esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Overview of Methodologies  
As illustrated in Figure 2, we first calculated the monthly SM and VPD based on the daily SM, T\({}_{\text{max}}\), T\({}_{\text{min}}\), and RH, then calculated SSMI and SVPDI using the gamma distribution function. Next, we obtained the spatial and temporal patterns of the photosynthetic capacity of vegetation based on the three sets of SIF data, then analyzed changes in the SIF under the conditions of different combinations of the SSMI and SVPDI. Finally, we investigated the effects of atmospheric droughts, soil droughts, and compound droughts on the SIF and analyzed the response characteristics of the SIF through a typical compound drought event.
# Safety Analysis and Condition Assessment of Corroded Energy Pipelines under Landslide Disasters
## 4 Results and Discussion
### Effect of Corrosion Dimension
In practical scenarios, it is vital to monitor the pipeline corrosion depth and assess the pipeline mechanical status according to the surface landslide displacement, ensuring the operational safety of pipelines.
# Practical Work of Three-Phased Induction Motor Modeling in a Classical Spreadsheet Software
## 2 Three-Phased Induction Motor
### Speed Variation and Variable Frequency Drives  
For a few decades, induction motors have been increasingly used in variable speed drive applications, whereas they were mainly used for fixed speed applications before. Currently, their use is estimated at around 40% of newly installed drives. A variable speed drive is an electro-technical piece of equipment supplying an electrical machine in such a way as to be able to vary its speed continuously: from standstill to its rated speed. The speed is proportional to an analog or digital electronic signal value. It consists of a rectifier followed by an inverter. The rectifier produces an almost direct current. From this direct current, the inverter (often PWM--Pulse-Width Modulated) creates a three-phase alternating voltage system, whose RMS value and frequency can be varied. Two main methods of speed control exist: scalar (or \(U/f\)) and vector control. Their major drawback is that they generate harmonic frequencies.
# Privacy-Preserving E-Voting System Supporting Score Voting Using Blockchain
## 5 The Proposed Voting Scheme  
In this section, a decentralized and self-tallying score voting system is proposed. At the start of an election cycle, the election administrator \(\mathcal{EA}\) publishes a candidate list \(\{\mathcal{C}_{1},\cdots,\mathcal{C}_{n_{c}}\}\) and defines a fixed number \(\mathcal{P}\) as the maximum evaluation score each candidate can receive. It is a necessary condition that the score assigned to each candidate should be no less than zero and no greater than \(\mathcal{P}\); likewise the sum of each score on the ballot must be equal to \(\mathcal{P}\). Our scheme, inspired by the Open Vote Network protocol [33], consists of three phases as follows. The _Setup_ phase generates the parameters for the system where the \(\mathcal{EA}\) is the entity that provides the candidate list as well as a list of eligible voters that they must authenticate. Each voter \(\mathcal{V}_{i}\) generates their secret and public key pair \(x_{i},y=g^{x_{i}}\). Then, during the _Vote_ phase, each voter \(\mathcal{V}_{i}\) sends his/her encrypted vote to the Blockchain as well as a proof that the submitted score falls within a publicly defined range \([0,P]\). To do this, we use a ZKP to prove that this condition is met. In order to accomplish this, we utilize a zero-knowledge set membership proof for each score to show that the score falls within the range without disclosing the assigned score. Finally, in the _Tally_ phase, all the ballots \(V_{i,i\in[n_{v}]}\) are tallied without the need for any entity's secret key, effectively enabling any entity to compute the results.
# Tibetan Speech Synthesis Based on Pre-Trained Mixture Alignment FastSpeech2
## 1 Introduction
Furthermore, its flexibility in modeling long-term dependencies enhances both the quality and smoothness of speech synthesis, making the FastSpeech2 model highly regarded in current research. In this paper we apply the FastSpeech2 model to Tibetan speech synthesis. But, in FastSpeech2, the external alignment tool MFA (Montreal forced alignment) [20] is utilized to ascertain the duration of Latin letters, resulting in errors and noise during the alignment of Latin letters due to the absence of distinct boundaries between different letters. To address this problem, we propose the mixture alignment FastSpeech2 model, which utilizes the idea of a mixture alignment mechanism in the linguistic encoder in PortaSpeech [21]. This model employs soft alignment at the level of Latin letters and hard alignment at the level of Tibetan characters, thereby enhancing semantic information comprehension and mitigating errors and noise attributed to the hard alignment of Latin letters. In addition, the majority of end-to-end speech synthesis systems require a substantial volume of paired high-quality speech data for model training [22]. But, the efficacy of directly applying the FastSpeech2 model to Tibetan speech synthesis is hindered by the objective lack of large-scale, high-quality Tibetan speech-text pairs. Consequently, this study pre-trains the FastSpeech2 baseline model using an English dataset, followed by transitioning the pre-trained model to Tibetan speech synthesis. The experimental results demonstrate that our proposed mixture alignment FastSpeech2 model excels in synthesizing audio quality, predicting Mel spectrograms, and achieving precise attention alignment. Furthermore, the use of pre-trained models has also contributed to some extent in enhancing the quality of synthesized speech. The main contributions of this work are summarized as follows:  
* We address the issue of misalignment caused by hard alignment at the Latin letter level by integrating the concept of mixture alignment into the FastSpeech2 model. Our proposed model, termed mixture alignment FastSpeech2, is applied to Tibetan speech synthesis, demonstrating significant improvements in alignment accuracy and synthesis quality. * Given the limited availability of Tibetan data, we advocate for the transfer of pre-trained model trained on larger datasets to the Tibetan language domain. This approach aims to enhance the naturalness of synthesized speech by leveraging the rich representations learned from abundant resources. In Section 2, we describe the constructed end-to-end speech synthesis architecture, including the model used and its improvement. Section 3 describes the experimental details and results. Finally, Section 4 summarizes the full paper.
## 4 Algorithms
### Algorithms for Score-Stability  
In this section, we describe algorithms for the generalized score-stability, hence also for score-stability.  
1. **The score-decreasing algorithm:** colleges start from a valid score vector \(\underline{t}_{0}\) (e.g., \(\underline{t}_{C}:=(M+\ 1,\ldots,M+1)\)). First, if there is a college, \(C_{i}\), that can lower its score limit without getting too many students, then \(C_{i}\) will decrease its score limit to the lowest score, such that \(C_{i}\) still gets a feasible set of students. Here, \(C_{i}\) chooses from free students and students who prefer \(C_{i}\) to their college; so, it chooses score limit \(P_{G_{i}}(E\setminus\mathcal{D}_{F}(P(\underline{t}))\). Then, we find another college, and iterate this score-decreasing step. (It is convenient to check \(C_{1}\) first, then \(C_{2}\), then all colleges one-by-one. After \(C_{m}\), we return to \(C_{1}\) again.) The algorithm terminates if no college wants to lower its score limit any more. As soon as no college can decrease its score limit, the score vector is stable. Let \(\underline{s_{C}}\) denote the stable score vector that we get by running the score-decreasing algorithm on \(\underline{t_{C}}\).  
**Theorem 5**.: _If stable score vector \(\underline{t}\) is the output of the score-decreasing algorithm with input \(\underline{t_{0}}\), where \(\underline{t_{0}}\) is valid, then \(\underline{t}\) is stable and is \(\underline{t}\), the maximum of all the stable score vectors that are not greater than \(\underline{t_{0}}\). Consequently, \(\underline{s_{C}}\) is the maximum of all stable score vectors. Furthermore, \(\underline{s_{C}}\) is applicant-pessimal._  
2. **The score-increasing algorithm:** colleges start with some critical score vector, \(\underline{t_{0}}\) (e.g., \(\underline{t_{A}}=(0,\ldots,0)\)), and keep on raising their score limits. If there is a college, \(C_{i}\), that has an infeasible set of students, then it raises the score limit to the lowest score where it becomes feasible. Therefore, it chooses \(P_{G_{i}}(F(P(\underline{t}))\). Then, another college, \(C_{j}\), increases the score limit and all colleges one-by-one. The algorithm stops if no college wants to raise its score limit. Let \(\underline{s_{A}}\) be the stable score vector the score-increasing algorithm outputs from input \(\underline{t_{A}}\).  
**Theorem 6**.: _If score vector \(\underline{t}\) is the output of the score-increasing algorithm with input \(\underline{t_{0}}\), where \(\underline{t_{0}}\) is critical, then \(\underline{t}\) is stable, and it is the minimum of all the stable score vectors that are not less than \(\underline{t_{0}}\). Consequently, \(\underline{s_{A}}\) is the minimum of all stable score vectors. Moreover, \(\underline{s_{A}}\) is applicant-optimal._  
**Theorem 7**.: _The score-decreasing and score-increasing algorithms run in polynomial time; the decreasing terminates in \(O(m^{2}n)\) steps, and the increasing stops in \(O(mn)\) steps._
# PRANAS: A Process Analytics System Based on Process Warehouse and Cube for Supply Chain Management
## 4 Applications to Process Analytics
### Process Discovery and Enhancement  
The first application is the process-oriented analytics. Process mining is a research discipline that combines business process modeling with data mining methods to convey useful results. The basic input for process mining techniques is prepared from event log, that is, records of the actual process execution. The preparation for process mining can be made through the process warehouse and the process cube. In detail, the wanted attributes can be selected from the process warehouse, and the result is designed into the process cube. Then, the process cube can support three purposes of process mining, such as process discovery, conformance checking, and process enhancement [3]. Moreover, the cube can also be used for operational support through three main tasks: Detect, predict, and recommend. For example, recommendation refers to guiding users in selecting the next proper activity over a running instance, as well as other kinds of recommendations, such as which performer had better take the next activity in terms of time or cost.  
Using the process cube, we can effectively preprocess the data. In this example process cube, we discover a subprocess model as shown in Figure 5a. After combining the discovered model with event data and metadata from the process cube, we then also apply a performer recommendation method, DTMiner, which stands for the decision tree miner [20]. This approach constructs decision trees based on event logs and recommends the best performer according to specific measures such as completion time or cost. When DTMiner is applied to a cell of interest in the process cube, the result shown in the Figure 5b can be obtained. Given a running instance, which has three completed activities, DTMiner uses historical event data to construct a decision tree, showing in red color the recommended performer for each possible path based on completion time or cost.  
Figure 4: Implementation of a process warehouse and a process cube for supporting operational data analysis in supply chain management.  
Figure 5: Two examples of process-oriented analytics using PRANAS with process mining tools. **(a)** Discovered subprocess model; **(b)** Decision tree for performer recommendation using DTMiner.
## 3 Results
### Driving Analysis of Influencing Factors
#### 3.3.1 Impact Analysis of Natural Factors on NEP
The interactions between DEM and precipitation and temperature and precipitation show a significant and strong non-linear enhancement, with explanatory powers of 0.308 and 0.299 for the vegetation carbon sink, respectively. In addition, there are strong correlations between the natural factors shown in Figure 10; especially, the correlation between precipitation, air temperature, and DEM is more obvious. Although the interaction between slope aspect and other natural factors is a non-linear enhancement relationship, its correlation is weak, and because its own explanatory power on vegetation carbon sinks is only 0.003, this also makes the combined explanatory power of the interactions with the other factors not strong. Overall, the influence of natural factors on vegetation carbon sinks is more complex, manifesting multiple interactions of factors. Based on the analysis of the parameter-optimal geographic detector, NEP is mainly affected by the interaction of DEM with precipitation and temperature, and DEM plays a dominant role in the single-factor interaction, while the interaction of DEM, temperature, and precipitation acting as a dominant factor in affecting the vegetation carbon sinks when the interaction of the factors is explored. The reason for this is that the Taihang Mountains are located in northern China, where the climate is dry and the region is relatively arid, so an increase in precipitation has a more significant effect on the growth of vegetation when DEM and air temperature are appropriate. Figure 8: Results of naturally driven single-factor probes.
# Enable Fair Proof-of-Work (PoW) Consensus for Blockchains in IoT by Miner Twins (MinT)
## 5 Experimental Study
### Experimental Results
#### 5.2.1 SSA-Based Detection on Static Single Miner Violation  
In this scenario, one dishonest miner uses twice as much CPU power as the assigned amount at \(t=200\) s. Figure 2(a) presents the network level observation at the fog server. The blue line is the average CPU usage for all 16 miners in this blockchain network, and the red line is the \(w_{n}\) value calculated using SSA algorithm as the score. The green line is the threshold \(h=0.607\), which is computed with \(t_{\alpha}=1.2815\). As shown by Figure 2(a), the fluctuation in the average CPU utility incurs a low peak in the distance score. However, applying the SSA algorithm on each miner twin individually avoids the false negative. Figure 2(b) shows that a significant peak is observed at \(t=200\) s.  
We also studied the impacts of different selections of the SSA parameters varying \(l\) and \(q-p\) combination. Figure 2(c) shows the consequence of increasing the value of \(l\) from 4 to 8 but with the same matrix size. The larger \(l\) leads to a more noise part with the signal; therefore, it would be more difficult to find a change in the signal time series. If the \(l\) is too small, which would cause underfitting, we might miss some part of the signal. Due to limited space, the figure is not included here.  
Meanwhile, the matrix size \(q-p\) also has significant impact on the detection distance score. Figure 3d shows that, by increasing the value of \(q-p\) to 24 while \(l=4\), the distance (red) line is smoother than in Figure 3b.
## 4 Discussion
The overexpression/altered expression of PTEN protein has been noticed in tumors [37]. Several studies have suggested a role for PTEN in hepatic injury [38; 39], but the expression pattern of PTEN protein among different groups was statistically insignificant in the current study. Mitochondria are the major source of endogenous reactive oxygen species, and altering mitochondrial homeostasis is a known mechanism for toxic compounds, including DEN [40; 41], EGCG treatment significantly improves mitochondrial health and number as determined by our TEM study; several studies [42; 43; 44] with EGCG are in accordance with our study. Further studies are needed to fully investigate this mechanism. Previous study have suggested that PARP is a family of proteins involved in a number of cellular processes including DNA repair and apoptosis [45]. This has been evidently established by an increase in apoptotic nuclei upon DEN-induced liver injury. However, EGCG decreases the presence of apoptotic nuclei as demonstrated by the TUNEL assay. Therefore, the present data advocate that administration of EGCG plays a role in the reduction of liver injury-related cell apoptosis, possibly by decreasing cleaved caspase-3 and cleaved caspase-9 levels. Thus, the accumulation of DEN in the liver caused several damaging effects, including marked pathological changes in liver enzyme activities, generation of reactive oxygen species, and DNA damage, as well as apoptosis. These effects were reversed by administration of EGCG. Additionally, EGCG administration displayed a clear effect on the cell cycle, accompanied by a decrease in the sub-G1 population (indicating apoptosis) and a significant increase in S or G2/M (indicating cell proliferation), the mechanism of which needs to be further elucidated. A previous finding was in accordance with the current finding and the study reported negative regulators of the protein kinases and cyclins, thus arresting the cell cycle at the eG\({}_{0}\)/G\({}_{1}\) phase [46], and EGCG, a chief compound of green tea, inhibited DNA synthesis and arrested the cell cycle at the G0/G\({}_{1}\) phase [47]. In conclusion, the result of the current study demonstrates that EGCG treatment reduced the amount of DEN-induced hepatic injury by decreasing mitochondrial oxidative stress and cellular apoptosis. Furthermore, it was also revealed that EGCG inhibits CRP, TNF-\(\alpha\), and IL-6 production and suppresses the inflammation process. Hence, EGCG can be used as a plausible therapeutic tool against liver injury and other liver-associated diseases. Conceptualization, S.A.A. and A.H.R. ; Formal analysis, M.A.A. ; Methodology, H.M.A. ; Supervision, H.M.A. ; Validation, A.A.K. ; Writing--original draft, S.A.A. and A.H.R. ; Writing--review & editing, M.A.A., A.A.K. and A.H.R. This work was funded and supported by Qassim University Research Deanship Grants # cams1-2018-1-14-S-3360. There are no conflict of interest regarding this study. The data used to support the findings of the current study are included within the article.
## 5 Conclusions  
The key objective of this work was to evaluate the wear rate and friction coefficient for ductile cast iron. In order to perform the sliding duration, load, and speed conditions, three different microstructures were chosen. These samples were composed of bainitic, ferrite, and perlite plus ferrite. Additionally, the ANN model based on the obtained data for the experimental testing was constructed to estimate the coefficient of friction utilizing two structures. The research findings can be summarized in the following way:  
* The COF decreased as the sliding speed increased under a constant load. Moreover, the COF displayed a similar pattern at a constant sliding speed under a high load;
* There was a reduction in the wear rate as the sliding speed increased. To be more precise, an increase in sliding speed from 70 cm/s to 114 cm/s caused a decline in the wear rate from (8.74 to 0.95) \(\times\) 10\({}^{-8}\) (g/cm) at the start of the experiment and from (9.8 to 1.82) \(\times\) 10\({}^{-8}\) (g/cm) at the end of the experiment;
* The wear rate increases with the increase in the applied load at constant sliding speed conditions due to the plastic deformation between the two surfaces, thus increasing the real contact area;
* The wear rate value was found to be lower for the bainite structure than the ferrite structure due to the higher hardness of the bainite structure;
* The ANN models with single and double hidden layers are able to accurately predict the friction coefficients;
* The ANN containing a single hidden layer was found to be more accurate than the that containing a double hidden layer. On the other hand, the best performance during the validation stage was recorded at an MSE of 0.012346 at epoch 20 in the single layer and 0.03833 at epoch 8 in the double hidden layer.  
Since ductile cast iron is involved in a vast range of tribological applications in the industry, the findings of this research can be used by other investigators to forecast the wear rate and coefficient of friction for potential usages. In addition, the proposed model (ANN) is a simple architecture that researchers can develop as future work to predict the roughness of the sliding surfaces or the number of nodules for ductile cast iron.  
Figure 10: Comparisons between the predicted and actual friction coefficients for (**a**) single and (**b**) double hidden layers.  
**Author Contributions:** Conceptualization, A.A.K. and M.M.H.; methodology, A.A.K.; software, M.M.H.; validation, A.A.K. and M.M.H.; formal analysis, A.A.K.; investigation, A.A.K. and M.M.H.; resources, A.A.K.; data curation, A.A.K.; writing--original draft preparation, A.A.K.; writing--review and editing, A.A.K. and M.M.H.; visualization, A.A.K.; supervision, M.M.H.; project administration, A.A.K.; funding acquisition, A.A.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## Materials and Methods
### Measurements and Data Processing
#### 2.2.2 Driving Performance  
To assess driving performance related to longitudinal and lateral control, the following parameters were calculated from a 10-s segment of DS recording data at a sampling rate of 60 Hz. Vehicle velocity, longitudinal acceleration, offset from lane center (lateral position), steering wheel acceleration (SWA), standard deviation of lateral position (SDLP), time headway (THW), and time to lane crossing (TLC) were recorded. SWA, which is utilized as the evaluation index of steering smoothness [26], was calculated from steering angle data. SDLP, which is used as the evaluation index of steering control [27], was calculated from lateral position data. THW is defined as the difference between the time when the preceding vehicle arrives at a point on the road and the time when the test vehicle arrives at the same point. TLC is defined as the time required to reach the edge of the lane, assuming that the vehicle velocity and steering angle are constant at a certain point while driving on a road [28].
## 3 Results
### Uncertainty of Presented Results  
Previous studies have shown that the simulated heat loss signature was 5% below the measured one presented in Figure 4a [47], and that the heat outputs given in Figure 4b agreed with the laboratory measurements within \(\pm\)2% [28]. A previous report also concluded that even the most advanced and detailed calculation models for the prediction of heat pump performance had an uncertainty of up to 10% [48]. This implies that the uncertainty of the HSPF-values presented in Figure 4c should also be within \(\pm\)10%, at best. According to the Swedish Energy Agency [49], in 2008 the combined CO\({}_{2}\) emissions for electricity generation in Scandinavian countries amounted to 75-100 kg per MWh. This means that there is an uncertainty in the CO\({}_{2}\) emissions shown in Figure 4d, which is within \(\pm\)15% of those of the presented graph. In addition, for hydraulically smooth pipes, the uncertainty in the prediction of the pipe friction factor is estimated to \(\pm\)5% [50]. This means that predictions of pressure loss and pumping power need in Figures 5 and 6, respectively, should also fall approximately within that range. Finally, according to the prevailing norm, the uncertainty of the preset valve throttling (K\({}_{\rm v}\)-value) must not exceed 10% [32]. Consequently, the uncertainty of the pressure loss shown in Figure 9 should not be greater than \(\pm\)10%. Taking all these factors into account, it can be concluded that the results presented in this study fall within commonly accepted engineering levels of accuracy.