esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Aqueous Decomposition of Ag\({}_{7}\)NO\({}_{11}\):SiO\({}_{2}\)  
The aqueous decomposition profiles of silver oxynitrate and Ag\({}_{7}\)NO\({}_{11}\):SiO\({}_{2}\) containing 0.1:1 SiO\({}_{2}\):Ag were also investigated. In brief, the solid powder products were dispersed into aqueous media at room temperature and, at set time intervals over seven days, were evaluated by XRD and SEM. Following dispersion into aqueous media it was observed that, with increasing relative ratios of silica, the Ag\({}_{7}\)NO\({}_{11}\):SiO\({}_{2}\) powders had an increased time of suspension as shown in Figure S5. The aqueous decomposition profiles were observed to be similar for silver oxynitrate and Ag\({}_{7}\)NO\({}_{11}\):SiO\({}_{2}\) as seen in Figure 6. Within two hours of exposure to water, silver oxynitrate was confirmed as the primary solid-state silver species, with minor components attributed to AgO and Ag\({}_{2}\)SO\({}_{4}\) confirmed by means of their respective standard diffraction patterns. Following 24 h in aqueous media, silver oxynitrate remained the primary solid-state compound, with minor components attributed to AgO and Ag\({}_{2}\)SO\({}_{4}\). Relative counts for AgO versus Ag\({}_{7}\)NO\({}_{11}\) were proportionally higher in Ag\({}_{7}\)NO\({}_{11}\):SiO\({}_{2}\). At the terminus of the seven-day study, AgO was identified as the primary solid-state species for both silver oxynitrate and Ag\({}_{7}\)NO\({}_{11}\):SiO\({}_{2}\), with secondary diffraction patterns attributed to Ag\({}_{2}\)SO\({}_{4}\). The recovered solids at each time point were imaged by SEM. Typical geometric crystalline structures of silver oxynitrate were observed in Figure 7A,D for both silver oxynitrate and Ag\({}_{7}\)NO\({}_{11}\):SiO\({}_{2}\). Subsequent to aqueous exposure, a crystalline morphology transformation was observed in Figure 7C,F previously identified to be associated with the formation of argentic oxide, AgO [22]. Retention of the silica framework in the co-deposition product was observed over the seven-day evaluation period as seen in Figure 7E,F.  
Figure 5: Powder X-ray diffraction patterns of (**A**) silver oxynitrate (Ag\({}_{7}\)NO\({}_{11}\)) and; (**B**) Ag\({}_{7}\)NO\({}_{11}\):SiO\({}_{2}\) containing 0.10:1, SiO\({}_{2}\):Ag, equivalents silica over a four month period under accelerated storage in an incubator at 40 \({}^{\circ}\)C. Solid-state silver compounds were identified as silver oxynitrate (Ag\({}_{7}\)NO\({}_{11}\), inverted red triangles), silver sulfate (Ag\({}_{2}\)SO\({}_{4}\), green circles), argentic oxide (AgO, blue squares), silver nitrate (AgNO3, orange diamonds), silver oxide (Ag\({}_{2}\)O, blue triangle), and metallic silver (Ag, grey arrows).
## 2 Materials and Methods
### Satellite Imagery  
#### 2.3.1 Landsat TM5 Imagery  
Atmospherically corrected surface reflectance Landsat TM5 (1999-2011) and Landsat 8 OLI (2013-2016) (both Collection 1, Tier 1) data were used to derive models of yearly cumulative NEE (Table 1). A query of the USGS archives through Google Earth Engine [42] yielded a catalog of 488 and 172 scenes of Mer Bleue, respectively, for the eddy covariance tower footprint area considered here (55,800 m\({}^{2}\)). Only snow- and cloud-free scenes for the study area were retained for the months spanning from April to November. The temporal frequency of the scenes in the collection was uneven between years, ranging from 5 to over 20. Therefore, in order for a year to be included in the models (Section 2.5.2), multiple scenes were required from all three seasons (i.e., spring (leaf off): prior to vascular vegetation green-up; summer: full foliage; and fall: senescent vascular vegetation). With these criteria, 175 scenes for TM5 and 63 scenes for OLI8 were retained (Table 3).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Year** & **Sensor** & **No. of Scenes Used** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 3: Number of Landsat TM5 and OLI8 (Path/Row: 15/28, 16/28 and 15/29) scenes used in the generation of the yearly cumulative net ecosystem exchange (NEE) models.
# Underwater 2D Image Acquisition Using Sequential Striping Illumination
## 2 Approach of Image Recovery from Sequential Striping Illuminated Images
### System Implementation  
The system implementation is illustrated in Figure 2. The DLP (Digital Light Processing) projector is used as the illumination source. The CCD video camera (GO-5000-PGE produced by JAI company, Denmark.) is controlled to record the frame images synchronously while the illumination scans across the object. The projected illumination pattern scans along the direction aligned with the x coordinate of the CCD image.  
Figure 2: System implementation.  
The illumination pattern \(I(x)\), specified in (5), is designed on the projector and the scanning is realized by sequentially translating the center of the illumination pattern on the projector imaging plane to a fixed displacement (1 pixel in our experiment), which is controlled through a computer program.  
Concerning the requirements of 3D measurement, the transverse distribution of the lighting stripe, generated from the projector, is designed to be a Gaussian distribution, so the shape of projected \(I(x)\) could facilitate detection of the center of the structured light in 3D computations. The width of the stripe, the parameter describing the normalized Gaussian distribution s, was set to 9 pixels.  
A schematic description of the sequential illumination process and an example of the sequential frame images obtained from a real object are illustrated in Figure 3.
## 1 Introduction
The development of effective tools to accurately predict the bioavailable fraction of metals constitutes a very important step in providing a risk assessment of the potential exposure of soil-dwelling organisms and can contribute to improvements to ecosystem health, as well as in remediated ecosystems. The methods for the measurement of TE availability are based on different techniques, including chemical extraction [9; 10; 12; 13; 21; 24; 25], modeling processes [26; 27], and bioassays [7; 8; 12; 13; 21; 24; 28; 29]. Of these, bioassays give a more direct measurement of biological responses, as well as the real bioaccumulation rate of TE [8; 12; 13; 21; 29; 30; 31]. The direct responses of organisms exposed to potentially polluting elements obtained by bioassays are indicative of the actual risk of pollutants [32]. Although these methods are time-consuming and relatively expensive (due to the different endpoints, exposure times, and uptake mechanisms involved), they are commonly used as efficient predictors of metal bioavailability [7; 12; 13; 21; 24; 25]. For this purpose, earthworms are considered to be the most important bio-indicators due to their intimate (external and internal) contact with the soil and the possibility they offer of evaluating the potential of TE bioaccumulation and food-chain transfer [7; 13; 33; 34; 31]. Studies published in recent years pointed out that the variation in the TE bioaccumulation in worms may be dependent on their bioavailability [35; 36; 37; 38; 39; 40], it is as affected by soil factors influencing metal solubility (pH and redox) and complexation with organic matter [41; 42; 43] However, these factors and mechanisms are still poorly understood. Moreover, earthworm studies in contaminated areas have mainly focused on the analysis of the assimilation of TE by earthworms in artificially contaminated soils. Relatively few studies have been conducted to examine the effectiveness of soil amendments at reducing TE bioaccumulation in soil organisms. Therefore, the aim of our research was to assess the impact of soil amendments characterized by different sorption properties and mechanisms (two types of compost, two types of biosolid, calcium phosphate, iron oxide, bentonite, rock waste, and limestone) on the level of TE (Cu, Zn, Pb, Cd, Ni, and Cr) bioavailability for earthworms. The research was conducted as a microcosm experiment using soil derived from heavily contaminated area subjected to long-term industrial pressure.
# Limitations of Using DAQ Cards for Absolute Vibration Measurement in Hydraulic Machines
## 5 Measurement of Absolute Vibrations of Hydraulics Machines Using DAQ Cards, Measurement Setup
### NI 6289  
The NI 6289 card from National Instruments (National Instruments Inc., Austin, TX, USA) is characterized by the following parameters:  
* resolution: 18 bits;
* number of analog inputs (multiplexed): 32/16 (asymmetric inputs/differential inputs);
* maximum sampling rate (when measuring a single channel): 625 kHz;
* maximum sampling rate (sum of channels used): 500 kHz;
* measurement ranges: \(\pm\)10, \(\pm\)5, \(\pm\)2, \(\pm\)1, \(\pm\)0.5, \(\pm\)0.2, and \(\pm\)0.1 V. The test results of the NI 6289 card are presented in Table 6 and Figure 10.  
The maximum value of the vibration velocity determined in the test does not exceed 0.041 m/s for the ranges of 10 and the cut-off frequency of 0.1 Hz. The vibration displacements reach 70 \(\upmu\)m for the range of 10 V and the filter frequency of 0.1 Hz. For the minimum voltage range and this frequency, the value of the vibration displacements is 12 \(\upmu\)m. A value not greater than 1 \(\upmu\)m is achieved for the range of 1 V (and lower) at a filtration frequency of not less than 1 Hz for the range of 2 V and the cut-off frequency  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline  & \multicolumn{2}{c}{**2 Hz**} & \multicolumn{2}{c}{**1.5 Hz**} & \multicolumn{2}{c}{**1 Hz**} & \multicolumn{2}{c}{**0.5 Hz**} & \multicolumn{2}{c}{**0.25 Hz**} & \multicolumn{2}{c}{**0.1 Hz**} \\ \hline
**Range** & **vrms** & **s**p-**p** & **vrms** & **s**p-**p** & **vrms** & **s**p-**p** & **vrms** & **s**p-**p** & **vrms** & **s**p-**p** & **vrms** & **s**p-**p** \\
**(V)** & **(mm/s)** & **(\(\upmu\)m)** & **(mm/s)** & **(\(\upmu\)m)** & **(mm/s)** & **(\(\upmu\)m)** & **(mm/s)** & **(\(\upmu\)m)** & **(mm/s)** & **(\(\upmu\)m)** & **(mm/s)** & **(\(\upmu\)m)** \\ \hline \(\pm\)10 & 0.049 & 8.60 & 0.059 & 13.90 & 0.070 & 21.78 & 0.096 & 51.13 & 0.104 & 82.49 & 0.114 & 188.71 \\ \(\pm\)5 & 0.025 & 4.40 & 0.029 & 6.37 & 0.035 & 10.74 & 0.046 & 22.88 & 0.059 & 51.00 & 0.062 & 97.25 \\ \(\pm\)2 & 0.010 & 1.66 & 0.012 & 2.73 & 0.014 & 4.28 & 0.020 & 10.87 & 0.024 & 21.90 & 0.025 & 38.59 \\ \(\pm\)1 & 0.006 & 1.01 & 0.006 & 1.29 & 0.008 & 2.34 & 0.010 & 5.16 & 0.012 & 10.32 & 0.014 & 20.73 \\ \(\pm\)0.5 & 0.003 & 0.55 & 0.004 & 0.81 & 0.005 & 1.4 & 0.006 & 3.33 & 0.007 & 5.66 & 0.010 & 26.44 \\ \(\pm\)0.2 & 0.002 & 0.41 & 0.003 & 0.57 & 0.003 & 1 & 0.005 & 2.9 & 0.006 & 6.26 & 0.008 & 14.8 \\ \(\pm\)0.1 & 0.002 & 0.37 & 0.002 & 0.57 & 0.003 & 0.96 & 0.004 & 2.31 & 0.006 & 5.67 & 0.007 & 13.68 \\ \hline \end{tabular}
\end{table}
Table 5: Vibration measurement results using the NI 6033E card for different cut-off frequencies of high-pass filters.  
Figure 9: Vibration measurements using the NI 6033E (National Instruments Inc., Austin, TX, USA) card depending on the cut-off frequency of the high-pass filter: vibration velocity (**a**) and vibration displacement (**b**).
## 5 Conclusions  
This paper first reviews the research on network performance prediction and network-intensive task scheduling. Due to the limitations of the service capabilities of data sources and the constraints of data user node collection capabilities, large-scale public remote sensing data collection has low efficiency and low user collection node utilization. In order to solve these problems, we propose a task scheduling model for open remote sensing data acquisition. An improved PSO-BP algorithm is proposed by improving the inertia weight and learning factor and introducing the dynamic precision adjustment function. In order to address the challenges in the dynamic task allocation of a large amount of remote sensing data, especially to improve the resource utilization of the collection nodes and collection speed, a multi-objective task scheduling model was established. With the consideration of the difference in the speed of the collection task performed by each node at different times, the TSCD-TSA dynamic task scheduling algorithm was developed to improve the traditional PSO optimization algorithm by using FCFS, MAX-MAX, and SJF algorithms. Comparative simulation experiments of four dynamic task scheduling algorithms, namely, FCFS-PSO, SJF-PSO, MAX-MAX-PSO, and PSO, were carried out on the CloudSim platform with the Sentinel2 acquisition task and the BP neural network algorithm as the prediction algorithm of the acquisition node network transmission capacity. The task execution of each node of various task scheduling algorithms was analyzed and compared, and the fitness value and convergence of various algorithms were analyzed. The experimental results show that with the increase in the number of tasks, the fitness value of the MAX-MAX-PSO algorithm presents a decreasing trend, and the convergence speed is obviously accelerated. In the future, we would like to establish a crowdsourced node network transmission capacity prediction model and use deep-learning-based algorithms  
Figure 5: Results of task execution of different algorithms.  
to compare with the current algorithms by analyzing the characteristic system of node network transmission capability.  
Conceptualization, Z.W. and L.B.; methodology, Z.W. and L.B.; software, X.L. and Y.C.; validation, X.L., Y.C. and M.Z.; formal analysis, X.L.; investigation, L.B. and Z.W.; resources, L.B., Z.W. and X.L.; data curation, X.L.; writing--original draft preparation, L.B. and X.L.; writing--review and editing, Z.W., L.B., X.L., Y.C., M.Z. and J.T.; visualization, X.L.; supervision, Z.W. and L.B.; project administration, J.T.; funding acquisition, J.T. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by TUOHAI special project 2020 of the Bohai Rim Energy Research Institute of Northeast Petroleum University under Grant HBHZX202002 and the Project of Excellent and Middle-aged Scientific Research Innovation Team of Northeast Petroleum University under Grant KYCXTD201903.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Key Techniques of near Real-Time Remote Sensing  
### Random Access Technology of Remote Sensing Users  
Users can access satellite Internet anytime and anywhere without relying on the terrestrial network based on the LEO broadband communication terminal, as shown in Figure 3.  
Under the mode of random access, the location and access time of the terminal are unknown to the gateway station; thus, the terminal can only acquire beam resources through competition. The process of terminal access under random access mode is shown in Figure 4 as follows:  
1. The terminal calculates the orbit of the satellite according to the built-in ephemeris, obtains the location of visible satellites, and then re-directs the antenna beam to complete the satellite tracking.  
Figure 3: Illustration of random access of remote sensing users to the satellite Internet.  
Figure 2: Workflow comparison of NRRS based on satellite Internet and traditional remote sensing.  
2. In the absence of ephemeris, the terminal scans the airspace to search for visible satellites and capture the broadcast signal. The ephemeris information in the broadcast signal is used to complete satellite ephemeris updates, hence realizing the satellite tracking. 3. The terminal sends a random access request over a dedicated access channel. 4. The gateway station determines whether the authentication is passed and accepts the application for terminal access.
# Mechanisms of Noise Transmission in Velocity Broad-Band Seismometers: Modeling and Analysis
## 3 Methodology  
The noise transfer function characterizes the shaping effects and mechanisms of the transmission path on noise. Our methodology centers on evaluating the transfer function that converts the inherent noise from each module into its EIN. We then further investigated the frequency spectral characteristics of these transfer functions, including amplitude characteristics, corner frequency, and 0 dB point frequency. The amplitude characteristics quantify the degree to which the transmission path amplifies or attenuates noise across different frequencies. The corner frequency represents the transition point in the noise-shaping characteristics of the transmission path, highlighting a shift in the underlying noise propagation mechanisms. The 0 dB point frequency identifies the threshold at which the transfer function ceases to amplify or attenuate noise. These analyses offer a foundation for developing effective noise mitigation strategies. Based on this, we propose a comprehensive self-noise model for the seismometer.
## 4 Bridge Degradation Identification and Model Update Results
### Analysis of Crack Detection Results  
This study applied the bridge crack detection algorithm model and particle swarm optimization (PSO) parameter optimization model proposed in Sections 2.2 and 2.3 to detect cracks in the point cloud data of Hedong Bridge from 2017. For the two parameters defined in the bridge crack detection algorithm--the distance threshold for adjacent points in density calculation and the adjustment factor for the sparsity threshold--a manual rough determination of the value range was completed. These ranges were then applied as constraints for parameter optimization, with the PSO algorithm iteratively selecting the most sensitive values for the two parameters.  
Figure 12: Elevations and plans of the continuous rigid frame section of Hedong Bridge (unit: cm).
# A Hyper Heuristic Algorithm to Solve the Low-Carbon Location Routing Problem
## 5 Simulation Experiment and Analysis
### Analysis of Model Correctness and Algorithm Performance  
There are no benchmarks for RLCLRPTWSPDHF, and the following examples are arranged according to the purpose of the experiment--15 simple examples were randomly generated, satisfying the following conditions: customer number \(N\in\{10,15,20\}\); depot number \(M\in\{2,3,5\}\); customer pickup quantity \(q\) and delivery quantity \(p\) obeyed \(U[100,1100]\) (kg); the customer time window was randomly generated and guaranteed \(l_{i}-e_{i}\geq ST_{i}\) (\(ST\in U[0,10]\) (minutes)); the vehicle type with different capacities was \(\{L1,L2,M\}\) fixed vehicle costs were \(FV\in[37.18,43.38,53.11]\) (yuan); depot capacity was \(CD\in\left\{104,1.5\times 10^{4}\right\}\) (kg); single depot costs, FD, were 200 yuan (Zone 3), 350 yuan (Zone 2), and 500 yuan (Zone 1); \(C_{vw}=0.3\) yuan/minute; \(C_{vw}=0.5\) yuan/minute; \(C_{f}=7.60\) yuan/minute; and \(C_{\omega 2}=38.49\) yuan/ton (Shanghai transaction price on 4 May 2018); Other data can be referred to in [17].  
To verify the correctness of the proposed model and the accuracy of the algorithm, the 15 examples above were solved by SESMAM-HH and CPL-EX. The results are shown in Table 2. In the table, the first column is the example name \(LN\times M-No\), N and M are the number of customers and depots, No. indicates the serial number at the same scale, TC is the total cost (yuan), Gap is the percentage between the two TCs, SD is the standard deviation of the total cost of five independent runs, MT is the average running time (s) of five independent runs, and Av. is the average.  
The results show that the proposed algorithm, SESMAM-HH, can find the optimal solution within a reasonable calculation time and ensure that the standard deviation of all instances is zero. Therefore, it can be proven that the proposed model is correct, and that the accuracy of the algorithm is high.
# Shear Behavior of Superposed Perfobond Connectors Considering Lateral Constraints
## 4 Equation for Calculating Shear Capacity
### Proposed Shear Capacity Equation
#### 4.2.1 Concrete Dowel and Perforating Rebar  
The shear contributions of the concrete dowel and perforating rebar are shown in Figure 17b. Both suffer from shear action on both sides. Thus, the shear action can be expressed as  
\[P_{\rm c}=2\tau_{\rm c}A_{0} \tag{7}\]  
\[A_{0}=A_{\rm s}\bigg{(}\frac{E_{\rm s}}{E_{\rm c}}-1\bigg{)}+A \tag{8}\]  
where \(A\) is the area of the hole (mm\({}^{2}\)); \(A_{\rm s}\) is the area of the perforating rebar (mm\({}^{2}\)); \(E_{\rm s}\) and \(E_{\rm c}\) are the elastic moduli of the perforating rebar and concrete (MPa), respectively; and \(\tau_{\rm c}\) is the shear strength of concrete under multi-directional stress.  
The shear strength of concrete in the pure shear state is \(\tau_{\rm p}=0.39f_{\rm cu}\)\({}^{0.57}\), as presented in [36]. However, because the concrete in the hole undergoes tri-axial compression through the perforating rebar, the perfobond rib, and the surrounding concrete [20; 22], the concrete strength increased to a certain extent. Hence, the concrete strength improvement coefficient \(k_{\rm p}\) was introduced. Thus,  
\[P_{\rm c}=2k_{\rm p}\tau_{\rm p}A_{0}=k_{0}f_{\rm cu}^{0.57}A_{0} \tag{9}\]  
The multi-hole perfobond connectors are equivalent to \(2n\) shear surfaces. Hence, the shear capacity of the concrete dowel and perforating rebar \(P_{\rm c}\) is expressed as  
\[P_{\rm c}=k_{0}nA_{0}f_{\rm cu}^{0.57} \tag{10}\]  
Figure 17: Shear calculation model of perfobond connectors: (**a**) Total shear capacity; (**b**) Shear contribution of concrete dowel and perforating rebar; (**c**) Shear contribution of lateral constraints; (**d**) Composition of lateral constraints.