esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Case I: Healthy vs. Ictal State  
The performance of CNN in classifying healthy and ictal states using LOSO CV is shown in Figure 5. The results demonstrate the robustness of the CNN model across multiple state classifications. The model yielded the highest Acc, Pre, Rec, and F1 of 99% when classifying healthy state Z compared to ictal state S. Additionally, the model achieved 96% accuracy in differentiating healthy O from ictal S, with Pre and Rec of 97% and 96%, respectively. The model also achieved 94% accuracy when considering the combined healthy states (Z+O) versus the ictal state S. The precision rate remains high at 96%, and the recall rate of 94% demonstrates the ability of the model to capture the majority of (Z+O) instances. These results demonstrate the ability of the CNN model to accurately characterize different states based on TFS images.  
The training and validation metrics of the CNN, as shown in Figure 5(a), exhibit different patterns in different classification scenarios. In the Z vs. S scenario, both normalized training accuracy (NTA) and normalized validation accuracy (NVA) fluctuated until the 10 th epoch, but then stabilized at 1.0. The normalized training loss (NTL) and normalized validation loss (NVL) consistently decreased to 0, indicating a stable learning process. For O vs. S, NTA started at 0.54, reaching 1.0 by the 7 th epoch, with NTL declining from 0.36 to 0. NVA steadily increased to 1.0, and NVL consistently decreased to 0, with minor fluctuationsfrom the 10 th to the 15 th epoch. In (Z+O) vs. (S), the model exhibited significant stability, with NTA reaching 1.0 by the 7 th epoch, NTL decreasing to 0, and NVA increasing to 1.0. This highlights the model's consistent performance across various classification scenarios.  
Figure 6b shows the normalized confusion matrices of the CNN for different classification scenarios. For Z vs. S binary classification, the model yielded 100% true positives (TP) and true negatives (TN), while avoiding false positives (FP) and false negatives (FN). In the O vs. S case, the model demonstrated strong performance, achieving a 100% TN  
Figure 5: Radar plot representation of the CNN performance metrics for classifying various combinations of healthy and ictal states.  
Figure 6: The (**a**) training and validation metrics and (**b**) normalized confusion matrix of CNN in classifying various combinations of healthy and ictal states.  
rate while accurately detecting ictal states. However, there were some instances where ictal states were misclassified as healthy states, leading to an 8.57% FN rate. Finally, in the (Z+O) vs. S classification, the model accurately identified S with a TP rate of 2.86%. Nonetheless, the model exhibited an FP rate of 17.14%, indicating the misclassification of Z+O as S state. Despite this, the model demonstrated high overall Acc and F1. Collectively, the results highlight the model's ability to effectively distinguish between these different states.
## 1 Introduction
### Effects on the Gastrointestinal System  
During short-duration spaceflight, the presence of gastrointestinal symptoms (e.g., diarrhea, vomiting, and inflammation of the gastrointestinal tract) are common due to microgravity exposure [35; 136; 137]. Still unknown however is whether acute, surgical conditions such as cholecystitis and appendicitis occur more frequently due to microgravity-induced stone formation or alterations in human physiology/anatomy, and immunosuppression [40]. Controlling for traditional risk factors associated with the development of these conditions (e.g., adequate hydration, maintenance of a normal BMI, dietary fat avoidance, etc.) may help mitigate the risk.  
During long-duration spaceflight, it is possible that prolonged radiation exposure could lead to radiation-induced gastrointestinal cancer. Gamma radiation exposure is a known risk factor for colorectal cancer via an absence of DNA methylation [138]. NASA has recently developed a space radiation simulator, named the \"GCR Simulator\", which allows for the more accurate radiobiologic research into the development and mitigation of radiation-induced malignancies [139]. Preflight colorectal cancer screening via colonoscopy or inflight screening via gut microbiome monitoring may be beneficial, but further research is required to demonstrate their clinical utility. Several studies, including the NASA Twins study have shown that microgravity could lead to alterations in an individual's gut microbial community (i.e., gut dysbiosis) [2; 140; 141; 142]. While changes to an individual's gut microbiome can cause inflammation of the gastrointestinal tract [143; 144], it remains unclear whether the specific alterations observed during spaceflight pose a risk to astronaut health. In fact, increased gut colonization by certain bacterial species is even associated with a beneficial effect on the gastrointestinal tract [2; 140]. (Table S1) Certain limitations of these studies, such as variations in genomic profile, diet, and a lack of adjusted confounders (e.g., the microbial content of samples) should be considered. Another potential consequence of prolonged microgravity exposure is the possibility of increased fatty-acid processing [145], leading to the development of non-alcoholic fatty liver disease (NAFLD) and hepatic fibrosis [146; 147].  
Further research is required to better understand gut microbial dynamics during space travel, as well as spaceflight-associated risk factors for the development of NAFLD, cholecystitis, and appendicitis.
## 2 Data and Methodology
### Methodology
#### 2.3.2 Optimization and Evaluation of Model Parameters  
The \(g_{xx}\) in the ET model and \(\varepsilon\) in the LUE model act as the most sensitive parameters for regional ET and GPP simulations [27; 41]. We used the aforementioned flux sites and the coefficient of determination (R\({}^{2}\)), root mean square error (RMSE), and Nash-Sutcliffe efficiency (NSE) to optimize these two parameters and further improve the model performance. The final, optimized parameters are shown in Table 2.  
R\({}^{2}\), RMSE, and NSE are widely employed to characterize the accuracy of geoscientific analyses [6; 21; 51]. We used these three indexes to evaluate our model's performance.  
\[R^{2}=\left(\frac{\sum\limits_{i=1}^{L}\left(x_{i}{}^{obs}-x_{i}{}^{\overline{ obs}}\right)\left(x_{i}{}^{sim}-x_{i}{}^{\overline{sim}}\right)}{\sqrt{ \sum\limits_{i=1}^{L}\left(x_{i}{}^{obs}-x_{i}{}^{\overline{obs}}\right)^{2} \sqrt{\sum\limits_{i=1}^{L}\left(x_{i}{}^{sim}-x_{i}{}^{\overline{sim}}\right) ^{2}}}}\right)^{2} \tag{17}\]  
\[NSE=1-\frac{\sum\limits_{i=1}^{L}\left(x_{i}{}^{obs}-x_{i}{}^{sim}\right)^{2} }{\sum\limits_{i=1}^{L}\left(x_{i}{}^{obs}-x_{i}{}^{\overline{obs}}\right)^{2}} \tag{18}\]  
\[RMSE=\sqrt{\frac{1}{t}{\sum\limits_{i=1}^{L}\left(x_{i}{}^{obs}-x_{i}{}^{sim} \right)^{2}}} \tag{19}\]  
where \(x^{obs}\) and \(x^{sim}\) are, respectively, the measured and modeled ET or GPP; \(x^{\overline{obs}}\) and \(x^{\overline{sim}}\) are the averages of the measured and modeled ET or GPP, respectively; and \(t\) is the sample count.
# Recognition of Food Ingredients--Dataset Analysis
## 2 Related Works
Based on the information obtained, the following answer to the research questions is presented:  
(a) Question 1: Are there digital solutions that recognize food from images of meals? Figure 4: Percentages of the architectures used in the articles. Figure 5: Percentages of the models used in the articles. Food recognition is a proven advantage for making the eating process simpler, more economical, and healthier. Various solutions can be applied to this task using artificial intelligence and machine learning algorithms, as mentioned in the articles [14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26]. All of the aforementioned articles are capable of recognizing food through a variety of approaches, resulting in varying degrees of accuracy. (b) Question 2: Are there digital solutions that propose recipes based on the re-recognition of ingredients or food images? Recipe recommendation is a valuable approach for many recipe management applications that do not yet utilize real-time food recognition mechanisms. The integration of this type of technology will streamline the process of meal decision-making. Although many of the models implemented for food recognition can be used for recipe recommendation, the majority of articles do not explicitly refer to this process. However, in articles [14; 17; 21], different approaches are used for this purpose. (c) Question 3: Are there solutions for proposing meals based on leftovers, taking into account the characteristics of each user? The issue of food waste has gained prominence in recent years, driven by the significant increase in consumerism and food diversity. This phenomenon, which has been growing at an alarming rate, has become a major issue, with consequences in the social, economic, and environmental spheres. Study [16] mentions a method that adapts to the user's eating habits. However, it is important to note that more responsible and healthier practices can be adopted through approaches that recommend recipes. Based on the analysis of the proposed studies, it was possible to conclude that the most widely used model was ResNet-50 and the most used dataset was Food-101. The next section will assess whether this combination can be used since the Food-101 dataset is very complex and the model may not perform satisfactorily.
## 3 Methodology
### The Proposed IRW System Design
#### 3.2.1 Unified Modeling Diagrams  
Unified Modeling Language (UML) is a standard language for visualizing, defining, creating, and documenting software used in the development of software. A UML diagram is based on the UML to visually represent a system and its main actors, roles, actions, or classes to understand, alter, maintain, or document information about the system [47, 48].  
After software and hardware requirements were met, we designed the proposed system using the Unified Modeling Language, as described below. The use case diagram of the proposed system is shown in Figure 4.  
The flowcharts in Figure 5 show the work flow of the proposed system.  
Figure 4: The use case diagram of the proposed system.  
Figure 3: Parallel running process.  
Finally, Figure 6 shows the flowchart of the sensor work flow.  
1. Use Case Diagram  
A use case is a scenario that describes how a system is used in a specific situation. Each use case includes an elaborate description of the activities, allowing quick understanding of the system's requirements. To create a use case, the various types of people (or devices) who use the system, as well as the functional requirements of the proposed system, must first be identified. In addition, we must determine the actors representing the roles that people, devices, subsystems, or other systems play as the system operates. An actor is anything that communicates or interacts with the system and is external to the system itself.  
Figure 4 illustrates the use case diagram of the proposed system and describes the interactions between the actors and the system.  
Figure 5: The flowchart of the entire proposed system.  
Figure 6: The flowchart of the temperature sensor.  
The actors involved in the use case diagram are listed in Table 1, along with a brief description of each, as well as the description of the use cases of the proposed system are listed in Table 2.  
The following is a list of the eight primary use cases, along with a brief description of each.  
2. Flowcharts of the Proposed System  
At this stage, a flowchart design was created. It was used to explain and simplify multistep processes or procedures based on the order of steps [49; 50].  
Two flowcharts were established for the proposed system to carefully illustrate the process. One was for the entire system, as shown in Figure 5, and the second, as shown in Figure 6, was for the temperature sensor, including the essential system functions and other system features.  
In order to facilitate the proposed system design, it was divided into two parts.
## 2 Materials and Methods
### TOC, TN and \(\delta^{13}\)C Values of SOM, FHA, BHA, and FA  
TOC, TN, and \(\delta^{13}\)C values of SOM, FHA, BHA, and FA were measured by gas chromatography (Thermo Scientific GC Combustion III) coupled with a continuum He-flow isotope ratio mass spectrometry (IRMS--Thermo Scientific Delta V Advantage) following a standard, semi-automated procedure. In brief, 10 mg of (CaCO\({}_{3}\) free) sample (sediment in the case of SOM, or FHA, BHA, and FA extracted from SOM) was conditioned in tin capsules and combusted on line, to release carbon dioxide (CO\({}_{2}\)) and nitrogen oxide (NOx).  
The latter was reduced to diatomic nitrogen on line, through a reduction column. Both CO\({}_{2}\) and N\({}_{2}\) were quantified by gas chromatography to yield the percentages of TOC and TN in the sample. Subsequently, CO\({}_{2}\) was run to the IRMS to determine the \(\delta^{13}\)C value.  
\(\delta^{13}\)C values are given relative to international reference Vienna-Pee Dee Belemnite (V-PDB), through calibration of raw values with certified reference materials (caffeine IAEA 600 and L-glutamic acid USGS 40). The analytical precision (1\(\sigma\) standard deviation) of \(\delta^{13}\)C analyses was <0.1%.
# Fusion Classification Method Based on Audiovisual Information Processing
## 4 Experimental Result and Analysis
### Comparative Test
#### 4.3.4 Model Feasibility Experiment  
In order to verify that the M-AVFA model proposed in this paper can work properly, the audiovisual event classification (AVE) dataset is selected for testing experiments and compared with other multimodal fusion classification methods. In this paper, STNet and PCTDF models are selected for comparative experiments. The experimental results are shown in Table 10.  
As shown in Table 10, the classification of audiovisual time on AVE dataset of the M-AVFA method proposed in this paper is superior to that of the PCTDF method and the STNet method. In terms of the Accuracy index, M-AVFA improves by 1.77% compared  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Accuracy** & **Recall** & **Precision** & **F1 Score** & **G-Mean** \\ \hline PCTDF & 81.23 & 81.14 & 80.96 & 81.05 & 81.12 \\ STNet & 81.46 & 81.37 & 81.68 & 81.52 & 81.27 \\ M-AVFA & 82.67 & 82.56 & 82.74 & 82.65 & 82.49 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Test on AVE dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fusion Strategies** & **Accuracy** & **Recall** & **Precision** & **F1 Score** & **G-Mean** \\ \hline Feature fusion & 0.77 & 0.76 & 0.78 & 0.77 & 0.77 \\ Decision fusion & 0.76 & 0.78 & 0.79 & 0.78 & 0.78 \\ M-AVFA & 0.82 & 0.83 & 0.83 & 0.84 & 0.83 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Ablation experiment of fusion strategy.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature Fusion Mode** & **Accuracy** & **Recall** & **Precision** & **F1 Score** & **G-Mean** \\ \hline \hline \multicolumn{1}{c}{Splice feature fusion} & 0.79 & 0.78 & 0.79 & 0.78 & 0.77 \\ Weighted feature fusion & 0.81 & 0.80 & 0.81 & 0.80 & 0.81 \\ SA feature fusion & 0.84 & 0.86 & 0.85 & 0.85 & 0.86 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Feature fusion ablation experiment.  
with PCTDF, and M-AVFA improves by 1.49% compared with STNet. From the perspective of G-Mean index, M-AVFA increased by 1.69% compared to PCTDF, and M-AVFA increased by 1.5% compared to STNet. From these, it can be seen that this method has advantages.
## 2 Methodology
### Principle of the HHT Algorithm  
HHT, proposed by [PERSON] et al. [25], is a novel method utilized for analyzing non-smooth and non-linear signals. The method consists of two main components: EMD and Hilbert Transform (HT) with its spectral analysis, with EMD being the primary component. HHT initially decomposes the signal adaptively into a series of IMFs through EMD. Subsequently, it performs HT on each IMF to extract the physically meaningful instantaneous frequency and instantaneous amplitude, enabling the observation of the signal's frequency domain characteristics over time. Unlike traditional signal processing methods, HHT does not require prior analysis or research or the establishment of basis functions. Instead, it employs an adaptive decomposition of the original signal into the sum of several IMFs based on its intrinsic properties [26]. Each IMF component represents a specific frequency component in the original signal and is decomposed sequentially from high frequency to low frequency. For a random temporal signal \(X(t)\), the HT yields the following result:  
\[Y(t)=\frac{1}{\pi}P_{V}\int_{-\infty}^{\infty}\big{[}X(\tau)/(t-\tau)\big{]}d\tau \tag{5}\]where \(P_{V}\) is the Cauchy principal value. In this way, the analytic signal of the original timing signal can be constructed from the mutually complex conjugates \(X(t)\) and \(Y(t)\) as follows:  
\[Z(t)=X(t)+jY(t)=a(t)\mathrm{e}^{\mathrm{i}\theta(t)} \tag{6}\]  
\[a(t)=\left[X^{2}(t)+Y^{2}(t)\right]^{1/2} \tag{7}\]  
\[\theta(t)=\arctan[Y(t)/X(t)] \tag{8}\]  
These functions contain information about the amplitude and phase of the signal. \(a(t)\) represents the amplitude function, while \(\theta(t)\) represents the phase function of the signal.  
The instantaneous amplitude and instantaneous frequency obtained by HT are functions of time. When the amplitude is expressed as a function of time and frequency, the time-frequency distribution spectrum of the amplitude is obtained, which is called the Hilbert spectrum, denoted as \(H(\omega,t)\). The integration over time for \(H(\omega,t)\) yields the Hilbert marginal spectrum as follows:  
\[H(\omega)=\int_{-\infty}^{\infty}H(\omega,t)dt \tag{9}\]  
The Hilbert marginal spectrum accumulates the amplitude at each frequency over the entire time period, representing the global contribution of energy (or amplitude) at each frequency. It reflects the cumulative amplitude of all the data in a statistical sense [24]. The marginal spectrum of the fluctuating pressure signal accurately represents the actual frequency components of the signal, with the peak of the marginal spectrum corresponding to the dominant frequency.
## 3 Results and Discussion
### Comparison Model with Real Operating Data
In the BL step, the pressure in Bed1 drops to the desorption pressure and the adsorbed CO\({}_{2}\) is removed from the adsorbent. According to the Langmuir isotherm, CO\({}_{2}\) comes out when the pressure drops, which in turn affects the mole fraction of CH\({}_{4}\), leading to the lowest value among all six steps. In the PU step, due to the injection of a higher mole fraction of CH\({}_{4}\) flowing through the bed from the counter-current direction, the remaining adsorbed CO\({}_{2}\) is removed, and the mole fraction of CH\({}_{4}\) becomes higher than that in the BL step. In the PPE step, the discharged gas from another adsorption bed comes in at the top of the bed. It has a lower CH\({}_{4}\) mole fraction than the PU step, but it does not maintain its value at this step and the CO\({}_{2}\) begins re-adsorbing. Therefore, in this step, the mole fraction of CH\({}_{4}\) is almost higher than the PU step along the adsorption bed length. Figure 12 shows the results of the CO\({}_{2}\) mole fraction in direction opposition to those of CH\({}_{4}\), because we assume that the proportions of biogas include only CH\({}_{4}\) and CO\({}_{2}\). Figure 11: The mole fraction of CH\({}_{4}\) at the cyclic steady state with Aspen Adsorption. This simulation makes it possible to obtain an average purity of biomethane of 92.08% over time, with a recovery of 81.63%. In addition, the results show that the real operating data fit well with the simulated results.
## 4 Result and Discussion
The predictions align well with calculated displacements, being evenly distributed along both sides of the central line in Figure 6. In summary, Model 2 demonstrates a more uniform residual distribution, higher fitting accuracy, and superior predictive performance compared to Model 1. To further validate the models presented in this study, Model 1 and Model 2 are compared with a classical empirical model (Figures 7 and 8). Figure 7 demonstrates that when \(I_{a}\) is 0.1 m/s, 0.5 m/s, and 1.0 m/s, Model 1 and the J07 Model exhibit similar trends with strong consistency. For clearer comparison with other classical models, Figure 8 shows the prediction results of seven models under different yield acceleration ratios, with \(PGA\) set to 0.05 g, 0.1 g, 0.2 g and \(I_{a}\) to 0.3 m/s, 0.7 m/s, and 1.5 m/s. Overall, the predicted displacements of Model 2 fall within the range of other models, indicating good applicability. Figure 5: The distribution of residual points of \(\log(u)\) in Model 2 with fitting parameters. (**a**) Distribution with \(a_{y}/PGA\); (**b**) distribution with \((a_{y}/PGA)^{2}\); (**c**) distribution with \((a_{y}/PGA)^{3}\); (**d**) distribution with \((a_{y}/PGA)^{4}\); (**e**) distribution with \(\log\left(I_{a}\right)\); (**f**) distribution with \(PGA\). Figure 6: The comparison of predicted displacement and calculated displacement of Model 2. Figure 7: Comparison of Model 1 with A&M Model and J07 Model under three \(I_{a}\) conditions.
# Tibetan Speech Synthesis Based on Pre-Trained Mixture Alignment FastSpeech2
## 2 Model Architecture
### Mixture Alignment Encoder
#### 2.2.4 Character-to-Letter Attention Module  
This study introduces a character-to-letter attention module that extends the hidden features at the Latin letter level. Specifically, \(\mathcal{H}^{\prime}_{char}\) serves as the query vector \(\mathbf{q}\), with \(\mathcal{H}_{l}\)  
Figure 4: Length regulator.  
functioning as both the key \(\mathbf{k}\) and the value \(\mathbf{v}\). The formula for calculating the attention weights is as follows:  
\[\alpha_{i}(\mathbf{q},\mathbf{k}_{i})=\frac{\exp(s(\mathbf{k}_{i},\mathbf{q}))}{ \text{sum}_{j=1}^{N}\exp(s(\mathbf{k}_{j},\mathbf{q}))}, \tag{2}\]  
\[s(\mathbf{k}_{i},\mathbf{q})=\mathbf{k}_{i}^{\text{T}}W_{qk}\mathbf{q}, \tag{3}\]  
where \(W_{qk}\) represents the learnable transformation matrix, and \(i\) denotes the attentional weight on the hidden feature of the \(i\)-th Latin letter. Then, using the computed attentional weights \(\alpha_{i}\), the output for each query vector is calculated as follows:  
\[\mathbf{y}(\mathbf{q},\mathbf{k},\mathbf{v})=\text{sum}_{i=1}^{N}\alpha_{i}( \mathbf{q},\mathbf{k}_{i})W_{v}\mathbf{v}_{i}, \tag{4}\]  
where \(W_{v}\) represents another learnable transformation matrix. To date, this study has achieved a Latin letter representation matching the spectrum's length \(\mathcal{H}_{l}^{\prime}=\mathbf{y}(\mathbf{q},\mathbf{k},\mathbf{v})\). To ensure that the query vector only accesses key-value pairs within the same character, a mask matrix based on the character boundary information from the input text encoder is constructed. This matrix is then incorporated into the attention weight \(\alpha\) matrix. Additionally, to account for the monotonic alignment of Latin letters with speech, positional encoding information is added to \(\mathbf{q}\), \(\mathbf{k}\), and \(\mathbf{v}\). For \(\mathbf{k}\) and \(\mathbf{v}\), this positional encoding information is \(\frac{i}{L_{c}}E_{kv}\), where \(i\) represents the position of each Latin letter within its corresponding character, \(L_{c}\) represents the total number of Latin letters in character \(c\), and \(E_{kv}\) is a learnable vector embedding. For \(\mathbf{q}\), the position encoding information is \(\frac{j}{T_{c}}E_{q}\), where \(j\) represents the frame's position within the character, \(T_{c}\) represents the total number of frames in the character, and \(E_{q}\) is another learnable vector embedding.  
To illustrate this process, Figure 5 shows the calculation principle of character-to-letter attention. This figure helps to visualize how the attention weights \(\alpha_{i}\) are computed and applied to obtain the final output \(\mathbf{y}(\mathbf{q},\mathbf{k},\mathbf{v})\).