esa-sceva/satcom-chunk-collection huggingface dataset:
# Meta-Learning for Mandarin-Tibetan Cross-Lingual Speech Synthesis
## 6 Evaluations and Analysis
### Multilingual Speech Synthesis Evaluation
#### 6.1.2 Subjective Evaluation  
For subjective evaluations, 20 speech samples were randomly selected from the test set. We conducted a mean opinion score (MOS) test and AB preference test to evaluate the voice quality of synthesized speech. In addition, we invited 30 native Mandarin and 30 Tibetan listeners as subjects. Mandarin subjects were invited to evaluate the synthesized Mandarin speech samples, while Tibetan subjects were invited to assess the synthesized Tibetan speech samples.  
In the MOS test, subjects were asked to rate the naturalness of the synthesized speech using a 5-point scale score. The average MOS scores of synthesized Mandarin speech samples are shown in Figure 5. We can find that the seq2 seq-M has a higher MOS score, and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Languages** & **seq2 seq-M** & **seq2 seq-T** & **shared-MT** & **separate-MT** & **meta-MT** \\ \hline Mandarin & 4.633 & - & 4.752 & 4.659 & 4.652 \\ Tibetan & - & 4.185 & 4.055 & 4.031 & 3.985 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The MCD values of different seq2 seq TTS models on the test sets. The seq2 seq-M and seq2 seq-T are Mandarin and Tibetan monolingual TTS models, respectively. The shared-MT, separate-MT, and meta-MT are Mandarin-Tibetan cross-language TTS models.  
Figure 5: The average MOS scores of synthesized Mandarin speech samples under 95% confidence intervals.  
the MOS scores of meta-MT and separate-MT are slightly higher than that of shared-MT from Figure 5. The average MOS scores of synthesized Tibetan speech are shown in Figure 6. The meta-MT has slightly higher average MOS scores for Mandarin and Tibetan speech synthesis compared with the separate-MT and shared-MT. These results are consistent with the objective results.  
In the AB preference test, the sentences were the same in both items within a pair. Each pair of synthesized speech samples was played at random. The subjects were asked to listen and judge the quality of which sample was better (or \"neutral\" means that the subjects had no preference). The preference results of the synthesized Tibetan and Mandarin speech samples are shown in Tables 2 and 3. We can find that the AB preference results are consistent with the objective and MOS evaluation results.
## 6 Conclusions
### Nomenclature  
A area, m\({}^{2}\)  
Bi Biot number \(Bi=h_{sf}L_{c}/\lambda_{f}\)  
Bo Bond number \(Bo\equiv\frac{(\rho_{l}-\rho_{c})\cdot\beta d_{P}^{2}}{4\sigma_{z}}=\frac{(\rho_{ l}-\rho_{c})\cdot\beta d^{\prime}}{\sigma_{z}}\)  
c\({}_{\rm P}\) constant pressure heat capacity, J\(\cdot\)kg\({}^{-1}\)K\({}^{-1}\)  
d\({}_{\rm P}\) mean particle diameter, m  
e thickness, m  
\(\dot{e}\) evaporation rate, kg\(\cdot\)m\({}^{-2}\)s\({}^{-1}\)  
h\({}_{\rm ef}\) distance from the heating plate to the vapor-liquid interface, m  
h\({}_{\rm IV}\) latent heat of evaporation, m\({}^{2}\)s\({}^{-2}\)  
h\({}_{\rm sf}\) interstitial heat transfer coefficient, W\(\cdot\)m\({}^{-2}\)K\({}^{-1}\)  
J(s) capillary pressure function  
k\({}^{\prime}\) intrinsic permeability, m\({}^{2}\)  
k\({}_{\rm r}\) relative permeability  
L\({}_{\rm C}\) characteristic length, m  
Nu Nusselt number  
P pressure, Nm\({}^{-2}\)  
PC capillary pressure, Nm\({}^{-2}\)  
Phs hydrostatic pressure, Nm\({}^{-2}\)  
r radius, m  
R universal gas constant, J\(\cdot\)mol\({}^{-1}\)K\({}^{-1}\)  
Ra Rayleigh number \(Ra=\frac{K\beta\beta\Delta Th}{1\
u\lambda_{f}/(\rho_{F})_{l}}\)  
Re Reynolds number \(Re_{p}=V\overline{d_{p}}/\
u(1-\varepsilon)\)  
S saturation  
Se specific surface, m\({}^{-1}\)  
s\({}_{\rm r}\) residual saturation  
t time, s  
t\({}_{\rm c}\) critique time, s  
t\({}_{\rm r}\) residence time, s  
T temperature, \({}^{\circ}\)C or K  
V volume, m\({}^{3}\)  
v velocity, ms\({}^{-1}\)  
v\({}_{\rm D}\) Darcy's velocity, m\(\cdot\)s\({}^{-1}\)  
\(\dot{W}\) power, W  
\(Z_{\rm bf}\) thickness of two-phase region, m
## 5 Experimental Results
### Comparison with Existing Datasets  
To assess the strengths and weaknesses of the Manchu music dataset, we compared it with several major existing music datasets. The comparison covered dimensions such as dataset size, audio quality, diversity, and application effectiveness. This analysis positions the Manchu music dataset within the context of existing datasets, highlighting its unique value not only for the preservation and transmission of cultural heritage but also as a valuable resource for digital and intelligent research in cultural heritage preservation.  
In terms of dataset size, although the Manchu music dataset is smaller than the Million Song Dataset, its focus on a specific ethnic music genre and its total duration of 500 h provide a sufficient amount of samples. Compared to the GTZAN and MAESTRO datasets, it offers more detailed support for ethnic music data. Regarding audio quality, the Manchu music dataset's quality surpasses that of the GTZAN and Million Song Dataset and is comparable to MAESTRO, which provides a solid foundation for high-quality audio analysis and model training.  
In terms of dataset diversity, the Manchu music dataset stands out for its variety in music types and regional distribution. Unlike the Million Song Dataset, which covers global popular music, the Manchu music dataset focuses on a specific ethnic music category, offering a unique resource for studying the characteristics of ethnic music. The specific contents are shown in Table 9.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **MSE** & **RMSE** & **MAE** \\ \hline MFCC & 0.025 & 0.158 & 0.120 \\ LPC & 0.030 & 0.173 & 0.125 \\ STFT & 0.028 & 0.167 & 0.122 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of experimental results on feature extraction and regression task performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **A** & **P** & **R** & **F1** \\ \hline Random Forest & 83.4\% & 81.0\% & 82.5\% & 81.7\% \\ k-NN & 78.9\% & 76.5\% & 77.8\% & 77.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of experimental results on sentiment analysis performance.  
The Manchu music dataset demonstrates superior performance in specific fields compared to other datasets, particularly in the research and application of ethnic music. Unlike the more general-purpose datasets like the Million Song Dataset and GTZAN, the Manchu music dataset offers more targeted research materials. This study reveals the unique value and advantages of the Manchu music dataset by comparing it with existing datasets.  
Although it may not match some large-scale datasets in terms of size, its high quality, diversity, and focus on a specific domain make it significantly important for the study of ethnic music. The dataset's application results indicate that it provides strong support for existing research in tasks such as music classification, generation, and analysis. This foundation paves the way for future research in related fields. The application effect is shown in Table 10.
## 3 The Proposed System  
### System Components  
Our defect detection system is comprised of four units: the defect generation unit, the pre-processing unit, the convolutional autoencoders unit, and the post-processing unit. Figure 1 shows overall architecture of the proposed system. The images coming from the camera are divided into small patches and processed one-by-one. In the defect generation unit, synthetic defects are generated using the characteristics of defects that the experienced workers described. Then, the generated defects are added to the non-defect training data. The preprocessing unit normalizes the images to make the dataset have a common scale. The trained convolutional autoencoder is used to reconstruct the input patches. Once the defective patches are used as input to the autoencoder, the defects can be located using the differences between the defective patches and the reconstructed images. The post-processing unit detects defects by using thresholding and morphology filtering.
## 5 Chain Synchronization
### Chain Synchronization Mechanism
#### 5.2.2 Chain Synchronization Mechanism Based on Signature Confirmation  
Assuming that the interval between the chain signatures of two communication nodes is \(d\), the sequence numbers \(seq_{\alpha}\) and \(seq_{\beta}\) corresponding to the two chain signatures \(s_{\alpha}\) and \(s_{\beta}\) should satisfy \(\beta=\alpha+d\). If \(\exists\delta,\alpha<\delta<\beta\), starting from \(p_{\delta}\), the attacker can use the algorithm \(Attack\) to tamper with the content of the message and make it successfully pass the receiver's message hash chain verification (it is challenging for the attacker to do this). Subsequently, the message tampered with by algorithm \(Attack\) is recorded as \(p_{\delta}^{*}=(m_{\delta}^{*},HC_{\delta}^{*})\), and the message hash chain constructed by the receiver according to \(HC_{\delta}^{*}\), \(HC_{\delta+1}^{*},\ldots,HC_{\beta-1}^{*}\) is recorded as \(HC^{\prime}(Attack)\). When the sender reaches the signature interval (or actively signs the chain as needed), the receiver must verify \(p_{\beta}=(m_{\beta},HC_{\beta},s_{\beta})\), satisfying as the following:  
\[\begin{cases}Ver(dk,s_{\beta})=1\\ h(h(m_{\beta})||HC^{\prime}(Attack)_{\beta-1})\
eq HC_{\beta}\end{cases}. \tag{5}\]  
Then the receiver needs to re-request \(p_{\alpha+1},p_{\alpha+2},\ldots,p_{\beta-1}\), and the receiver's message hash chain needs to be reconstructed from \(HC_{\alpha}^{\prime}\).
## 4 Proposed Resource Allocation Scheme
### Tradeoff between Customer Satisfaction and Service Provider Profit
#### 4.3.1 Satisfaction Optimization with a Profit Bound  
This strategy is for service providers who aim to sustain a minimal unit profit \(\rho_{min}\) for each request. This value is predetermined by some market analysis by the service provider for each type of requested task [19]. Algorithm 1 summarizes the steps of the First Fit Satisfaction algorithm for user Quality-of-Experience (QoE) maximization. At first, the incoming requests are sorted in decreasing order according to priority, \(w_{r}\), so that the request with the highest priority gets quicker access to the BBU. The target of this algorithm is to elect that BBU for each request, \(r\in R\), which offers the fastestprocessing of one particular request. The faster execution or processing of a request bestowed by the BBU ensures a higher value of user satisfaction received by end user from a particular BBU. The BBU, \(Y_{b}\), providing the highest value of satisfaction, \(\kappa_{r}\), is picked out to be provisioned for one request, \(r\in R\). Therefore, the total time required to process a request, \(r\in R\), on BBU, \(b\in B\), is enumerated by the ratio of total data to be processed of a request, \(d_{r}\), to the processing capacity of the BBU \(\eta_{b}\) as:  
\[\zeta_{b,r}=\frac{d_{r}}{\eta_{b}} \tag{14}\]  
**INPUT:** Processing Capacity of all BBUs, \(\eta_{b},\forall b\in B\), priority of each incoming request, \(w_{r}\), on a scheduling interval and QoS of the incoming requests.  
```
1:Sort the incoming requests in decreasing order according to the value of \(w_{r}\)
2:for each incoming request \(r\in R\) in the sorted array do
3:\(\kappa_{r}=0\) and \(Y_{b}=0\)
4:for each available BBU \(b\in B\) in the BBU pool do
5: Calculate \(\zeta_{b,r}\) using Equation (14)
6: Calculate satisfaction of running a request on BBU b
7:\(\kappa_{b,r}=q_{r}-(\zeta_{b,r}+\varpi_{r})\)
8:if\(\kappa_{r}\leq\kappa_{b,r}\)then
9:\(\kappa_{r}=\kappa_{b,r}\)
10:\(Y_{b}=b\)
11:endif
12:endfor
13: Select BBU \(Y_{b}\) for processing the request \(r\in R\)
14:endfor
```  
**Algorithm 1** First Fit Satisfaction Algorithm for Maximizing User Satisfaction  
In this FFS algorithm, the BBU is selected focusing on maximizing the value of user satisfaction or user Quality-of-Experience (QoE). It searches for that BBU which can process earlier than the QoS requirement of a particular request. The BBU which provides the maximum distance from the value of QoS of a request, \(q_{r}\) to the value of processing time, \(\zeta_{b}^{r}\), is selected for a request. A higher value of this distance assures higher satisfaction.
# BDS-3 Triple-Frequency Timing Group Delay/Differential Code Bias and Its Effect on Positioning
## 2 Data and Methods
### Triple-Frequency Positioning Model of BDS-3  
Multifrequency IF combination is commonly used in precise positioning to effectively eliminate ionospheric first-order term errors, but different linear combination coefficients result in different noise levels. UC cannot eliminate ionospheric errors, but it can use the ionospheric delay as the parameter to mitigate the ionospheric effects. This implies that more ionospheric information might be included in UC. In addition, UC is more flexible in multifrequency positioning without amplifying noise. The commonly used triple-frequency positioning model of BDS-3 includes two IF models (IF123, IF1213) and a UC model (UC123) [28].
## 4 Mechanical Properties of Geopolymer Concrete
### Factors on Workability of GPC and PCC  
One of the most significant drawbacks of geopolymer concrete (GPC) is low workability due to high viscosity and cohesion in this type of concrete. Diverse methods are employed to improve concrete workability. The simplest method to increase the workability of fresh concrete is adding water and raising the water-cement ratio. Alternatively, keeping the ratio of water to cement constantly in a controlled range and appending plasticizer or superplasticizer to concrete content can be considered the best method in terms of the improvement of workability in this kind of concrete. GPC workability as an important topic of the literature review needs to be assessed in this section. In a study by [PERSON] and [PERSON] [30], the effect of superplasticizer (SP) for the workability enhancement of fly ash-based GPC was evaluated using a slump test and mini flow results, as well as rheology behavior. The results indicated that the effect of both superplasticizers in GPC was remarkably less beneficial than that in OPC. Their efficacy on fly ash was distinct in each category. The effectiveness of Polycarbosylate (PC) on class C was more considerable compared to class F fly ash, which is attributed to high-value calcium ion participating in the content of class C fly ash. As for another type of superplasticizer, Naphthalene, investigated in this study, no significant difference associated with its effect on two types of fly ash was observed, however, the effect of this type of superplasticizer was slightly better than that of PC while testing on class F type. In addition to the results mentioned above, the performance of these two superplasticizers respective to other experimental factors was examined in other studies. [PERSON] and [PERSON] [10] have expressed a similar view in terms of concrete workability for GPC. In this study, naphthalene, melamine, and modified Polycarbosylate was investigated for increasing workability and strength in class F fly ash GPC paste which was activated by two different activators composed of 8 M sodium hydroxide solution along with a multi-compound activator, including 8 M sodium hydroxide and sodium silicate with SiO\({}_{2}\)/Na\({}_{2}\)O ratio of 2. The laboratory results demonstrated that these types of activators and superplasticizers are effective factors on workability and strength of fly ash-based GPC.
# Study on Lightweight Model of Maize Seeding Object Detection Based on YOLOv7
###### Abstract  
Traditional maize seedling detection mainly relies on manual observation and experience, which is time-consuming and prone to errors. With the rapid development of deep learning and object-detection technology, we propose a lightweight model LW-YOLOv7 to address the above issues. The new model can be deployed on mobile devices with limited memory and real-time detection of maize seedlings in the field. LW-YOLOv7 is based on YOLOv7 but incorporates GhostNet as the backbone network to reduce parameters. The Convolutional Block Attention Module (CBAM) enhances the network's attention to the target region. In the head of the model, the Path Aggregation Network (PANet) is replaced with a Bi-Directional Feature Pyramid Network (BiFPN) to improve semantic and location information. The SIoU loss function is used during training to enhance bounding box regression speed and detection accuracy. Experimental results reveal that LW-YOLOv7 outperforms YOLOv7 in terms of accuracy and parameter reduction. Compared to other object-detection models like Faster RCNN, YOLOv3, YOLOv4, and YOLOv5l, LW-YOLOv7 demonstrates increased accuracy, reduced parameters, and improved detection speed. The results indicate that LW-YOLOv7 is suitable for real-time object detection of maize seedlings in field environments and provides a practical solution for efficiently counting the number of seedling maize plants.  
YOLOv7; seedling maize; detection model; lightweight; attention models Article
# Impact of Atmospheric Pressure Microwave Plasma Treatment on Quality of Selected Spices
## 3 Results and Discussion
### Piperine Content in Black Pepper Seeds  
Piperine content (Figure 9) in the investigated black pepper seeds was equal to 5.79-6.94% d.m and was similar to the values reported previously in the literature (5.61-7.42%) [56]. Plasma treatment affected the piperine content significantly (\(p<0.05\)) only when irradiation time lasted 15 and 30 s. In these cases, its value increased by 13 and 17%, respectively. When microwave plasma application lasted 45 and 60 s, piperine content reached 5.97 and 6.11%, respectively, and the values were the same as in untreated samples from a statistical point of view. The research performed by [PERSON] et al. [29] demonstrated that cold plasma treatment, performed both directly and remotely, minimally affected piperine content. It is worth emphasizing that in the case of the cited research, the plasma was generated using either radio frequency (direct application) or microwave (remote application).  
Figure 8: Scanning electron microscope(SEM) images of plasma treated black pepper seeds.
## 6 Discussion
According to information theory and the concept of perfect secrecy, there must be zero mutual information between the plaintext and ciphertext, as well as between the ciphertext and the key. In addition to these two methods, a method of distributing the secret parts of the HPD matrix as a key can be realized. Implementing this method for distributing keys between the sender and receiver involves a few steps:  
1. Matrix definition--In this step, the sender establishes the secret HPD matrix. 2. Secret sharing scheme--In this scenario, the sender implements [PERSON]'s Secret Sharing to divide the secret into several parts [32]. 3. Distributing shares--Each share can be given to a different participant or kept in separate locations, ensuring that no single share discloses information about the original HPD matrix. 4. Embedding in cover media--The sender applies a steganographic method (such as LSB--Least Significant Bit embedding) to incorporate each share into the cover medium, making sure that the alterations do not significantly change the appearance or integrity of the medium. 5. Reconstruction and security considerations--The key and shares are well-protected from unauthorized access. It is advisable to encrypt the shares before embedding for added security. 6. Testing and validation--In this phase, the sender and receiver realize tests to verify that both the embedding and extraction processes function correctly, preserving the quality of the cover medium while enabling successful reconstruction of the secret HPD matrix based on our previous experimental tests in papers [30,31]. Dividing the HPD matrix (as stego-key) into several parts and distributing them enhances the overall system's security. If one segment is compromised, the complete key remains protected. This strategy complicates the efforts of adversaries to conduct cryptanalysis or reverse-engineering, as they would require multiple pieces of information instead of just a single key. Distributing the parts of the HPD matrix also introduces redundancy, allowing the overall secret to be reconstructed even if some parts are lost or damaged. Additionally, this method improves access control, as different key segments can be assigned to various individuals or locations, reducing the risk of unauthorized access. In collaborative settings, sharing parts of a key allows multiple contributors to participate in the steganographic process while ensuring that no single individual has full access to the key. This distribution increases the system's complexity, making it more challenging for potential attackers to grasp the entire structure or methodology used in the steganography. Using this method to distribute secret key parts significantly enhances the security and integrity of steganographic communications.
# Cyber Physical Systems Dependability Using CPS-IOT Monitoring
## 2 Related Work
### Autonomic Computing  
Autonomic computing provides the potential to transform the CPS of the future into a computing environment that can manage itself through a feedback loop using the MAPE structure initially proposed by IBM [33]. Using the MAPE structure, the CPS can be monitored through sensing, analysed through different modelling processes, and execute relevant mitigation processes through actuation when the CPS has moved away from its optimal operation. A decision-making framework for dynamically adjusting a dynamic IoT environment to changing requirements is proposed in [34]. The framework uses a combination of a finite-state machine model and a game theoretic decision-making method for extracting efficient strategies. The usability of the framework is demonstrated through a smart greenhouse-based use case. A Hierarchical fog-assisted Computing architecture (HiCH) is proposed in [35] for remote IoT-based patient monitoring systems with autonomous data management and processing performed at the edge of a network. Moving away from the conventional Observe-Decide-Act (ODA) control strategy [36], HiCH exploits and customises MAPE-K autonomic computing with an adaptive control loop that achieves monitoring in the sensor layer, analysis in the cloud layer, planning in the fog layer, and execution in the fog layer. A cloud-based middleware framework is proposed in [37] to provide a set of cloud services for self-adaptive IoT collaboration services in a cloud environment. In such an environment, domain-dependent components are separated from the layers that leverage existing middleware frameworks. Besides domain-dependent components and operations, the framework includes support for MAPE cycles on Virtual Machine and collaboration between multiple systems.  
The literature surveyed above reveals a clear research gap in terms of traffic engineering techniques and models for CPS-IoT. It also points to the state-of-the-art in autonomic computing models for CPS-IoT dependability, which is gaining momentum with a number of papers applying self-configurable techniques to the IoT domain.