esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Formulation of Multi-Criteria Optimization Problem  
### Problem Description  
The DDFJSP-VPT can be described as follows: a set of \(n\) jobs \(J=\{J_{1},J_{2},\ldots,J_{n}\}\) must be processed in \(w\) flexible job-shop-type factories \(\{U_{1},U_{2},\ldots,U_{w}\}\). Each job \(J_{i}\) consists of one or more operations \(\{O_{U_{1}},O_{U_{2}},\ldots,O_{in_{j}}\}\), and each factory \(U_{l}\) has \(m_{l}\) machines \(\{M_{l1},M_{l2},\ldots,M_{lm}\}\). Once a job is assigned to a designated factory, all of its operations must be processed in it, and each operation can be assigned to one or more machines in this factory. When operation \(O_{ij}\) is assigned to machine \(M_{lk}\) in factory \(U_{l}\), its theoretical processing time is \(p_{ij}^{lk}\). However, due to various uncertainties in real-world production, the actual processing time of each operation is subject to fluctuations and is denoted as \(\tilde{p}_{ij}^{lk}\). The scheduling consists of three subproblems: the factory assignment of jobs, machine selection for operations, and the sequencing of operations in an uncertain environment arising from changes in processing times. The objective is to optimize the performance of the scheduling system while ensuring all constraints are satisfied.  
As is the case in much of the existing literature, the following assumptions are made: (1) all machines are available and all jobs can begin processing from the initial moment. (2) Each factory has the capacity to process all jobs. (3) Each machine can process at most one operation at a time. (4) All operations of the same job must be processed within the same factory. (5) Each operation can be processed by only one machine. (6) Once a job begins processing, it cannot be interrupted. (7) The transport time, setup time, and release time are not considered. (8) Only the processing phase of jobs is considered. Disassembly and assembly phases are not included.
## 1 Introduction
If the MHD pump is combined with a conventional pump, in that case the power consumption load from the conventional pump could be reduced and also MHD pump-based cooling could be targeted for the thermal management of higher power density applications. There are research opportunities on optimizing the design and operation of a hybrid approach that combines convectional pump and MHD pump-based cooling to significantly enhance the cooling capabilities of electronic devices and systems with increasing energy densities. To the best of the authors' knowledge and conducted literature review, the open literature is missing a concrete research study on the thermal management of traction inverters with high power density using hybrid MHD-convectional pump-based cooling systems. The research gaps in the open literature are explored by proposing the hybrid MHD-conventional pump-based cooling system for the thermal management of higher power density traction inverters in the present work. Therefore, the objective of the present study is to numerically investigate the cooling performance characteristics of traction inverters using MHD pump-based cooling systems with water and ferrofluid coolants. The influence of various conditions of inlet velocity, electromagnetic parameters, and volume fraction is studied on outlet velocity, inverter maximum temperature and Nusselt number. The cooling performance characteristics of traction inverters are investigated under the influence of various factors using MHD pump-based cooling systems with and without integration of conventional pumps.
## 4 Discussion
(2018) that achieved an accuracy of 60.3% on 151 samples with combination of DPCA (dynamic principal component analysis) and GMM (Gaussian mixture model) technologies, our system demonstrated higher accuracies of 79.8% and 79.1%. In future implementations of our system in hospitals, the tolerant head height of the patient used to determine the maximum safe region should be adjusted to the patient's height. Furthermore, more subjects could be recruited in future work to eliminate inter-subject interference and improve the generality of the eNightTrack system. Additionally, prior YOLO models, such as YOLO-NAS, could be utilized for head detection. Similarly, an emergency MOT technique could substitute the tracking model adopted in this experiment. The registered 3D data mentioned in Section 3.2 could be utilized for 3D tracking, which could be more comprehensive. Clinical trials should be implemented before it is finally applied to hospital use.
# A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions
## 8 Conclusions
If a solution is not found, the unavoidable consequence of such a significant limitation will be the lack of reliability of USVs in harsh DM scenarios. The present work identified a trend in DM involving aquatic environments: the use of heterogeneous fleets of unmanned systems working together, with promising results and applications. USVs, UAVs, and UUVs have complementary advantages and weakness. Even though UUVs and UAVs have their limitations, they can provide different perspectives to disaster sites by performing measurements and going to regions where USVs cannot go. USVs can carry a large payload, depending on its size, and provide energy and communications infrastructure for UUVs and UAVs as a moving station. However, the use of USVs for such purpose is still evolving, and live tests are still restricted to competitions. Another problem constantly occupying researchers is conformance with international naval regulations. Among them, the COLREGS and country-specific rules which must be respected to avoid collision accidents. USVs share similar concerns with autonomous cars, where the discussion of responsibility in case of accidents is still not clarified. Thus, regulatory and legal concerns bring together a considerable amount of problems which are being individually addressed, case by case, by each country and research group. This paper was partially funded by CAPES/Brazil, under project 88887.115590/2015-01 and 88887.215325/2018-00, Pro-Alertas program. It was also financed in part by the CAPES/FAPERGS. We would like to thank the support of the librarians at PUCRS during this endeavor. The authors declare no conflict 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. The following abbreviations are used in this manuscript: \begin{tabular}{l l}
3D & Three Dimensional \\ COLREGS & COLLision REGulations at Sea \\ CRASAR & Center for Robotic-Assisted Search and Rescue \\ DM & Disaster Management \\ FP7 & Seventh Framework Program \\ GNC & Guidance Navigation and Control \\ GPS & Global Positioning System \\ HAB & Harmful Algal Bloom \\ ICARUS & Integrated Components for Assisted Rescue and Unmanned Search \\ LiDAR & Light Detection And Ranging \\ OASIS & Ocean-Atmosphere Sensor Integration System \\ SAR & Search and Rescue \\ UAV & Unmanned Aerial Vehicle \\ UMV & Unmanned Marine Vehicle \\ USV & Unmanned Surface Vehicle \\ UUV & Unmanned Underwater Vehicle \\ \end{tabular}
## 3 SCH Nonlinear Mathematical Model (NMM)
### Nonlinear Model Modification  
The advantage of a nonlinear model is that it contains dynamic features of a wider flight envelope. However, the accuracy of pure nonlinear modeling of helicopters is always unsatisfied. On the contrary, the linear models obtained from system identification method can provide more accurate results but only under the particular test conditions. Therefore, in this study, a linear modification method was designed to improve whole-model performances. The method extracted several essential nonlinear parameters and modified them with reference to system identification results.  
In this section, the identification results adopt previous experience on SCH in References [25; 26]. The procedure was conducted in frequency domain, assisted of CIFAR software [13] (developed by NASA Ames Research Center). Table 4 gathers the main identified parameters from CIFAR and Figure 7 plots the frequency comparisons with flight results. As shown in Figure 7, the frequency responses of the linear identification model (IDM) matches the flight data in high quality. The cost value quantifies the matching quality. The smaller the cost value, the higher the matching degree in frequency domain. According to CIFER, a cost value lower than 100 can reflect a good quality of the identification results [13]. In Table 4, the cost values meet the requirement. In the following section, the modification will be conducted firstly in hover condition and then in forward flight condition.
# A Framework for Fall Detection Based on OpenPose Skeleton and LSTM/GRU Models
## 2 System Architecture and Method
### Experiment Environment  
The neural network model is trained on the Google Colaboratory platform [38], using CPU and GPU for training, and the software environment is Keras 2.3.1, as shown in Table 3. We use three different models, namely RNN, LSTM, and GRU for experiments and compare their performance in fall detection.  
For training, we randomly divide 1140 groups of mixed datasets into 80% training set and 20% test set, resulting in 916 training sets and 224 test sets. The training sets are set up in batches and invested a number of training data to learn in each set. This study sets 916 to the same size as the training set. The reason for this is that the model can find the gradient to the best value faster. However, if there are more data or the hardware is slower, insufficient memory problems may occur. We also evaluated the performance under different Learning Rate (LR), which is the step size to find the best value. If LR is too large, the best value will not be found, and the training speed will be too small.  
Table 4 shows the parameters for the training model. There are four datasets, which include the original dataset, the one with minimum and maximum normalization, the one with relative position normalization, and the one with linear interpolation plus relative position. According to the different datasets, we set 50 and 30 inputs, which are the original 25 joint points and the (\(x,y\)) coordinates of 15 joint points, respectively, as the feature value  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **Learning Rate** & **Batch Sizes** & **Epochs** \\ \hline
**RNN** & & & \\ \hline
**LSTM** & 0.1, 0.01, 0.001 & 916 & 500 \\ \hline
**GRU** & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model training parameters.  
Figure 13: Fall down model architecture.  
input. There are 64 to 1024 hidden neurons in the hidden layer. Because different datasets require different hidden layers and the number of hidden neurons, we evaluated a different range of numbers to find the best choice. Because there are missing points, it is necessary to use the excitation function whose output is centered at 0, so that when the input is 0, the output is also 0.
## 4 Monte Carlo Simulations  
Monte Carlo simulation (MCS) has proven to be a valuable approach for addressing problems involving numerous uncertain variables, making it an attractive method for addressing the challenging issue of ballistic penetration model uncertainty. MCS has been applied by many researchers for geotechnical applications, including probabilistic stratification in site characterization [50], analysis of the load-deflection behavior for laterally loaded piles [51], random finite element method for bearing capacity analyses [52], and more.  
In MCS, the response of a complex system is iteratively computed using a sequence of randomly generated samples as inputs. In order to conduct an MCS, a significant number of random samples are generated based on predefined probability distributions, and these samples are subsequently utilized as inputs for the analysis. The higher the number of iterations, the more accurate the prediction and the more computationally expensive the analysis is. A sample size of 10,000 was used for this analysis because iterations using larger sample sizes (100,000 and 1,000,000) yielded marginal changes in the results with considerable computational time increase.  
Traditional MCS, while accurate, is computationally intensive. Some researchers introduce methods to overcome this drawback. For example, [PERSON] and [PERSON] [53] introduced a modified Weighted Uniform Simulation (WUS) method designed to efficiently handle correlated non-normal random variables through the Nataf transformation. The method transforms the correlated non-normal random variables into independent standard normal variables, allowing for a more efficient analysis with reduced sample sizes under the same accuracy requirements. In addition to the aforementioned weighted simulation technique, several variance reduction techniques can be used to improve MCS [21]. Examples of these techniques used in the current study are the Latin Hypercube Sampling (LHS) and Gaussian Process Response Surface, in addition to the common pseudorandom sampling, as discussed next.
## 5 Discussion
### Bird Individual Identification and the Complexity Classification  
Even though the automatic algorithm shows better efficiencies in complex vocalizations (see Table 5), this is mainly due to the algorithm's discarding ability for impostor matching. In this sense, a more specialized automatic region extraction algorithm may improve the efficiency of the proposed  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Species** & **Semi-Automatic Individual** & **Automatic Individual** \\  & **Verification Algorithm** & **Verification Algorithm** \\ \hline (a) _Automolus rubiginosus_ & 85.58 & 75.84 \\ \hline (b) _Synallaxis erythlothorax_ & 81.6 & 76 \\ \hline (c) _Cardinalis cardinalis_ & 96.94 & 92.98 \\ \hline (d) _Cercomacra tyrantina_ & 25 & 82.81 \\ \hline (e) _Hylorchilus sumichrasti_ & 55 & 56.5 \\ \hline (f) _Myiozetetes similis_ & 82 & 82.9 \\ \hline (g) _Pitangus sulphuratus_ & 25 & 75.78 \\ \hline (h) _Psarocolius montezuma_ & 47.6 & 85.71 \\ \hline (i) _Amazona viriligenalis_ & 29.4 & 55.55 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of individuals identification performance test (%). Species with complex vocalizations are written in bold.  
automatic algorithm for genuine matching. As expected, the semi-automatic algorithm shows better efficiencies in non-complex vocalizations because the main information from the bird vocalization in the recordings is easy to extract from the spectrogram.  
According to the efficiency of the algorithm and the spectrogram characteristics of each vocalization, some differences about what is (or is not) a complex sound (for example, variations of time-frequency and number and type of syllables) could be established. This classification term does not exist in the literature, but it is presented in this article to make a fairer comparison of the algorithm (and for possible bird identifications that could be carried out in future investigations).
# Two Decades of Groundwater Variability in Peru Using Satellite Gravimetry Data
## 2 Materials and Methods
### Methods
#### 2.3.6 Precipitation-GWS Relationship  
The precipitation data have been collected for the northern, central, and southern regions of Peru for the years 2003-2023 along with a comparative analysis of precipitation variability and groundwater storage, as well as their potential effects. The data are presented in Appendix A (Tables A1-A3).  
_Linear Trend Calculation of Precipitation_  
To assess trends in annual precipitation across Peru's Northern, Central, and Southern regions from 2003 to 2023, the simple linear regression model was applied. This approach is suitable for detecting linear relationships between time (independent variable) and annual precipitation (dependent variable), under the assumption that long-term climate change induces gradual variations. The analysis was performed independently for each region to identify potential geographic differences in rainfall patterns.  
\[\text{Y}=\text{mX}+\text{b} \tag{6}\]  
where:  
Y: is the estimated annual precipitation.  
X: is the year.  
m: is the slope of the line (average annual variation in precipitation).  
b: is the intercept (value of Y when X = 0).  
The calculations are shown in Appendix B, and the Results Section details the obtained values.
## 2 Materials and Methods
### Intelligent Inspection System
#### 2.3.4 Machine Learning  
The processing method for shape and quality profile classification is a feed-forward neural network (FFNN) composed of three layers: input, hidden, and output. Figure 18 presents a diagram of the FFNN architecture, where the input is the feature vector, the hidden layer has a different number of neurons for each experiment in this work, and the output is just one neuron.  
The \(D_{\Delta,\beta}(k)\), \(M_{d,\beta}(k)\), and \(r_{d,\beta}(n)\) are one-dimensional and single-channel feature vectors that can be codified with an FFNN. The FFNN is a less computationally complex classifier than the classifiers most used in the literature related to CNC process monitoring. These classifiers are SVM, RNN, and CNN [15; 16]. Moreover, the FFNNs learn with hundreds of feature vectors, while SVM, RNN, and CNN require tens of thousands of feature vectors to learn. Then, the shape and quality profile classification method is an  
Figure 16: \(M_{\beta}(k)\) frequency from the difference between \(M_{g,c}(k)\) and \(M_{g,s}(k)\).  
Figure 17: \(R_{\delta}(k)\) behavior from the difference between \(M_{g,c}(k)\) and \(M_{g,s}(k)\).  
Figure 18: FFNN architecture.  
FFNN composed of three layers: input, hidden, and output. The inputs are the features of the profile, \(F(k)\). The hidden layer is composed of \(N\) neurons, defined by Equation (7).  
\[\gamma_{1,j_{1}}=f(\omega_{1}(k)F(k)+\omega_{1,o}) \tag{7}\]  
where \(\gamma_{1,j}\) is the hidden layer, \(\omega_{1}(k)\) is the weights, \(\omega_{1,o}\) is the bias, \(j_{1}\) is the neuron index of the hidden layer, \(j_{1}=0,\ldots,J\), and \(f(\cdot)\) is the RELU activation function that helps the FFNN to converge faster than other activation functions [37]. The output layer is defined by Equation (8).  
\[\gamma_{2,j_{2}}=f(\omega_{2}(k)M_{\Delta,\beta}(k)+\omega_{2,o}) \tag{8}\]  
where \(\gamma_{2,j_{2}}\) is the output layer, \(j_{2}\) is the neuron index of the output layer, \(\omega_{2}(k)\) are the weights, \(\omega_{2,o}\) is the bias, and \(f(\cdot)\) is the sigmoid activation function used to represent a binary classification with one neuron [38].
# Speech Sentiment Analysis Using Hierarchical Conformer Networks
###### Abstract
The sparsity of the data is also a difficult problem for feature extraction. To increase the accuracy of speech emotion feature extraction and reduce the computational cost, we introduce a new architecture for speech sentiment analysis using speech sequences in this paper. As shown in Figure 1, the architecture of the framework has two main parts: a local feature learning module and a global feature learning module. First, the raw speech signal data are input into the local feature extraction module, and the spatial features of emotion are extracted by convolution through the conformer, and then the spatial and temporal features of emotion are learned through a hierarchical strategy. Second, the global feature extraction module adaptively adjusts the associated global feature weights based on the relevance of the input features. At last, sentiment classification is performed through the central loss function and the softmax function. The main innovations of our approach include the following:  
1. We present a framework for speech sentiment recognition using hierarchical conformer networks and an attention-based GRU model. 2. The conformer model learns and extracts the spatio-temporal features of speech signals and the semantic associations of the sentiment information. The Gated Recursive Unit (GRU) of the attention-based network layer adaptively adjusts the weights of the computed global features. The weights of the global features are adjusted according to the local features and the global features. 3. The conformer model can more accurately select the deep-level features of speech signals without increasing the computational cost. It solves the computational cost issues of traditional models and improves the accuracy significantly. The other parts of this article work are as follows: Section 2 describes relevant recent work on sentiment analysis; Section 3 introduces our proposed model framework and methodology; Section 4 presents experimental evaluation and discussion; Section 5 reports experimental results and analysis; Section 6 summarizes the work and the future perspectives.
## 1 Introduction
Furthermore, single-frequency measurements limit our knowledge of the ear function at other frequencies, which are crucial for perceiving complex stimuli such as speech. In contrast, wideband acoustic immittance (WAI), also known as wideband tympanometry (WBT), uses a short click or chirp stimuli to enable the evaluation of the middle ear across a wide range of frequencies (250-8000 Hz) [27]. The test provides an analysis of wideband absorbance (WBA) or wideband reflectance (1-absorbance) [23]. The absorbance results are plotted as a function of frequency and range between 1, indicating absorption of all the energy from the stimulus, and 0, indicating reflection of all the energy [28]. Normative WBT reference values vary across populations according to ethnicity and age [27]. There is a growing understanding that WBT contributes crucial additional information to the study of conductive hearing loss, both of middle- and inner-ear origin. [PERSON] et al. [29] demonstrated specific atypical WBT findings in patients with superior semicircular canal dehiscence, confirmed by CT, which was absent in the control group. Specifically, individuals with superior canal dehiscence showed a notch of energy reflectance near 1000 Hz. No difference was found between the two groups with the standard 226 Hz tympanometry. Several studies found unique WBT patterns in patients with inner and middle-ear pathologies apart from ABG, such as large vestibular aqueduct [30], cochlear hypoplasia, incomplete partition I, incomplete partition II, cochlear aplasia, and labyrinthine aplasia [31]. Ears with CI were also found to be associated with WBT pattern differences compared to persons with normal hearing, including a reduction in WBT at frequencies up to 1200 Hz [29; 30]. Both studies hypothesized that WBT variations are related to increased inner-ear stiffness. Since implanted ears are usually associated with severe to profound SNHL, it is crucial to characterize WBT patterns related to the implantation from those related to the hearing loss itself. The primary aim of this study was to compare the WBT findings for ears with severe to profound SNHL in patients who underwent cochlear implantation to the non-implanted ears of the same individuals with similar SNHL but without CI. The secondary aim of this study was to compare WBT in ears with normal hearing to ears with SNHL with or without CI.