esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Wastewater Collection, Pretreatment, and Analysis  
Samples of anaerobic digested swine wastewater (ADSW) were collected from Xin Wu Feng Co., Ltd. (Branch office: Yong'an Town, Changsha, Hunan Province, China). The raw ADSW sample was centrifuged at 8000\(\times\)\(g\) for 10 min, followed by immediate filtration through 0.45 \(\upmu\)m nylon membranes to remove insoluble solids. Sterilization was performed via autoclaving at 121 \({}^{\circ}\)C for 20 min. For physicochemical analysis, the pH value was measured using a glass electrode. Total nitrogen (TN) was quantified by ultraviolet spectrophotometry after oxidation with alkaline potassium persulfate. Total phosphorus (TP) was determined by ammonium molybdate spectrophotometry. Ammonium nitrogen (NH\({}_{4}\)+-N) was measured using sodium chloride reagent-based spectrophotometry. Chemical oxygen demand (COD) was quantified using the dichromate method. Total organic carbon (TOC) and inorganic carbon (IC) were analyzed according to the Chinese National Standards. The compositions of ADSW are summarized in Table 1.
# Effect of Mix Proportion Parameters on Behaviors of Basalt Fiber RPC Based on Box-Behnken Model
## 3 Results and Discussion
### Influence of Parameters on the Performance of BFRPC  
#### 3.2.1 Fluidity  
The fluidity prediction model obtained by BBD method is reported in Table 7 and is given by Equation (1). According to the \(p\) values of various factors obtained from ANOVA, it could be seen that the influence degree of factors on fluidity was: \(B>D>A>BD>AD\).  
The effect of water/binder ratio (\(B\)) and sand/binder ratio (\(A\)) on fluidity (\(R1\)) of the mixture under the condition of different silica fume content, is shown in Figure 3. It was found that \(B\) had the most effect on the fluidity according to the results of ANOVA. It always showed a positive proportional  
Figure 2: Predicted and measured values of experimental properties. (**a**) Fluidity; (**b**) flexural strength and (**c**) compressive strength.  
linear relationship between the \(w\)\(b\) ratio and fluidity as can be seen from the contour line in Figure 3. Obviously, the more water added into the mixture, the more free water there was, so the fluidity of mixture increased.  
Meanwhile, there were interactions of \(BD\) and \(AD\). By controlling \(D\) as a variable, three contour plots of \(AB\) could be obtained as Figure 3 shows. It was observed that the influence of \(A\) and \(B\) on fluidity was linear, but the linear trend gradually changed with the variation of silica fume content (\(D\)). As shown in Figure 3a, when the silica fume content (\(D\)) was less than 0.38, a decrease of the sand/binder ratio (\(A\)) led to an increase of fluidity values. As the critical point, when silica fume content (\(D\)) approached 0.38, the fluidity of the mixture would no longer be affected by the sand volume (\(A\); Figure 3b). When the silica fume content was greater than 0.38, the fluidity of the mixture improved with the increase of the sand volume (\(A\)) as illustrated in Figure 3c. Meanwhile, with the increase of \(D\), the fluidity of the mixture presented an overall downward trend. The fluidity of mixture will affect the construction workability of concrete, but its mechanical properties should be considered comprehensively to carry out the mix design.
# Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond
## 3 Results and Discussion
### Reported Application of AI to RT
#### 3.2.2 Image Registration  
Image registration and fusion algorithms are currently applied in RT and are considered a critical component of contour propagation and dose accumulation among consecutive RT treatments, as well as in online or offline adaptive plan optimization. Automated rigid registration or deformable image registration (DIR) often requires manual tuning due to possible relevant patient anatomy and setup changes compared with the available images. This task is time-consuming but can be aided by AI-based algorithms to assist the radiation oncologist. In [12; 13], DL-based methods were implemented to learn similarity metrics for image registration purposes, allowing for fast, user-independent, non-rigid inter-modality registration  
It should be noted that non-negligible uncertainties still exist, potentially affecting applications based on DIR [14]. The validation of the DIR-based algorithms against expert contours is necessary, as recommended by the American Association of Physicists in Medicine's Task Group (TG) 132 report [15] and as applied in several papers [16; 17; 18; 19; 20; 21; 22; 23].
# Machine-Learning-Based Optimization for Multiple-IRS-Aided Communication System
###### Abstract  
Due to the benefits of the spectrum and energy efficiency, intelligent reflecting surfaces (IRSs) are regarded as a promising technology for future networks. In this work, we consider a single cellular network where multiple IRSs are deployed to assist the downlink transmissions from the base station (BS) to multiple user equipment (UE). Hence, we aim to jointly optimize the configuration of the BS active beamforming and reflection beamforming of the IRSs that meet the UE's QoS while allowing the lowest transmit power consumption at the BS. Although the conventional alternating approach is widely used to find converged solutions, its applicability is restricted by high complexity, which is more severe in a dynamic environment. Consequently, an alternative approach, i.e., machine learning (ML), is adopted to find the optimal solution with lower complexity. For the static UE scenario, we propose a low-complexity optimization algorithm based on the new generalized neural network (GRNN). Meanwhile, for the dynamic UE scenario, we propose a deep reinforcement learning (DRL)-based optimization algorithm. Specifically, a deep deterministic policy gradient (DDPG)-based algorithm is designed to address the GRNN algorithm's restrictions and efficiently handle the dynamic UE scenario. Simulation results confirm that the proposed algorithms can achieve better power-saving performance and convergence with a noteworthy reduction in the computation time compared to the alternating optimization-based approaches. In addition, our results show that the total transmit power at the BS decreases with the increasing number of reflecting units at the IRSs.  
intelligent reflecting surfaces; joint beamforming optimization; machine learning; deep-reinforcement-based learning +
Footnote †: journal: Electronics Electronics Letters  
15 March 2022  
Received: 26 March 2022  
Accepted: 28 March 2022  
Published: 4 April 2023
## 2 Materials and Methods
### NEP Map Production  
The grassland and cropland NEP regression tree models were applied to the U.S. Great Plains using the input spatial data applicable to each model and time period to produce weekly NEP maps. For example, the cropland model was implemented for week 1 of 2000 by using all week 1 data from 2000 (NDVI, Week, PCP, TMIN, and TMAX), all annual data from 2000 (AMP, DUR, EOSN, EOST, MAXN, MAXT, SOSN, SOST, TIN, and CTM), along with the static input datasets (30-year normal data, AWC, CP, BD, and SOC). In this example, the cropland model would yield the week 1 NEP cropland estimates for 2000. Through this model implementation process, 936 weekly, 250 m NEP maps for grassland and cropland of the U.S. Great Plains for 2000-2008 were produced (468 grassland NEP maps and 468 cropland NEP maps). Following the initial map development, a masking step was performed to remove areas where the dominant crop, during the 2000-2008 time period, was something other than corn, soybeans, winter wheat, or alfalfa (Figure S2).
## 6 On-Site Constructions of the Vibrated Grout-Enriched CSGR
### Application  
The Tangba Protection dam has a total length of 2.7 km, with a crest elevation of 336.10 m and the filling volume of CSGR material reached 373,000 m\({}^{3}\). The vibrated grout-enriched CSGR with a slurry rate of 9% was used in the protection and seepage control layer on both the upstream and downstream areas. Through sequential rolling and layer-by-layer construction, the total amount of vibrated grout-enriched CSGR used is 20,867 m\({}^{3}\). Figure 12 illustrates the completed Tangba Protection Dam. The quality test results in the construction process are presented in Table 11. The protective layer of the dam meets the design requirements of C\({}_{90}\)15W6F50. The T-shaped surface water stop totals 10,163 m. Its installation and fixation were straightforward, ensuring fast construction speed, easy replacement and repair in subsequent stages (Figure 13).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Slurry Addition Rate (\%)**} & \multirow{2}{*}{**Surface Quality**} & \multicolumn{2}{c}{**Compressive Strength (MPa)**} & \multirow{2}{*}{**Impermeability Grade**} & \multirow{2}{*}{**Freeze Resistance Grade**} \\ \cline{3-3} \cline{5-6}  & & **Minimum Value** & & & **Mean Value** \\ \hline \multirow{3}{*}{7\%} & surface less slurry & 12.2 & 15.8 & W8 & F50 \\  & the surface can be slurried, & 14.4 & 17.4 & W9 & F50 \\ \cline{1-1}  & the slurry is suitable & & & & \\ \cline{1-1}  & the surface can be slurried, & 15.2 & 19.8 & W11 & F75 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Test results of different slurry addition rate.  
Figure 12: The completion of Tangba CSGR dam.  
From 15 to 16 May 2020, the first phase of water storage was completed, with the water level rising from 326 m to 330 m. Subsequently, from 15 to 16 September 2021, the second phase of water storage concluded, resulting in the water level increasing from 330 m to 335 m (see Figure 14). The water storage operations have been ongoing for nearly four years. Notably, optical fiber leakage measuring devices installed after the transverse joint have detected no leakage points, indicating the commendable comprehensive anti-seepage performance of the dam body. It is worth highlighting that during the impoundment period, the leveee endured the '8 18' catastrophic flood in 2020. Despite the large-scale disaster in Leshan and Qianwei, it effectively withstood the excessive flood, ensuring the safety of life and property for over 11,000 residents and more than ten major enterprises in Tangba Township. The safety and reliability of the dam have met the anticipated standards.
# 3D DNA Nanostructures: The Nanoscale Architect
## 5 Applications
### Encapsulation
#### 5.1.1 Nanocasting  
Control over shape and permeability has been vital to synthesizing shaped gold and silver nanoparticles using a hollowed DNA nanostructure as a mold via a process termed _nanocasting_[92; 93] (Figure 6D). The nanocasting process utilizes seeded-growth nanoparticle synthesis but with the seed beginning within a shaped cavity that the growing nanoparticle will conform to. This process has been demonstrated with silica nanotubes [94]  
Figure 5: Reinforcement of DNA nanostructures via coating. (**A**) Applying a coat of silica on DNA nanostructures has been shown to increase stiffness and stability (reprinted with permission from Springer Nature [80]); (**B**) Protein coatings have been shown to increase immunocompatibility of DNA nanostructures as well as resist degradation by DNase I (reprinted with permission from Wiley-VCH [86]); (**C**) Peptidoid coatings maintain stability of DNA nanostructures in low Mg\({}^{2+}\) concentrations and in the presence of DNase I (reprinted with permission from National Academy of Sciences [83]).  
and protein shells [95] but without the same degree of shape variety as DNA nanostructures. The advantage of using DNA nanostructures to form the mold is the wide variety of shapes, such as cubes, discs, triangles, as well as their compositions, such as into Y-branch shapes. Expanding the available geometries of other technologies is, in isolation, already a significant research problem, while shape design via DNA origami is generalized across a variety of geometries. Currently, while still effective upon the shape of the metallic nanoparticle, the flexibility of DNA nanostructures slightly yields to the forces of nanoparticle growth upon its inner walls. Thus so far, the process mandates accurate timing, rather than fully relying on the walls of the DNA mold, to terminate nanoparticle growth, but methods as yet untested for nanocasting for improving the rigidity of DNA nanostructures could be later shown to improve nanocasting quality [78; 80]. Nonetheless, the results show enticing potential for the bottom-up synthesis of plasmonic nanoparticles [92] or nanowires [96].
###### Abstract  
Type 1 diabetes mellitus (DM1) is a metabolic disease derived from falls in pancreatic insulin production resulting in chronic hyperglycemia. DM1 subjects usually have to undertake a number of assessments of blood glucose levels every day, employing capillary glucometers for the monitoring of blood glucose dynamics. In recent years, advances in technology have allowed for the creation of revolutionary biosensors and continuous glucose monitoring (CGM) techniques. This has enabled the monitoring of a subject's blood glucose level in real time. On the other hand, few attempts have been made to apply machine learning techniques to predicting glycaemia levels, but dealing with a database containing such a high level of variables is problematic. In this sense, to the best of the authors' knowledge, the issues of proper feature selection (FS)--the stage before applying predictive algorithms--have not been subject to in-depth discussion and comparison in past research when it comes to forecasting glycaemia. Therefore, in order to assess how a proper FS stage could improve the accuracy of the glycaemia forecasted, this work has developed six FS techniques alongside four predictive algorithms, applying them to a full dataset of biomedical features related to glycaemia. These were harvested through a wide-ranging passive monitoring process involving 25 patients with DM1 in practical real-life scenarios. From the obtained results, we affirm that Random Forest (RF) as both predictive algorithm and FS strategy offers the best average performance (Root Median Square Error, RMSE = 18.54 mg/dL) throughout the 12 considered predictive horizons (up to 60 min in steps of 5 min), showing Support Vector Machines (SVM) to have the best accuracy as a forecasting algorithm when considering, in turn, the average of the six FS techniques applied (RMSE = 20.58 mg/dL).  
diabetes mellitus; machine learning; feature selection; time series forecasting +
Footnote †: journal: Applied Sciences
# Study on Seismic Reduction Measures of a Diaphrag Wall--Underground Structure System
## 2 Underground Structure-Isolation Layer-Diaphragm Wall System
### The Effect of Isolation Layer  
#### 2.2.1 Deformation Analysis of Main Structure  
The change curve of the structural lateral displacement along the height when the relative displacement between the top and bottom of the structure reaches the peak value is shown in Figure 8a. It can be seen that the structural lateral deformation is obviously affected by the isolation layer segmented form. Compared with the case of only the diaphragm wall, the integral isolation layer obviously amplifies the interstory displacement of the station structure. However, the displacement between floors is slightly enlarged when the segmented isolation layer is used, but the overall lateral displacement curve of the structure is close to that of the case with only the diaphragm wall. This is the main reason why the rigid isolation layer is used to connect the diaphragm wall with the top plate, middle plate and bottom plate.  
Figure 8b shows the lateral displacement of structure by setting the segmented isolation layer with different stiffness. It can be seen that the lateral deformation of the structure is obviously affected by the stiffness of the flexible isolation layer material. As the flexible isolation layer changes from soft to hard, the relative displacement between the top and bottom of the structure gradually decreases. When the elastic modulus of the flexible isolation layer is taken as 31,500 MPa, the relative displacement reaches the minimum value. By observing the shape of the curve, it can be found that the lateral deformation curve of the structure has an obvious turning point at the connection between the rigid and flexible isolation layer. The deformation characteristic is less affected by the stiffness change of the flexible isolation layer.  
Figure 7: Input seismic records.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Soil Stratum** & **A** & **B** & **\(\gamma_{0}\) (\(\times 10^{4}\))** \\ \hline Miscellaneous fill & 1.2 & 0.4 & 1.69 \\ Silty clay & 1.2 & 0.47 & 5.8 \\ Pebbles & 1.25 & 0.31 & 8.99 \\ Sand & 1.15 & 0.33 & 5.36 \\ Silt & 1.05 & 0.49 & 5.4 \\ \hline \end{tabular}
\end{table}
Table 3: The values of the Davidenkov model parameters.  
Figure 8c shows the lateral displacement of structure by setting the segmented isolation layer thickness. The lateral deformation of the structure is obviously affected by the thickness of the layer. With the thinning of the layer, the relative displacement between the top and bottom of the structure gradually decreases. When the thickness of the layer is 0.2 m, the relative displacement reaches the minimum. The segmented isolation layer of 0.2 m thick is enlarged by 3.18% compared with the case without the diaphragm wall. Compared with the case with only the diaphragm wall, the segmented isolation layer is enlarged by 1.82%.
## 5 Numerical Studies  
The influences of the mechanical uncertainties and thermal deformation on the transmission errors of a spur gear pair were examined using the proposed method with numerical examples. The tooth contact analysis of numerical examples is under 2D simplification, and we focus on studying the influence of uncertainties on 2D spur gear pairs. We considered three numerical examples for investigating the influences of the uncertainties and transmission errors with a comprehensive error. Table 1 summarizes the design parameters and material properties of the spur gear pair used in the numerical example. There are two cases of gear pairs, i.e., steel and plastic. For lubrication, we consider its effect on the steel gear pairs only since, in general, plastic gear pairs do not use lubrication due to material characteristics.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Design Parameter** & **Gear1** & **Gear2** \\ \hline Number of teeth & 40 & 40 \\ Normal module (mm) & 2.5 & 2.5 \\ Normal pressure angle (deg) & 20 & 20 \\ Facewidth (mm) & 40 & 40 \\ Material & Steel, Plastic & Steel, plastic \\ Torque (Nm) & 20 & \\ Speed (r/min) & 200 & \\ \hline
**Design Parameter** & **Rack** & \\ \hline Normal module (mm) & & 2.5 \\ Addendum (mm) & & 2.5 \\ Dedendum (mm) & & 3.125 \\ Fillet radius (mm) & & 0.95 \\ \hline
**Material Property** & **Steel(18 CrNiMo7-6)** & **Plastic(MC901)** \\ \hline Young’s modulus (MPa) & 206,000 & 2150 \\ Thermal expansion coefficient (\(10^{-6}/\text{\textdegree C}\)) & 11.5 & 85 \\ Density (T/mm\({}^{3}\)) & \(7.83\times 10^{-9}\) & \(1.13\times 10^{-9}\) \\ Conductivity (W/(m × K)) & 50 & 0.29 \\ Heat capacity (mJ/(T × K)) & \(4.58\times 10^{8}\) & \(1.67\times 10^{9}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specification of a spur gear pair.
# Evaluating Levels of Automation in Human-Robot Collaboration at Different Workload Levels
## 2 Materials and Methods
### Dependent Variables
#### 2.7.3 Constructs  
The dependent variables were defined through two constructs: QoT execution and usability. These constructs were derived from the objective and subjective measures explained above (mapping is provided in Figure 6). They were adapted to the context of human-robot collaboration from the ISO 9241-151 guideline [40; 45] as follows:  
Figure 6: Mapping of the measures into constructs for assessment. (O—objective measures; S—subjective measures).  
**Quality of task (QoT) execution**. The extent to which specific goals in a task are accomplished to a specified degree of accuracy for a specified time period [46]. This construct involves effectiveness and efficiency of the collaboration. Effectiveness of the collaboration was evaluated by the accuracy and completeness of the task which the human and robot cooperate to execute. The efficiency of the collaboration depends on resources such as time and human effort spent to achieve the required goal [47].  
**Usability.** The extent to which the robotic system can be used to achieve specified goals with effectiveness, efficiency and satisfaction in a specified context of use (adapted from [40]). This construct, in this study, is composed of effectiveness, efficiency in addition to satisfaction derived from the perceived ease of use, perceived workload and perceived reliability of the system. All these variables could affect the degree to which the human operator believes that working with the robot will be free of difficulty or great effort. This is an adaption from [44] in the information technology domain to the context of HRC. They constitute the user's perception regarding use of the system and is essential to ensure that the human can successfully team up with the robot to achieve such collaboration [35]. A negative user perception could lead to disuse of the support the robot can provide in the collaboration [48]. In the current study, the usability construct was comprised of the QoT measures, along with other user perceptions on ease of use, workload, and reliability.
# Two-Tier Cooperation Based High-Reliable and Lightweight Forwarding Strategy in Heterogeneous WBAN
## 4 Experiments Analysis and Discussion
### Comparison Model and Metrics
Network throughput refers to the total amount of data successfully transmitted to the sink node. Thus, the networking throughput is the sum of the throughput of all sensors. The higher the throughput, the more stable the network and the better the performance is.