esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Primary Findings
### Holistic Analysis of Findings
#### 4.1.5 Established Partnerships  
The fifth category studies the companies' collaborations with international I4.0 stakeholders. The interview analysis revealed that 60% of the interviewees had active international collaborations. Furthermore, 33% of the interviewees had no active international partners but were learning I4.0 practices from foreign cases (Figure 6). The rest, 7%, had neither collaborations nor self-learning.  
The gained analysis in categories 1-4 showed a low level of I4.0 awareness and numerous obstacles toward I4.0 project implementation. These factors inevitably affect the high rate of active international collaborations with I4.0 stakeholders. Moreover, there is a lack of I4.0 solutions providers within the country. Therefore, companies seek ready-made solutions from international experience (Q21). Big companies with sufficient resources enjoy direct international collaborations and participate in employee exchange programs for training. On the country, SMEs usually have fewer resources and limited access to international experience. Therefore, domestic enterprises are becoming self-learners (Q22). They study international I4.0 practices from courses, conferences, articles, etc.  
Figure 5: The most demanded technologies and tools of Industry 4.0 in Kazakhstan.  
However, the absence of the established process for work with foreign partners harms the project quality (Q23). Domestic companies fear not satisfying the requirements of foreign colleagues because of high demands and standards.
# Two-Stage Single-Channel Speech Enhancement with Multi-Frame Filtering
## 2 Related Research  
### Speech Enhancement Based on Filters  
Multi-channel filters are widely applied in multi-channel speech enhancement, since they can utilize the extra spatial information contained in speech signals, thus achieving good enhancement results. There has been a long history of research on how to design filters with better enhancement performance, and many filters have been proposed, among which the classical ones are the Wiener filter, minimum variance distortionless response (MVDR) [10], minimum power distortionless response (MPDR) [11], etc.  
Recently, low-distortion filters for single-channel speech enhancement have been proposed, such as MFMVDR [12] and multi-frame Wiener filter (MFWF) [13]. Since single-channel signals have no additional spatial information, these single-channel filters focus on modeling the relationship between adjacent frames.
# Paraphrase Identification with Lexical, Syntactic and Sentential Encodings
## 4 Experiments
### Ablation Study  
We recall that our model utilizes lexical and syntactic encodings including the baseline model. Moreover, the syntactic encoding integrates different features. We thus conducted ablation studies to empirically examine the impact of these features/encodings. The results are shown in Table 6.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**MRPC Dataset**} \\ \hline
**Model** & **Acc** & **F1** \\ \hline Str Align [48] & 78.3 & 84.9 \\ BERT\_base [11] & 84.8 & 88.9 \\ GenSen [49] & 78.6 & 84.4 \\ ERNIE 2.0 [25] & 86.1 & **89.9** \\ Trans FT [50] & - & 89.2 \\ StructBERT\_base [51] & 86.1 & **89.9** \\ FreeLB-BERT [55] & 83.5 & 88.1 \\ ELECTRA-Base [56] & **86.7** & - \\ LSSE (Our model) & 86.3 & **89.9** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative results with related work including state-of-the-art method: Str Align is based on attributed relational graphs. Bold font shows the best result.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**QQP Dataset**} \\ \hline
**Model** & **Acc** \\ \hline BiMPM [46] & 88.2 \\ SSE [58] & 87.8 \\ LSSE (Our model) & **90.6** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative results in accuracy by using QQP: Bold font shows the best result.  
Table 6 shows the results by using the MRPC dataset (Supplementary Materials). Overall, we can see that integrating different features from the contexts is effective as LSSE was the best performance. The results both without R-GCNs and BERT token encoding (-R-GCNs and -BERT TokenE) and without R-GCNs and sentential encoding (-R-GCNs and -SentE) are worse than those without R-GCNs (-R-GCNs). This shows that the combination of the sentential and lexical encoding is effective for paraphrase identification.  
We note that the result by \"-SentE\" is better than that with \"-SentE and -PE\". This means that the combination of R-GCNs output, BERT token encoding and position encoding is better than that with only R-GCNs output and BERT token encoding. We can see a similar observation that the combination of sentential encoding, R-GCNs output, BERT token encoding, and position encoding more works well than that with sentential encoding, R-GCNs output, and BERT token encoding because our LSSE is better than the result by \"-PE\". From these observations, we can conclude that the combination of syntactic features and position encoding contributes to performance improvement.
# Enhancing Smart Agriculture by Implementing Digital Twins: A Comprehensive Review
## 1 Introduction
A comparative accumulation of such implementations and critical components in three tables, while addressing technical aspects, led to significant conclusions regarding DT implementations and related limitations that seem beneficial to investigate. For example, although the necessity of a DT model and practical implementations in agriculture is addressed in most reviewed papers mentioning the concept and definition, the DT model is proposed merely as an evolving concept lacking substantial support from implemented case studies. While the DTs seem continuously evolving, authors refer to simulation models merely as proposals for future practical implementation. Our research seeks to expand the scope of Agriculture 4.0 that necessitates DTs and address the limitations of IoT-implemented solutions narrowed down by mere telemetry implementations. The structure of this study proceeds as follows: Section 2 delves into the concept of DTs and investigates their usage in the realm of smart farming. Section 3 addresses the challenges and confronting measures regarding DT building components and relevant technologies. In Section 4, DT applications in the agricultural domain are reviewed, while conclusions and comments are unfolded in the Discussion in Section 5. Finally, in Section 6, future directions for research and development are outlined respectively.
# Harnessing Biotechnology for the Remediation of Organic Pollutants in Coastal Marine Ecosystems
## 3 Organic Pollution in Coastal Marine Ecosystems
### Pesticides and Herbicides  
Agricultural runoff and urban landscing introduce various pesticides and herbicides into coastal marine ecosystems, where they persist in sediments and accumulate in marine organisms. The most concerning groups are organochlorine pesticides (OCPs); though banned in many countries, residues of DDT, chlordane, and heptachlor continue to be detected in marine sediments [38], disrupting endocrine functions in fish and birds.  
Organophosphates (e.g., chloropyrifos, malathion) can enter water bodies, particularly surface water, through various pathways, including irrigation effluents, industrial discharges, agricultural runoff, uncontrolled application, transport during rainfall, unintended spills, and waste from agricultural activities [39; 40; 41]. Widely used in modern agriculture, these neurotoxic compounds inhibit acetylcholinesterase in marine invertebrates and fish, leading to nervous system disorders [39]. Glyphosate and atrazine are common herbicides that disrupt photosynthesis in marine phytoplankton and seagrasses, altering primary production and food web dynamics [42].  
Pesticides and herbicides contribute to large-scale disruptions such as eutrophication, harmful algal blooms, hypoxia, and biodiversity loss [43], ultimately influencing Microbial Succession Theory as affected microbial populations shift to favor pollutant-resistant species, often at the expense of ecological balance.
## 3 Methodology  
### Parameterization Dataset and Experimentation Dataset  
In this work, two different datasets are used: one for parameterization and one for experimentation. The parameterization dataset takes into account a year from which two different days per every month are selected, comprising a total of 24 days. The criteria for selecting the two days of each month are as follows: one day with almost no irradiance variations associated with cloud presence; and a second day with cloud presence that generates unexpected variations in the irradiance profile. This dataset is only used for the estimation of the parameters of every model (active power and THD). Once the parameters of the model are estimated, a different dataset is used for experimentation. On the other hand, 8 days, taken through the year, are analyzed to form the experimentation dataset. Each one of these days is different from those selected for the parameterization dataset. This selection is performed considering the following: two representative days per season of the year are selected to assess the model under different environmental conditions. Moreover, for each season, one day with only a few clouds through the day (or none if it is possible) is selected. For the second day of each season, one with many abrupt irradiance variations due to cloud presence is selected. In this way, it is possible to assess the performance of the methodology under different scenarios.
## 3 Proposed Network Traffic Predictor and Traffic Classification Agents
### Proposed Network Traffic Predictor Agent Architectural Framework  
A given NTPA is equipped with an \"input unit\" that enables it to receive a network traffic processing request and network state sampling/measuring points. Furthermore, NTPA is equipped with several facts that are model features that could be the amount of incoming network traffic, the available bandwidth in a given link, IP addresses, source and destination ports, and labels (protocols) [20]. The main target to focus on is the \"cognitive/reasoning unit\". It is the brain of NTPA that enables it to have reasoning capability, which is typically a DL model. The \"planning strategy\" component of NTPA is the steps for the action to be performed in accomplishing the required processing. Another component of NTPA is a validation unit. It is composed of validation rules for the final decision, for example, knowing the amount of arriving network traffic of given traffic classes that helps make decisions. Finally, we have an \"output or action\" unit. This is the final outcome that could be a prediction result to be sent to the other agents. Figure 5 (bottom) shows the building blocks of an NTPA.  
Figure 5: Network traffic classifier agent (**top**) and network traffic predictor (**bottom**) agent architecture.
## 2 Materials and Methods
### Hop Cones Analysis
#### 2.12.4 High-Performance Liquid Chromatography with Ultraviolet (HPLC-UV) Analysis  
The HPLC system was equipped with a pump, on-line vacuum degasser, auto-sampler, Peltier column oven, UV-Vis detector (Perkin Elmer Series 200) and autosampler (Perkin Elmer series 220). The chromatographic data was analyzed using a Perkin Elmer Total Chrome workstation (version 6.3.1). HPLC was equipped with Luna C18:2 column (5 \(\upmu\)m, 100 A, 250 mm \(\times\) 4.6 mm) (Phenomener(r), Castel Maggiore, Bologna, Italy). Solvent A (water (H\({}_{2}\)O) + 0.1% orthophosphoric acid (H\({}_{3}\)PO\({}_{4}\)) and solvent B (methanol (CH\({}_{3}\)OH) + 0.1% H\({}_{3}\)PO\({}_{4}\)) were used for the mobile phase. Chromatographic conditions were set as follows: the flow rate was 1.5 mL min\({}^{-1}\) in isocratic, the column temperature was set at 30 \({}^{\circ}\)C; the injection volume was 10 \(\upmu\)L; the analysis was performed using eluent A 5% and eluent B 95% for 15 min. Chromatograms were acquired at 314 nm. Three injections from three independent extractions were performed for each sample. For the quantification of \(\upalpha\)- and \(\upbeta\)- acids, a calibration curve was obtained from dilution of ICE-3 standard, according to the official method (Analytica-EBC, method 7.7).
## 3 Methods
### Hydrological Model Calibration and Validation  
The calibration process aims to find the optimal parameters to minimize the objective function. The calibration for each parameter is performed independently of the others, to avoid interdependence between the parameters of the production and transfer functions. The calibration is performed both automatically and manually, to assess the sensitivity of the model to small variations of the parameters. Three efficiency criterions were used to evaluate the agreement between observed and simulated hydrographs; The Nash Sutcliffe (NS) efficiency coefficient [52] (Equation (9)) and the relative error calculated based on the observed and estimated total volume runoff and peak flow (bias) (Equations (10) and (11))  
\[NS=1-\frac{\sum_{i=1}^{n}(Q_{o,i}-Q_{s,i})^{2}}{\sum_{i=1}^{n}\big{(}Q_{o,i}- \overline{Q_{o}}\big{)}^{2}} \tag{9}\]  
\[BIAS_{V}=\ \frac{(V_{s}-V_{o})}{V_{o}} \tag{10}\]  
\[BIAS_{Q}=\ \frac{(Q_{s}-Q_{o})}{Q_{o}} \tag{11}\]  
where \(Q_{o,i}\) and \(Q_{s,i}\) are the observed and the simulated flow, respectively, and \(\overline{Q_{o}}\) is the average observed flow. \(V_{s}\) and \(V_{o}\) are the simulated and the observed runoff depth, respectively.  
The CN parameter can be identified as the initial soil moisture condition in the model. Several studies have shown that this parameter can be related to different soil moisture indicators, obtained from field measurements [53, 54, 55], or soil moisture models or from satellite data [56] to reproduce the CN of flood events. In the present study, the approach previously used by [18,48] in a similar basin of South Morocco is implemented: the ESA-CCI soil moisture is used to estimate the initial soil moisture condition. All the model parameters including the CN are first calibrated. Then a linear regression is established between the CN and the ESA-CCI soil moisture the day before of each flood event.  
For validation, a leave-one-out procedure (or Jack Knife) is considered, with each flood event in turn removed from the sample, the model is recalibrated on the remaining events. The mean values of the model parameters obtained after recalibration are used for the validation of the event that was removed. For the CN, the value is estimated from the linear regression against ESA-CCI soil moisture.
## 3 Methodology
### Gaussian-Peak Waveform Retracker  
In general, the waveforms echoed from the surfaces of small- to medium-sized inland water bodies should behave as those described in [PERSON] et al. [32; 35] for either specular or quasi-specular surfaces. In these previous studies, careful attention is given to the absolute backscattered energy of the waveforms in order to better classify them. However, this approach is left out of the scope of this study as the intention is to evaluate the performance of the algorithm, taking into account only the position of the peak without considering its absolute value in terms of power, as the absolute echoed power may not be representative of the kind of surface if only a fraction of the illuminated footprint contains water.  
Figure 4 (left panel) displays a typical FFSAR waveform over this kind of water body, exhibiting a dominant approximately sinc-like function around the range bin with the highest echoed power. With the presented FFSAR processing configuration, every sample corresponds to a difference of roughly 5 cm in range. As the considered targets are expected to behave as quasi-specular surfaces, the surface range is assumed to be around the range with the highest power.  
The range retracker approach is kept simple: since the actual peak of the sinc inherent to the waveform might not be precisely sampled (as depicted in the right panel of Figure 4), we employ a Gaussian function to estimate the waveform's peak and consequently determine the range. An evaluation of the number of points utilized in the Gaussian fitting revealed that 3 points yielded accurate results, with further increases in the number of points resulting in changes below 1 mm. Exploring the benefits of using more complex retrackers is left out of the scope of this study.
## 2 Materials and Methods  
### Sample Preparation  
Dental sample blocks for computer-aided design (CAD) and computer-aided manufacturing (CAM) of zirconia, LD glass ceramics, and VITA _En_ were sourced from VITA Zahnfabrik H. Rauter GmbH & Co., Bad Sackingen, Germany. Zirconia in VITA YZ T manufacturing grade contained ZrO2 at 90-95 wt.%, yttria (Y2O3) at 4-6 wt.%, hafnium oxide (HfO2) at 1-3 wt.%, and alumina and pigments at \(<\)1 wt.% [36]. The LD glass ceramic in VITA AMBRIA(r) grade contained silica (SiO2) at 58-66 wt.%, Li2O at 12-16 wt.%, zirconia at 8-12 wt.%, alumina at 1-4 wt.%, phosphorus pentoxide (i.e., phosphorus(V) oxide, also known as phosphoric anhydride) (P2O5) at 2-6 wt.%, and other minor fractions of elements at 1 wt.% or less [37]. VITA ENAMI(r) is a hybrid ceramic composed of a dual-network structure, with approximately 86% ceramic (feldspathic-based ceramic) and 14% highly cross-linked acrylate polymer by weight [38]. VITA _En_ is reported to have a high purity level of \(>\)99%.  
The samples were milled into flat oval plates with dimensions of 5 \(\times\) 10 \(\times\) 3 mm\({}^{3}\), in accordance with, and exceeding the requirements of, ISO 6872:2024 [39] (Dentistry) 'Ceramic Materials', which specifies the preparation and surface finish for dental ceramics such as zirconia and LD glass ceramic materials. In addition, the accuracy of CAD/CAM digitized milling and finishing of test samples compiled with the requirements of the ISO 12836:2015 [40] (Dentistry) 'Digitizing Devices for CAD/CAM Systems for Indirect Dental Restorations' standard. After milling, the samples were sintered at 1500 \({}^{\circ}\)C, with the zirconia samples undergoing an additional firing at 810 \({}^{\circ}\)C.  
All samples were polished to achieve a flat surface using a stepwise approach that included the use of a Universal Polisher, Diacera HP Medium and DVA Zircon-Brite polishing paste (EVEUR EVE Ernst Vetter GmbH, Keltern, Germany), and the JOTA Zir Gloss Laboratory Kit (JOTA AG Rotary Instruments, Ruthi, Switzerland). Following polishing, the samples were steam-cleaned with demineralized water to remove any residual polishing compounds. They were then cleaned with a neutral detergent solution (Extran(r) MA 05, Sigma Aldrich, St. Louis, MO, USA) and subjected to ultrasonic cleaning in deionized water for 10 min before being used in laser ablation experiments.
## 2 Experiment 1: Differences in Controller Operation Sounds Based on Player Skill
### Results  
First, the Shapiro-Wilk test confirmed that \(L_{\max}\) may satisfy the normality assumption (\(p=0.899>0.05\)). In addition, the Bartlett test confirmed the homoscedasticity of variance in \(L_{\max}\) for each player's skill (\(p=0.726>0.05\)) and each game controller (\(p=0.787>0.05\)). The ANOVA of the differences in \(L_{\max}\) confirmed a significant primary effect for the player level (\(F(1,2)=82.45,p=0.007<0.01,\text{partial}\,\eta^{2}=0.49\) (large)) and game controller (\(F(1,2)=55.93,p=0.024<0.05,\text{partial}\,\eta^{2}=0.39\) (large)). The interaction between the player level and game controller was not significant for \(L_{\max}\) (\(F(1,2)=14.81,p=0.31>0.05,\text{partial}\,\eta^{2}=0.39\) (large)).  
The figures on the left and right sides in Figure 2 exhibit the maximum sound pressure levels for the players and game controllers, respectively. The findings of multiple comparisons illustrate that \(L_{\max}\) was significantly smaller for beginners as opposed to intermediate (\(t(14)=5.40,p=0.002<0.01,d=1.95\) (large)) and advanced (\(t(10)=6.48,p<0.001,d=2.37\) (large)) players. The \(L_{\max}\) was also significantly different in the game controllers: The operation of GameCube controllers was significantly louder compared to that of Switch Pro controllers (\(t(17)=2.99,p=0.041<0.05,d=1.60\) (large)).  
Figure 2: Difference in maximum sound pressure level \(L_{\max}\) for (**left**) player level and (**right**) game controller.
# CFD and Energy Loss Model Analysis of High-Speed Centrifugal Pump with Low Specific Speed
## 4 Conclusions  
In this paper, CFD simulations and the energy loss model are combined to calculate different power losses and to analyze the performance of the high-speed centrifugal wa  
Figure 9: Various lost power under different flows. (**a**) Energy loss power; (**b**) proportion of energy loss power.  
ter pump with the low specific speed. The simulation results are validated against the experimental results. The main conclusions of this paper are as follows.  
1. ELM/CFD can better predict the performance of the high-speed centrifugal pump with the low specific speed. Comparing the experimental results with the calculated results, it is found that the results of ELM/CFD are more consistent with the experimental values, with the relative deviation of pump head and efficiency less than 2% and 5% under 50% to 185% design flowrate, respectively.
2. Under different flowrates, the variations of the pressure in the high-speed centrifugal pump along the streamline are the same, but the amplitude is quite different. As the flowrate increases, the low-pressure area expands and the minimum static pressure decreases. Meanwhile, the reflux phenomenon at the junction of the hydraulic components alleviates.
3. ELM/CFD can accurately analyze the optimization direction of the pump. As for the high-speed centrifugal water pump with the low specific speed, the factors in restricting the efficiency of the pump under smaller flowrates are the hydraulic loss and volume loss of the impeller, and the centrifugal pump under large flowrates could be optimized by reducing the hydraulic loss of the subsequent channel.  
As for the low specific speed pump with a fully sealed structure, the empirical correlation method ignores the influence of leakage and overestimates the pump performance. The ELM method is mainly based on empirical parameters, which will affect the accuracy of the performance prediction. The ELM/CFD method can obtain more accurate prediction of the performance of the high-speed centrifugal pump than the empirical correlation method, and this method more directly reflects the optimization direction. The research results in this study could provide references on the practical engineering application of the performance prediction and the optimization of pumps.  
Data curation, X.L. and B.Z.; investigation, X.L.; project administration, R.X.; software, H.Z.; supervision, R.X. and Y.H.; writing--original draft, X.L. and B.Z.; writing--review and editing, T.L. All authors have read and agreed to the published version of the manuscript.  
This project is supported by the National Natural Science Foundation of China (51976150) and the Youth Innovation Team of Shaanxi Universities.  
Not applicable.  
The authors declare no conflict of interest.