esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Results
In particular we can notice from Figure 6 how the increase in \(C_{QoS}\) leads to a rapid increase in the total network cost in the SPRPA solution because it is not able to limit the total QoS degradation cost as highlighted in Figure 8. Conversely the APRPA solution is able to apply the appropriate countermeasures, to over-allocate the processing resource and to reduce the total QoS degradation cost as highlighted in Figures 7 and 8 where we notice an increase in total resource allocation cost (due to the resource over-allocation) and a decrease in total QoS degradation cost. Finally the APRPA algorithm is compared with our previous ATPA solution [5; 6] in Figure 9 in which traffic prediction is only performed. We can observe the better performance of APRPA with 10% gain. The main reason is that APRPA performs a prediction on processing capacities requested by aggregated traffic; convesely ATPA performs the forecast on the single SFC only. Figure 7: Comparison of the SPRPA and APRPA algorithms in terms of total allocation Cost as a function of the QoS degradation cost \(C_{QoS}\) when the MI duration \(T_{m}\) equals 10 min and the allocation time \(T_{a}\) is chosen to be 10 min (\(S=1\)), 30 min (\(S=3\)) and 60 min (\(S=6\)). Figure 8: Comparison of the SPRPA and APRPA algorithms in terms of total QoS degradation Cost as a function of the QoS degradation cost \(C_{QoS}\) when the MI duration \(T_{m}\) equals 10 min and the allocation time \(T_{a}\) is chosen to be 10 min (\(S=1\)), 30 min (\(S=3\)) and 60 min (\(S=6\)). Figure 9: Comparison of the ATPA and APRPA algorithms in terms of total Cost as a function of the QoS degradation cost \(C_{QoS}\) when the MI duration \(T_{m}\) equals 10 min and the allocation time \(T_{a}\) is chosen to be 10 min (\(S=1\)), 30 min (\(S=3\)) and 60 min (\(S=6\)).
# Enhancing Recommendation Accuracy of Item-Based Collaborative Filtering via Item-Variance Weighting
###### Abstract  
Recommender systems (RS) analyze user rating information and recommend items that may interest users. Item-based collaborative filtering (IBCF) is widely used in RSs. However, traditional IBCF often cannot provide recommendations with good predictive and classification accuracy at the same time because it assigns equal weights to all items when computing similarity and prediction. However, some items are more relevant and should be assigned greater weight. To address this problem, we propose a niche approach to realize item-variance weighting in IBCF in this paper. In the proposed approach, to improve the predictive accuracy, a novel time-related correlation degree is proposed and applied to form time-aware similarity computation, which can estimate the relationship between two items and reduce the weight of the item rated over a long period. Furthermore, a covering-based rating prediction is proposed to increase classification accuracy, which combines the relationship between items and the target user's preference into the predicted rating scores. Experimental results suggest that the proposed approach outperforms traditional IBCF and other existing work and can provide recommendations with satisfactory predictive and classification accuracy simultaneously.  
recommender system; item-based collaborative filtering; predictive accuracy; classification accuracy; item-variance weighting Article
## 2 Seismic Resilience Evaluation Framework
### Seismic Resilience Index Assessment Procedure  
Resilience (\(Re\)) is a term used to describe the ability of tunnels to recover after being subjected to seismic damage. The most widely used analytical definition is adopted in this work. The following formula can be used to calculate \(Re\)[10, 13]:  
\[Re=\frac{\int_{t_{0}}^{t_{h}}Q(t)\mathrm{dt}}{t_{h}-t_{0}}, \tag{6}\]  
where \(Q(t)\) represents the tunnel functionality at time \(t\) under the functionality recovery function, \(t_{0}\) is the earthquake occurrence time (\(t_{0}\) is set as 0 in this study), and \(t_{h}\) is the investigated time point. As defined in Equation (3), the resilience index (\(Re\)) can be graphically illustrated, as shown in Figure 7.  
Figure 6: An illustrative case of using the fragility function for a given \(IM\) to calculate the direct seismic loss of tunnels.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Damage States (DS)** & \(R_{i}\)**, Repair Cost (\% Replacement Cost)** \\ \hline No damage & 0\% \\ Minor damage & 10\% \\ Moderate damage & 25\% \\ Extensive to complete damage & 75\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Repair ratio of tunnels under different damage states [28].  
The seismic resilience index (\(Re\)) of the examined tunnel exposed to a certain _IM_ level can be derived using the fragility and restoration functions. The latter describes the speed of restoration of functionality for the different \(DS\)s and the probabilities of occurrence for each \(DS\), as discussed in the following sections. Hence, the tunnel functionality at time \(t\) can be computed as Equation (7) [14; 15]:  
\[Q(t)=\sum_{j=1}^{4}Q\big{[}DS_{j}\big{|}t\big{]}P\big{[}DS_{j}\big{|}IM\big{]}, \tag{7}\]  
where \(Q[DS_{j}\big{|}\,t]\) represents the function to restore the functionality of the tunnel at time \(t\) and damage state \(j\), and \(P[DS_{j}\big{|}\,IM]\) stands for the conditional probability of a damage state \(j\) for an event with a given _IM_, which can be calculated by Equations (4) and (5).
## 3 Numerical Simulations
### Simulation Model
#### 3.1.2 Validation of the Model on Frequency  
The mode shapes of the modal analysis are shown in Figure 3. It can be seen that the first and third models correspond to the Z direction, which is horizontal, and the second and fourth correspond to the vertical direction. And following that, the modals in the vertical direction are greater. It can be seen that the fifth, sixth, and eighth modals are in the vertical direction; only the seventh modal is in the horizontal direction.  
Figure 3: (**a**) The first-order modal; (**b**) the second-order modal; (**c**) the third-order modal; (**d**) the fourth-order modal; (**e**) the fifth-order modal; (**f**) the sixth-order modal; (**g**) the seventh-order modal; and (**h**) the eighth-order modal.  
Figure 2: The geometry dimension and FEM model of Chenxi Bridge (m). (**a**) The geometry dimension of Chenxi Bridge; (**b**) the section dimension for the beam (mm); and (**c**) the FEM model of Chenxi Bridge.  
The modal analysis results using the finite element model are presented in Table 1. A comparison with the measured frequencies reveals that the first two frequencies are almost the same. According to the root mean squared error calculation for the first four frequencies, the values of the RMSE are 3.6% and 4.2% for the eight frequencies, which shows that the two data can be seen as the same and shows the validation of the FEM model.
## 5 Potential Challenges
### Hybrid NOMA  
5G and next-generation mobile networks are envisioned to run on a mix of MA techniques instead of the absolute replacement of the present OMA approach by NOMA. System performance and capacity advantages from NOMA are maximized only when paired users have dissimilar channel gains. Nevertheless, in cases where users had comparable channel gains, the literature's pairing strategies resulted in a net drop in throughput. Instead of abandoning these users, it is preferable to employ or create more sophisticated DL technologies to help improve their performance.
## 3 Experiment Results
### Repeatability Experiment  
The full-circle measurement of the six DOF motion errors of the target axis was repeated nine times. At the same position, half of the fluctuation range of the nine measurements was defined as the repeatable value of this position. The maximum value of all the 13 positions was selected as the repeatability value of each motion error measured by the device.  
The original data consist of various errors, and the real motion errors of the target axis can be obtained through error compensation. First, the initial values of the first position were subtracted from all the original data. Thus, the constant influence of errors, such as installation errors and manufacturing errors, could be eliminated. Then, the error crosstalk was compensated for according to the above equations. Finally, the installation errors due to the coaxiality deviations of the two axes were compensated for by function fitting. The final results of the repeatability experiment are shown in Figure 6.  
The repeatability value of the angular positioning error was 28.0\({}^{\prime\prime}\). The repeatability values of tilt motion errors around the \(y\) axis and \(x\) axis were 2.8\({}^{\prime\prime}\) and 3.9\({}^{\prime\prime}\), respectively. The repeatability value of the radial motion error along the \(y\) axis was 2.8 \(\upmu\)m. The repeatability value of axial motion error was 0.5 \(\upmu\)m. The radial motion error along the \(x\) axis was 1.3 \(\upmu\)m. As shown in Figure 6, the results at 0 and 360 degrees are not the same. There are mainly two reasons for this. One is that our device has repeatability values, which result in the noncoincidence of the measuring points. Our experimental results show that the maximum repeatability value of the angular motion error was 28.0\({}^{\prime\prime}\). The other is that the measured axis itself has a large repeatability value. The positions of 0 degrees and 360 degrees are the same in theory, but they are not in fact. Due to the characteristics of the measured axis, some motion errors of the measured axis near the 0 point vary greatly. Thus, even a small angular positioning error will lead to a large change in motion error.  
The error model helps us not only obtain error data much closer to the real value but also compensate for the influence of error crosstalk on the repeatability measurement. As shown in Table 3, the repeatability values of translational motion errors were significantly improved by removing the fluctuations introduced by other angular motion errors.  
Figure 5: Measurement device.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Motion Errors**} & **Repeatability after** & **Repeatability after** & **Percentage** \\  & **Subtracting the Initial Value** & **Compensating for the Crosstalk** & **Decline** \\ \hline Radial error along the \(y\) axis & 4.1 \(\upmu\)m & 2.8 \(\upmu\)m & 32\% \\ Axial error & 0.8 \(\upmu\)m & 0.5 \(\upmu\)m & 38\% \\ Radial error along the \(x\) axis & 4.5 \(\upmu\)m & 1.3 \(\upmu\)m & 71\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Repeatability changes before and after crosstalk compensation.
# Biofilm Lifestyle in Recurrent Urinary Tract Infections
## 4 Biofilm Formation Patterns in Some Clinically Important Uropathogens
### Staphylococcus spp.  
_Staphylococci_ are divided into two major groups: the coagulase-positive staphylococci which are mostly pathogenic, and the coagulase-negative staphylococci (e.g., _S. epidermidis_ and _S. saprophyticus_), which are frequent inhabitants of the human gastrointestinal tract, cervix, urethra, vagina, perineum, and rectum [90; 91]. One infamous member of the coagulase-positive staphylococci is _S. aureus_, which is an opportunistic inhabitant of the nasopharyngeal epithelium and axillae skin and flips easily to pathogenic form. Coagulase-negative staphylococci are normally less virulent and express fewer virulence factors. _Staphylococcus saprophyticus_ is a common uropathogen associated with 10-20% of uncomplicated UTIs in sexually active young women [90]. This bacterium has high ability to survive in hostile toxic conditions, in addition to a remarkable tolerance to disinfection and heavy metal treatment. _S. aureus_ represents one of the most common causes of infection at all body tissues. _S. aureus_ has been associated lately with high levels of multidrug resistance, especially with the recent increase in prevalence of methicillin resistant _S. aureus_ (MRSA) and vancomycin resistant _S. aureus_ (VRSA), which are challenging to eradicate and usually associated with high mortality rates [92]. _S. aureus_ has many virulence factors: (i) surface adhesins that allow for adhesion and biofilm initiation, (ii) surface structures that allows evading host immunity and escaping phagocytosis (capsular polysaccharide and immunoglobulin binding protein A); and (iii) toxins that induce tissue damage and immune evasion, e.g., leukocidin, \(\alpha\)-toxin, \(\beta\)-toxin, enterotoxins and toxic shock syndrome toxin. _Staphylococci_ are commonly associated with high incidence of biofilm formation that is mediated through the expression of a clumping factor which has high affinity for fibrinogen and fibrin proteins. This factor promotes attachment to blood clots, injured tissues, fibrinogen-coated catheters, and indwelling medical devices [91; 92; 93; 94]. _Staphylococci_ biofilms are problematic to eradicate with antibiotics alone, and often require removal of the device [92; 93].
# Research on a Mongolian Text to Speech Model Based on Ghost and ILPCnet
## 2 Para-WaveNet Mongolian Speech Synthesis Model  
In recent years, ParaNet has been widely used for English and Chinese speech syntheses. Since Mongolian language knowledge is more complex than English writing and pronunciation, some complex pronunciation rules require a large amount of training data to be learned. Especially when certain rules appear less frequently in the training data, it is difficult for the neural network to fully learn these rules. Coupled with the lack of a Mongolian audio corpus, this leads to errors in synthesized speech. If ParaNet is directly used in Mongolian speech synthesis, this will result in word skipping and wrong words when input directly in the form of Mongolian text. Phonemes are the most basic units of pronunciation, such as initial consonants and finals in Mongolian, which themselves reflect pronunciation attributes. Therefore, using phoneme sequences as inputs can effectively avoid these error problems.  
In order to solve the abovementioned problems, this paper improves the front-end module of the ParaNet acoustic model and adds the Mongolian character to the phoneme module. By inputting the converted phoneme sequence into the acoustic model, the pronunciation of the text can be simulated more accurately, and the <phoneme sequence, speech > data pair can be used to more effectively match the sound spectrum of the speech. These greatly reduce the word error rate of synthesized speech, further improving the sound quality of synthesized Mongolian speech. The overall structure of the model is shown in Figure 1.  
Figure 1: Model of Mongolian text to speech based on improved Para-WaveNet.  
Traditional Mongolian letters have different writing forms at the beginning, middle, and end of the word, for example, the beginning of the word is expressed as \"\(\pi\)\", the middle of the word is expressed as \"\(\pi\)\", and the end of the word is expressed as \"\(\circ\)\". This change in writing increases the complexity of training the Mongolian speech synthesis model and the generated speech also has problems, such as word skipping and missing words. Therefore, in order to solve the complex writing problems in Mongolian, in the front-end module of Mongolian character conversion into phonemes, Mongolian characters are first converted into their corresponding Latin forms [14], and then the Latin-form characters are converted into phonemes through preprocessing. Among them, the process of converting Latin characters into phonemes is called grapheme to phoneme (G2P). The processing flow is shown in Figure 2.  
In the vocoder part, the WaveNet vocoder is used to generate the final waveform and synthesize Mongolian speech. WaveNet is a type of convolutional neural network (CNN). In WaveNet, the CNN takes the original signal as the input and outputs one synthetic sample at a time by sampling from the SoftMax distribution of the signal value.
# Relating Sentinel-1 Interferometric Coherence to Moving Events on Grasslands
## 3 Results and Discussion
In the following two subsections, we analyse the impact of precipitation and time separation on the measured coherence in cases when a moving event is known to have happened. These analyses will help to understand the general effects regardless of specific knowledge about species and soil conditions.
## 3 Results and Discussion
### Toxicity Tests in Laboratory
#### 3.2.2 Microtox(r) Bioassay  
The highest inhibition of bioluminescence, which corresponds to an IC\({}_{50}<1000\) mg L\({}^{-1}\) dry weight, is shown in the samples collected at SE (Figure 2b). Therefore, this station was only under the limit assumed for sediment toxicity (1000 mg L\({}^{-1}\) dry weight) according to the Canadian standards [23]. On the other hand, the sediments obtained from the RSP, and used as a control station, showed the highest values of IC\({}_{50}\) (12,186.67 mg L\({}^{-1}\) dry weight). No significant differences (\(p<0.05\)) were observed between each station and the control.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Element** & **AR** & **AL** & **SE** & **RSP** \\ \hline As & \(\mu\)g/g & 10.22 & 9.77 & 9.84 & 7.14 \\ Cd & \(\mu\)g/g & 0.27 & 0.26 & 0.26 & 0.13 \\ Co & \(\mu\)g/g & 8.63 & 7.73 & 7.70 & 5.82 \\ Cu & \(\mu\)g/g & 37.42 & 35.37 & 35.51 & 18.00 \\ Fe & \(\mu\)g/g & 23,990 & 21,920 & 22,140 & 15,239 \\ Ni & \(\mu\)g/g & 25.42 & 23.39 & 23.56 & 14.00 \\ Pb & \(\mu\)g/g & 29.65 & 27.30 & 31.73 & 22.30 \\ Zn & \(\mu\)g/g & 75.89 & 66.79 & 69.65 & 49.00 \\ OC & \% & 1.18 & 1.06 & 1.16 & 0.83 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summarized results of chemical analyses in sediments from the studied stations (Adapted from [55]).
## 3 Results and Discussion
### Post-Harvest Evaluation of Citral-Treated Table Grapes during Storage
oryzae_ spoilage in table grapes under temperature conditions higher than the usually used to preserve the product and simulating an accidental cold chain breakage. Citral was also more effective than other molecules identified in EOs to preserve grape quality; only the combination of modified atmosphere packaging (MAP) and amounts of eugenol, thymol, or menthol (0.5 mL) higher than those used in this paper for citral successfully counteracted the microbial decay of grape samples (cv. Crimson Seedless) [56]. Figure 3: Inoculated table grapes (cv. Italia) packed in HDPE bags with different citral doses (A equal to MIC value of 0.0125 \(\upmu\)L/cm\({}^{3}\) and B equal to 4-fold MIC value of 0.05 \(\upmu\)L/cm\({}^{3}\), respectively) and stored at 12 \({}^{\circ}\)C and 85% relative humidity for 15 days. CTR: control without citral. Bars are means \(\pm\) standard deviations (\(n=3\)). Figure 2: _R. oryzae_ ITEM 18876 decay incidence percentages on the inoculated table grapes (cv. Italia) packed in HDPE bags with different citral doses (A equal to MIC value of 0.0125 \(\upmu\)L/cm\({}^{3}\) and B equal to 4-fold MIC value of 0.05 \(\upmu\)L/cm\({}^{3}\), respectively) and stored at 12 \({}^{\circ}\)C and 85% relative humidity for 15 days. CTR: control without citral. Bars are means \(\pm\) standard deviations (\(n=3\)). Bars with different lowercase letters represent significantly different averages (\(p<0.0001\); [PERSON]’s test; \(F(8.18)=89.297\), \(p=6.732\times 10^{-13}\)). Recent efforts in \"green technology\" development aimed at replacing conventional and not eco-friendly methodologies have allowed the increase in yields and the stability of EOs and their components, such as citral [57; 58]; thus, taking into account the results presented in this paper, the latter could be successfully applied in the development of antimicrobial and or edible packaging without economic disadvantages and under a circular economy. The formulation of natural food ingredients that are alternative to synthetic compounds used to preserve grapes' shelf life is also a feasible and reliable process.
## 4 Results and Discussions
### Comparison of Uncontrolled and Controlled Responses  
Figure 9a, Figure 10a illustrate the time history of the acceleration responses of the cabinet subjected to two specific records, which characterized for high-frequency (Pohang) and low-frequency (Impevall) earthquakes. As seen in these figures, the significant reductions in acceleration response can be achieved with a TMD system. Thus, the stability of the electric cabinet can be improved to protect the installed electrical devices. Moreover, the elastic response spectrum analysis of 5% viscous damping is also performed, as illustrated in Figure 9b, Figure 10b. The maximum acceleration in the cases of using TMD can be decreased by up to 30% compared with those from the without TMD case.  
Figure 8: Response spectra (5% viscous damping) of selected ground motions: (**a**) High-frequency motions; (**b**) low-frequency motions.  
Figure 10: Electric cabinet’s performance under Impevall earthquake: (**a**) Acceleration response; (**b**) response spectra.  
Figure 9: Electric cabinet’s performance under Pohang earthquake: (**a**) Acceleration response; (**b**) response spectra.
## 5 The Proposed ECPSO-MTP for Data Clustering
### Test Datasets  
Test datasets, including artificial and UCI datasets, are commonly used to testify to the effectiveness of clustering approaches. In this comparison experiment, eight datasets, consisting of two artificial and six UCI datasets, have been previously reported and adopted as benchmarks in research. Two artificial datasets, i.e., Data_9_2 and Square4, are generated manually by existing literature [64]. Six UCI datasets, i.e., Iris, Newthyroid, Seeds, Yeast, Glass, and Wine, are from the UCI Machine Learning Repository [65]. Additional details of these test datasets are briefly described in Table 1.
# Prediction of Grain Porosity Based on WOA-BPNN and Grain Compression Experiment
## 5 Analysis of Prediction Results
### Experimental Validation of the WOA-BPNN for Porosity Prediction Model  
#### 5.3.1 Grain Cell Box Experiment  
To validate the effectiveness of the established WOA-BPNN model in practical applications, the experimental data from the grain cell box experiment were input into the WOA-BPNN model. Subsequently, the predicted porosity values from the WOA-BPNN model were compared with the experimental values. The grain cell box experiment is depicted in Figure 12, with the internal dimensions measuring 600 mm \(\times\) 600 mm \(\times\) 600 mm. During the experiment, vertical pressure is applied to the storage material inside the box by a rubber airbag, and an external air pump provides stable gas pressure for the airbag. A 2-mm thick plexiglass plate is placed between  
Figure 11: The regression of the WOA–BPNN: (**a**) Based on the training set; (**b**) Based on the validation set; (**c**) Based on the testing set; (**d**) Based on the complete dataset.  
Figure 10: The training stage of the WOA–BPNN.  
the material and the rubber airbag, and the sidewalls around the airbag are coated with a thin layer of petroleum jelly to minimize friction. This setup ensures that the bottom of the airbag can evenly distribute the load on the top surface of the entire grain pile.  
The grain cell box experiment was conducted to determine the porosity of wheat as a storage material. The wet base moisture content of the wheat pile was 10.89%, the initial porosity (the porosity of the wheat in the natural stacking state at a vertical pressure of 0 kPa) was 0.429, and the particle density was 1250 kg/m\({}^{3}\). The porosity of wheat was measured under vertical pressures of 0 kPa, 25 kPa, 50 kPa, 75 kPa, 100 kPa, 125 kPa, and 150 kPa, resulting in seven sets of experimental data for validation.