esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Pseudo-Dynamic Test Overview and Result Analysis
### Experiment Results and Analysis
#### 4.5.3 Analysis of the Load-Displacement and Displacement-Time History Results  
Figure 22 shows the load-displacement curve of the non-reinforced specimen for 200 cm/s\({}^{2}\) and the load-displacement curve of the reinforced specimen (PD-LLFD-V) for 200-500 cm/s\({}^{2}\). Figure 23 compares the seismic response displacement-time history curves of the non-reinforced (200 cm/s\({}^{2}\)) and reinforced specimens (200-500 cm/s\({}^{2}\)). Table 12 compares the seismic response strength ratios and displacement ratios, which are important factors for seismic performance assessment, from the experiment results for the reference (200 cm/s\({}^{2}\)) and reinforced specimens (200-500 cm/s\({}^{2}\)).  
According to the above-mentioned figures and table, the LLFD method increased the seismic response strength by approximately 1.74 times under the input ground motion of 200 cm/s\({}^{2}\), 2.42 times under 300 cm/s\({}^{2}\), 3.00 times under 400 cm/s\({}^{2}\), and 3.51 times under 500 cm/s\({}^{2}\) compared to the reference specimen (PD-RC). These results are also well reflected in Figures 16-20, which compare the final failure situations. Compared to the reference specimen, the LLFD method increased the displacement response ratio by approximately 0.14 times for 200 cm/s\({}^{2}\), 0.23 times for 300 cm/s\({}^{2}\), 0.40 times for 400 cm/s\({}^{2}\), and 0.54 times for 500 cm/s\({}^{2}\). The seismic response displacement for the same seismic load (200 cm/s\({}^{2}\)) was suppressed by approximately 86%, confirming the excellent seismic energy absorption capacity and validity of the proposed LLFD method.  
*\({}^{\text{1}}\)\(R_{s}\): Maximum strength ratio of the NBSD-strengthened specimen relative to the non-strengthened specimen. \({}^{\text{2}}\)\(R_{d}\): Maximum displacement ratio of the NBSD-strengthened specimen relative to the non-strengthened specimen.  
\end{table}
Table 12: Comparison of strength ratio and displacement ratio of seismic response.  
Figure 23: Comparison of time and displacement of seismic response.
## 3 Performance Analysis for Single SWIPT Relaying System  
In this section, we first derive the average SNR and the moment-generating function (MGF) of \(\gamma_{\rm id}\). Based on the derived MGF, the average BER for BPSK modulation is subsequently obtained. Moreover, asymptotic expressions for the BER are presented to offer further analytical insights.
# Manifolds-Based Low-Rank Dictionary Pair Learning for Efficient Set-Based Video Recognition
## 5 Discussion  
Video recognition [29; 30; 31] is an important direction in computer vision and video processing research. To date, many effective video recognition methods have been developed, which can be grouped into two categories: temporal-spatial- and spatial-based video recognition methods. In this study, only the spatial information of the video was considered; thus, each video was considered an unordered image set. Additionally, the video recognition task degenerated to a set-based video recognition or an image set classification task. According to the speed and accuracy requirements for image set classification tasks, our MbLRDPL  
Figure 4: The (**a**) recognition rate (%), and (**b**) computing time (s) of different methods on ETH-80 dataset. These sub-figures demonstrate that our MbLRDPL achieves the highest recognition rate (95%) with the fastest computing time.  
uses static modeling, i.e., the covariance matrix or linear subspace, to model each image set, which considerably reduces the number of samples and accelerates the computing speed. Additionally, a new powerful classifier called LRDPL was also proposed, which not only increases the classification performance but also has a quick computing speed. Tables 2 and 3 show the classification accuracy, training time, and testing time of different methods on the Honda and YTC datasets; the experimental results demonstrate that our proposed method obtained the highest classification accuracy with the fastest computing time (training plus testing time). In the object classification task, i.e., Figure 4, the same phenomenon can be observed. Specifically, compared with static modeling methods, our MbLRDPL provides higher classification accuracy and reduces the computing time; compared with dictionary learning methods, MbLRDPL effectively increases the accuracy. This means that the static modeling strategy can capture the discriminative features of image sets, and the LRDPL classifier has powerful classification ability.  
As a fundamental video processing algorithm, the proposed MbLRDPL can be applied to many video oriented tasks, such as video-based fugitive tracking, video retrieval, video-based security monitoring, even action recognition, micro expression recognition, etc. In addition, since MbLRDPL requires less testing/inference time while maintaining recognition accuracy, it can be applied to some IoT edge devices via combining with deep features.  
Since MbLRDPL uses the iterative strategy to solve the parameters, we plotted the convergence curves for the Honda and ETH-80 datasets to verify the convergence of the MbLRDPL method. The convergence curves are shown in Figure 5, which shows that our proposed MbLRDPL method can quickly converge (not more than 6 iterations), so we set the maximum iterations to 20.
# Mechanical and Acoustic Emission (AE) Characteristics of Rocks under Biaxial Confinements
## 3 Results and Discussion  
### Basic Mechanical Parameters of Rocks  
The uniaxial compression tests using cubic specimens were carried out first to determine the basic mechanical parameters of granite, marble, and sandstone, as shown in Table 2. Granite has the highest uniaxial compressive strength \(\sigma_{\text{ucs}}\) and elastic modulus \(E_{y}\), while sandstone has the lowest \(\sigma_{\text{ucs}}\) and \(E_{y}\). The [PERSON]'s ratio \(\mu\) of granite, marble, and sandstone are 0.25, 0.27, and 0.31, respectively. The P-wave velocities \(V_{L}\) of granite, marble, and sandstone measured by an ultrasonic vibration are 4.26, 3.58, and 2.34 km/s, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Rock Types** & \(\sigma_{\text{ucs}}\) (MPa) & \(E_{y}\) (GPa) & \(\mu\) & \(V_{L}\) (km\(\cdot\)s\({}^{-1}\)) \\ \hline Granite & 89.86 & 52.25 & 0.25 & 4.26 \\ Marble & 45.39 & 27.67 & 0.27 & 3.58 \\ Sandstone & 27.71 & 12.07 & 0.31 & 2.34 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Basic mechanical parameters of granite, marble, and sandstone.  
Figure 5: Testing apparatuses: (**a**) overall view, (**b**) acoustic emission (AE) sensors distribution, (**c**) biaxial compression setup, (**d**) loading condition, (**e**) AE sensor, (**f**) measuring method of strain during biaxial compression loading. Note: The numbers 1–12 represent the TRW-3000 operating system, high-speed camera with an operating system, AE control system, spotlight, TRW-3000, servo-controller system, object stage, extensometer, platen, specimen, AE sensors with screw holders, and data line, respectively.
## 5 Results and Discussion
### On the Variability of Model Performance when Grouped by the Considered Scenarios
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Station**} & \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**MAE**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**R2 Score**} \\ \cline{3-8}  & & **Mean** & **Std. Deviation** & **Mean** & **Std. Deviation** & **Mean** & **Std. Deviation** \\ \hline \multirow{3}{*}{BU04} & ST\_CNN\_v1 & 77.39 & 8.82 & 111.99 & 18.56 & 0.78 & 0.02 \\  & ST\_CNN\_v2 & 53.07 & 5.82 & 78.64 & 11.76 & 0.89 & 0.02 \\  & ST\_Dilated\_CNN & 47.48 & 8.51 & 71.25 & 13.49 & 0.91 & 0.02 \\ \hline \multirow{3}{*}{LE05} & ST\_CNN\_v1 & 63.5 & 1.82 & 88.03 & 1.88 & 0.87 & 0.04 \\  & ST\_CNN\_v2 & 42.86 & 2.21 & 67.09 & 3.47 & 0.93 & 0.02 \\  & ST\_Dilated\_CNN & 54.12 & 9.14 & 74.06 & 10.72 & 0.91 & 0.0 \\ \hline \multirow{3}{*}{P03} & ST\_CNN\_v1 & 66.63 & 5.4 & 97.15 & 12.37 & 0.85 & 0.02 \\  & ST\_CNN\_v2 & 44.37 & 7.08 & 67.69 & 10.5 & 0.93 & 0.01 \\  & ST\_Dilated\_CNN & 61.15 & 11.22 & 82.74 & 14.37 & 0.89 & 0.01 \\ \hline \multirow{3}{*}{P07} & ST\_CNN\_v1 & 88.54 & 8.41 & 115.57 & 11.88 & 0.74 & 0.05 \\  & ST\_CNN\_v2 & 52.87 & 12.87 & 83.17 & 22.72 & 0.87 & 0.03 \\  & ST\_Dilated\_CNN & 56.63 & 12.2 & 82.03 & 18.16 & 0.87 & 0.02 \\ \hline \multirow{3}{*}{SG01} & ST\_CNN\_v1 & 71.42 & 8.92 & 100.68 & 15.63 & 0.85 & 0.01 \\  & ST\_CNN\_v2 & 47.51 & 7.01 & 74.9 & 12.6 & 0.92 & 0.01 \\  & ST\_Dilated\_CNN & 63.62 & 7.97 & 83.89 & 13.08 & 0.9 & 0.01 \\ \hline \multirow{3}{*}{SO01} & ST\_CNN\_v1 & 119.83 & 20.57 & 162.58 & 32.13 & 0.57 & 0.15 \\  & ST\_CNN\_v2 & 133.11 & 15.09 & 165.49 & 12.85 & 0.54 & 0.14 \\  & ST\_Dilated\_CNN & 89.27 & 9.33 & 117.33 & 13.61 & 0.78 & 0.01 \\ \hline \multirow{3}{*}{ZA02} & ST\_CNN\_v1 & 65.55 & 3.78 & 88.21 & 3.07 & 0.89 & 0.03 \\  & ST\_CNN\_v2 & 54.52 & 2.39 & 73.37 & 4.07 & 0.92 & 0.01 \\ \cline{1-1}  & ST\_Dilated\_CNN & 44.53 & 4.44 & 64.14 & 6.55 & 0.94 & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Mean and standard deviation values of the performance metrics obtained in Scenario 3. Figure 10: Boxplots of the performance metrics achieved by the proposed models when grouped by the scenarios considered in terms of (**a**) MAE, (**b**) RMSE, and (**c**) R2 score values computed.
# A Period Training Method for Heterogeneous UUV Dynamic Task Allocation
## 4 Simulation Experiment
### Dynamic Case Settings with Emergencies  
All UUVs start from the same base, whose coordinates are (12.5, 12.5), and return to the base when all their tasks are finished. We set three cases with emergencies in simulation experiments.  
1. Case 1: We randomly generate 40 irregular task areas as the initial settings. Four heterogeneous UUVs with different velocities and search ranges need to conduct these tasks with preplanned orders and search coverage paths. The original task situation is shown in Figure 5a.  
Figure 4: The training process of PTM.  
In Figure 5a,b the irregular blue areas are the task areas that need to be searched, the red rectangles are the corresponding approximate area model, and the black star represents the base. The Euclidean distance and the search time of UUVs in all areas can be approximately calculated using the simplified area model, which is a fast and efficient method of evaluating allocated task schemes.
2. Case 2: We consider that the UUVs find a group of new task areas while performing case 1. Similarly, the new target areas are approximated using a bounding rectangle. To search the new task areas, the algorithms need to reallocate the task schemes in real time.
3. Case 3: Following case 2, we assume that UUV 4 is broken and cannot search for any tasks in case 3. To deal with the emergency, the rest of the unfinished tasks are quickly assigned to the other UUVs according to objective function (1) and constraint (4).
# Experimental Activities on a Hydrogen-Fueled Spark-Ignition Engine for Light-Duty Applications
## 4 Results and Discussion
### Effects of Injection Strategy
The thermal  
Figure 11: IMEP, GIE, COVIMEP, ISFC and ISNOx at 1500 rpm engine speed, under low-load conditions and different dilution conditions. Results obtained using the DI system maintaining the SOI at \(-\)70 CAD aTDC in all tests. Figure 10: Maximum in-cylinder pressure and HRR peaks, characteristic combustion periods (CA10, CA50, and CA90), spark timing and combustion duration obtained at 1500 rpm engine speed and low-load conditions for various dilution conditions. The results were obtained using the DI system and the SOI was maintained at \(-\)70 CAD aTDC in all tests. efficiency hovers around 40%, and the CCV remains consistently below or close to 1%, underscoring the noteworthy operational stability. Delaying the SOI results in a substantial enhancement in the engine performance after IVC. The GIE increases from 40% to nearly 43%, accompanied by CCV levels consistently below 1-2% (SOI = \(-\)70 CAD aTDC). Consequently, the IMEP experiences an increment, and the ISFC decreases to 70 g/kWh. However, a notable instability peak emerges when the injection initiates at \(-\)65 CAD aTDC. At this juncture, the CCV escalates to 4%, and the GIE exhibits a slight decline, indicating that the zenith of performance has been attained. Nevertheless, what remains intriguing is what the graph conceals. The subsequent SOI (\(-\)60 CAD aTDC) could not be assessed due to elevated CCV, jeopardizing the operational stability due to an excess of misfiring cycles. This underscores the influence of critical factors, such as the mixing preparation, the flow velocity field, and the turbulence levels within the combustion chamber, which are adversely impacted by delayed injection and contribute to a decline in the combustion stability. It is evident that NOx emissions consistently maintain low levels across the majority of the SOI sweep, primarily attributed to the suppressive influence of elevated dilution rates on NOx formation via the thermal mechanism. However, when the injection timing is further delayed beyond \(-\)110 CAD aTDC under low-load conditions, a discernible rise in NOx emissions becomes apparent. This trend signifies the initiation of a stratified charge, wherein a locally enriched mixture forms, resulting in elevated flame temperatures and consequently augmented NOx production.
# Hydropower Functional Zoning with Crowdsourced Geospatial Data: A Case Study in Sichuan Province
## 4 Discussion
### Development Suggestions Based on Hydropower Functional Regions  
#### 4.2.1 Soil Erosion  
Soil erosion has been a prominent problem that threatens the ecological environment of basins in Sichuan Province. Hydropower-engineering development is inevitably accompanied by surface disturbances, which can easily induce soil erosion. Hence, analyzing the distribution and severity of soil erosion based on zoning results can effectively facilitate zone administration and decision-making in river basins. The zones with hydraulic soil erosion mainly belong to regions with either no hydropower stations or with only one. Hence, these regions either suffer no interference or slight interference from hydropower engineering. In future hydropower planning, it is suggested to avoid the construction of hydropower stations in these regions or to pay close attention to the full implementation of water-soil-conservation engineering measures and decrease the disturbances during the construction process.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hydroelectric-Development Intensity** & **Hydropower Stations (Unit)** & **Total Installed Capacity (10,000 kW)** \\ \hline Core affected zones of all hydropower & 23 & 1303.85 \\ stations & & \\ \hline Affected zones of middle-sized hydropower & 0 & 0 \\ stations & & \\ \hline Core affected zones of middle-sized & 35 & 632.26 \\ hydropower stations & 17 & 4599.4 \\ \hline Construction zones & 49 & 1502.43 \\ \hline Affected zones of large-sized hydropower & 20 & 1270.8 \\ stations & & \\ \hline Core affected zones of large-sized & 27 & 1635.35 \\ hydropower stations & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Statistics of the different hydroelectric-development intensities.
## 2 Materials and Methods
### Respiratory Epithelial and Fibroblast Cells Isolation and Culture  
Nasal turbinate discarded during turbinctomy was obtained from consenting patients and cleaned of mucus and blood three times using Dulbecco's phosphate-buffered saline (DPBS; Gibco, Waltham, MA, USA) supplemented with 1% (\(v/v\)) antibiotic/antimycotic (Gibco, Waltham, MA, USA). The epithelium layer was minced into 1 mm pieces and digested with 0.6% (\(w/v\)) collagenase type I (Worthington, Lakewood, NJ, USA) supplemented with 1% (\(v/v\)) antibiotic/antimycotic for 60 min in a shaker incubator at 37 \({}^{\circ}\)C. After tissue digestion, it was centrifuged (Hettich Zentaifugen, Westphalia, Germany) for 5 min at 2370\(\times\)\(g\). Next, the supernatant was discarded and the pellet containing fibroblasts and RECs was washed with DPBS, followed by re-centrifugation for 5 min at 2370\(\times\)\(g\). Then, the cells were cultured in defined keratinocytes serum-free medium (DKSFM; Gibco, Waltham, MA, USA), F-12, and Dulbecco's modified Eagle's medium (DMEM) in a 2:1:1 ratio, supplemented with 5% fetal bovine serum (FBS) (DKSFM:F-12:DMEM + 5%FBS). Cells were incubated at 37 \({}^{\circ}\)C in a 5% CO\({}_{2}\) incubator (RS Biotech, Irvine, U.K.), and media were changed every 2 days. Once confluent (80-90%), differential trypsinization of fibroblasts was performed using 0.05% trypsin-EDTA (Capricorm Scientific, Ebsdorfergrund, Germany) with 3 min incubation at 37 \({}^{\circ}\)C. This step allowed selective detachment of fibroblasts from the culture plate while leaving colonies of RECs in place. The REC colonies in 6-well plates were left to reach confluency. The trypsinized fibroblasts were then seeded back into another 6-well plate and cultured in F-12:DMEM (1:1) + 10% FBS, with media being replaced every 2 days until 80-90% confluency.
## 2 Materials and Methods
### Satellite Data
#### 2.3.3 Digital Elevation Model (DEM)  
The Shuttle Radar Topography Mission (SRTM) Global 1 arc-second digital elevation model (DEM) is an extensive global elevation dataset providing a resolution of approximately 30 m [73]. It was derived through single-pass interferometry during the space shuttle Endeavour mission in February 2000. The SRTM dataset was processed using the Google Earth Engine (GEE) platform, where it was cropped within the Area of Interest (AOI).
###### Abstract  
The effluent from municipal wastewater treatment plants is a major point source of contamination in Canadian waterways. The improvement of effluent quality to reduce contaminants, such as pharmaceuticals and personal care products, before being released into the environment is necessary to reduce the impacts on organisms that live in the river downstream. Here, we aimed to characterize the metabolic and gill physiological responses of rainbow (_Etheostoma caeruleum_), fantail (_Etheostoma flabellare_), and greenside (_Etheostoma blentioides_) darters to the effluent in the Grand River from the recently upgraded Waterloo municipal wastewater treatment plant. The routine metabolism of darters was not affected by effluent exposure, but some species had increased maximum metabolic rates, leading to an increased aerobic scope. The rainbow darter aerobic scope increased by 2.2 times and the fantail darter aerobic scope increased by 2.7 times compared to the reference site. Gill samples from effluent-exposed rainbow darters and greenside darters showed evidence of more pathologies and variations in morphology. These results suggest that darters can metabolically adjust to effluent-contaminated water and may also be adapting to the urban and agricultural inputs. The modification and damage to the gills provide a useful water quality indicator but does not necessarily reflect how well acclimated the species is to the environment due to a lack of evidence of poor fish health.  
Etheostoma species; wastewater effluent; field metabolic rate; gill pathology; respiratory; pharmaceuticals +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Soil Column Moisture Profile  
In the transient infiltration test, a series of moisture content and suction measurement points were installed along the soil column from bottom to top to obtain the moisture content and suction distributions of the entire soil main cross-section. This moisture profile can be used to calculate the volume of water from one cross-section to another within a certain period of time. The suction profile can be used to calculate the hydraulic gradient. If only one variable, i.e., the moisture profile or suction profile, is measured, the other variable could be obtained from the SWCC [25]. Figure 13 shows the soil column moisture profile and the total hydraulic head profile versus time. Assuming that the coordinate of the top surface of the soil column is 0 cm, the gravity head is numerically equal to the depth of each cross-section, and the distance between any two test points in this experiment is 10 cm, so the total head profile can be calculated for each moment and position of the soil column by Equation (8). The matric suction at the corresponding moisture content can be calculated based on the SWCC in Figure 12.  
Figure 12: SWCC and PSD of the compacted soil sample.  
The change in the moisture content or matric suction along the vertical direction could be divided into three stages. At the beginning of infiltration, the moisture content near the top increases rapidly, and the matric suction decreases rapidly. With the migration of water, the increase in soil moisture content at the top slows, and the range of the rapid increase in moisture content evolves from gradual to deeper positions until reaching the bottom of the soil column. In the third stage, the wetting front reaches the bottom of the soil column, and the moisture content at each position increases or the decrease rate of matric suction decreases until the moisture content at each position of the soil column tends to reach the same level, and the entire unsaturated infiltration process becomes saturated infiltration, indicating the end of the infiltration test.
# Adaptive Coherent Receiver Settings for Optimum Channel Spacing in Gridless Optical Networks
## 4 Experimental Results and Discussions  
### Experimental Setup  
A set of experiments were conducted to get the quantitative benefits with real-world Flex-Grid settings using Ciena 6500 ROADMs. We used three 100G QPSK channels with the center one being the probe. Spacing, in GHz, is defined as the distance between the center of probe channel from the center of the interferer. We used the setup, shown in Figure 10, with three WaveLogic3 (c) transponders operating at 35 GBaud, two as interferers and one centered as the probe (i.e., \"two neighbors\"). Since most digital signal processing techniques for clock recovery are sensitive to optical impairments, our sweeps were done over multiple PDL, DGD, SOP scramblers (PC1 / PC2) and OSNR values. The latter is controlled by adjusting a VOA at the egress of an ASE source.  
Figure 9: Jitter margin as function of channel spacing and link OSNR.
## 3 Proposed ATMALS-GSK Algorithm
### Atmals-Gsk
The overall operation of the proposed ATMALS-GSK algorithm can be seen in Figure 3.  
The initial value, range of variation, and discretized level for each parameter in the ATMALS-GSK algorithm are provided in Table 1. In this table, the parameter \(P_{LS}\) represents the probability of applying a local search change for each decision variable of the \(N_{LS}\) randomly selected solutions at every iteration of the ATMALS-GSK algorithm. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & **GSK** & **ATMALS-GSK** & **Range of Variation** & **Discretized Levels** \\ \cline{2-5}  & **Fixed Value** & **Initial Value** & **Variation** & **Decretized Levels** \\ \hline \(K\) & 10 & 10 & [5; 30] & 1 \\ \(K_{f}\) & 0.5 & 0.5 & [0.2, 0.8] & 0.1 \\ \(K_{r}\) & 0.9 & 0.9 & [0.85, 1] & 0.01 \\ \(P\) & 0.1 & 0.1 & [0.05, 0.15] & 0.01 \\ \(P_{LS}\) & N/A & 1 & [1, 2]/_ProbSize_ & 0.1 \\ \(N_{LS}\) & N/A & 0.05 \(\times\)_PopSize_ & N/A & N/A \\ \(RP_{G}\) & N/A & 5 & [3; 5] & N/A \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of fixed and adaptive parameters in GSK and ATMALS-GSK algorithms.