esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient VQ Codebook Search Algorithm Applied to AMR-WB Speech Coding
## 5 Conclusions  
This paper presents a BSS-VQ codebook search algorithm for ISF vector quantization in the AMR-WB speech codec. Using a combination of a fast locating technique and lookup tables, an input vector is efficiently assigned to a search subspace where a small number of codeword searches is carried out and the aim of remarkable search load reduction is reached consequently. Particularly, a trade-off can be made between the quantization accuracy and the search load to meet a user's need when performing a VQ encoding. This BSS-VQ search algorithm, providing a considerable search load reduction as well as nearly lossless speech quality, is experimentally validated as superior to MTIE, DI-TIE, and EEENNS approaches. Furthermore, this improved AMR-WB speech codec can be adopted to upgrade the VoIP performance on a smartphone. As a consequence, the energy efficiency requirement is achieved for an extended operation time period due to computational load reduction.  
This research was financially supported by the Ministry of Science and Technology under grant number MOST 105-2221-E-167-031, Taiwan. The author felt deeply indebted as well to the co-authors of two cited papers, including [PERSON], [PERSON], [PERSON] and [PERSON], for their contribution as the basis of this work.  
**Conflicts of Interest:** The author declares no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**MOS**} \\ \cline{2-4}  & \multicolumn{1}{c}{**Mean**} & \multicolumn{1}{c}{**STD**} \\ \hline Full search & & 3.931 & 0.329 \\ \hline  & 0.90 & 3.930 & 0.328 \\  & 0.91 & 3.931 & 0.328 \\  & 0.92 & 3.931 & 0.330 \\  & 0.93 & 3.931 & 0.328 \\ BSS-VQ (\(TQA\)) & 0.94 & 3.930 & 0.329 \\  & 0.95 & 3.933 & 0.329 \\  & 0.96 & 3.931 & 0.330 \\  & 0.97 & 3.930 & 0.330 \\  & 0.98 & 3.932 & 0.328 \\  & 0.99 & 3.933 & 0.328 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison on mean opinion score (MOS) values using the PESQ algorithm among various methods.  
Figure 2: Comparison of overall search load reduction among various approaches.
# Method for Resonant Frequency Attenuation in Dynamic Audio Equalizer
## 2 Materials and Methods
### Threshold Curve with Standard Deviation  
The magnitude spectrum is used to generate a curve that approximates the shape of the audio spectrum, while disregarding the peaks of the signal. This is achieved by applying the standard deviation rule of thumb, which states that 95% of a group of data is between two times the standard deviation above the mean and two times the standard deviation below the mean [13]. The standard deviation is calculated by octave bands, and each value is multiplied by four because the spectrum is normalized. The calculated value is then taken as a threshold. The threshold values per octave band \(TB\) are converted to decibels as shown in Equation (8).  
Figure 4: Spectral Flatness coefficient values for the audio files used to obtain the threshold value between frequency distributions.  
\[TB=20\log(4(\sqrt{\frac{1}{N-1}\sum_{n=0}^{N}(a[n]-\mu)^{2})}). \tag{8}\]  
The function \(a[n]\) is the amplitude for a frequency band number, \(N\) is the number of frequencies in an octave band, and \(\mu\) is the mean amplitude corresponding to the frequencies within an octave. As demonstrated in Figure 6, the spectrum is divided into octave bands, and the threshold values obtained with Equation (8) are presented. This equation facilitates the calculation of a threshold value that considers the amplitudes of the corresponding band, while disregarding the prominent peaks.  
With each representative value per octave, a linear interpolation is performed along all center frequencies of the distribution calculated in Section 2.2 within the range of 31.5 Hz to 16,000 Hz. Values that fall outside the specified range are maintained at the same value at both ends; for example, the octave value of 31.5 Hz is identical for lower frequencies, and likewise, values for frequencies above 16 kHz will have the same value as that frequency, as shown in Equation (9). Figure 7 shows the threshold curve generated by interpolating the 30 points of the center frequency of the selected distribution.  
\[a[f[n]]=\begin{cases}a[f_{ol}]+\frac{a[f_{ol}]-a[f_{ol}]}{f_{on}-f_{ol}}(f[n]- f_{ol}),&\text{if }31.5\text{ Hz}<f[n]<16,000\text{ Hz}\\ a[31.5],&\text{if }f[n]<=31.5\text{ Hz}\\ a[16,000],&\text{if }f[n]>=16,000\text{ Hz}\end{cases}. \tag{9}\]
## 3 Image Quality Evaluation  
This study establishes a dual-metric quantitative analysis framework combining peak signal-to-noise ratio (PSNR) and the Structural Similarity Index Measure (SSIM) to assess noise in computational holographic reconstructions.  
The MSE-based PSNR quantifies pixel-level signal distortion between original and reconstructed images:  
\[PSNR=10*\log_{10}\left(\frac{MAX^{2}}{MSE}\right) \tag{21}\]  
Here, MAX denotes pixel dynamic range (255 for 8-bit images), where logarithmic conversion maps errors to decibel (dB) scale, linearly characterizing global fidelity degradation under compression or noise interference.  
MSE (Mean Squared Error) formulation:  
\[MSE=\frac{1}{MN}\sum_{i=1}^{M}\sum_{j=1}^{N}\left(I_{O}(i,j)-I_{R}(i,j)\right) \tag{22}\]  
where \(I_{O}(i,j)\) and \(I_{R}(i,j)\) represent original and reconstructed pixel values.  
SSIM advances beyond pixel-wise metrics by decomposing image information into luminance (L), contrast (C), and structure (S) components, mimicking human visual system's nonlinear perception:  
\[SSIM=\frac{\left(2\mu_{x}\mu_{y}+C_{1}\right)\left(2\sigma_{xy}+C_{2}\right)} {\left(\mu_{x}^{2}+\mu_{y}^{2}+C_{1}\right)\left(\sigma_{x}^{2}+\sigma_{y}^{ 2}+C_{2}\right)} \tag{23}\]  
Here, \(\mu_{x}\), \(\mu_{y}\): local window means (luminance components); \(\sigma_{x}^{2}\), \(\sigma_{y}^{2}\): local window variances (contrast measures); \(\sigma_{xy}\): cross-covariance (structural similarity). Stabilization constants C\({}_{1}\) and C\({}_{2}\) (dynamically scaled) prevent division singularity. The normalized [0, 1] SSIM range effectively evaluates texture preservation and visual naturalness in holographic reconstructions [22].  
The combination of PSNR and SSIM provides a comprehensive framework for holographic noise analysis: PSNR quantifies the linear relationship between noise intensity and signal-to-noise ratio attenuation, while structural distortions induced by noise can be captured by SSIM. This combined approach ensures both mathematical rigor in noise characterization and alignment with human visual perception, thereby establishing a theoretical foundation for evaluating holographic algorithm robustness under complex noise conditions.  
Figure 3 illustrates the Matlab simulation flowchart for calculating PSNR and SSIM metrics, comprising three primary steps: image preprocessing, PSNR calculation, and SSIM calculation.
## 3 The Proposed Model
### Process Involved in GEO-FS Technique
A novel place of GEs is then provided as: \[x_{j}^{t+1}=x_{j}^{r}+\triangle_{x_{j}}^{t}\] (5) When the fitness function \(j\) offers superior to the preceding places after their memory is upgraded with novel places. * The transition from exploration to exploitation: The GEO technique utilizes the attacking co-efficient \(p_{a}\) and the cruising co-efficient \(p_{c}\) for switching from the state of exploration to exploitation. \(p_{a}\) and \(p_{c}\) is calculated utilizing the subsequent linear expression: \[p_{a}=p_{a}^{0}+\frac{t}{T}\Big{|}p_{a}^{T}-p_{a}^{0}\Big{|},\] (6) \[p_{c}=p_{c}^{0}+\frac{t}{T}\Big{|}p_{c}^{T}-p_{c}^{0}\Big{|},\] (7) where \(p_{a}^{0}\) and \(p_{c}^{0}\) are correspondingly the primary values to propensity for attacking \(p_{a}\) and for propensity for cruising \(p_{c}\), \(t\) signifies the present iteration, \(T\) denotes the maximal count of iterations, \(p_{a}^{T}\) and \(p_{a}^{T}\) are correspondingly the last values to propensity for attacking \(p_{a}\) and to propensity for cruising \(p_{c}\). The fitness function (FF) of the GEO-FS technique assumes the classifier accuracy and the number of chosen features. It maximizes the classifier accuracy and minimizing the set size of chosen features. Thus, the subsequent FF utilized for evaluating individual solutions is demonstrated in Equation (8). \[Fitness=\alpha\times ErrorRate+(1-\alpha)\times\frac{\#SF}{\#All\_F} \tag{8}\]  
where \(ErrorRate\) implies the classifier error rate utilizing the chosen features. \(ErrorRate\) is computed as the percentage of inappropriate classification (by 5-ANN classifier) to the number of classifiers developed, formulated as a value amongst zero and one [22]. (\(ErrorRate\) implies the complement of classifier accuracy), \(\#SF\) denotes the number of chosen features, and \(\#All\_F\) denotes the entire number of elements from the original dataset. \(\alpha\) denotes utilized for controlling the significance of classifier quality as well as subset length. During the experiments, \(\alpha\) is fixed to 0.9.
## 2 Jamming Attack Models  
### Military Models for Electronic Warfare  
Military jammers have no constraints of energy (power supply) or radiated frequency (RF) power and liberally use their resources with the philosophy of crushing the pea-nut (target network) with the sledge hammer (brute RF power). However, they do exercise restraint and use less RF power only to evade detection. There are three basic jamming attack models used in electronic warfare by the military:  
* _Spot Jammer_ is a jammer which knows the exact radio frequency of the target network, and attacks the network on that frequency (spot frequency) only. It requires less power to jam the network, and is the most efficient and effective jammer. However, it suffers from the disadvantage that the target network can change the frequency (channel surfing/frequency hopping) to evade jamming.
* _Swep Jammer is_ a jammer which does not know the target frequency, and therefore sweeps across the probable spectrum either periodically or aperiodically, thus jamming the affected networks temporarily. They are less efficient and effective than the spot jammer, but can attack several networks and impose restrictions on freedom of frequency-hopping by the target network.
* _Barrage Jammers_ cover a large bandwidth of the radio spectrum at a time, leaving very little scope for the target network to evade jamming. Also, they can jam a number of networks simultaneously. Barrage jammers require high RF power to maintain the required power spectral density of jamming.
## 5 Performance Evaluation
### Ranging Accuracy
SNR = 20 dB, \(\text{V}=8\). From the above discussion, DDBDM offers excellent navigation performance in the scenario of the massive LEO satellites. The low MI of DDBDM ensures that its ranging accuracy effectively improves as the SNR of the INAC signal increases, and its block structure makes it virtually unaffected by communication signals from other satellites. Additionally, the separation of the navigation and communication subcarriers of DDBDM makes the signal processing overhead of the navigation receiver much lower than that of other schemes, making it easier to collaborate with the GNSS to enhance PNT service.
## 2 Materials and Methods
### Statistical Analysis  
The sequences were analysed with the QIIME2 (Quantitative Insights Into Microbial Ecology) pipeline, version 2024.2 ([[https://docs.qitime2.org](https://docs.qitime2.org)]([https://docs.qitime2.org](https://docs.qitime2.org)) accessed on 20 April 2024). Sequences of low quality (<20) and sequences of <200 bp were removed. Sequences with at least 99% similarity were clustered and used as ASVs. The bacteria genus with a relative abundance greater than 1% of the total genera was defined as the dominant genus [18]. The'vegan' package in the R program ([[https://www.r-project.org](https://www.r-project.org)]([https://www.r-project.org](https://www.r-project.org)) accessed on 24 April 2024) was used to calculate the diversity index. On the one hand, ANOVA and Tukey HSD tests were used to compare the alpha diversity of the microbial communities in different blocks. Species richness is represented by the Chao1 index, while species diversity is represented by the Shannon index. On the other hand, a principal coordinate analysis (PCoA) and non-metric multidimensional scaling (NMDS) based on Bray-Curtis distance were used to analyse the differences in the microbial communities between different blocks. A redundancy analysis (RDA) was used to investigate the correlation between the physico-chemical parameters and the dominant bacterial genus. The positive and negative correlations between the physico-chemical parameters of the reservoirs and the dominant bacterial genus were also evaluated using heat maps. In order to analyse the effect of the two blocks on the microbial co-occurrence patterns, we used the 'hmisc' and 'igraph' packages in R to construct a co-occurrence network and set the Spearman correlation coefficient \(|\,r\,|>0.6\) and \(p<0.05\) as an effective relationship. To describe the topological characteristics of the network, the Gephi software ([[https://gephi.org](https://gephi.org)]([https://gephi.org](https://gephi.org)) accessed on 15 April 2024) was used to calculate a set of data (such as total nodes, total edges, average degree, average clustering coefficient, average path length, network density, and modularity index). The visualisation was performed using the Fruchterman-Reingold layout and was colour coded according to the modules. Finally, the FAPROTAX database [19], which links species classification and functional annotation, was used to predict potential microbial ecological functions in the study samples. The ASVs classification table was compared with the FAPROTAX 1.2.6 database using the R program, and the predicted functional annotation results of the microbial communities were output.
# Multi-Model Identification of HVAC System
## 1 Introduction
The disadvantage of this method is the complication in computing the nonlinearity measure. After deciding each local model, the global model can be determined by combining the local models using either so-called hard or soft switching approaches. In the hard switching approach, the best possible local model is selected and analyzed. In the soft switching approach, the global model is formed by a weighted sum of the local models. There are many weighting functions, such as gap metric based weighting [8], Bayesian weighting functions [9], and trapezoidal functions [10]. The gap metric-based weighting method has been extensively used; see [11; 12; 13]. The advantages of using a weighted gap in the multi-model approach include (1) the performance can be incorporated in selecting operating points, and (2) soft switching, which avoids high-frequency switching. The proposed method of identifying local models uses simultaneous clustering and regression, then a global model using a weighted sum of local models is tested on a simulated HVAC system. In this test, the simulated building has three zones. The proposed method is used to identify the behavior of the ventilation system in stabilizing the zones' temperature, while there are different sources of disturbances and outdoor temperature changes in a range of \(-25\)\({}^{\circ}\)C to \(25\)\({}^{\circ}\)C. The proposed method is also tested on a real-world dataset. The results show that the proposed method is capable of identifying that the operating conditions of the nonlinear HVAC system are changing due to disturbances. The remainder of the paper is organized as follows: In Section 2, the proposed simultaneous clustering and regression method is presented. In Section 3, the global model is built using the gap metric method. Section 4 presents the identified models based on datasets obtained from a simulated ventilation system and real-world dataset using the proposed multi-model framework. In Section 5, concluding remarks are drawn.
## 2 System on Power Generation using Double-Stator Generator  
### Power Mapping of Double-Stator Permanent Magnet Generator  
The generator that used in this estimation is a high power density double-stator permanent magnet generator (DSPMG) [11]. The detailed design procedure of such a type of machine is presented in Reference [16]. Figure 1 shows the structure of the DSPMG and the generator is simulated in a finite element analysis tool for the power density characteristics. The maximum power is captured during modeling when the load reduced from the maximum (maximum impedance value) value and is plotted as the power mapping characteristics.  
The performance of the double-stator generator uses the power mapping approach in which the spectra of the power and the voltage generated at various speeds are computed is as shown in Figure 2a,b. Figure 2a shows the power mapping for the generator. In order to get this power mapping, it is tested with various load conditions for speed variations. The output is coupled to the rectifier and capacitor for the generation of direct current (DC) voltage. The electronic load resistance is changed from 3 to 100 ohm and the generated output power is plotted. The maximum power generated is 180 W at the rotational speed of 1500 rpm with a load 10 ohm. The relation between the speed, the load and generated power is clearly understood through this power mapping data. Besides that, the relation between the voltage, current and output power in Figure 2b is calculated using Equations (1) and (2) and the efficiency characteristics are shown as in Figure 2c.  
\[V = I\times R \tag{1}\] \[P = I\times V \tag{2}\]  
Figure 1: Structural configuration of the double-stator permanent magnet generator.
# Smartphone Accelerometer for Gait Assessment: Validity and Reliability in Healthy Adults
## 2 Materials and Methods
### Procedure  
The experimental procedure was based on a previous study [16]. The subjects wore comfortable clothes and socks and were instructed to walk on the GAITRite mat under the following three conditions while firmly attaching a smartphone with an accelerometer to the second sacral vertebra, which is the center of gravity of the body, with an elastic band: (1) preferred walking speed, (2) slow walking speed of their choice, and (3) fast walking speed of their choice (Figure 1).  
For the familiarization process before the walking task, the participants naturally walked on the GAITRite mat twice with the smartphone attached to their waist. No measurements were taken during this process. After the familiarization process, the sequence of speed conditions was randomized for each participant using a computer-generated randomization algorithm to minimize potential order effects. For example, some participants performed the preferred walking speed condition first, followed by the slow and fast walking speed conditions, while others followed different sequences. This randomization ensured that potential fatigue or learning effects did not systematically influence the results. All conditions were measured four times, and the average value was calculated.  
The measurer instructed the subjects, \"Walk at your usual walking pace (Preferred), slower than your usual walking pace (Slow), and faster than your usual walking pace (Fast)\". Five minutes were given between each condition to avoid potential fatigue effects or overlap, and a 1-min rest period was given between repeat measurements. To exclude accelerators and decelerators during the gait cycle from the measurements, the subjects stood at a line marked 2 m before the start of the GAITRite mat, started walking when they heard the start signal, and stopped when they reached a line marked 2 m after the end of the mat. If the subject stepped into the inactive areas at either end of the mat where no pressure sensors were distributed or stepped off the mat during walking, the measurement was retaken.  
This study consisted of a single-group, repeated-measures design. To exclude the order effect that repeated measures may cause, the order of repeated measures for each subject was randomized. Research personnel responsible for data collection were blinded to the assigned speed condition to eliminate potential bias during measurement. All repeated  
Figure 1: Flowchart.  
measures were performed within 1 h by the same physical therapist. The physical therapist involved in the measurements had over 3 years of clinical experience.
# Load Frequency Optimal Active Disturbance Rejection Control of Hybrid Power System
## 4 Experimental Results and Analysis  
In this section, an investigation is conducted on a multi-source hybrid power system by using the proposed optimal ADRC method. The IEEE-39 Bus system with PVG and WTG was used as a baseline for the experiment [20]. This system has three control areas: WTGs and PVGs are added in certain nodes. Each control area has five generators. The G1, G4, and G9 are reheat thermal generators. The G2, G3, G5, G6, G7, G8, and G10 are hydroelectric generators. \(G_{WT}\) and \(G_{PV}\) are WTG and PVG separately. Area 1 includes G1, G2, G3, \(G_{WT1}\), and \(G_{PV1}\). Area 2 includes G4, G5, G6, G7, and \(G_{PV2}\). Area 3 includes G8, G9, G10, \(G_{WT2}\), and \(G_{PV2}\). All the experiments are finished on the Matlab/Simulink platform. The parameter values of the multi-area power system are given in Table 1. The participation factors of the generators of each area are given in Tables 2-4. The participation factor of the reheat thermal generator is 52% in all these three areas.  
The total transfer function for each control area can be derived according to the model parameters and generator types given above, combined with the models of each generator in Section 2. This is a complex work. Model-wise-based methods require designing the controller according to the resulting precise model. The advantage of this proposed strategy is that the total transfer function model of the system does not need to be reduced. When the total disturbance of the system is estimated, so precise control can be implemented.  
Comparative results of reducing frequency deviation and tie-line power variation using LQR, LQR with ESO [20], and the proposed method are presented and analyzed.
# Numerical Analysis of Ultrasonic Multiple Scattering for Fine Dust Number Density Estimation
## 2 Theoretical Background on Multiple Scattering of Acoustic Waves
In the ISA, \(l_{S}\) is further related to the number density \(n\), given by  
\[l_{s}=\frac{1}{\alpha}=\frac{1}{n\sigma_{t}}, \tag{8}\]  
where \(\sigma_{t}\) is the total scattering cross-section of one scatterer. Note that the theory introduced in this section can apply to both 2-D and 3-D conditions without loss of generality. In 2-D, \(n\) has a unit of (1/m\({}^{2}\)), and \(\sigma_{t}\) (m). In 3-D, \(n\) and \(\sigma_{t}\) have a unit of 1/m\({}^{3}\) and m\({}^{2}\), respectively. Equation (8) depicts the fundamentals of the proposed fine density estimation technique: the number density of scatterers can be estimated using \(\alpha\) obtained from the ensemble average wave responses and the analytically computed \(\sigma_{t}\). More detail about the ISA theory can be found in [22]. The proposed fine dust density estimation technique is detailed further in the following section.