esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation of Debonding Effect in Internal Replacement Pipe System Under Lateral Loading
## 3 Surface Preparation and Its Impact on Debonding
### Continuous IRP System with Different Levels of Adhesive Strength
#### 3.3.1 Deflection Performance  
Figure 36 compares the deflection performance of the three IRP systems, including the continuous, 12.7 mm (0.5 inch), and 152.4 mm (6 inch) discontinuous configurations for the specific case of a 20 MPa (2.9 ksi) shear strength on the adhesive. It can be observed that the continuous IRP yields higher strength and, therefore, lower deflection compared with the other two discontinuous systems. Specifically, at the maximum load of 71.2 kN (16 kip) for the location of invert 591.833 mm (23.3005 inches), the continuous IRP system exhibits a deflection of 0.543 mm (0.021 inches), while the 12.7 mm (0.5 inch) discontinuous IRP system shows a deflection of 5.410 mm (0.213 inches), and the 152.4 mm (6 inch) discontinuous IRP system exhibits a deflection of 26.442 mm (1.041 inches).  
Figure 35: Local deformation failure in the IRP layer of a 152.4 mm (6 inch) discontinuous IRP system at a maximum load of 54.7 kN (12.3 kip). Courtesy data from the University of Colorado Boulder.
## 4 Simulation Results
### Comparison of the Round at Which the WSN Begins to Decline  
In this section, we will analyze the network's decline from three aspects: the number of rounds until the death of the first node, the number of rounds leading to the decline in network coverage, and the number of rounds leading to the decline in information integrity. Combined with the contents of Sections 4.2-4.4, our results are shown in Table 3 and Figure 18. It can be seen that compared with the traditional protocols, including LEACH, DEEC, and GSEN, the CH selection mechanism of partition based on the uniformity of the CH distribution proposed in this paper can balance the network energy consumption well, thus greatly delaying the number rounds until the death of the first node, the number of rounds leading to the decline in network coverage, and the number of rounds leading to the decline in information integrity. Then, by constructing the optimal path from each CH to the BS, the ICRM proposed in this paper can further reduce the network energy consumption, thus optimizing the above three aspects.
## 4 Modulation Strategies  
Pulse width modulation (PWM) is generally used for regulating the power converter AC output voltage. The desired (reference) AC output voltage is achieved by regulating the duty cycle of the switching equipment. PWM methods were intended to eliminate harmonic components in the output voltage and increase the magnitude of the output voltage at any switching frequency. Figure 8 demonstrates the classification of various pulse width modulation techniques commonly used in multilevel converters. These are the PWM techniques adopted in VSC applications based on the switching frequency requirements. Figure 9 shows the simulation results with the (a) Sine PWM with the level-shifted carrier (SPWMLSC), (b) Sine PWM with the phase-shifted carrier (SPWMPSC), (c) Sine with the third harmonic injected level-shifted carrier (STHILSC), and (d) Sine with the third harmonic injected phase-shifted carrier (STHIPSC).  
Figure 7: Control block diagram.
## 1 Introduction
The water may contain various contaminants, including dissolved minerals, organic matter, and microorganisms, which can affect its suitability for various applications [34]. Additionally, the water quality may vary over time, depending on the geological, hydrological, and operational conditions of the tunnel system [35]. Therefore, appropriate measures are taken to monitor and maintain the quality of water from tunnels to ensure its safe and sustainable use for domestic and agricultural purposes [36]. To assess the corrosive effects and suitability for drinking and irrigation, Piper diagrams and Wilcox plots are utilized. The Piper diagram interprets the hydrochemical data and represents the composition of the sample [37]. It is a graphical representation of water chemistry that can be used to identify the dominant cation and anion species present in the water, including the presence of dissolved salts and ions that can cause corrosion in metal structures such as concrete linings, rock bolts, and other supporting structures [38,39]. If the water chemistry indicates high chloride levels, measures such as cathodic protection or corrosion inhibitors can be used to protect metal structures from corrosion [40]. The Wilcox diagram is a graphical tool that helps to determine the suitability of water for irrigation purposes by analyzing the water'ssalinity and sodicity levels. It plots the water's sodium adsorption ratio (SAR) against the total dissolved solids (TDS) concentration to classify water into different irrigation classes based on its potential effects on soil structure and plant growth [41]. Water with high TDS and SAR values can negatively affect soil structure and crop yield. The SAR level can cause soil dispersion and reduce water infiltration rates, while high TDS levels can cause salt accumulation in the soil and plant tissues [42]. Previous studies have primarily focused on the quantitative analysis of tunnel water, neglecting its qualitative aspects. This study provides a comprehensive analysis of the hydrological conditions in the Lowari Tunnel, including both quantitative and qualitative analyses of tunnel water. The study also assesses the suitability of tunnel water for drinking and irrigation purposes, which is an important consideration for the sustainable use of tunnel water.
# TSA-GRU: A Novel Hybrid Deep Learning Module for Learner Behavior Analytics in MOOCs
## 6 Implications and Future Directions  
The TSA-GRU model achieves impressive results in the context of knowledge tracing, with a test accuracy of 95.60% and a test loss of 0.0209 after five epochs. Given the speed in obtaining these results, it appears the model can handle capturing very complex sequential dependencies in student learning data and limit overfitting, making it useful for real-time education systems. With long- and short-term dependency modeling capacity, TSA-GRU is well-positioned to provide personalized learning experiences and employ powerful situational performance predictors. However, several limitations should be noted. The sparse attention mechanism requires the user to tune threshold values manually, which may limit generalizability to other datasets or learning contexts. In addition, the current model does not leverage any affective or contextual learning signals, which would provide additional opportunities for improved prediction performance and personalization. Furthermore, we recognize that the ASSISTments 2009-2010 dataset, while still commonly utilized in the educational modeling community, does not capture the intricacies and variability of contemporary MOOC platforms, such as edX, Coursera, or XuetangX. Although we decided upon this dataset based on its sequential structure and its common benchmarking use for temporal models, we will certainly develop our work for better generalizability and provide a more accurate instantiation of contemporary e-learning environments where possible. Future work will require the consideration of new and heterogeneous datasets that possess multimodal learner behavior (e.g., text, video, exercises), richer engagement signals, and modality-dependent features, such as the real-world use of TSA-GRU across a number of possible MOOC implementations. In future work, we intend to research the use of reinforcement learning technologies to cater for adaptive learning path generation through the dynamic performance of each student. We will also research meta-learning means of quickly adapting to new learners, courses, or contexts with little retraining. Other routes of research include using graphic neural networks to understand the relational structures in the learning data, applying self-supervised pretraining to enrich the feature extraction, and improving the interpretability of models to provide actionable outputs for educators; however, the most important immediate route of exploration for TSA-GRU is to evaluate it in live adaptive learning systems as, from an overall practical view, it is important to implement it in real settings.
# A Network Analysis on Cloud Gaming: Stadia, GeForce Now and PSNow
## 2 Related Work  
Several research papers have already studied cloud gaming under different perspectives [6]. Authors in [6] focused on the study of cloud gaming platforms and optimization techniques. At the same time, several works provided general frameworks and guidelines for deploying cloud gaming services from the technical [7,8] and business [9] points of view. Other studies focused on the factors affecting the subjective QoE of users [10,11] or proposed novel techniques to improve it [12,13]. Some works proposed approaches for optimizing multimedia streaming [14, 15, 16] or GPU usage [17] in the specific context of cloud gaming. Interestingly, in 2012, the authors of [18] concluded that the network infrastructure was unable to meet the strict latency requirements necessary for acceptable gameplay for many end-users.  
Industrial pioneers of cloud gaming such as Online, StreamMyGame, and Gaikai have already been the object of study [19, 20, 21, 22]. However, research papers targeting the services recently launched by leading tech companies such as Google and Sony have not yet been published. Only Stadia has been studied by [PERSON] et al. [23], who investigated its network utilization; however, their work did not compare with other platforms, nor explore mobile scenarios. This paper closes this gap, and first characterises the generated traffic in terms of employed protocols and network workload.
## 3 Conclusions and future perspective  
Graphene based nanoadsorbents have been proven to play the vital role in the selective adsorption of heavy metal ions, such as Pb(II), from wastewater. The selective recovery and reuse of valuable adsorbents and pollutants are the important challenges for the adsorption technology using nanoadsorbents with favorable structures, morphology, superior adsorption capacities, and ease of separation. Graphene is always thanked for its unique property of functionalization, used to alter its properties and, consequently, its applications. This review exploited recently-developed graphene-based novel nanoadsorbents for the effective and selective removal of Pb(II) ions from wastewaters. In addition to the various own functionalities, GO was further surface modified with the negatively-charged functional groups to enhance the selective and effective Pb(II) ion adsorption from wastewaters. Due to wide varieties of surface functional groups, high surface area, and a preponderance of exposed edges planes, GO exhibited remarkable superior adsorption of Pb(II) ions. In addition, GO composites with ion scavengers (for example, EDTA), metal nanoparticles, magnetites, polymers, and aerogels have been reviewed for the adsorption of Pb(II) ions under the influence of temperature, pH of the medium, adsorbate, and sorbent loadings, etc., and exhibited superior adsorption than pristine GO for Pb(II) ion adsorption from an aqueous solution. One of the major advantages revealed by the graphene-based nanoadsorbents is the recovery of adsorbents and adsorbates even after a series of life cycles. Although simple and effective routes for GO-based nanoadsorbents with high surface area and pore size for superior adsorption of heavy metal ions are still in demand, graphene-based nanoadsorbents have proven their potential adsorbability for the expediency of mankind and the environment.  
**Funding:** This study was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by The Ministry of Science, ICT and Future Planning (2019R1A2B5B02069683).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Overview of the Study Area
### General Situation of Hydrogeology  
The aquifers within the study area are mainly developed into two types: loose-layer pore aquifers and bedrock fracture aquifers. The bedrock fracture aquifer is further divided into weathered bedrock aquifers, normal bedrock aquifers of the Zhiluo Formation, and normal bedrock aquifers of the Yan'an Formation. The relative aquifers in the study area are the loess aquifer of the Lishi Formation and the red soil aquifer of the Baode Formation.  
The weathered bedrock aquifer, the principal aquifer in the mine, is widely distributed in the southern flank of the minefield and is sporadically exposed in the area of the Kaukau Wusu Trench. Its lithology consists of a suite of yellow-green and grey-green medium-grained sandstones, sandy mudstones, and siltstones. According to the boreholes and pumping test data, the thickness of the weathered bedrock aquifer in the study area is 14.7-49 m, with an average thickness of 26 m; the permeability coefficient varies from 0.00342 to 1.4714 m/d; and the spatial distribution of water-bearing properties is uneven.
# Action Recognition Network Using Stacked Short-Term Deep Features and Bidirectional Moving Average
## 4 Experimental Results
### Accuracy  
In this section, in order to verify the effectiveness of the proposed method, we compared the accuracy with the other methods, including the baseline. The experiment was conducted in two datasets: UCF101 and KTH dataset. Table 3 shows the action recognition accuracy comparison performed in the UCF101. When comparing the performance in UCF101 with same environment in Section 4.2, 2 DCNN, which is a baseline in our method, has a low accuracy of 48.40%. Another model, the Ishan method, also has a low accuracy of 50.90%. In addition, the C3D method which later fused in our proposed method has an accuracy of 70.02%. Therefore, when comparing our proposed method with 2 DCNN, Ishan method, C3D, the proposed method showed a 23.63%, 21.13%, 2.01% improved performance.  
Table 4 shows the accuracy comparison performed in the KTH dataset. This experiment shows an increased performance similar to Table 3. When it is performed on the KTH dataset, the recognition accuracy of the 2 DCNN is 62.28% and C3D is 66.20%. Therefore, when compared with our proposed method, which has an accuracy of 73.61, the accuracy of our proposed method is improved by 11.33%, 7.41%. Our proposed method is a late fusion of 2 DCNN-based Stacked Short-Term Deep features and Bidirectional Moving Average method and C3D, respectively, without deteriorating the existing performance, even though late fusion with C3D was potentially performed.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Model** & **Accuracy (\%)** \\ \hline
2 DCNN [5] & 62.28 \\ C3D [10] & 66.20 \\ Proposed method & 73.61 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy comparison in KTH dataset.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Moving Average Parameter: \(\alpha\)** & **Accuracy (\%)** \\ \hline
0.5 & 53.89 \\
0.6 & 54.50 \\
0.7 & 55.19 \\
0.8 & 55.43 \\
0.9 & 55.97 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experiment of Bidirectional exponential moving average parameter \(\alpha\).  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Model** & **Accuracy (\%)** \\ \hline
2 DCNN [5] & 48.40 \\ [PERSON] et al. [15] & 50.90 \\ C3D [10] & 70.02 \\ Proposed method & 72.03 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy comparison in UCF101 dataset.
## 3 Monitoring and Verification
### Simulation Analysis
#### 3.1.2 Displacement Field Analysis  
The displacement contour maps can be obtained by sectioning slices along the tunnel's longitudinal axis and excavation direction to comprehend the extent of displacement changes after simulating tunnel excavation and support. Figure 7 depicts the vertical displacement contour on a cross-sectional view.  
Figure 7 exhibits that during the tunnel excavation process, the tunnel crown undergoes a settlement of approximately 4.5 cm, while the surrounding rock at the bottom experiences an uplift of about 2.8 cm. Relative to other areas, deformations are most pronounced at the crown and bottom. The vertical displacement of the surrounding rock on both sides of the arch rib measures 0.46 cm, which is within the safe range. In areas farther from the tunnel face, the slope of the displacement contour map becomes gentle. The steepest slope is apparent near the tunnel face, suggesting that as tunnel excavation progresses, vertical displacement continually increases. However, the rate of increase decelerates as one moves further from the tunnel face. This observation indicates that the impact of subsequent excavation on previously excavated sections decreases as the distance from the tunnel face expands.
## 4 Results and Performance Analysis
### Spilling/Plunging and Plunging Breaking Waves on a Plane Beach  
Both numerical approaches reproduce the breaking waves quantitatively well in both tests (T1 and T2). In fact, in the horizontal direction of the surf zone, the following zones can be distinguished [97]. Initial wave deformation occurs in what has been termed the shoaling zone (section 76-49), where wave profile is characterized by a rapid change in shape. Subsequently, the wave reaches the breaking point in the outer zone (section 48-47) originating an overturning jet whose strength depends on the type of breaker. In the inner surf zone (section 45), the wave undergoes a gradual transformation into a turbulent bore, until reaching the swash zone. Figure 4a,b show the snapshot of the free surface for both the cases of a spilling/plunging (T1) and a plunging breaking wave (T2).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**TEST**} & \multicolumn{2}{c}{**Time**} & \multirow{2}{*}{\(\Delta\)x, \(\Delta\)y (m)**} & \multicolumn{2}{c}{**Wave Breaking**} \\  & **Simulation (s)** & & **Criterion** & **N\({}_{\text{nodes}}\)(\(-\))** \\ \hline \hline \begin{tabular}{c} **D/C** \\ **Galerkin\_T1** \\ \end{tabular} & 60 & 0.04 & \(\frac{\frac{\alpha}{\sqrt{g}[h]}}{\sqrt{g[h]}}>0.2\) & 5607 \\ \hline \begin{tabular}{c} **D/C** \\ **Galerkin\_T2** \\ \end{tabular} & 60 & 0.04 & \(\frac{\alpha}{\sqrt{g}[h]}>0.3\) & 5607 \\ \hline \
\begin{tabular}{c} **D/C** \\ **Galerkin\_T3** \\ \end{tabular} & 25 & 0.05 & \(\frac{\alpha}{h}>0.8\) & 4505 \\ \hline
\begin{tabular}{c} **D/C** \\ **Galerkin\_T4** \\ \end{tabular} & 40 & 0.4 & \(\frac{\alpha}{h}>0.8\) & 3857 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Main characteristics of the non-hydrostatic D/C Galerkin simulations.  
Figure 4: _Cont_.  
As an example, in Figure 5a,b, both laboratory and numerical wave surface elevations, at vertical sections 49, 48 and 45, have been plotted for T1 and T2.  
Figure 4: DualSPHysics model: Snapshots of free surface and vorticity field for (**a**) spilling/plunging, breaking wave (T1) and (**b**) plunging breaking wave (T2).  
Figure 5: Computed and measured surface elevation for (**a**) spilling/plunging breaking wave (T1) and (**b**) plunging breaking wave (T2).
# Feature-Based Image Watermarking Algorithm Using SVD and APBT for Copyright Protection
## 4 Experimental Results and Comparative Analyses
### Performance Comparisons
In terms of BER and NC, the proposed scheme based on APDCBT has excellent performance.
## Materials and Methods  
### Traffic Noise Spectrum  
The spectrum of traffic noise can be influenced by a variety of factors including flow rate, type, and speed of vehicles, road surface, and weather conditions. There is a huge body of work examining the spectra of road traffic noise sound pressure level (SPL) recorded in many countries taking into account the aforementioned factors [34; 35; 36]. In most of these studies, significant low-frequency SPL content has been identified for vehicle speeds lower than 50-60 km/h (31-37 mph) caused by the vehicles' engine and transmission and the interaction of the tires with the road surface. Spectra peaks commonly shift to higher frequencies when the vehicle speed increases. Nevertheless, traffic noise spectra levels usually decrease in magnitude at frequencies higher than 1 kHz. The one-third octave band normalized spectra of noise source for A-weighted road traffic noise given in the European Standard EN1793-3:199719 [37] is widely used in Europe as a reference. For the present work, the road traffic spectrum source was taken from [38], which in turn was based on the cited standard and is shown in Figure 1.  
Due to the decrease, at a certain point, of the curve depicted in Figure 1, _IL_ is typically studied in relation to frequencies lower than 3 or 4 kHz. However, there is another relatively common source of noise that needs to be considered, specifically, emergency sirens (e.g., ambulance, police car, etc.), which feature a higher frequency content, as reported by [PERSON] [39] and [PERSON] [40].  
The radiated acoustic power is a recommendable method to characterize these types of sound sources, as it is independent of the measurement distance and mounting location of the siren. An example of siren noise recorded by [PERSON] [40], which has been chosen for the present work, is shown in Figure 2.  
The sound power levels (A-weighted) of the siren selected were the _\"UK fire brigade\"_ type (Figure 2). In any case, the results of the one third octave band sound power measurements in [40] showed that  
Figure 1: A-weighted sound power level (Lw) spectrum. Road traffic noise spectrum according to [38].  
Figure 2: A-weighted sound power level (Lw) spectrum of a typical siren by [PERSON] [40].  
all sirens exhibited similar sound power levels for the same frequency range including the French or Italian ambulance sirens presented by [PERSON]. Therefore, the election of any other siren for this work would have been practically inconsequential, providing similar results.  
As can be observed, the spectral content of typical sirens reach frequencies close to 10 kHz. Therefore, in this work, unlike in previous studies, an extended frequency range of up to 10 kHz was assumed, along with an additional specific scenario in which an ambulance siren sound source was located at a typical height of 2.5 m, as described in the following section.
## 3 Results and Discussion
### Flow Parameters
The results presented in Figure 6a showed that the SJA with chevron orifice has lower efficiency than at SJA with circular orifice, but the efficiency is higher in the case of SJA with rounded cutouts than at SJA with the circular orifice. These facts suggest that the sharp cutouts are unfavorable in terms of flow parameters and caused the losses of energy e.g., by the deflection of the stream. The rounding of cutouts reduces these effects and at higher velocity causes reduction of pressure loss, and consequently increases the efficiency of the actuator. Based on the presented results, it is impossible to make bolder conclusions, but this should be investigated in future studies. The time mean-reaction method allows measuring only the primary flow parameters of the SJ. The flow visualization of the chevron SJ was made by [PERSON] et al. [26; 27] for the actuator with a chevron nozzle. Therefore, it should be considered whether the SJ generated by the investigated actuators will be similar to those presented by [PERSON] et al.1 [26; 27]. This question can only be confirmed only with the use of the flow visualization. However, the velocity measurements presented by [PERSON] and [PERSON] [29] suggest that it is so. The increase of the SJ turbulence intensity in some distance from the orifice, significant differences in the values velocities (average, maximum, minimum), and increase of the actuator efficiency indicate that the chevron orifice has a direct impact on the flow organization of SJ.