (Grant No. 20K011).  
**Data Availability Statement:** The data presented in this study are available on reasonable request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Palladium-Deposited Molybdenum Disulfide-Based Hydrogen Sensor at Room Temperature
## 4 Results
### Gas Sensor Mechanism  
This section describes the basic behavior of the MoS\({}_{2}\)-based hydrogen sensor. Here, we explain the scientific principles of how sensors detect the presence of H\({}_{2}\).  
The operational framework of the Pd-deposited MoS\({}_{2}\)-based hydrogen sensor arises from the multifaceted functions of MoS\({}_{2}\) within the system. Specifically, MoS\({}_{2}\) assumes two critical roles that synergistically contribute to the sensor's functionality. Firstly, MoS\({}_{2}\) serves as the channel through which the current flows. Simultaneously, MoS\({}_{2}\) undertakes the task of adsorbing oxygen molecules onto its surface, thereby generating oxygen ion species. This process induces an increase in the resistance of MoS\({}_{2}\). This resistance increase can be attributed to the transformation of the adsorbed oxygen into oxygen ion species, involving the exchange of electrons with MoS\({}_{2}\) atoms.  
Figure 3 shows the mechanism of the Pd-deposited MoS\({}_{2}\)-based hydrogen sensor. In the absence of injected H\({}_{2}\), MoS\({}_{2}\) tends to adsorb oxygen on the MoS\({}_{2}\) surface, leading to the generation of oxygen ion species. Following the injection of H\({}_{2}\), Pd plays a pivotal role. Pd dissociates the H\({}_{2}\) molecules, subsequently absorbing the liberated H. A notable phenomenon referred to as the \"spillover effect\" ensues, wherein hydrogen diffuses from the Pd layer into the MoS\({}_{2}\) material. In this dynamic, hydrogen and oxygen ion species combine on the surface of MoS\({}_{2}\), culminating in the production of H\({}_{2}\)O and free electrons. These generated electrons are then introduced into the MoS\({}_{2}\) channel, inducing a change in the current flow. Consequently, the Schottky barrier, which governs the flow of electrical charge at the metal-semiconductor interface and experiences alteration, ultimately manifesting as a measurable current change. As such, the hydrogen sensor detects the presence of H\({}_{2}\) leakage through the observed variation in current.  
Figure 2: Schematic of the Pd-deposited MoS\({}_{2}\)-based hydrogen sensor.
## 3 Research Methods
### Stability Analysis of the Landslide  
The GeoStudio 2018 R2 (version number: 9.1.1.16749) software, a Canadian geotechnical engineering analysis tool, was employed for landslide seepage simulation, stability analysis, and displacement calculation. The built-in SEEP/W module was utilized for landslide seepage simulation, while the built-in LANDSLIDE/W module was used for landslide stability analysis. The numerical computational model established is approximately 700 meters long and 400 meters high, as shown in Figure 11. For model grid division, a hybrid approach of triangular and quadrilateral elements was employed, considering both computational accuracy and convergence ease. The minimum size of the model elements was controlled at 5 m. The model consisted of a total of 2480 grid elements and 2535 grid nodes.  
Based on geological surveys and laboratory experiments, the physical and mechanical parameters of the materials are presented in the Table 3. Knowing the saturated volumetric water content of different materials of the landslide body, the soil-water characteristic curve (SWCC) and the relationship curve between the permeability coefficient and matrix suction of the landslide body can be estimated and plotted using the Fredholm model function [23], as shown in Figure 12.  
During the period from 2018 to 2022, the Guangdong Hydropower Station recorded a maximum daily rainfall of approximately 30 mm. This data prompted analyses of reservoir bank landslide stability under two distinct scenarios: the influence of reservoir water level fluctuations alone and the combined effects of reservoir water level fluctuations and 30 mm/days rainfall.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Materials**} & **Permeability** & **Unit Weight** & **Cohesion (kPa)** & **Frictional Angle** &
\begin{tabular}{c} **Saturated** \\ **Volumetric** \\ **Moisture Content** \\ \end{tabular} \\ \hline Colluvial deposits & \(7.0\times 10^{-5}\) & 18 & 16 & 22 & 0.26 \\ Landslide deposits & \(8.98\times 10^{-5}\) & 19 & 23 & 25 & 0.21 \\ Phylltic slate & \(1.16\times 10^{-5}\) & 20 & 30 & 27 & 0.10 \\ Bedding rock & \(5.79\times 10^{-7}\) & 25 & 650 & 41 & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Material parameters in the numerical model.  
Figure 11: Illustrative diagram of the model’s grid division and computational boundaries.
# An Acoustic Feature-Based Deep Learning Model for Automatic Thai Vowel Pronunciation Recognition
## 4 Materials and Methods
### Spectrogram Conversion
#### 4.2.1 Preprocessing  
Proper preprocessing of input data is one of the keys to good feature representation. The Thai vowel speech signals were preprocessed using the LibROSA [53] library in Python, a package for audio and music analysis. In the preprocessing step, the monophonic speech signal was downsampled from 44,100 Hz to 16,000 Hz sampling rate. The audio analysis method used small frames of the signal that were spaced by a hop length. The length of the window was 2048 samples (approximately 128 ms), and the hop size was 512 samples (approximately 32 ms). The speech signal was converted into a time-frequency representation based upon the short-time Fourier transform (STFT). The discrete-time STFT was calculated using the fast Fourier transform algorithm (FFT). The mathematical representation of STFT [54] is written as:  
\[X[m,\omega]=\sum_{n=-\infty}^{\infty}x[n]w[n-m]exp^{-j\omega n} \tag{1}\]  
In Equation (1), \(x[n]\) denotes the sequence of a discretized time-domain signal to be transformed, \(w[n]\) denotes the window function, \(m\) denotes the time index, \(\omega\) denotes the frequency, and \(X[m,\omega]\) denotes the STFT of the time-domain sequence.
## 2 Materials and Methods
### Variable-Frequency Complex Demodulation Algorithm  
We used TFS obtained from EEG signals to objectively capture the changing dynamics in the central nervous system during normal and seizure conditions. To overcome the challenge of limited data samples, we employed a segmentation strategy that equally divided the EEG signals into seven segments. This increased the dataset and provided a more robust basis for TFS analysis. The foundation of our analysis is the use of the variable-frequency complex demodulation (VFCDM) technique. This approach has been extensively described in previous literature [21; 23] and extensively validated with various physiological signals, such as blood pressure [21], renal blood flow [24], heart rate variability [25], and electrodermal activity [26; 27].  
The two-step process involved in estimating the TFS of EEG using VFCDM is as follows: First, an initial estimate of the TFS is obtained using fixed-frequency CDM (FFCDM) or complex demodulation. Second, the VFCDM approach is used to improve the time-frequency resolution by selecting the dominant frequencies of interest.
## 4 Fault Detection
### Fault Detection Making Algorithm  
Based on the dual observers as shown in Figure 4, they return two flags. Flag1 and Flag2 represent the judgement results from the methods presented in the Sections 4.1 and 4.2, respectively. As for Flag1, the residues of roll, pitch, \(x\), \(y\), and \(z\) are taken into consideration. When any of the residues mentioned exceeds the upper bound or lower bound as presented in Equations (38) and (39), the fault alarm Flag1 would be triggered. However, using only Flag1 would lead to an incorrect fault alarm. As for Flag2, the estimated fault would be filtered by moving average. If any of the results exceed the threshold, it is treated as fault happening. The fault signal of from the 1 st observer reacts quickly when fault happens, but residues may still converge due to the property of observer. The fault signal of the 2 nd observer requires time to converge while it would eventually close to the fault happening. With the combination of Flag1 and Flag2, the decision making regarding whether the true fault happens or not would be made in the judgment block as shown in Figure 4. By default, Flag1 = Flag2 = 1, which represents the normal status. Therefore, the real-time fault double confirmation procedure, as shown in Table 1, is applied:  
\begin{table}
\begin{tabular}{c c} \hline \hline if & Flag1 \(\parallel\) Flag2 == 0 => Fault\_True = 1 \\ if & Fault\_True == 1 \\ if & Flag1 \& Flag2 == 1 holds for 0.5 sec \\  & \(\rightarrow\)Fault\_True = 0 and reset Flag1=Flag2=1; \\  & \(\rightarrow\)return status is considered as normal \\  & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Rotor fault judgement procedure.  
Figure 4: Fault detection scheme.  
Based on the judgement procedure, if any of the flags denotes a fault is happening, the fault detection result would be considered as a fault happening first. In order to prevent wrong judgments, a mechanism is further imposed to pull back a faulty alarm. That is, if both of the flags denote normal flight for a time period, the fault detection would return to \"normal\".
## 3 Optimization of SM-OGFC by Response Surface Methodology (RSM)
### RSM Model Validation  
The validation of the model can be examined with the diagnostic tab in the design-expert software. The normal plot of the residuals of \(-15\)\({}^{\circ}\)C splitting strength is shown in Figure 3. A straight line almost covers all the points, and therefore, there is a normal distribution of errors in the experiments. In addition, there is no abnormal pattern or structure in the residuals plot (Figure 4).  
The difference between predicted and actual values of the response is shown in Figure 5. A straight line almost cover all the points, and therefore there are negligible errors between the predicted and actual responses. Thereupon, the proposed mathematical regression is authentic.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Source** & **Sum of Squares** & **df** & **Mean Square** & **F-Value** & \(p\)**-Value** \\ \hline Model & 10.6 & 14 & 0.76 & 44.51 & \textless{}0.0001 \\ A-Asphal-aggregate ratio & 5.06 & 1 & 5.06 & 297.6 & \textless{}0.0001 \\ B-Silane coupling agent content & 0.16 & 1 & 0.16 & 9.12 & 0.0086 \\ C-Mixing temperature & 0.059 & 1 & 0.059 & 3.47 & 0.0823 \\ D-Compaction times & 0.23 & 1 & 0.23 & 13.29 & 0.0024 \\ AB & 0.11 & 1 & 0.11 & 6.69 & 0.0206 \\ AC & 0.086 & 1 & 0.086 & 5.03 & 0.0405 \\ AD & 0.12 & 1 & 0.12 & 6.89 & 0.0191 \\ BC & \(5.06\times 10^{-4}\) & 1 & \(5.06\times 10^{-4}\) & 0.03 & 0.8654 \\ BD & 0.39 & 1 & 0.39 & 22.77 & 0.0002 \\ CD & 0.016 & 1 & 0.016 & 0.96 & 0.3439 \\ A\({}^{2}\) & 5.94 & 1 & 5.94 & 349.09 & \textless{}0.0001 \\ B\({}^{2}\) & 0.49 & 1 & 0.49 & 28.95 & \textless{}0.0001 \\ C\({}^{2}\) & 0.28 & 1 & 0.28 & 16.54 & 0.001 \\ D\({}^{2}\) & \(9.14\times 10^{-3}\) & 1 & \(9.14\times 10^{-3}\) & 0.54 & 0.475 \\ Residual & 0.26 & 15 & 0.017 & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: ANOVA Analysis of \(-15\)\({}^{\circ}\)C splitting strength response model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline Std.Dev. & 0.13 & R\({}^{2}\) & 0.9765 \\ \hline Mean & 3.19 & R\({}_{\mathrm{Adj}}\)\({}^{2}\) & 0.9546 \\ C.V.\% & 4.09 & R\({}_{\mathrm{Pred}}\)\({}^{2}\) & 0.9240 \\ \hline PRESS & 0.82 & Adeq Precisior & 21.416 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Credibility analysis of modified regression equation.  
Figure 4: Residual versus predicted values of \(-15\)\({}^{\circ}\)C splitting strength.  
Figure 5: Predicted against actual responses of \(-15\)\({}^{\circ}\)C splitting strength.  
Figure 3: Normal probability plot of residuals of \(-15\)\({}^{\circ}\)C splitting strength.
# Tripartite Quantum Key Distribution Implemented with Imperfect Sources
## 3 Operation of Proposed Reference-Frame Independent (RFI) Protocol
### Measurement  
After receiving the photons, [PERSON] and [PERSON] perform measurements using the bases \(b_{i}\in\{X,Y,Z\}\) and \(c_{i}\in\{X,Y,Z\}\), respectively, with probabilities \(p_{z}\) and \(p_{c}\). Additionally, the measurements of [PERSON] and [PERSON] are represented by a set of positive-operator valued measures (POVMs), \(\{\hat{M}_{0\beta},\hat{M}_{1\beta},\hat{M}_{f}\}\), where \(\hat{M}_{0,\beta}\) (\(\hat{M}_{1\beta}\)) with \(\beta\in\{X,Y,Z\}\) equates to obtaining the bit value of 0 (1) if [PERSON] and [PERSON] choose basis \(\beta\). \(\hat{M}_{f}\) represents an inconclusive event and is assumed to be the same for all the bases. They then choose uniform random bits \(r_{i}^{\prime}\in\{0,1,\emptyset\}\) and \(r_{i}^{\prime\prime}\in\{0,1\emptyset\}\) to store their outcomes. Here, the symbol \(\emptyset\) corresponds to an inconclusive result and is assumed the same for all bases. In this protocol, [PERSON], [PERSON], and [PERSON] share a common aligned measurement basis, \(Z_{A}=Z_{B}\), \(Z_{A}=Z_{C}\), while the other measurements bases, \(X\) and \(Y\), are allowed to slowly vary by an arbitrary angle, \(\beta\). Due to drift in reference frames, the measurement bases complementary to the \(Z\) basis are provided by  
\[X_{B} =\cos\beta X_{A}+\sin\beta Y_{A},\ X_{C}=\cos\beta X_{A}+\sin\beta Y _{A},\] \[Y_{B} =\cos\beta Y_{A}-\sin\beta X_{A},\ Y_{C}=\cos\beta Y_{A}-\sin\beta X _{A}.\]
# Marine microalgae Biomolecules and Their Adhesion Capacity to _Salmonella enterica_ sv. Typhimurium
###### Abstract  
Different molecules have been tested as analog receptors due to their capacity to bind bacteria and prevent cell adhesion. By using in vitro assays, the present study characterized the aqueous and alkaline extracts from microalgae _Pavlova lutheri_ and _Pavlova gyrans_ and evaluated the capacity of these extracts to adhere to enterobacteria (_Salmonella_ Typhimuriumium). The aqueous and alkaline extracts of both species were fractionated via freeze-thawing, giving rise to soluble and insoluble (precipitate) fractions in cold water. The obtained fractions were studied using thermogravimetric, methylation analyses, and using 1D and 2D NMR techniques. The cold-water-soluble fractions obtained from the aqueous extracts were mainly composed of highly branched (1\(\rightarrow\)3),(1\(\rightarrow\)6)-\(\beta\)-glucans, whereas the cold-water-precipitate fractions were constituted by (1\(\rightarrow\)3)-\(\beta\)-glucans. The alkaline extract fractions showed similar compositions with a high protein content, and the presence of glycosides (sulfoquinovosylglycerol (SQG), digalactosylglycerol (DGG)), and free fatty acids. The linear (1\(\rightarrow\)3)-\(\beta\)-glucans and the alkaline extract fractions showed an adhesion capacity toward _Salmonella_. The chemical composition of the active fractions suggested that the presence of three-linked \(\beta\)-glucose units, as well as microalgal proteins and glycosides, could be important in the adhesion process. Therefore, these microalgal species possess a high potential to serve as a source of anti-adhesive compounds.  
microalgae; _Pavlova_; \(\beta\)-glucans; glycosides; adhesion capacity; enterobacteria
# The Analysis of Experimental Deployment of IGLUNA 2019 Trans-Ice Longwave System
## 6 Communication Channel Analysis  
The simultaneous registration of the P1A (maximized concentration of RF energy in the transmission [4], similar to plasmasphere-aiming test transmissions [30]) and A3E-SC emissions on the maximum range location allowed the description of the received electric field value as a function of the signal's bandwidth B\({}_{\text{UA}}\) (signals from the under-ice antenna) and the calculation of the channel capacity C [bit/s] as a function of the signal's bandwidth [31]. Figure 16 presents the latter function plotted. It can be clearly seen that this type of communication channel does have a maximum available capacity at approximately 2.5 kHz of 15.5 kbit/s. This capacity shows that the system, despite its limitations (very low power, inconvenient polarization), already complies with the requirements for the bit rates needed by the modes A and B of the DRM30 digital modulation system [32]-if the bandwidth could be increased to min. 4.5 kHz (i.e., by an additional non-linear component).  
Figure 14: The E field strength (RMS) calculated for IGLUNA 2019 LW experiment using different formulas.  
Figure 15: The selected E field strengths (RMS)—four lowest models—shown for larger (>1 Mm) distances for the IGLUNA 2019 LW experiment.  
The bit error rate (BER) for the deployed system has been analyzed as a function of the net bitrate [8] for two bandwidths: 2.5 kHz (maximum from Figure 16) and 4.5 kHz (minimum for the employment of the 'Digitale Radio Mondiale below 30 MHz' standard-the DRM30 [32]). Considering the net bitrate equal to the available channel capacity, for 2.5 kHz and 15.5 kbit/s (from Figure 15) the BER value is 0.2362; for 4.5 kHz and 7 kbit/s, the BER drops to 0.0757 [8]. Both these values remain too high for a successful implementation of the DRM30, as the minimum BER described as the 'limit of service' is equal to \(10^{-4}\)[32]. This issue can be rectified by the increase of the SNR--the increase of the transmitting power.