esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Audio-Based Emotion Detection Component: GEVA
### Voice Features Extraction
Statistic flux tells us if the signal is concentrated around an average amplitude or if there are many variations in the volume of the signal. Figure 4: The three audio domains in which voice feature extraction is performed. 3. **Rolloff-95** corresponds to the frequency value at which 95% of the signal energy is already contained. For example, if the signal is contained between 200 and 3,000 Hz, maybe, at a frequency of 2,800, the amount of energy contained within the range of zero to 2,800 is already 95% of the total. Then, the RollOff-95 value is 2800. 4. **Centroid** represents the median of the signal spectrum. That is, the frequency the signal approaches the most. It is frequently used to calculate the tone of a sound or voice (timber). Our vocal apparatus produces very different values to those produced by a violin or a flute. 5. **Zero-crossing rate** indicates the number of times that the signal crosses the zero (x axis). Typically, the ground noise crosses much more times the x axis than a verbal sound. 6. **Signal-to-noise ratio (SNR)** allows us to relate the voice signal volume (RMS) to the noise signal volume. In order to do that, the average volume value of the noise when there is no voice activity is calculated. Besides, the average volume value of the received signal, when the voice activity is detected, is also calculated. Finally, SNR is obtained by dividing the first obtained value by the second one. Therefore, this statistic allows, in noisy environments, for differentiating the user voice from the noise better than just using RMS. 7. **The communicative rhythm** serves to obtain more information about the state of the user. This parameter is obtained by counting the number of words pronounced per minute. Each word is separated from the previous one by a brief instant of silence or a pitch peak, which are identified. This sum of silences and pitch-peaks is multiplied by 60, and this result is divided by the duration (in seconds) of the speech. This statistic is very useful to distinguish emotions, because each of them have typically different communication rhythms.
# Toward a Comprehensive Understanding and Evaluation of the Sustainability of E-Health Solutions
## 5 E-Health Sustainability Model
### E-health Sustainability Model
#### 5.2.5 Economic  
The economic dimension contains three aspects, which are the affordability of the e-health solution, sustainable funding, and financial benefits. The affordability of e-health refers to the degree to which all expenses of the e-health solution are covered. This is not limited to the technical expenses but extends to operational costs such as staff salaries and required equipment. However, even in the case of the capability of affording the e-health solution, it is of great importance to have sustainable funding for the e-health solution. Therefore, the sustainability of funding must be considered and ensured when approving the e-health affordability, as it is not a one-time activity. The financial benefits aspect concentrates on the direct or indirect pecuniary or material benefit that is obtained by implementing and using the e-health solution [47]. This can be in a form of direct cost-saving (cost reduction) or indirect cost-saving through operative, regulative, or social aspects, that eventually affects the financial business aspects [47]. This aspect can interrelate with the outcome's identification aspect in the organization dimension. However, it is worth noting that it depends on the healthcare organization's goals and objectives for using the e-health solution. In other words, if the healthcare organization does not consider financial benefits in the adoption of e-health, then this aspect can be eliminated in the context of the healthcare organization.
# A Novel Decomposed Optical Architecture for Satellite Terrestrial Network Edge Computing
## 4 Experimental Demonstration of Decomposed Optical Architecture
### Network Performance of Decomposed Computing Prototype  
The network performance of the decomposed computing prototype is assessed in terms of end-to-end access latency and packet loss. The network latency of the decomposed prototype includes four components: the processing delay in the flow controller and network interface of the source and destination hardware cubes, packet switching in the NOS, and fiber transmission delay. The end-to-end access latency of the implemented computing prototype with its breakdown is illustrated in Figure 6a, in which the latency is recorded when the processor cubes send instructions to the memory cubes. The customized transmission protocol is designed for the computing prototype with decomposed hardware to minimize the protocol processing delay. It is shown that, based on customized transmission protocol, the processor cube contributes 20.63 ns to handle data access instructions before sending optical packets out, while the memory cube takes 28.27 ns for receiving instructions and finding the target data. The NOS switching delay is higher compared to other components because it has more processing procedure including control signal processing, switch driver delay, and acknowledgement signal generation. Utilizing fast packet switching by NOS and customized transmission protocol for decomposed hardware, the implemented computing prototype performs minimal end-to-end access latency of 122.3 ns. By applying the Application Specific Integrated Circuit (ASIC) to implement the function of the decomposed hardware instead of the FPGA chip, the end-to-end access latency can be reduced still further.  
Figure 5: BER and eye diagram of decomposed computing prototype.  
To evaluate the network transmission stability of the computing prototype with decomposed hardware, the packet loss is cumulatively measured during 80-h operation time. In the assessment, the processor cube sent 5.8 \(\times\) 10\({}^{-11}\) instructions to memory cube in total. As shown in Figure 6b, the cumulative packet loss of the implemented computing prototype with decomposed hardware is less than 1.9 \(\times\) 10\({}^{-12}\) in the end. Only 11 packets are lost during initialization stage before setting up the alive link. This is because the Clock and Data Recovery (CDR) procedure is required during initialization stage at receiver part. After finishing the initial CDR procedure, the NOS based network can provide a stable and no packet loss interconnection for the decomposed hardware cubes. This is because that the link keeps active and the clock is locked by inserting blank packets, avoiding resetting up the link.
## 4 Experiments and Results  
### Baseline Noise of the Experimental Setup  
The baseline noise of the experimental setup in the test conditions of the laboratory without the introduction of a heat source was measured first. The laboratory was in a closed environment with no airflow disturbance. The room temperature was approximately \(25\)\({}^{\circ}\)C, and the hot plate was not activated. When the laboratory environment was stable, the images of the specimen were recorded at \(1\) Hz over \(5\) min, and a total of \(300\) images were obtained. The middle \(100\) images were selected. The first image of the \(100\) images was taken as the reference image, and the remaining \(99\) images were correlated to the reference image to complete the DIC measurement. The displacement and strain fields of the \(99\) images were obtained. The Region of Interest (ROI) in DIC measurement was set to \(452\times 452\) pixels, as shown in Figure 7. The pixel equivalent was \(0.109\) mm/pixel. The subset radius was \(14\) pixels and the subset spacing was \(1\) pixel. An example of the calculation result is shown in Figure 8.  
Figure 5: Measured object.  
Figure 6: Experimental device diagram: (**a**) The camera is facing vertically to the measured object; (**b**) Temperature control box; (**c**) Hot plate in front of the measured object.  
In order to quantify the errors of the displacement field and strain field, two standard deviations, the spatial standard deviation and the temporal standard deviation, were calculated according to Reference [30]. The spatial standard deviation is calculated by calculating the standard deviation of each displacement field and strain field data to quantify the spatial variation of the displacement and strain. Then, the spatial standard deviation is averaged over 99 images. The temporal standard deviation is calculated by calculating the standard deviation of the 99 displacement fields or the strain field corresponding subsets to quantify the temporal variation. Then, in the ROI region, the standard deviations of all the subsets are spatially averaged. Table 1 shows the calculated spatial standard deviation and temporal standard deviation. According to References [31; 32; 33; 34], for a DIC system with extremely well-controlled experimental noise sources, the error does not exceed 0.001 pixels, and the DIC system in a general laboratory environment does not exceed 0.01 pixels. From Table 1, it can be seen that the displacement standard deviations, both spatial and temporal, did not exceed 0.005 pixels, which is generally accepted by the DIC community as being a reasonable noise floor for typical experiments.  
Figure 8: Representative contour plots of the displacements and strains computed in the DIC measurement without the introduction of a heat source: (**a**) Displacement in U direction; (**b**) displacement in V direction; (**c**) strain \(\varepsilon_{\text{xx}}\); (**d**) strain \(\varepsilon_{\text{xy}}\); (**e**) strain \(\varepsilon_{\text{yy}}\).  
Figure 7: Region of Interest (ROI) in DIC measurement.
## 5 Results and Discussion  
### Experiment I: Charging of TES Tank  
#### 5.1.1 Variation of Water Temperature and Ice Thickness  
The variation of water temperature and ice thickness is shown in Figure 8. The water temperature decreased rapidly with charging time during the first 6 h, but the decrease became more gradual when the water temperature was lower than 5 \({}^{\circ}\)C. Before 6 h, the upper layer's water temperature was higher than the lower and middle layers. From 0 to 6 h, the lower layer's temperature was higher than that of the upper and middle layers. After 9 h, the water temperature remained almost unchanged and was in the range of 1.4 \({}^{\circ}\)C to 2 \({}^{\circ}\)C. The water density increases with the decrease in temperature when the water temperature is higher than 4 \({}^{\circ}\)C. The water density drops as the temperature decreases when the water temperature is lower than 4 \({}^{\circ}\)C. Hence, the sinking of colder water leads to the upper layer's water temperature being higher than the lower and middle layers when water is higher than 4 \({}^{\circ}\)C. On the contrary, the floating of colder water leads to the lower layer's water temperature being higher than the upper and middle layers when the water temperature is lower than 4 \({}^{\circ}\)C [38].  
It can be seen from Figure 8 that the water around the coils begins to freeze at 3.5 h. Initially, the ice thickness of the lower layer was more than the upper and middle layers. After 7 h of charging, the lower layer's ice thickness was less than the upper and middle layers. The temperature of the lower layer was more than the upper and middle layers after 6 h, which resulted in a reduced ice thickness in the lower layer after 7 h of charging. The ice thickness of the upper, middle, and lower layers after 12 h of charging was 42 mm, 42 mm, and 39 mm, respectively, and the average ice thickness was 41 mm, which is close to the designed thickness of 40 mm. The mass of ice from the two refrigeration units was 143.3 kg and 141.3 kg, respectively, after 12 h of charging. The total ice storage capacity was 284.6 kg, which meets the design requirements of having a capacity of less than 272.5 kg.
## 3 The Framework
### Qualification of Requirements  
The term qualification refers to the testing activity that ensures the solution has its conformance to requirements [6]. During the specification stage, tests that verify the satisfaction of the requirements and validate the design and manufacturing stages should also be defined. The tests are generalized into five different types:  
1. Virtual prototype performance testing: These tests are done through simulations to the motor by the different domains. It should be carried out in the conceptual design phase.  
Figure 7: Validate requirements and motor type activity.  
Virtual system application performance testing: These tests integrate the motor into the application, but they are also carried out through simulations during the conceptual design phase.
3. Prototype performance testing: These tests are carried out on the manufactured prototype in a laboratory. These tests include type testing, routine testing, or any research testing.
4. Manufacturing tolerance testing: These tests are done to components when interested in mass production.
5. System application performance testing: These tests integrate the motor into the application and ensure the correct performance of the motor with the load system. Previously, factory acceptance tests may be included only if the manufactured motor is considered to be the final solution. Figure 8 illustrates the relationship among these generalized tests and the requirements packages. The realization of these tests should verify the satisfaction of the requirements within each package.
## 3 Data and Methods
### CNNs
#### 3.1.1 Architecture of CNNs
The result is a non-linear combination of selected features that are used to classify the data. The final classification is performed on the last layer, which represents the output layer of the CNN. To calculate the class predicted error, loss functions are used in the output layer; the error value reveals the difference between the actual output and the predicted one. The predicted error is optimized through the CNN learning process. In the case of binary classification, the following loss functions are used: binary cross-entropy loss, Hinge loss, and squared Hinge loss. The proposed CNN uses the binary cross-entropy loss function, a detailed description of which is given in Section 3.1.2. Cross-entropy loss is one of the most commonly used loss functions for training classifiers. One of the properties of this function is to provide a clear signal for model updates, making it suitable for classification tasks. One of the problems encountered with training neural networks on small datasets is overfitting [24]. Along with underfitting, such an undesirable machine learning behavior is the main cause for the poor performance of algorithms. Overfitting is a phenomenon when a model performs well on training data, but shows not so good performance on test data. Thus, the poor ability of the model to generalize new, previously unused examples makes it useless. In order to prevent overfitting, a regularization technique called dropout is often used in neural networks [25]. The key idea of the dropout layer is that randomly selected units from the neural network are ignored during the training of a model, i.e., they are \"dropped out\" randomly. This allows for a reduction in the interdependent learning of neurons: since a neuron can no longer rely on the presence of other neurons, it is forced to learn more robust features. The dropout layer is placed before the layer to which we want to apply the dropout. The pre-processing layers are used in the early stages of a CNN, and include data augmentation. Data augmentation is a set of methods used to artificially expand the size of the training dataset [26]. This technique is often used on small datasets, as it allows the model to avoid overfitting and improve its performance. The essence of this method is to perform random (but realistic) transformations to existing images to create a set of new variants without altering their natures. Typical data augmentation approaches in image classification are cropping, reflection, translation, rotation, zoom, and contrast adjustment. The pre-processing layers are only active during model training.
# An Upgraded Version of the Binary Search Space-Structured VQ Search Algorithm for AMR-WB Codec
## 3 Proposed Search Algorithm
### The TTIE Search Algorithm  
As a preliminary to the TTIE algorithm, a TIE algorithm is briefly described below. First of all, a codeword captured from the preceding frame is viewed as a reference codeword \(\mathbf{c}_{r}\) in the current frame, and the Euclidean distance between \(\mathbf{c}_{r}\) and an input vector \(\mathbf{x}\), symbolized as \(d(\mathbf{c}_{r},\mathbf{x})\), is calculated [16]. A set, consisting of all the codewords \(\mathbf{c}_{i}\) and meeting the condition \(d(\mathbf{c}_{r},\mathbf{c}_{i})<2d(\mathbf{c}_{r},\mathbf{x})\), is referred to as a candidate search group (\(CSG\)), represented as  
\[\text{TIE:}\;CSG(\mathbf{c}_{r})=\{\mathbf{c}_{i}\;\mid\;d(\mathbf{c}_{r}, \mathbf{c}_{i})<2d(\mathbf{c}_{r},\mathbf{x})\},\;1\leq i\leq CSize, \tag{13}\]  
where \(CSize\) denotes the total number of the codewords. \(CSG(\mathbf{c}_{r})\) is the search space over which a current codebook search is conducted. Additionally, a lookup table, listing all the codewords sorted by the Euclidean distance, is constructed in advance for the TIE search algorithm.  
Subsequently, the TTIE algorithm works as illustrated in Figure 1 and stated in Algorithm 2. Just as in the TIE case, a codeword captured from the preceding frame is viewed as a reference codeword \(\mathbf{c}_{r}\) in the current frame, and then a codebook search is performed over \(CSG(\mathbf{c}_{r})\) using (13). As in the TIE case, the condition \(d(\mathbf{c}_{r},\mathbf{c}_{k})<2d(\mathbf{c}_{r},\mathbf{x})\) is checked. If the condition is true, then \(d(\mathbf{c}_{k},\mathbf{x})\) is computed, and another condition \(d(\mathbf{c}_{k},\mathbf{x})<d(\mathbf{c}_{r},\mathbf{x})\) is checked. If the condition is true, then \(\mathbf{c}_{r}\) is replaced with \(\mathbf{c}_{k}\), and the search scope \(CSG(\mathbf{c}_{r})\) is updated. \(CSG(\mathbf{c}_{r})\) updates and shrinks each time through the above-stated loop.  
```
Step 1. Build a TIE lookup table. Step 2. Given a \(\mathbf{c}_{r}\), compute \(d(\mathbf{c}_{r},\mathbf{x})\), and then \(CSG(\mathbf{c}_{r})\) is found directly in the TIE lookup table, that is, \[CSG(\mathbf{c}_{r})=\{\mathbf{c}_{k}\ \mid\ k=1,2,...,N(\mathbf{c}_{r})\},\] (14) where \(\mathbf{c}_{k}\) and \(N(\mathbf{c}_{r})\) denote the codewords and the number thereof in \(CSG(\mathbf{c}_{r})\), respectively. Step 3. Starting at \(k=1\), obtain \(d(\mathbf{c}_{r},\mathbf{c}_{k})\) from the lookup table. Step 4. If \((d(\mathbf{c}_{r},\mathbf{c}_{k})<2d(\mathbf{c}_{r},\mathbf{x}))\), then compute \(d(\mathbf{c}_{k},\mathbf{x})\), and perform Step 5. Otherwise, let \(k=k+1\), and then repeat Step 4, until \(k=N(\mathbf{c}_{r})\). Step 5. If \((d(\mathbf{c}_{k},\mathbf{x})<d(\mathbf{c}_{r},\mathbf{x}))\), then replace \(\mathbf{c}_{r}\) with \(\mathbf{c}_{k}\), update new \(CSG(\mathbf{c}_{r})\), let \(k=1\), and repeat Step 3. Otherwise, let \(k=k+1\), and then repeat Step 4, until \(k=N(\mathbf{c}_{r})\).
```  
**Algorithm 2** Search procedure of ITIE
# Optimal Pattern Synthesis of Linear Array Antennas Using the Nonlinear Chaotic Grey Wolf Algorithm
## 6 Data Analysis
### Ten-Element Sparse LAA with Constraint on the Minimum Element Spacing  
The fourth example focuses on a 10-element sparse LAA with the objective of minimizing the PSLL. The main lobe width was configured to be \(22^{\circ}\), while the sidelobe regions were designated as \(\theta=\left[0^{\circ},79^{\circ}\right]\cup\left[101^{\circ},180^{\circ}\right]\). The minimum element spacing was \(0.5\lambda\). The NCGWO algorithm was executed with a population size of 40 and at most 1000 iterations, which is consistent with previous studies [37; 38].  
Figure 13 depicts the sparse LAA pattern using the NCGWO algorithm and a comparison with the results of the GWO, IGWO, WDO, and ACO algorithms. Figure 13 presents the iteration curves of the GWO, IGWO, and NCGWO algorithms. The NCGWO algorithm outperforms both IGWO and GWO, as it can more quickly converge towards optimal values. Figure 14 shows the 3D radiation patterns of the 10-element sparse LAA before and after the NCGWO algorithm, where the NCGWO algorithm effectively reduces the PSLL in an intuitive manner.  
Table 6 shows the optimization results. The PSLL of the NCGWO algorithm was \(-19.0848\) dB, which was \(0.0148\), \(0.0236\), \(1.6748\), and \(0.0348\) dB lower than those of the GWO, IGWO, PSO, and WDO algorithms, respectively. Previous studies [35; 36] did not provide information on the running time; therefore, the present study independently executed the algorithm 50 times and calculated the average. As shown in Table 6, the NCGWO algorithm requires less time than other algorithms.  
Figure 14: 3D radiation patterns of the SLL of a 10-element sparse linear array antenna: (a) before and (b) after.  
Figure 13: (a) Radiation patterns of the SLL of a 10-element sparse linear array antenna; (b) iteration curve.
## 2 Results and Discussion
### Extraction Time  
Three different extraction times (10, 20, and 30 min) were then tested at 60 \({}^{\circ}\)C using the DVB/CAR/PDMS fiber. As shown in Figure 2A, the extraction times tested showed a negligible effect on the number of metabolites detected, however, an extraction time of 20 min led to the highest total peak area. The total peak areas in extractions longer than 20 min were dramatically decreased due to the partial desorption of some volatile metabolites from the SPME fiber through a competition phenomenon which is based upon the equilibrium between analytes and the fiber coating (Figure 2B) [18]. The competition phenomenon results in different quantitative recoveries for different extraction times, whereas fiber composition and extraction temperature contributed to the qualitative composition as well as the quantitative recovery of volatile metabolites in alfalfa plants. A final extraction time of 20 min was selected due to better metabolite adsorption efficiency and quantitative recovery using the DVB/CAR/PDMS fiber.  
The optimized SPME extraction method for the large-scale volatile metabolic profiling of alfalfa consisted of a DVB/CAR/PDMS fiber, an extraction temperature of 60 \({}^{\circ}\)C, and an extraction time of 20 min. The absorptive composition of the SPME fiber was the most important parameter for optimization as it had the greatest effect on both the qualitative and quantitative volatile profiles. The extraction temperature and time were also important parameters, because they have a substantial impact on the transfer and equilibrium between the sample analyte matrix and the SPME fiber [19; 29].
## 3 Proposed Scheme
### Encryption and Aggregation
#### 3.2.3. Decryption Process  
In this way, the aggregation process is actually the decryption process, in which the random keys cancel out and individual consumption in a billing period or the spatial aggregation in a neighborhood is revealed. Hence the combination of simple modular addition with noise addition reduces the costly encryption and decryption operation in public key cryptography.  
Figure 3: Communication process data between the pairwise nodes and GW.