esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring the Effectiveness of the Phase Features on Double Compressed AMR Speech Detection
## 6 Experimental Setup  
### Database  
Double-compressed AMR audio detection experiments are conducted on the TIMIT dataset [39]. TIMIT is a well-known audio dataset commonly used for speech and speaker recognition. It comprises 6300 speech recordings sampled at 16 kHz from 630 different speakers, with each speaker having 10 different utterances. Among these utterances, two speech recordings (SA1 and SA2) are shared by all speakers.  
For the AMR-NB codec, all speech signals in the database are initially downsampled to 8 kHz, as the AMR-NB codec operates on narrowband signals. Subsequently, all speech signals in the database are compressed using the AMR-NB codec at eight different bit rates, ranging from 4.75 kbps to 12.2 kbps. This process yields a total of 50,400 SC AMR-NB speech recordings (\(6300\times 8=50,400\)). These SC AMR-NB files are then decoded and recompressed at eight different bit rates to generate DC AMR-NB signals. Consequently, we obtain a total of 403,200 double-compressed AMR-NB audio files (\(6300\times 8\times 8=403,200\)).  
A similar procedure to the one used for the AMR-NB database generation is applied to generate the SC and DC AMR-WB speech signals. The speech signals in the TIMIT database, sampled at 16 kHz, are utilized to generate the AMR-WB-coded signals. Since the AMR-WB codec employs nine different bit rates ranging from 6.6 kbps to 23.85 kbps, the AMR-WB database comprises a total of 56,700 SC speech signals (\(6300\times 9=56,700\)) and 510,300 DC speech signals (\(6300\times 9\times 9=510,300\)).  
In the experiments, one-second-long speech signals are utilized. If a signal's duration exceeds 1 s, its first one-second portion is cropped. Conversely, if the signal is shorter than 1 s, its samples are duplicated and appended to the signal to achieve a one-second duration. For each dataset, 25% of the speech signals are allocated for training the DC signal detection system, while the remaining signals are used for validation and testing. Since SA1 and SA2 utterances are shared by all speakers in the dataset, the validation set comprises only these signals. To ensure the effectiveness of the model in distinguishing DC signals from SC signals, the training and test sets are disjoint in terms of speaker allocation.  
Figure 5: Proposed parallel LSTM system for DC AMR speech detection.  
This prevents the system from potentially learning to differentiate between speakers rather than detecting DC signals.
## 3 Methodology
### Word Embedding
#### 3.3.2 fastText  
Many strategies for incorporating morphological information into word representations have been proposed in recent years. Previous word embedding algorithms represented each word in the lexicon as a separate vector, with no shared parameters. In particular, they ignored internal word structure, which is a significant drawback for morphologically rich languages.  
fastText, an open-source project by Facebook Research, is a fast and effective approach to learning word representation and performing text classification that is widely used for NLP. Instead of learning word representations, the fundamental goal of the fastText embeddings is to analyze the internal structure of words. This is especially effective in morphologically rich languages, as it allows learners to independently learn representations of distinct morphological forms of words [14].  
Using Skip-Gram, the likelihood of the context, given by a word \(t\), is parametrized by word vectors using a scoring function \(s\):  
\[s(w_{t},\ w_{c})=U_{w_{1}}^{T}V_{w_{c}} \tag{4}\]  
With \(u\) and \(v\) chosen from the input and output embedding matrices. The scoring function in fastText is as follows:  
\[s(w_{t},\ w_{c})=\sum_{g\ \in\ G_{w_{1}}}Z_{g}^{T}V_{w_{c}} \tag{5}\]  
\(G_{w_{1}}\) represents the set of n-grams in word \(w_{t}\), and \(Z_{g}\) represents the vector of the \(g\)th n-gram. The vector of the context word \(w_{c}\) is denoted by \(V_{w_{c}}\).
## 2 Materials and Methods
### Description of the Hardware and Software Platforms on Which the Tests Were Conducted
\begin{table}
\begin{tabular}{c c c c} \hline
**Chip Manufacturer:** & **AMD** & **Intel** & **Nvidia** \\ \hline \multirow{2}{*}{Model name} & ASRock Radeon RX & Intel ARC A750 8 GB & ASUS TUF Gaming \\  & 7900 XT Phantom Gaming & Limited Editon & GeForce RTX 4090 \\ \hline Chip name & Navi 31 XT & DG2-512 & AD102-300-A1 \\ \hline Architecture & RDNA 3.0 & Xe HPG & Ada Lovelace \\ \hline \multirow{2}{*}{Number of computer units} & 84 (computer units) & 28 (Xe cores) & 128 (streaming \\  & 5376 (stream processors) & 448 (Xe vector engines) & multiprocessors) \\  & & 16384 (CUDA cores) \\ \hline Number of dedicated & \multirow{2}{*}{168 (AI accelerators)} & \multirow{2}{*}{448 (XMX engines)} & \multirow{2}{*}{512 (Tensor cores)} \\ AI computer units & & & \\ \hline VRAM & 20 GB GDDR6 320 bit & 8 GB GDDR6 256 bit & 24 GB GDDR6X 384 bit \\ \hline VRAM bandwidth & 800 GB/s & 512 GB/s & 1008 GB/s \\ \hline Typical board power (TBP) & 315 W & 225 W & 450 W \\ \hline \end{tabular}
\end{table} 
Table 2: Technical parameters of the graphics cards. \begin{table}
\begin{tabular}{c c c c} \hline  & **AMD** & **Intel** & **Nvidia** \\ \hline Graphics driver version & 6.8.5 & 2:2.99.917 & 550.107.02 \\ \hline Framework for running calculations on the GPU & HIP 6.2.41134 & OneAPI 2024.2.1 & CUDA Toolkit 12.4 \\ \hline AI library & ROCm 6.2.2 & OpenVINO 2024.4.0 & CuDNN 8.9.7 \\ \hline \end{tabular}
\end{table} 
Table 3: AI driver and library versions. Performance testing was conducted on Python 3.10.12 using the same or close to the same version of the libraries for all graphics cards when not available. Where applicable, the configuration/extensions of the library prepared for a given graphics card were used. A list of the versions of libraries used for performance tests is presented in Table 4. Compatibility with neural network training and inference frameworks was checked based on the latest versions of libraries available for a given device, if any. Compatibility was checked for the following libraries: PyTorch, Tensorflow, Google JAX, SciKit, ONNX, OpenCV, and Numba.
# Solid-Phase Microextraction Techniques and Application in Food and Horticultural Crops
## 3 Flavors and Aromas (Volatile Organic Compounds) in Foods Using HS-SPME
### Volatile Organic Compound Profile in Oranges Correlated with Farming Practice  
[PERSON] et al. investigated the use of HS-SPME GC-MS in the analysis of aroma compounds in the fresh fruit of two orange cultivars, Navalina and Salustiana (_Citrus sinensis_ L. [PERSON]), grown via conventional methods and organically [36]. Their aim was to use the aroma profiles of fresh fruit pulp to determine if orange samples were grown via conventional methods or organically. A number of volatile aldehydes, ketones, alcohols, terpenoids, esters, and also 2-ethyl furan were assessed. Analysis consisted of headspace sampling using a 50/30 \(\upmu\)m CAR/PDMS/DVB fiber (StableFlex/SS) (Supelo). Both incubation and extraction were carried out at 60 \({}^{\circ}\)C. The incubation time was 10 min and the extraction time was 30 min, with the sample being continuously agitated at 500 rpm. The sample desorption time was 15 min at a temperature of 250 \({}^{\circ}\)C. They found that conventionally farmed oranges had a higher concentration of esters, while organically farmed oranges had a higher concentration of nervl acetate, geranyl acetate, and some monoterenes. Applying partial least squares discriminate analysis to the data enabled them to successfully predict what farming method was used in the production of the orange samples.
## 3 Results
### Descriptive Statistics and Reliability
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Outcome Variable** & **Mean** & **SD** & **ICC (95\% CI)** & **TE (\%)** \\ \hline \multicolumn{4}{c}{Sustained Contractions} \\ \hline Peak force (N) & 582.6 & 199.7 & 0.97 (0.95–0.98) & 6.5 (5.6–7.9) \\ Peak RFD [N/s] & 5546.9 & 271.8 & 0.59 (0.39–0.83) & 23.1 (12.1–34.5) \\ RFD 0–50 [N/s] & 3190.4 & 1594.8 & 0.61 (0.43–0.75) & 37.2 (32–45) \\ RFD 0–100 [N/s] & 3007.4 & 1126.7 & 0.82 (0.71–0.89) & 18 (15.4–21.8) \\ RFD 0–150 [N/s] & 2556.6 & 919.7 & 0.90 (0.84–0.94) & 12.4 (10.6–15) \\ RFD 0–200 [N/s] & 2143.9 & 750.7 & 0.94 (0.91–0.97) & 8.8 (7.6–10.7) \\ RFD 50–100 [N/s] & 3517.0 & 1367.4 & 0.78 (0.66–0.87) & 20.8 (17.9–25.1) \\ RFD 100–150 [N/s] & 2837.3 & 1004.4 & 0.89 (0.83–0.94) & 12.5 (10.7–15.1) \\ RFD 150–200 [N/s] & 2353.1 & 823.6 & 0.90 (0.84–0.94) & 12.3 (10.5–14.8) \\ \hline \multicolumn{4}{c}{Ballistic Contractions} \\ \hline Peak RFD [N/s] & 7454.1 & 4466.2 & 0.61 (0.39–0.85) & 24.2 (11.1–36.6) \\ RFD 0–50 [N/s] & 3207.6 & 1330.7 & 0.65 (0.51–0.77) & 32.7 (28.9–38.1) \\ RFD 0–100 [N/s] & 3187.9 & 1008.5 & 0.89 (0.83–0.93) & 12.1 (10.6–14.1) \\ RFD 0–150 [N/s] & 2676.7 & 840.8 & 0.89 (0.84–0.94) & 11.5 (10.2–13.4) \\ RFD 0–200  
for the sustained task (ICC = 0.94). Typical errors ranged between 8.6% and 40.1% for the sustained contractions and between 11.0% and 39.6% for the ballistic contractions. As in the ankle extension task, the typical errors for the knee task decreased as the time interval increased.
# Use Electroencephalogram Entropy as an Indicator to Detect Stress-Induced Sleep Alteration
###### Abstract  
An acute stressor can cause sleep disruptions. Electroencephalography (EEG) is one of the major tools to measure sleep. In rats, sleep stages are classified as rapid-eye movement (REM) sleep and non-rapid-eye movement (NREM) sleep, by different characteristics of EEGs. Sleep alterations after exposure to an acute stress are regularly determined by the power spectra of brain waves and the changes of vigilance stages, and they all depend on EEG analysis. Herein, we hypothesized that the Shannon entropy can be employed as an indicator to detect stress-induced sleep alterations, since we noticed that an acute stressor, the footh shock stimulation, causes certain uniformity changes of the spectrograms during NREM and REM sleep in rats. The present study applied the Shannon entropy on three features of brain waves, including the amplitude, frequency, and oscillation phases, to measure the uniformities in the footh shock-induced alterations of sleep EEGs. Our result suggests that the footh shock stimuli resulted in a smoother and uniform amplitude as well as varied frequencies of EEG waveforms during REM sleep. In contrast, the EEGs during NREM sleep exhibited a smoother, but less uniform, amplitude after the footh shock stimuli. The result depicts the change property of brain waves after exposure to an acute stressor and, also, demonstrates that the Shannon entropy could be used to detect EEG alteration in sleep disorders.  
acute stress disorder; electroencephalogram (EEG); footh shock; information theory; sleep sciences
## 3 Results and Discussion
### Objective Function Minimization
Table 1 implies that for fixed-fixed and fixed-pinned cases, the optimal solutions from each mode varies significantly, and the objective function is either dominated by one of the modes or an irregular combination of them. Fixed-roller case, on the other hand, is contradictory with the first two BC cases. Optimal combinations obtained from first, second, and third modes are evidently similar with each other (ranging around 21 th model number), as well as the optimal objective function solution. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \begin{tabular}{c} **Boundary** \\ **Conditions** \\ \end{tabular}  & **Optimal** & **Parameter** & **Combinations** & **\textless{K, M\textgreater{}}** \\ \hline \hline \multicolumn{2}{c}{**Mode 1**} & **Mode 2** & **Mode 3** & **Objective** \\ \hline \multirow{3}{*}{Fixed-Fixed} & \(<\)3, 15\(>\) & \(<\)10, 19\(>\) & \(<\)5, 11\(>\) & \(<\)10, 19\(>\) \\ \cline{2-5}  & \(I_{member}=3.33\times 10^{-5}\)m\({}^{4}\) & \(1.2\times 10^{-5}\  
To understand the difference between the fixed-roller case and the other cases, the modal frequencies obtained from each case are investigated. Looking at the first modal frequency of the updated models, it can be observed that the fixed-fixed and fixed-pinned cases have very high errors (4%, 55%), although the second (0.3%, 2.8%) and the third (0.5%, 1.1%) modal frequencies are represented well. In contrast, fixed-roller case represents all three modes with a fair and even accuracy such as 5.8%, 0.2%, and 2.5%. These results show that the arch support kivities are decisive to set the proportion between the first modal frequency and the others, and the fixed-roller case performs significantly better than the other BC cases. According to Figure 9, comparing the ratio between the modal frequencies, it is seen that the BCs qualitatively do not have a significant effect on the updated mode shapes. On the other hand, without the correct proportion between modal frequencies, even if one or two modes are accurately identified, the remaining mode will have a very high error value. This phenomenon can be proven with a sensitivity study, yet it is the beyond of the scope, and therefore is not addressed further in this paper. Specifically, releasing the arch support kivities in the longitudinal direction can tremendously increase the accuracy of the FEM modal frequencies. Conclusively, an accurate FEM is developed with the presented model updating procedure, and such model can be used to simulate the seismic performance of the structure.
## 1 Introduction
HEFA was able to outperform the parent algorithms and GA, but with a longer computation time than GA [23]. BF-PSO hybrid is composed of BFO (Bacterial Forage Optimization) and PSO (Particle Swarm optimization), which was formed to improve the BFO's ability to tackle multi-modal functions [24]. ACO has been hybridized with a Pseudo-Parallel GA (PPGA) for solving set of optimization problems, and PPGA-ACO obtained successfully the best minimum with minimal computational effort as compared to PPGA, GA, and neural networks [25]. The HS-BA (Harmony Search and Bees Algorithm hybrid) made the best results on eight out of 14 data instances of the University Course Timetabling Problem (UCTP), as compared to VNS (Variable Neighborhood Search), BA (Bees Algorithm), and TS (Tabu Search) [26]. The explorative ability of FA was enhanced by adding GA, and the hybrid was tested on a number of benchmarks and gained better results than standard FA and a number of PSO variants. However, it was often either outperformed by, or at best comparable to, FAs that use Gaussian distribution (Brownian motion) instead of Levy flights, or use learning automata for parameter adaptation [27]. For further reading about hybrid algorithms, their methods, and strategies, please refer to the following: GA and BFO [28, 29], PSO and SA [30], GA and SA [31], ACO and TS [32], and GA and PSO [33]. In this study, a new hybrid stochastic optimization method was developed, which uses features from the two algorithms, MA and KHA. The aim of this paper is to present the new algorithm and to evaluate its performance in comparison with the original algorithms. The remainder of this paper is divided as follows: Sections 2 and 3 introduce the Monkey Algorithm and the Krill Herd Algorithm, respectively. Section 4 introduces the proposed hybrid algorithm. The numerical experiments performed to evaluate the modification are presented in Section 5. The results of the numerical experiments are presented and discussed in Section 6. Finally, Section 7 summarizes the conclusions of this study.
## 3 Materials and Methods
### Fractal Dimension Combination Pipeline
#### 3.2.2 Feature Extraction  
The feature extraction phase employed nine distinct combinations of fractal dimension (FD) metrics, drawn from a core set of seven FD methods, each capturing unique aspects of EEG signal complexity.  
The Katz FD [51] estimates how the waveform evolves over multiple scales by assessing the ratio between the total traversal distance and the signal's maximum vertical (or horizontal) extent, revealing complexities tied to motor-related activity. The Petrosian FD [52] highlights rapid signal fluctuations by counting sign changes in the first derivative, linking them to essential cortical chaos. The Higuchi FD [53] decomposes signals at various downsampled scales to track their effective length, a process sensitive to temporal dynamics. A refinement was made to the HFD function to correct an issue identified in the previous implementation. The earlier function occasionally returned negative values due to an incorrect sign convention in the log-log regression step. The updated function corrects this by properly computing the log-log scaling and ensuring positive HFD values. The box-counting FD [47] overlays a grid of boxes, quantifying self-similar, nonlinear structures frequently associated with motor tasks. The MFDFA FD [49] expands upon standard fluctuation analysis by measuring multifractal properties across multiple time scales, reflecting the diverse variability within EEG signals. The DFA FD [48] removes underlying drifts to reveal long-range correlations in non-stationary data, revealing organized patterns often hidden in cortical signals. The correlation dimension FD [50] quantifies chaotic behavior by examining how points cluster in increasing dimensional embeddings, capturing complex dynamics in EEG during motor imagery.  
\begin{table}
\begin{tabular}{c c c} \hline
**1 st FD** & **2 nd FD** & **3 rd FD** \\ \hline Petrosian & Box-Counting & DFA \\ Petrosian & MFDFA & DFA \\ Petrosian & Correlation Dimension & MFDFA \\ Higuchi & Correlation Dimension & DFA \\ Katz & Petrosian & Higuchi \\ Katz & Petrosian & Box-Counting \\ Katz & Correlation Dimension & MFDFA \\ Katz & Petrosian & Correlation Dimension \\ Katz & Box-Counting & Correlation Dimension \\ \hline \end{tabular}
\end{table}
Table 1: The list of fractal dimension combinations.  
By combining these seven FD methods into nine feature sets, we gain a comprehensive view of the signal's fractal properties, from delicate textural details to larger chaotic structures, eventually enhancing EEG classification in the motor imagery context.
# Domain Adaptation Network with Double Adversarial Mechanism for Intelligent Fault Diagnosis
## 5 Conclusions  
This paper proposes a DAN-DAM model for the inconsistency of sample distribution under variable working conditions. This model achieves both domain-level alignment and class-level alignment. The feature extraction stage uses a deep convolutional neural network, while using MMD to align the features of the source and target domains. The domain-level alignment is realized by adversarial of the feature extractor and the domain discriminator and, at the same time, the GRL is added to realize the automatic inversion of the gradient. Class-level alignment is achieved by using adversarial feature extractor and two classifiers, while Wasserstein distance is used to measure the difference between the two classifiers. The results of multiple different transfer experiments on two different experimental platforms show that the average diagnostic accuracy of the DAN-DAM model on the open dataset is about 98% and the average diagnostic accuracy on the private dataset is about 97%. Moreover, compared with other diagnostic models, the diagnostic accuracy is high and the stability and the generalization ability are strong.  
Although the proposed method has achieved a relatively good diagnostic effect, it still has certain limitations. In the early stage, it did not consider the large discrepancy in sample distribution to train the model. We will continue to study and optimize the proposed method in the future to expand the application scope of this method.  
Conceptualization, K.X. and R.L.; methodology, K.X.; software, K.X.; validation, K.X. and R.L.; formal analysis, K.X.; investigation, J.L.; resources, S.L.; data curation, M.Z.; writing--original draft preparation, K.X.; writing--review and editing, K.X.; visualization, K.X.; supervision, R.L.; project administration, S.L. and X.L.; funding acquisition, K.X., S.L. and J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Special Project of the National Key Research and Development Program of China (2020 YFB1709801), the China Scholarship Council, the National Natural Science Foundation of China (51975276), the Postgraduate Research and Practice Innovation Program of Jiangsu Province, China (KYCX21_0230) and the National Science and Technology Major Project of China (2017-IV-0008-0045).  
Figure 12: The confusion matrix of the DAN-DAM model in two domains for private datasets.
# Blockchain-Embedded Strategic Options for Satellite Operators: Sell, SataS, or Dual?
## 2 Literature Review and Marginal Contributions
### Literature Review  
#### 2.1.1 Satellite-as-a-Service  
As a core component of the Space-Air-Ground Integrated Network (SAGIN), satellite services have drawn attention in the literature. [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) clarified the services that satellites may provide from different dimensions. For example, satellites can be used as platforms for running software applications provided by customers, enabling cloud computing ([PERSON] and [PERSON], 2018). By forming a collaborative computing network in space, invalid data transportation is reduced. In addition, satellite communication has become an important component of the telecommunications industry. [PERSON] et al. [PERSON] et al. (2018) pointed out that typical application areas of satellite communication include video streaming, television relay, and broadband internet, where remote locations are connected to existing high-speed backbone networks through satellites operating in orbit. There are also services that revolve around the full life cycle of a satellite, such as development, launch, and operation ([PERSON] et al., 2018). In addition to these, another relevant service is digitization, which allows customers to obtain required data ([PERSON] and [PERSON], 2018). Spire Global has similar services related to the delivery of Earth observation datasets. There has been extensive research based on the satellite services mentioned above. [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) investigated the Starlink satellite project's impact on internet provider services in emerging economies. [PERSON] et al. [PERSON] et al. (2018) found that current satellite-ground IoT task offloading schemes rarely consider service deployment based on network function virtualization (NFV) and proposed a service deployment strategy based on deep reinforcement learning to solve the service deployment problem. Much of the previously discussed research used a technical research methodology. Theoretical research that has examined the impact of satellite services is limited. Here, we consider two types of services provided by satellite operators: selling satellites and SataaS.