esa-sceva/satcom-chunk-collection huggingface dataset:
# UAV Communication Recovery under Meteorological Conditions
## 4 UAV Communication Recovery Strategy with Results  
In this section, we first summarize the expressions for the fitted curves of the UAV's maximum coverage area (\(S_{max}\)), optimal height (\(H_{opt}\)), and compensated path loss (\(PL_{cps}\)) along with their corresponding fitting parameters. Then, based on these expressions, we propose an algorithm to restore communications between the UAV and ground users under various meteorological conditions. Finally, we give some results regarding the recovery coverage of the UAV under different weather conditions in the four regions and present a discussion. Fitted Curve Expressions for UAV's Maximum Coverage Area (\(S_{max}\)), Optimal Height (\(H_{opt}\)), and Compensated Path Loss (\(PL_{cps}\))  
In this subsection, we present the expressions for the fitted curves of the UAV's maximum coverage area (\(S_{max}\)), optimal height (\(H_{opt}\)), and compensated path loss (\(PL_{cps}\)) along with their corresponding fitting parameters, as summarized in Table 11. For the convenience of readers, we use blue, purple, and pink to distinguish the fitting parameters corresponding to \(S_{max}\), \(H_{opt}\), and \(PL_{cps}\). The relationships between the rainfall rate (\(R_{\rm rain}\)), the maximum coverage area of the UAV (\(S_{max}\)), the optimal height (\(H_{opt}\)), and the compensated path loss (\(PL_{cps}\)) can be modeled using an exponential formula, with \(a\), \(b\), \(c\), and \(d\) as fitting parameters. For fog conditions, a simple linear expression suffices to describe the relationships between the liquid water density (\(R_{\rm fog}\)) and \(S_{max}\), \(H_{opt}\), \(PL_{cps}\), where \(p\) and \(q\) represent the fitting parameters. The relationships between the snowfall rate (\(R_{\rm snow}\)) and \(S_{max}\) and \(H_{opt}\) can be described using the same exponential expression as in the rainfall case. Meanwhile, the relationship between the snowfall rate and \(PL_{cps}\) can be modeled using a formula similar to a Fourier expansion, with \(t\), \(u\), \(v\), and \(w\) as fitting parameters.
# Refractive Index Sensing through Surface Plasmon Resonance in Light-Diffusing Fibers
###### Abstract  
In this paper, we show that light-diffusing fibers (LDF) can be efficiently used as host material for surface plasmon resonance (SPR)-based refractive index sensing. This novel platform does not require a chemical procedure to remove the cladding or enhance the evanescent field, which is expected to give better reproducibility of the sensing interface. The SPR sensor has been realized by first removing the cladding with a simple mechanical stripper, and then covering the unclad fiber surface with a thin gold film. The tests have been carried out using water-glycerin mixtures with refractive indices ranging from 1.332 to 1.394. The experimental results reveal a high sensitivity of the SPR wavelength to the outer medium's refractive index, with values ranging from \(\sim\)1500 to \(\sim\)4000 nm/RIU in the analyzed range. The results suggest that the proposed optical fiber sensor platform could be used in biochemical applications.  
Keywords:light-diffusing fibers (LDF); surface plasmon resonance (SPR); optical sensors +
Footnote †: journal: Applied Sciences
## 6 Conclusions  
By establishing the mechanical model of the carrier-based aircraft in the catapult-assisted takeoff tensioning state and release gliding state, as well as the dynamics model and friction model of the holdback bar device, the release process of the holdback bar was simulated and analyzed, and the effects of the structural parameters of the holdback bar and the friction coefficient on the sensitivity of the release load were studied. The following conclusions were obtained.  
1. The release load of the holdback bar is crucial to the catapult-assisted takeoff speed of the carrier-based aircraft, and the accurate control of the release load is the key to ensure the catapult-assisted takeoff performance.
2. In determining the structural parameters of the contact surface of the holdback bar release mechanism, consideration should be given to the effect of changes in the angle of the contact surface on the sensitivity of the release load; when the angle of the contact surface is between 30\({}^{\circ}\) and 50\({}^{\circ}\) the effect on the sensitivity of the release load is small, in order to ensure the stability of the release load, the angle of the contact surface of the holdback bar should be selected within this range.
3. The release load increases with the increase of friction coefficient of each contact surface of the holdback lock mechanism. When the friction coefficient is greater than the critical value of 0.3, the release load increases rapidly; in order to make the release load more stable, the friction coefficient should be selected in the range of 0.1 to 0.3, when the impact on the sensitivity of the release load is small.
4. As for repeatable holdback bar, in order to ensure the stability of the release load when repeated, and to accurately control the magnitude of the release load, the factors affecting the sensitivity of the release load should be thoroughly considered in design and applications.  
Methodology, analysis, validation, writing--original draft, E.Z.; simulation Z.Z.; writing--review and editing H.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 11: Variation curve of released load with friction coefficient.
## Testing Site  
### Site Description  
The selected testing site experienced debris flow a few years ago and is still susceptible to additional debris flow because of several geological characteristics of the site, including steep slope angle (over 32\({}^{\circ}\)) and saturated soil condition on slope. According to the geological map issued by the Korea Institute of Geology, Analysis, Mining (KIGAM), the testing area mainly consists of gneiss some granite terrane. The testing site belongs to Mt. Geohwa, South Korea, where the altitude and area are approximately 200 m and 1.2 km\({}^{2}\), respectively, and the latitude and longitude of the top of the mountain are N 36\({}^{\circ}\)29\({}^{\prime}\)15.7313\({}^{\prime\prime}\) and E 127\({}^{\circ}\)18\({}^{\prime}\)40.5986\({}^{\prime\prime}\). A drone aerial survey was performed to obtain a topographic map and digital elevation model. Figure 1 shows the topographic map: the area consists of one main stream and several branch streams. The vertical length and area of the main stream are approximately 206 m and 14,675 m\({}^{2}\), respectively. Figure 1 also indicates the presence of the debris barrier and check dam at the site to prevent additional debris flow (N 36\({}^{\circ}\)29\({}^{\prime}\)04.3147\({}^{\prime\prime}\), E 127\({}^{\circ}\)18\({}^{\prime}\)44.7017\({}^{\prime\prime}\)). The main stream was divided at four points, selected by considering slope characteristics: point A (N 36\({}^{\circ}\)29\({}^{\prime}\)
# Single-Channel Blind Source Separation of Spatial Aliasing Signal Based on Stacked-LSTM
## 7 Conclusions  
In this paper, we propose a deep learning model based on Stacked-LSTM to solve the single-channel blind source separation problem, in which two source signals are mixed into one mixed signal. The proposed model can separate the mixed signals and restore them to two source signals by learning useful information from big data. Through training the time domain mask, the mapping of the hybrid matrix in the neural network is obtained, avoiding the high error of iteratively solving the hybrid matrix directly. Experiments show that the proposed model has good generalization performance not only in pure environment but also noisy environment compared with classical methods or other deep learning models. The separation loss is reduced by 10.09-38.17 dB.  
**Author Contributions:** conceptualization, M.Z.; methodology, M.Z. and X.Y.; software, M.Z.; validation, J.W. and Y.Y.; formal analysis, X.G. and Y.F.; writing--original draft preparation, M.Z.; writing--review and editing, X.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Chinese Academy of Sciences Space Science Strategic Leading Science and Technology Project, grant number XDA15060100. This research was funded by the Chinese Academy of Sciences Strategic High-Tech Innovation Fund, grant number GQRC-19-14.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Results and Discussion
\begin{table}
\begin{tabular}{c c} \hline \hline
**Low Level Descriptors (LLDs)** & **Statistical Features** \\ \hline Root mean square (RMS) Energy & Position max/min \\ Quality of Voice & Arithmetic mean, standard deviation \\ Zero crossing rate & Skewness \\ Jitter Local & Kurtosis \\ Jitter DDP (difference of periods) & Linear regression coefficient 1/2 \\ Shimmer Local & Quadratic \& Absolute linear regression error \\ F0 by Sub-Harmonic sum (SHS) & Quartile 1/2/3 \\ Loudness & Quartile range 2-1/3-2/3-1 \\ Probability of Voicing & Percentile 99 \\ Harmonics to noise ratio (HINR) by Autocorrelation function (ACF) & Up-level time 75/0 \\ MFCC & Percentile 1 \\ Line spectral pairs (LSP) Frequency & Percentile range 1-99 \\ Log mel frequency band (MFB) & Onsets number \\ F0 Envelope & Duration \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Acoustic features used in the literature [38]. Figure 7: Recognition rates of discrete emotions on baseline, timbre all, timbre selected, and the combination of baseline and timbre selected acoustic features for IEMOCAP. The experimental results in this work showed the effectiveness of the timbre features, which consisted of spectral and harmonic features. Timbre features can characterize emotions that share similar acoustic contents in the arousal dimension, but are different in the valence dimensions, such as anger and happiness. Moreover, we investigated whether timbre features can be used not only to discriminate instrument sounds, music emotion recognition [17; 18], and music mood classification [20], but also to classify emotions in a speech signals. Spectral shape and harmonic features complement each other to describe the voice quality of a sound. When we combined timbre selected and baseline acoustic features, the results improved significantly compared to the rest of the acoustic feature sets for EMO-DB. The difference between the results of timbre and the combination of the baseline and timbre selected acoustic feature sets was not high (around 3%) for IEMOCAP. Timbre selected and the baseline acoustic features complement each other for both databases.
# A CNN-Based Method for Heavy-Metal Ion Detection
## 3 CNN Structure
### Fully Connected Layer and Output Layer  
The fully connected layer was present in the classification and regression networks of the HMID-NET. The fully connected layer is mainly used to integrate features to accomplish the operation of classification or regression prediction. It can map the extracted features from the feature extraction network to a few neurons for prediction.  
HMID-NET can perform both classification and regression of the ion concentrations in solutions. The classification and regression networks were placed side by side. In the classification network, the ReLU function was used as the activation function for the fully connected layer. This was because the classification network used cross-entropy loss, which is self-normalizing. In the regression network, the Softmax function was used as the activation function for the output layer. This mapped the concentration predictions between 0 and 1, reducing the gradient loss during training.  
The output of the classification network was three neurons. The data in these three neurons corresponded to the probability that the solution contained \(Cd^{2+}\), \(Zn^{2+}\), and \(Cu^{2+}\), respectively. The labels corresponding to the maximum values of these three data were the predicted solution types.  
In the regression network, the output of the three neuron values correspond to the predicted concentrations of \(Cd^{2+}\), \(Zn^{2+}\), and \(Cu^{2+}\), respectively. However, the concentration prediction requires taking into account the prediction of the classification network. For example, if an ion is predicted to have the class \(Zn^{2+}\), the regression network result is [a, b, c]. At this point, the ion species is \(Zn^{2+}\), and the concentration is a. The solution does not contain \(Cu^{2+}\) or \(Cd^{2+}\); so, results b and c are not considered.  
For a fully connected layer network with an input layer of 800 and an output layer of 3, see Table 1 The effect of changing the size of the hidden layer on the accuracy of the network is shown. We consider the relationship between the concentration error, classification accuracy, and the number of parameters, and choose 64 as the number of neurons in the middle hidden layer.
## 5 Experimental Setup
### Soft Labels for Distillation  
As mentioned above, NLLB-200-3.3B was considered as the teacher in our experiments. The Urdu sentences of the training data (see Section 4) were translated to English by NLLB-200-3.3B. We sampled a few hundred English translations, manually examined them, and found that the some English translations were low in quality, often featuring repetitive words (over-generation) and generation of contextually incorrect words. We captured the confidence scores for each translation in order to understand how uncertain the models were about generating outputs. For this, we used the negative log-likelihood (NLL) [2] loss  
\begin{table}
\begin{tabular}{l r r r} \hline \hline  & **Sentences** & \multicolumn{2}{c}{**Vocabulary**} \\  & & **Urdu** & **English** \\ \hline Train & 188,130 & 152,943 & 166,848 \\ Valid & 1000 & 5488 & 4996 \\ IN22-Gen & 1023 & 6708 & 7387 \\ FLORES-200 & 994 & 5775 & 5336 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data statistics.  
of the predicted sequence. By applying the exponential function to the negative of this loss, we approximated the probability of the sequence, as shown in Equation (8):  
\[\text{Confidence}=\exp\left(-\sum_{j=1}^{J}\log p(t\mid s)\right) \tag{8}\]  
where \(j\) represents a position in the target sequence, \(s\) is the source sentence, and \(p(t|s)\) represents the probability of the target token being at position \(j\) given \(s\). The sequence-level negative log-likelihood computes the joint probability of the entire sequence [2].  
We observed that the teacher model usually produced low confidence scores during inference when dealing with low-quality translations. The opposite case was observed as well, i.e., high confidence scores were associated with the good-quality translations. In Figure 4, we show English translations of four Urdu sentences produced by the teacher with both low and high confidence scores. As can be been from Figure 4, translations with high confidence scores are generally of good quality, whereas those with low confidence scores tend to be of lower quality. This encouraged us to leverage the confidence scores estimated by the teacher for KD. We discarded those instances for which the teacher was quite uncertain in generating translations. For our experiments, we used a grid of threshold values (the first quartile, mean, and third quartile) in the filtering process in order to examine the tradeoffs between model performance and training cost.  
We followed the standard student-teacher setup in order to build our baseline student models; in other words, we treated the English translations of Urdu training sentences as pseudo-target labels for model training, appended them to the original training data, and built the baseline student model. The process of training the student model is shown in Figure 5.  
Figure 4: Examples of English translations of Urdu sentences produced by the teacher model with high and low confidence scores. The Urdu source sentences are accompanied by their English transliterations.
_Aerospace_**2015**, \(2\), 392-422; doi:10.3390/aerospace2030392  
_Aricle_  
**Unmanned Aerial _ad Hoc_ Networks: Simulation-Based Evaluation of Entity Mobility Models' Impact on Routing Performance  
**[PERSON] 1, [PERSON] 1,*, [PERSON] 1 and [PERSON] 2**  
1 Systems and Computer Engineering, Carleton University, 1125 Colonel By Drive, Ottawa, ON K1S 5B6, Canada; E-Mails: [EMAIL_ADDRESS] (J.-D.M.M.B.); [PERSON] (M.S.-H.)  
2 Communications Research Centre Canada, 3701 Carling Avenue, PO Box 11490, Station H, Ottawa, ON K2H 8S2, Canada; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +1-613-520-3573; Fax: +1-613-520-5727.  
Academic Editor: [PERSON]  
_Received: 18 February 2015 / Accepted: 17 June 2015 / Published: 30 June 2015_  
**Abstract:** An unmanned aerial _ad hoc_ network (UAANET) is a special type of mobile _ad hoc_ network (MANET). For these networks, researchers rely mostly on simulations to evaluate their proposed networking protocols. Hence, it is of great importance that the simulation environment of a UAANET replicates as much as possible the reality of UAVs. One major component of that environment is the movement pattern of the UAVs. This means that the mobility model used in simulations has to be thoroughly understood in terms of its impact on the performance of the network. In this paper, we investigate how mobility models affect the performance of UAANET in simulations in order to come up with conclusions/recommendations that provide a benchmark for future UAANET simulations. To that end, we first propose a few metrics to evaluate the mobility models. Then, we present five random entity mobility models that allow nodes to move almost freely and independently from one another and evaluate four carefully-chosen MANET/UAANET routing protocols: _ad hoc_ on-demand distance vector (AODV), optimized link state routing (OLSR), reactive-geographic hybrid routing (RGR) and geographic routing protocol (GRP). In addition, flooding is also evaluated. The results show a wide variation of the protocol performance over different mobility models. These performance differences can be explained by the mobility model characteristics, and we discuss these effects. The results of our analysis show that: (i) the enhanced Gauss-Markov (EGM) mobility model is best suited for UAANET; (ii) OLSR, a table-driven proactive routing protocol, and GRP, a position-based geographic protocol, are the protocols most sensitive to the change of mobility models; (iii) RGR, a reactive-geographic hybrid routing protocol, is best suited for UAANET.  
Keywords:mobility models; routing; UAANET; UAV +
Footnote †: journal: Journal of the Acoustical Society
## 1 Introduction  
In a system where safety is essential, such as a nuclear power plant, it is vital to judge the system state in an emergency operation correctly. The judgment of the system state depends on the operator's situation awareness (SA), which is the perception of environmental elements within time and space, the comprehension of their meaning, and the immediate projection of their status [1, 2]. This definition describes the perceptual aspect of information processing, the mental activities that process the perceived information, and the resulting mental activity [3].  
While SA is not equivalent to performance, a higher SA is generally correlated with higher performance. High SA has improved safety in aviation control rooms [4] and nuclear power plant control rooms [5, 6, 7] by reducing mistakes and accidents. [PERSON] [1] reported that SA in a complex system causes 88% of human errors. [PERSON] et al. [8] analyzed 177 marine accidents and found that 71% of human errors were SA-related. [PERSON] et al. [9] showed that 81.5% of human errors in anesthesia were related to SA. Therefore, it can be seen that the operator's SA is crucial for a system's safety. In this context, studies on SA have been conducted from various perspectives.  
In particular, since humans receive most of the information through sight, gaze movement has been used as a crucial indicator in SA. Gaze movement analysis has been used to analyze SA in various fields such as civil aviation cockpit settings [3], nuclear power plant control rooms [10], aviation [11], and health care [12]. As described above, although some studies have been conducted on the characteristics of gaze movement in SA, there has been no study on the gender characteristics of gaze movement in the SA process. As the number of female workers increases even in large systems, such as nuclear power plants, it is relevant to compare and analyze men and women's SA and gaze movement characteristics  
Although it is not a study on SA, several research results show a difference in the visual characteristics of men and women during information processing. [PERSON] [13] said that there are differences in the visual perception characteristics of men and women in spatial exploration. [PERSON] [14] said that there are differences in the characteristics between men and women from various viewpoints concerning human information processing. [PERSON] et al. [15] stated that, from a cognitive information processing's perspective, men tend to pay more attention to core information consistent with personal goals, whereas women tend to pay attention to various information types. Therefore, there may be differences according to gender in visual attention to the same object. From this fact, it can be hypothesized that there may be a gender difference in gaze movement in the SA of the system state. Therefore, a study was conducted to compare and analyze men and women's SA and gaze movement characteristics by making a simulator for emergency scenarios in nuclear power plants.
# Multi-Satellite Observation-Relay Transmission-Downloading Coupling Scheduling Method
## 2 Problem Description
### Improvement of Observation Solution Local Search Based on Satellite Cluster  
_Profit-State Evaluation_  
In the article, the improvement process for the local search in the traditional tabu search algorithm is as follows: When there is no solution superior to the historical optimal solution in the neighborhood, the neighborhood solution is evaluated by the satellite cluster profit-state evaluation function, and the local search direction is selected according to the evaluated value. The satellites' subsystem states are adjusted by modifying satellite observations, inter-satellite data transmission, and satellite-ground data downloading actions. The purpose of the improvement process is to escape local optima. In this subsec  
Figure 2: Flowchart of the improved tabu search method.  
tion, descriptions of the observation solution search neighborhood and the satellite cluster profit-state evaluation function are provided.