esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Total Phenolic Content (TPC) and Total Flavonoid Content (TFC)  
#### 3.3.1 Total Phenolic Content (TPC)  
Figure 4 shows the effect of adding CP and PP on the bread's total phenolic content (TPC). Carrots and pumpkins are raw products that are particularly rich in bioactive compounds, including carotenoids, phenolic compounds, terpenoids, and sterols [67]. The results indicate that the addition of both CP and PP affects the total phenolic content of the bread (\(p<0.05\)). While wheat bread without additives contained 2.65 mg/g of phenolic compounds, bread with 10%, 20%, and 30% CP contained 6.67, 18.28, and 26.11 mg/g, respectively. The TPC of bread with a 10% PP and CP addition did not differ; however, significant differences in amounts were noted with greater additions of bread powders.  
Bread enriched with 20% CP contained 30% more phenolic compounds than bread with the same amount of PP. [PERSON] et al. [68] indicated a higher TPC in wheat bread enriched with 20% PP (16.16 mg/g). The differences in content may have been mainly due to the different species and origin of the pumpkin, as well as the different methods used to prepare PP (freeze-drying vs. traditional drying). [PERSON] et al. [69] showed in their study that PP obtained by freeze-drying contained significantly fewer total phenolic compounds than those obtained by the conventional drying method (at 40 to 80 \({}^{\circ}\)C). When phenolic compounds were analyzed, it was shown that treatment at higher temperatures and shorter drying times produced a dried product with a higher content of phenolic compounds [69]. [PERSON] and [PERSON] [70] also report that bioavailable phenolic content is increased with conventional drying compared to freeze-drying.  
[PERSON] et al. [71] reported a significant increase in the content of phenolic compounds in bread enriched with freeze-dried carrot pomace powder, thus recognizing carrots as a valuable raw material for increasing the nutritional value of wheat bread. In our study, a 30% addition of PP to bread resulted in an almost tenfold increase in the content of phenolic compounds compared to the control bread (\(p<0.05\)).
## 3 Methodology
### Models Used  
As previously mentioned, the following deep learning models were used to address the problem. Below, the main technologies used in each predictive model are listed for comparison with our proposed model:  
1. LSTM [18]: Long Short-Term Memory (LSTM) networks are a type of Recurrent Neural Network (RNN) designed to capture long-term temporal dependencies in sequential data.
2. CNN+LSTM [19]: This hybrid architecture combines Convolutional Neural Networks (CNNs) for extracting local spatial features with LSTM networks for modeling temporal dependencies, making it suitable for multivariate time series prediction.
3. GAT [20]: Graph Attention Networks (GATs) apply attention mechanisms to assign adaptive weights to neighboring nodes in a graph, enhancing predictive performance in structured data scenarios.
4. GraphSAGE [21]: GraphSAGE is a graph learning method that generates node embeddings by aggregating features from neighboring nodes, suitable for inductive learning.
5. GraphSAGE+LSTM: This combined model integrates GraphSAGE for capturing spatial relationships within a graph and LSTM networks for modeling temporal dynamics, offering a spatiotemporal solution for sequential graph-structured data.
6. Transformer [22]: The Transformer architecture leverages self-attention mechanisms to model long-range dependencies without relying on recurrence. It allows for the parallel processing of sequences and has demonstrated strong performance in tasks such as natural language processing, machine translation, and time series forecasting.
# Enhanced Scheduling of AI Applications in Multi-Tenant Cloud Using Genetic Optimizations
## 2 Problem Definition and the Basic Model  
### Overview of GPU Utilization in Multi-Tenant Environments  
GPUs are costly resources when compared to CPUs and other computing resources. In multi-tenant environments executing ML workloads, it is common to encounter significant underutilization of GPUs. Reports from various industries indicate that the average GPU utilization often remains below 50%, which poses substantial challenges in resource planning within large-scale AI server clusters [12; 13]. Notably, in Alibaba's cloud machine learning platform, PAI, approximately 76% of all GPUs show an average utilization of less than 10% [22], highlighting a systemic issue where many training tasks do not fully leverage the capabilities of modern GPUs [23; 24].
# Weather Radar in Complex Orography
## 3 Challenges
### Maintenance and Operation  
The second category of challenges specific to mountainous regions concerns operation and maintenance. In most cases, radar sites on mountains are unmanned, and access is not guaranteed all year round. Automatic monitoring and the remote control of both the radar hardware and building technology are mandatory. The possibility of monitoring several components of the transmitter, antenna system, receiver, heating, ventilation, door lock status and house climate allows for the early detection of errors and appropriate preparation measures prior to a maintenance trip to the site. It is good practice to have maintenance tools, measurement equipment and a stock of spare parts on site.
## 3 Methods
### Performance Evaluation  
To evaluate the performance of the ML regression models, the root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (\(R^{2}\)) between the measured and predicted values were used to assess the performance of the models. The RMSE, MAE and \(R^{2}\) are calculated as follows:  
\[RMSE(y,\,\hat{y})=\sqrt{\frac{1}{n_{samples}}\sum_{i=0}^{n_{samples}-1}(y_{i}- \hat{y_{i}})^{2}} \tag{14}\]  
\[MAE(y,\,\hat{y})=\frac{1}{n_{samples}}\sum_{i=0}^{n_{samples}-1}|y_{i}-\hat{y_ {i}}| \tag{15}\]  
\[R^{2}(y,\,\hat{y})=1-\frac{\sum_{i=0}^{n_{samples}-1}(y_{i}-\hat{y_{i}})^{2}}{ \sum_{i=0}^{n_{samples}-1}(y_{i}-\overline{y})^{2}} \tag{16}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
\begin{tabular}{c} 1 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 8: Parameter settings to determine the optimal hyperparameters for the gradient boosting regression tree (GBRT) model.  
where \(\overline{y}=\frac{1}{n_{samples}}\sum_{i=0}^{n_{samples}-1}y_{i}\), \(\hat{y}_{i}\) is the estimated cotton LAI value, \(y_{i}\) is the measured cotton LAI value and \(n_{samples}\) is the number of validation datasets. The higher the \(R^{2}\), the smaller the RMSE and MAE, and thus the higher the model precision and accuracy.
# Methodology for Obtaining High-Quality Speech Corpora
## 4 Assessing the Quality of Speech Corpora
### Technical Parameters of Speech Recordings
The use of consumer-grade devices for speech recording can lead to significant differences in the acquired voice data compared to recordings obtained using high-quality microphones because of differences between the recording devices, and even such basic properties as amplitude and fundamental frequency are affected [87], not to mention other sound features. Vocal microphones of various characteristics can be easily found on the market at various prices; if a directional pattern is selected, speakers should be recorded on-axis, in the pickup direction, where the microphone is the most sensitive. Additionally, pop filters can be used. Speech data are sometimes recorded from phone call recordings or from voice call apps. Since such apps are very popular, and speech recordings can be easily obtained this way, they can also be included in speech corpora. However, users should be aware that these recordings contain data of different quality, determined by the software and equipment used, which introduce distortions, e.g., via signal compression, noise reduction, or equipment noise addition [88]. If noisy recordings are also planned, then the requirements regarding the signal level and noise level should be given, the SNR of such recordings should be measured, and the measurement results should be provided to the users of the corpus. The quality of the ASR depends on the quality of the recordings. [PERSON] and coauthors [89] showed that noise below a minimum threshold does not degrade ASR performance, and increasing noise above a maximum threshold does not further degrade this performance. Various tools are available for calculating the SNR; see [90] for an example. There are also measures available to assess the quality of speech. The speech intelligibility index (SII), which ranges between 0 for unintelligible speech and 1 for perfectly intelligible speech, is defined as the sum, over all frequency bands, of products of each band importance function and band audibility function; there are tools available for SII calculations [91]. The speech clarity index, C\({}_{50}\), is defined as the early-to-late arriving sound energy ratio, expressed in [dB], with a 50 ms boundary between early reflections (with a positive impact on clarity) and late reflections (detrimental); the higher the C\({}_{50}\), the better. C\({}_{50}\) is derived from the room impulse response and illustrates the influence of the reverberant properties of the room on speech clarity. Neural networks can also be used to assess speech signal quality.
# A Sensitivity Analysis of a Computer Model-Based Leak Detection System for Oil Pipelines
###### Abstract  
Improving leak detection capability to eliminate undetected releases is an area of focus for the energy pipeline industry, and the pipeline companies are working to improve existing methods for monitoring their pipelines. Computer model-based leak detection methods that detect leaks by analyzing the pipeline hydraulic state have been widely employed in the industry, but their effectiveness in practical applications is often challenged by real-world uncertainties. This study quantitatively assessed the effects of uncertainties on leak detectability of a commonly used real-time transient model-based leak detection system. Uncertainties in fluid properties, field sensors, and the data acquisition system were evaluated. Errors were introduced into the input variables of the leak detection system individually and collectively, and the changes in leak detectability caused by the uncertainties were quantified using simulated leaks. This study provides valuable quantitative results contributing towards a better understanding of how real-world uncertainties affect leak detection. A general ranking of the importance of the uncertainty sources was obtained: from high to low it is time skew, bulk modulus error, viscosity error, and polling time. It was also shown that inertia-dominated pipeline systems were less sensitive to uncertainties compared to friction-dominated systems.  
oil pipeline; computational pipeline monitoring; real-time transient model-based leak detection; uncertainties; leak detectability; state estimation +
Footnote †: journal: Accepted: 14 August 2017;
## 5 Data Analysis Results and Research Findings  
Statistical analysis was conducted to evaluate the psychometric features of the measurement item scales based on the valid 71-response data collected during the study. Given that the measurement scales were adapted (the researcher made extensive modifications) to suit the research objectives, all measurement scales were subjected to exploratory factor analysis (EFA). Hence, this study applied EFA to assess the suitability of the measurement instrument in examining the challenging factors influencing government intention to adopt cloud services.
## 3 Materials and Methods
### Test Data Augmentation  
To assess the decay in SR performances when real-world audio phenomena occur, two augmentation techniques were applied to validation data only to check how the performance of a pre-trained SR model evolves. The techniques used were as follows:  
Figure 2: The methodology pipeline that leads to the trained models for each speaker (ID), starting with the non-labeled DEMOS dataset that is then prepared for one-vs.-all classification, augmented with noise and reverb, and transformed into feature matrices for MFCCs, RASTA data, and merged.  
* Regarding noise, we chose the addition of pink noise with varying SNR levels. A pink noise profile was chosen because it is a good approximation of real-world noises, and as its peculiar 1/f curve makes it more prone to be perceptually detected by the human ear. The noise was added by generating a random pink noise signal on MATLAB(r), version 9.13.0 (Mathworks Inc., Natick, MA, USA [35]) and by re-weighing it so that its power in dB gave the desired SNR concerning the original signal. Five different SNRs (SNR = 25, 20, 15, 10 and 5) were used to generate progressively noisier audios.
* Regarding reverb, we opted for the addition of reverb with different SNRs. Reverberation is very common in real-world speech recordings, and this is especially problematic when the acoustic environment consists of large rooms. For the purpose of this study, a convolutional reverb based on the Impulse Response (IR) of a real acoustic space was applied to the signals with the aid of REAPER(r), version 7.15 (Cockos Inc., San Francisco, CA, USA [36]). To better reproduce real-world reverb situations, five different reverb volumes were used to generate progressively more reverberated audios. The amplitudes of the reverberated (wet) signals added to a dry signal with 0 dB gain are as follows, measured in dB and ordered from the least to the most intrusive: \(-40\), \(-33\), \(-27\), \(-20\), and \(-12\). As an example, a reverb of \(-12\) dB means that the wet signal was mixed lower than the dry, bringing it to a signal-to-reverb ratio of 12 dB or \(10\log_{10}(12)=10.79\). The amount of reverb was chosen empirically by progressively worsening the intelligibility of the raw speech. More experiments were performed using also 0 dB and 10 dB reverb/wet signals, but the results were not relevant or were difficult to interpret, and thus were omitted.
## 4 Conclusions  
A set of formulas that enable the analytical calculation of the temperature of an SC cell subjected to charging and discharging processes at constant power have been presented. The traditional RC-series model has been used for both the electrical and thermal analysis. In this regard, the electrical and thermal parameters are normally provided by manufacturers or can be obtained by means of simple experimental tests. By applying the so-called Incomplete Gamma Function, the proposed formulation renders strictly accurate results for any operation mode (including high-power demand situations). This approach represents a breakthrough in the field of SCs because analytical results identical to those obtained by utilizing numerical methods are now provided. Therefore, the evolution of the cell temperature over time can be straightforwardly calculated from the cell parameters (electrical and thermal resistance and capacitance), charging and discharging power, the initial internal voltage and temperature, and the ambient temperature. Moreover, the cell temperature can be also expressed as a function of any other electrical variable (current, internal and external voltages, energy losses, state of charge, etc.), providing this way a useful and simple tool for designing, dimensioning, cooling, or developing new control algorithms.  
Conceptualization, J.F.P.; methodology, J.F.P. and M.G.M.; software, M.G.M., A.S.G. and J.G.N.; validation, M.G.M., M.F.C.; formal analysis, J.F.P. and A.S.G.; investigation, M.G.M., J.F.P. and M.F.C.; writing--original draft preparation, J.F.P.; writing--review and editing, J.G.N., M.F.C., G.A.O. and A.S.G.; supervision, J.G.N. and G.A.O.; project administration, G.A.O. and M.G.M.; funding acquisition, G.A.O. and M.G.M. All authors have read and agreed to the published version of the manuscript.  
This work has been funded by the Spanish Government, Innovation Development and Research Office (MEC), under research-grants DPI2017-83804-R, and DPI2017-89186-R.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Methodology
### MDF Processing Mechanism  
The processing mechanism of MDF is usually divided into three parts according to the length of the press: the fast closing part, the spacing holding part, and the constant thickness part, as shown in Figure 1. Depending on the slab material, slab thickness, and density requirements, the spacing of the hot board has different settings [29]. The combination of the pitch setting curve and the actual pitch curve reflects the pressure of the slab in the press. In the interval maintaining portion, the thickness of the slab is highly variable, so it is necessary to adjust the spacing of the hot pressing plates near the exit of the hot press, thereby setting the thickness of the slab and stabilizing its thickness [30]. Usually, considering the rebound characteristics of the slab, the pitch setting of the fixed thickness section should be slightly smaller than the set slab thickness value.  
Through the above MDF type mechanism analysis, it can be known that the continuous flat hot pressing system has a complicated process control mechanism, which brings difficulty for accurate control of the slab thickness, especially when there is a deviation between the real thickness and theset thickness of the slab. Note that the dimensions of the MDF board are determined using the ISO 16895:2016 standard. The slab thickness correction needs to be achieved through a reasonable and effective control mechanism. The control of slab thickness is an important part and is always related to the final quality of the production [31]. The failure mode of the fixed thickness section is also an important part of the deviation control of the MDF fixed thickness section. The impact of different failure modes on the quality of medium-density fiberboard products and the high yield pass rate are predicted in advance. By analyzing the failure mode of the fixed thickness section, the corresponding code position can be obtained, and the principle of the Hamming code is used to control the deviation level. For the deviation control of MDF, this study integrates the FMEA failure mode expression into the Hamming tree coding analysis method to achieve the MDF fixed thickness deviation control.  
The hot pressing mechanism shown in Figure 1 takes 37 sets of cylinder arrays as an example and analyzes the hot pressing pressure changes in three different regions and states. From the trend in the figure, it can be seen that Groups 1-6 are a low pressure area, and after 6 groups, the pressure is flat as a pressure holding area, while the pressure needs to be increased because the board needs to be given a thickness when entering the high pressure area.
## 2 Results and Discussion
### Mnt-Hepn
#### 2.3.2 MNT-HEPN (II)-KNTase-COG2445 Composite  
The KNTase (kanamycin nucleotidyltransferase) enzyme transfers a nucleoside monophosphate group to aminoglycosac antibiotics, such as kanamycin, leading to the deactivation of the antibiotic. The NThese domain of KNTases is homologous to the MNT domain [76], allowing KNTase to function as an antitoxin within the TA family rather than an aminoglycosaculate NThese in certain cases [77]. Thus, KNTase and HEPN form a TA pair representing the MNT-HEPN TA family [77]. The COG2445 domain, which belongs to the DUF86 protein superfamily (Table 1), has been identified as a HEPN-associated domain [75]. Therefore, the KNTase-COG2445 composite forms a putative TA pair.  
MNT-HEPN (II) of _Geobacillus_ sp. ZGt-1 and _G. thermolevorans_Gts  
In strain ZGt-1, the aa sequence of the protein encoded on contig 12_83 is identical to that of the protein product of *_11500 (Table S1; global alignment in Figure S5a). These proteins represent putative HEPN (II) toxins, as they harbor the COG2445 domain (Table 1). Moreover, both of the putative HEPN proteins harbor the Rx4-6H catalytic motif mentioned above in the form of R(98)NIAVH(103), where N is a polar aa (asparagine, Asn). These two putative toxins have been annotated as DUF86 domain-containing proteins without specific identification (Tables S1 and S2). Therefore, the current study helped in identifying them as putative HEPN toxins.  
In strains ZGt-1 and Gts, each of the upstream genes encoded on contig 12_84 and that with the locus tag *_11510 potentially shares the same two-gene operon with the HEPN (II) coding gene and codes for a putative KNTase-harboring antitoxin (Table 1). The aa sequences of the putative antiotoxins are identical (Table S1; global alignment in Figure S5b).  
It is likely that both of the KNTase protein products of contig 12_84 and *_11510, in strains ZGt-1 and Gts, respectively, function as putative antiotoxins that neutralize the putative HEPN toxin, as was the case for _Shevanella oneidensis_[77]. Here as well, we noticed that the two genes of the putative TA pair in each strain overlap by 11 nt.  
In addition to harboring the R(98)NIAVH(103) motif, the HEPN-containing proteins in both strains harbor E(14)RCLKR(19) that is in line with the EX3 KR motif reported by [PERSON] et al., 2013 to be harbored by many HEPN-containing proteins whether they have the Rx4-6H motif or not [76]. However, HEPN-containing proteins are poorly conserved generally [76].  
Neither _G. kaustophilus_ HTA426 nor _G. thermodentirificans_ NG80-2 has the MNT-HEPN TA family.
## 2 Literature Review
### Rough Set  
Rough set (RS) theory, introduced by [PERSON] [29], is a mathematical tool to deal with vagueness and uncertainty. The core concept of RS theory involves utilizing existing knowledge bases to approximate and characterize imprecise or uncertain information through established knowledge repositories. This method provides an objective way to describe and process uncertainties without requiring any prior information beyond the dataset being processed. It relies solely on intrinsic data relationships derived from equivalence classes and indiscernibility relations.  
Integrating RS with machine learning algorithms can enhance the accuracy of behavior classification. By learning from historical behavior data, systems can predict behaviors that may occur in the future. [PERSON] [30] developed a three-level model based on RS to understand complex Indian consumer retail shopping behavior in the grocery segment, treating behavioral traits as rules. [PERSON] [31] incorporated RS with reinforcement learning, which could be applied to a monocular vision system for tracking moving targets and studying swarm behavior. [PERSON] et al. [32] introduced a multi-modal and multi-criteria conflict analysis approach, combining RS with deep learning. This approach used a deep residual network as a feature extractor and classified objects according to decision attributes derived from RS. [PERSON] et al. [33] made use of deep learning to train a set of gestures, which served as a rough estimate of hand pose and orientation. [PERSON] et al. [34] proposed a video mining learning pattern of behavior, granulating detailed action sequences into molecular behavioral units, then applying RS for attribute reduction and extracting decision rules from the resulting feature table, enabling interpretable pattern mining, validated through preliminary experiments. The generated rule sets are highly explainable and can be directly translated into decision-making criteria for behavior recognition.  
The previous discussion highlights that traditional behavior recognition methods struggle to capture the temporal evolution characteristics of actions, typically achieving recognition only in narrow-domain single-person scenarios. Monomodal visual approaches exhibit weak discrimination capabilities for similar behaviors and fail to resolve ambiguities within actions. Consequently, this paper integrates ByteTrack with TSM to address real-time multi-target tracking and behavior recognition challenges in multi-driver scenarios. Additionally, it introduces a decision optimization method based on RS, effectively distinguishing ambiguous actions, such as smoking and playing on a phone.