esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Strategies for Improving Protein Digestibility
### Enzymatic Hydrolysis  
Enzymatic hydrolysis involves breaking down proteins into smaller peptides and amino acids using proteolytic enzymes. This approach improves digestibility and enhances the bio-functional properties of proteins, such as antioxidant or antihypertensive activities. Enzymatic hydrolysis is commonly employed in producing protein hydrolyses for specialized applications, such as infant formulas, medical nutrition, and sports supplements.  
Enzymatic hydrolysis enhances protein digestibility by breaking down high-molecular-weight proteins into smaller peptides and amino acids, making them more accessible to digestive enzymes. A study demonstrated that limited hydrolysis using papain significantly reduced the molecular weight of a soy protein isolate and corn zein mixture while increasing the proportion of random coil structures [145]. These structural modifications facilitated protein breakdown, resulting in a remarkable improvement in in vitro digestibility. [PERSON] et al. [146] indicated that enzymatic hydrolysis significantly improves the digestibility of plant proteins by breaking down large, complex protein structures into smaller peptides and free amino acids, making them more accessible to digestive enzymesin the gastrointestinal tract. This process also eliminates antinutritional factors, such as phytate, that form insoluble complexes with proteins and minerals, otherwise hindering enzymatic degradation and nutrient absorption [3]. By degrading cell walls and disrupting protein-phytate complexes, hydrolysis enhances the bioavailability of vital nutrients like calcium and improves the overall nutritional value of plant proteins [147]. In addition, [PERSON] et al. [148] showed in a study on rapeseed that enzymatic hydrolysis significantly enhances protein digestibility by breaking down complex protein structures into smaller peptides and amino acids, making them more accessible to digestive enzymes. The study demonstrated that proteolysis improves digestibility and enhances functional properties. As a result, hydrolyzed plant proteins offer better digestibility, addressing one of the primary limitations of plant-based diets.
# Applying Automatic Translation for Optical Music Recognition's Encoding Step
## 4 Results
### Experimental Results
As can be seen in Figure 10, the SMT approach slightly outperformed the transformer on the smaller evaluation subsample. However, when the size of the dataset increased, the transformer became more accurate than SMT. In fact, as the size of the dataset got larger, the difference between both approaches became larger and the deviations got smaller, so the differences are more significant. These results clearly confirm our hypothesis that with enough data, the transformer clearly outperforms the SMT approach. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Contextual SMT** & **SMT** & **Seq2 Seq-Attn** & **The Transformer** \\ \hline Camera-PrIMuS & & & & \\ Pitch & 1.28\% & 22.74\% & 1.70\% & 0.42\% \\ Dotted notes & 0.90\% & 11.73\% & 2.60\% & 0.33\% \\ Pitch altered by key & 0.97\% & 16.31\% & 1.73\% & 0.27\% \\ \hline Overall & 1.58\% & 23.70\% & 2.04\% & 0.50\% \\ \hline FMT & & & & \\ Pitch & 10.43\% & 9.03\% & 11.34\% & 16.32\% \\ Dotted notes & 14.22\% & 11.21\% & 19.18\% & 18.32\% \\ Pitch altered by key & 15.91\% & 20.63\% & 21.10\% & 18.74\% \\ Slurs and ties & 14.11\% & 11.35\% & 15.03\% & 19.33\% \\ \hline Overall & 11.10\% & 9.61\% & 9.75\% & 15.30\% \\ \hline Zaragoza & & & & \\ Pitch & 23.67\% & 68.43\% & 91.78\% & 90.63\% \\ Dotted notes & 24.32\% & 68.47\% & 92.43\% & 91.35\% \\ Pitch altered by key & 24.76\% & 63.59\% & 94.17\% & 91.74\% \\ Slurs and ties & 50.00\% & 80.00\% & 90.00\% & 90.00\% \\ \hline Overall & 35.31\% & 68.60\% & 89.50\% & 86.31\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Masked SER for some translation categories. The overall SER is also shown for easy comparison. Other relevant dimensions to take into account in the comparison of these machine translation approaches are the amount and type of computational resources required by each of them. Table 5 shows the total time consumed by each approach, the peak memory and the amount of CPU/GPU used to train and to translate one fold of each of the evaluated datasets. Note that neural approaches require a GPU to be trained, while statistical models are able to be trained on CPUs. For this reason, SMT models were trained and run on a machine with an AMD Opteron(tm) 6128 CPU with 8 cores and 64 GB of RAM, and the neural models were trained on a machine with an Intel(r) Xeon(r) E3-1230 v5 CPU with 8 cores, 32 GB of RAM and a GeForce GTX 1080 GPU. It is worth mentioning that the time performance of the neural models is highly dependent on the GPU model used; for example, we tested the Seq2 Seq-Attn model with a NVIDIA RTX 2080 GPU, and reduced the training time by 25%, thereby only taking one day to train a model.
## 3 Results
### Real Image Testing
The running time of the method proposed in this paper appeared to be relatively long, as shown in Table 2. To analyze the source code, we employed MATLAB's runtime analysis tool. Our analysis revealed that the feature symbol search algorithm accounted for a significant portion of the computing time, specifically 80.1%. Considering this finding, we are convinced that there is ample scope for improvement. Thus, one of our forthcoming research areas will focus on optimizing the feature symbol search algorithm.
# Quantum Implementation of AIM: Aiming for Low-Depth
## 3 Quantum Circuit Implementation of AIM
### LinearLayer
As a result, we can save 128, 192, and 256 qubits (CNOT gates also) for AIM-I, -III, and -V, respectively. Table 4 shows the quantum resources required for Linearlyer of AIM-I, -III, and -V.  
Although we did not describe it earlier, the vector extracted from the hash value is XORed with the result vector of the matrix-vector multiplication. This step corresponds to a classical quantum implementation. Thus, we only apply X gates to the input vector depending on the bit values of the public vector, instead of using CNOT gates, which is a more efficient approach. Lastly, \(\mathsf{Mer}(\mathsf{e}_{*})\) and \(\mathsf{FeedForward}\), which XOR the input with the output, are performed. Note that \(\mathsf{FeedForward}\) is a quantum-quantum implementation, so CNOT gates are used. Finally, Table 5 shows the quantum resources required for the AIM quantum circuits. Our proposed AIM quantum circuits require a significant number of ancilla qubits, which is due to the Karatsuba multiplication method [23] we adopted. Most of the ancilla qubits are used for the multiplication operations inside \(\mathsf{Mer}\). While we allow for a large number of qubits, we provide low \(T\)-depth and full depth. In particular, since a single multiplication has a \(T\)-depth of only 4, the \(T\)-depth of our proposed quantum circuit is very low. In the trade-off between qubit count and depth, we use metrics such as Toffoli depth \(\times\) qubit count (\(TD\times M\)) and full depth \(\times\) qubit count (\(FD\times M\)), which are common metrics for quantifying the performance of quantum circuits.
## 5 Proposed Source Authentication Schemes  
In this section, we propose three source authentication schemes for code dissemination supporting dynamic packet size. Source authentication, the most significant security requirement for code  
Figure 4: An example of the Bloom filter.  
dissemination in WSNs, ensures that a new code image is really sent by the BS and not altered in transit. Note that, for simplicity, we explain our schemes in the first hop which is between the BS and the neighbor node but this procedure continues to all sensor nodes in the WSN over multihop communications.
# Effects of Long-Term Fertilization on Phosphorus Form and Availability in Black Soil
## 1 Introduction
Utilizing the national long-term positioning experiment on black soil fertility and fertilizer effectiveness, we employed the chemical continuous extraction method in conjunction with \({}^{31}\)P NMR technology. Our objective was to investigate the impact of mineral (N, P, and K) and organic (pig manure) fertilization on the content and availability of various phosphorus forms. This study was conducted as part of a long-term field experiment focusing on continuous maize (_Zea mays_ L.) cropping in the black soil region of northeast China. Our hypotheses were as follows: (1) long-term organic amendment practices will improve the content of various forms of phosphorus when compared with the application of chemical fertilizer only and (2) the content of orthophosphate will increase after the long-term use of organic amendments, thereby enhancing its bioavailability.
# Amharic Speech Search Using Text Word Query Based on Automatic Sentence-like Segmentation
###### Abstract  
More than 7000 languages are spoken in the world today. Amharic is one of the languages spoken in the East African country Ethiopia. A lot of speech data is being made every day in different languages as machines are getting better at processing and have improved storing capacity. However, searching for a particular word with its respective time frame inside a given audio file is a challenge. Since Amharic has its own distinguishing characteristics, such as glottal, palatal, and labialized consonants, it is not possible to directly use models that are developed for other languages. A popular approach in developing systems for searching particular information in speech involves using an automatic speech recognition (ASR) module that generates the text version of the speech where the word or phrase is searched based on text query. However, it is not possible to transcribe a long audio file without segmentation, which in turn affects the performance of the ASR module. In this paper, we are reporting our investigation on the effects of manual and automatic speech segmentation of Amharic audio files in a spiritual domain. We have used manual segmentation as a baseline for our investigation and found out that sentence-like automatic segmentation resulted in a word error rate (WER) closer to the WER achieved on the manually segmented test speech. Based on the experimental results, we propose Amharic speech search using text word query (ASSTWQ) based on automatic sentence-like segmentation. Since we have achieved lower WER using the previously developed speech corpus, which is in a broadcast news domain, together with the in-domain speech corpus, we recommend using both in- and out-domain speech corpora to develop the Amharic ASR module. The performance of the proposed ASR is a WER of 53% that needs further improvement. Combining two language models (LMs) developed using training text from the two different domains (spiritual and broadcast news) allowed a WER reduction from 53% to 46%. Therefore, we have developed two ASSTWQ systems using the two ASR modules with WERs of 53% and 46%.  
speech segmentation; spoken term detection; automatic speech recognition; manual speech segmentation
## 5 Numerical Examples
### Specific Examples
#### 5.2.1 Example 1  
In the first example, we start with standard values that a oriented at the literature, see Table 2, and compare variations in abundance replanting with frequency replanting. Specifically, different variants of abundance replanting and frequency replanting are simulated, namely, abundance replanting with \(\hat{\epsilon}\in\{0.5,0.8\}\) (-), and frequency replanting with \(\varepsilon\in\{0.7,0.8\}\) (-).  
Figure 2 illustrates that all cases exhibit similar qualitative behavior: The sum of \(S\) and \(I\) approaches a carrying capacity. The value of \(S\) initially increases, reaching a maximum in cases 1a and 1c, whereas in case 1b, the decrease is only slight, and in 1d, \(S\) appears to increase monotonically within the visualized time window. The variable \(I\) behaves in a manner complementary to \(S\): it increases monotonically in 1a and 1c. In 1b, it rises to a local maximum, then slightly decreases to a local minimum, before increasing slightly again.  
The situation for \(U\) is clearer; in all examples, it increases to a maximum before subsequently decreasing. Complementarily, \(W\) exhibits S-shaped curves that resemble the behavior of a logistic equation.  
In subsequent examples, specific parameters are varied, while the other variables are kept at reference levels.  
Figure 2: Simulation results for Example 1. First row: abundance replanting with (a) \(\hat{\epsilon}=1\) (-), (b) \(\hat{\epsilon}=0.5\) (-). Second row: frequency replanting with (c) \(\varepsilon=1\) (-), (d) \(\varepsilon=0.5\) (-).
## 2 Materials and Methods
### Angular Loss Modelling
#### 2.6.1 [PERSON] and Ruiz Model  
Direct irradiance angular losses, \(F_{\text{B}}\), can be obtained with the expression of the incidence angle modifier (IAM) developed by [PERSON] and [PERSON] [71,72] and adopted by the IEC 61853-2 Standard [59] to interpolate the _IAM_ data obtained after following the standard testing procedures. The model expresses _IAM_ as a function of the angle of incidence (\(\theta\)) and includes a dimensionless fitting parameter, \(a_{\text{r}}\) (Equation (10)):  
\[F_{\text{B}}=IAM=1-\left[\frac{1-\exp\left[\frac{-\left(\cos\theta\right)}{a _{\text{r}}}\right]}{1-\exp\left(\frac{-1}{a_{\text{r}}}\right)}\right] \tag{10}\]  
The fitting parameter \(a_{\text{r}}\) relates the angular reflectance curve to the angle of incidence, with higher values implying a larger angular dependence of the module's reflection losses. Typical values for \(a_{\text{r}}\) for modules with clean flat front glass were experimentally found to range from 0.20 to 0.23 [39], depending on the PV cell technology, which has a small influence on angular losses compared to the 'air/glass' interface. Modules with anti-reflecting coatings show a better angular response for high angles of incidence, i.e., above 40\({}^{\circ}\). In addition, it should be considered that surface soiling might increase \(a_{\text{r}}\) by several hundredths (e.g., from 0.17 to 0.20 or 0.27, depending on the type and degree of soiling).  
Expressions accounting for diffuse, \(F_{\text{D}}\), and ground-reflected, \(F_{\text{A}}\), irradiance losses were developed by [PERSON] and [PERSON] from the _IAM_ expression (Equations (11) and (12)):  
\[F_{\text{D}}=1-\exp\left[-\frac{1}{a_{\text{r}}}\left(c_{1}\left(\sin\beta+ \frac{\beta-\sin\beta}{1-\cos\beta}\right)+c_{2}\left(\sin\beta+\frac{\beta- \sin\beta}{1-\cos\beta}\right)^{2}\right)\right] \tag{11}\]  
\[F_{\text{A}}=1-\exp\left[-\frac{1}{a_{\text{r}}}\left(c_{1}\left(\sin\beta+ \frac{\pi-\beta-\sin\beta}{1+\cos\beta}\right)+c_{2}\left(\sin\beta+\frac{\pi -\beta-\sin\beta}{1+\cos\beta}\right)^{2}\right)\right] \tag{12}\]  
where \(\beta\) is the tilt angle of the module, \(c_{1}\) has an adjusted value of 43\(\pi\)[71], and \(c_{2}\) is a parameter which is a linear function of \(a_{\text{r}}\) (values of \(c_{2}\) and \(a_{\text{r}}\)[71,72] can be used to determine \(c_{2}\) by linear extrapolation).
## 2 Proposed Adaptive Finite Step Length Algorithm
### Summary of the Proposed Algorithm  
Three features of the proposed AFSL algorithm for reliability analysis are concluded as: (i) inheritance, that is, the proposed algorithm process features of the FSL algorithm are as simple as those of the HL-RF algorithm but superior in robustness; (ii) efficiency, the proposed algorithm is more efficient than the FSL algorithm due to the iteration point adaptive optimization method based on the Armijo line search being employed; (iii) availability, the AFSL algorithm is suitable for various nonlinear LSFs by establishing the relationship between the initial step length and the initial iteration point. Consequently, the proposed algorithm can efficiently deal with various reliability analysis problems, especially for highly nonlinear functions. The framework of the proposed AFSL algorithm is shown in Figure 3, and the main steps are summarized as follows:  
(1) Let \(k=0\), and set the start point \(\mathbf{X}^{0}\).  
(2) Transform the random variables \(\mathbf{X}\) into standard normal random variables \(\mathbf{U}\) by Equation (2).  
(3) Calculate the sensitivity factor \(\mathbf{\alpha}^{k}\) by Equation (11).  
(4) Determine the new point \(\mathbf{U}^{k+1}\) based on Equation (10).  
(5) Check convergence. If \(\left\|\left\|\mathbf{U}^{k+1}-\mathbf{U}^{k}\right\|\right/\left\|\mathbf{U}^{k}\right\|\leq\varepsilon\) (\(\varepsilon\) is a small positive number), then stop, \(\beta=\left\|\mathbf{U}^{k+1}\right\|\) and go to step (8); otherwise, go to step (6).  
(6) If \(k=0\), set \(k=k+1\) and go to step (3); otherwise, go to step (7).  
(7) If \(\left\|\mathbf{U}^{k+1}-\mathbf{U}^{k}\right\|<\left\|\mathbf{U}^{k}-\mathbf{U}^{k-1}\right\|\), set \(k=k+1\) and go to step (3); otherwise, optimize the iteration point \(\mathbf{U}^{k+1}\) based on Equation (14), set \(\lambda^{k}=c\lambda^{k}\) and \(k=k+1\), go to step (3).  
Figure 3: Framework of the proposed AFSL algorithm.  
(8) The failure probability is estimated by Equation (5).
# Intelligent Reflecting Surface-Assisted Wireless Communication Using RNNs: Comprehensive Insights
## 6 Results
### Results of Individual RNN Techniques  
We first describe the results of the RNN techniques with single and double layers. The BER performance of the single-layer LSTM is shown in Figure 11. At an SNR value of 30 dB, the BER reaches \(8.18\times 10^{-4}\). However, when the number of layers is increased, the performance decreases to \(9.47\times 10^{-4}\). This decline is likely due to the feature extraction process being less effective in the double-layer configuration compared to the single-layer setup. A similar trend is observed in Figure 12, which depicts the SER for both the single-layer and double-layer LSTM models. The single-layer BiLSTM, on average, performs better than the LSTM, with the best performance achieved by the double-layer BiLSTM, as shown in Figures 11 and 12. At 30 dB SNR, the BER is \(2.89\times 10^{-4}\), and the SER is \(9.95\times 10^{-4}\). For the single-layer GRU, the BER performance is better than both the LSTM and the single-layer BiLSTM. In contrast, the double-layer GRU shows better performance across all SNR ranges, with the second-highest BER performance, as GRU is effective in feature extraction.  
Figure 10: Model training summary (**a**) LSTM-BiLSTM model and BiLSTM-LSTM model, (**b**) LSTM-GRU model and GRU-LSTM model, and (**c**) BiLSTM-GRU model and GRU-BiLSTM model.  
Figure 9: Model training summary; (**a**) LSTM single and double layer, (**b**) BiLSTM single and double layer, and (**c**) single and double layers.
# Application of Gas Foil Bearings in China
## 3 Challenges and Prospects
### The Stability of Gas Foil Bearing  
The supporting force provided by the gas foil bearings is nonlinear due to the influence of geometric and structural parameters. At the same time, the high-speed rotor system also has relatively complicated dynamic characteristics, which makes stability analysis of the rotor bearing system very complicated. Larger vibration amplitudes are prone to appear in the rotor system. Subsynchronous vibration may cause serious accidents with rotating machinery. At present, a great deal of research has been carried out on subsynchronous vibration of foil bearings. However, the in-depth mechanism of the emergence of subsynchronous vibrations has not yet been found. In order to reveal the influencing mechanism of foil bearing stability, some researches could be carried out from various points of view, such as establishing a complete dynamic solution model considering gas film, foil thickness, and rotor structure, and improving experimental conditions. It is significant to analyze the formation mechanism of the non-linear bearing capacity of the gas foil bearing through experiments and theoretical calculations.
# Comparative Study for Multi-Speaker Mongolian TTS with a New Corpus
## 2 Related Work  
For mainstream languages, such as English and Mandarin, there are many free and publicly available TTS datasets. For example, LJspeech [19] is a single-speaker dataset for English. To enrich the speaker diversity, some multi-speaker TTS datasets have been released, for example Librits [20] and VCTK [21] with different accents for English and AiShell [22] for Chinese.  
For low-resource languages, such as Mongolian, the available resources are pretty limited. We note that there are some methods for learning by generating additional annotated data that can improve TTS synthesis with low-resource data, such as data augmentation [23; 24] and cross-linguistic mapping [25]. Other approaches, such as unsupervised learning [26], semi-supervised learning [27], and transfer learning [28], have also achieved good results.  
In order to promote the development of Mongolian TTS, some works built their own Mongolian TTS corpus and designed various models to achieve good results. For example, [PERSON] et al. established the first emotionally controllable Mongolian TTS system and achieved eight emotional embeddings by transfer learning and emotional embedding [29]. [PERSON] et al. introduced a new method to segment Mongolian words into stems and suffixes, which greatly improved the performance of the Mongolian rhyming phrase prediction system [30]. Immediately after that, [PERSON] proposed a DNN-based Mongolian speech synthesis system, which performs better than the traditional HMM [31]. In addition,he introduced the Bidirectional Long-Term Memory (BilstM) model to improve the phrase break prediction step in the traditional speech synthesis system, making it more applicable to Mongolian [32]. Unfortunately, none of the Mongolian TTS datasets from the above works have been released publicly and are not directly available to the public. We also found that some datasets in related fields, such as M2 ASR-MONGO [33] for Mongolian speech recognition, have been made public recently. However, the speech recognition corpus cannot be applied in the TTS field due to environmental noise and improper speaking style issues, etc.  
We previously released the single-speaker MnTTS dataset [16], called MnTTS. The total duration of the MnTTS is 8 h, and it was recorded in a studio by a professional female native Mongolian announcer. However, the duration and speaker diversity still need to be further expanded. In a nutshell, it is necessary to construct a high-quality multi-speaker Mongolian TTS dataset to further promote Mongolian TTS research, which is the focus of this paper. We will introduce the details of the MnTTS2 in the following subsection.