esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance
### Energy  
Sensor nodes are usually powered by batteries and thus have very limited energy, therefore maximizing the network lifetime is the main objective of the majority of WSN research works. With data compression, the traffic is reduced and energy consumption is also reduced and the network lifetime can be extended. However ACT is composed by many compression techniques which could be expensive in terms of energy and thus energy efficiency should be compared. Figure 17 shows the comparison of energy consumption. The energy consumption by the CPU and the Radio (Network Interface Card) was estimated. CPU was used by the sensing component, compression component, and transmission component. The Radio consumption is the summation of energies consumed by the sending and receiving. If a packet is dropped somewhere in a network during transmission, the transmissions from the source node to the relaying node that dropped the packet become unnecessary and thus the energy which was consumed by the radio is wasted (\"Waste\" in the figure). CODA shows minimum amount of total energy consumption. With CODA, there is not much computation and thus the energy consumption by the CPU is very smaller than other schemes. However, CODA showed very poor network efficiency in Figure 14, which means lots of packet drop, and thus lots of energy was wasted. CODA with compression consumed less energy on the Radio and more energy on the CPU. Therefore the total amount of consumed energy is more than CODA whereas the amount of wasted energy is less than the CODA. CRRT stays in middle stage between CODA and CODA-Comp. ACT shows highest energy consumption than other scheme. With ACT, CPU consumed lots of energy due to the compression task, however the entire network efficiency was higher than other schemes. Therefore the wasted energy was less than other scheme.  
Figure 17: Energy Consumption.
###### Abstract  
In this paper, a hybrid storage system solution consisting of flywheels and batteries with a Lithium-manganese oxide cathode and a graphite anode is proposed, for supporting the electrical network primary frequency regulation. The aim of the paper is to investigate the benefits of flywheels in mitigation of the accelerating aging that li-ion batteries suffer during the grid frequency regulation operation. For this purpose, experimental aging tests have been performed on a lithium-manganese oxide battery module and an electrical battery model which takes into account the battery aging has been developed in a Simulink environment. Then, a flywheel electrical model has been implemented, taking into account the thermal and the electromechanical phenomena governing the electrical power exchange. This more complete model of a hybrid storage system enables us to simulate the same aging cycles of the battery-based storage system and to compare the performances of the latter with the hybrid storage system. The simulation results suggest that suitable control of the power shared between the batteries and the flywheels could effectively help in countering Li-ion battery accelerated aging due to the grid frequency regulation service.  
Keywords:flywheel; Li-ion; battery aging; frequency regulation +
Footnote †: journal: Applied Sciences
# Low-Cost RSAC and Adsorption Characteristics in the Removal of Copper Ions from Wastewater
## 3 Results and Discussion
### Adsorption Experiments
#### 3.1.5 Effect of Ambient Temperature  
The fitted curves at 25 \({}^{\circ}\)C are shown in Figure 8. The fitting parameters can be obtained from the adsorption isotherm. It can be observed from Table 10 that the fit coefficients R\({}^{2}\) of both Langmuir and Freundlich models are greater than 0.95 at different temperatures, which indicates that both models could well express the isothermal characteristics of the adsorption of copper ions by RSAC particles. This also implies that the adsorption isotherm characteristics of the adsorbent for copper ions could fit two or more adsorption isotherm models under certain conditions [32]. Based on the Langmuir model, the K\({}_{\text{L}}\) and the q\({}_{\text{max}}\) which increase as temperature increases indicates that the intermolecular binding and the adsorption capacity may increase as temperature increases. Based on Freunlich model, the low 1/n value indicates that the adsorption process could undertake easily. Meanwhile, the K\({}_{\text{F}}\) which represents the absorbability increases as temperature increases [33]. The two models show that the adsorption of copper ions by RSAC particles is a heat-absorbing process. This could be explained from different aspects. Firstly, the cooper ion needs the energy to approach the RSAC particles and overcome the resistance from the liquid film of the particles to reach the internal active sites. Secondly, the physical adsorption may release heat since the intermolecular force (Van der Waals force) between adsorbates and adsorbents contributed to the main effect during adsorption, which made the molecular kinetic energy decrease by releasing thermal energy [34]. Another study also found that the adsorption process of fly ash involves physical adsorption [35]. In conclusion, chemisorption is an endothermic reaction while physical adsorption is an exothermic reaction, and the adsorbed thermal is more than the released thermal, causing the increase in temperature to promote the adsorption reaction, which is consistent with previous research conclusions [36; 37].  
Figure 7: Effect of metal cations on the adsorption of copper ions by RSAC particles.
###### Abstract
This makes them well-suited to BMI tasks, as the input to the algorithm is already encoded in the form of the recorded neuronal spikes. SNNs have also been shown to require less computational power than traditional ANNs [22; 23; 24]. However, traditional SNNs, like traditional RNNs, have problems capturing long-term information within data. We have extended the SNN by adding a spiking long short-term memory layer (SLSTM), which can help capture temporal dynamics in the neural data. We hypothesized that it would perform better than current SNN techniques. We also used SNN models with trainable membrane potential thresholds to increase activation sparsity. Traditionally, when SNNs are used in classification problems [25; 26] the spikes are used as the output. However, for regression-based problems, the membrane potential is used as the output [27]. Instead of taking this approach, to further augment the SNN, we introduced a unique output scheme using a linear weighting of output spikes to mimic the biology of the motor cortex more closely. Since the brain communicates using spikes, we hypothesized that this method could perform better than a membrane-based SNN. A spike-weighted SNN would also be less dependent on the starting conditions compared to the membrane potential, which must always start at 0. In this study we tested two different methods of regression in SNNs (spike-weighted and membrane potential) against two alternative decoders (LSTM [16] and Unscented Kalman filter) [13]. We used each decoder to reconstruct positional data from the input spike data in a regression problem and evaluated their performance by seeing how closely they matched the ground truth, which is the actual joystick positions, using the Pearson correlation coefficient (\(\mathbf{r}\)) and the coefficient of determination (\(R^{2}\)). Kalman filters and LSTMs have previously been used for regression problems in brain-machine interfaces [16].
## 1 Introduction
However, from the literature, few researches have been carried out to identify both the location and depth of a crack in a rotor with these super-harmonic features. For a cracked rotor system, various crack parameters correspond to various amplitudes of super-harmonic components. For crack localization there must be some spatial information in the corresponding features, therefore, if there is just one measurement point, it is not possible to distinguish the cracks at different locations with varied depths. Hence, more measurement points are needed. In view of the difficulties in taking measurement in rotating rotors, only two points are measured for crack identification. It is believed that two measurement points are enough, only if accurate model information is available. Hence the key to identify a crack in rotors with two measurement points is to obtain a crack model as accurate as possible (it should be noted that the model here is not limited to a structural model but also a mapping between any two variables). However, to obtain an accurate model between crack parameters and features, many samples are required, which will be quite costly and the efficiency will be quite low. Therefore, how to obtain the accurate relationship with much fewer samples and to boost its efficiency are challenges. A Kriging surrogate model is a statistics-based interpolation method [51]. It has been increasingly utilized in optimization problems in which large computing resources are required [52], and it has also been used in parameter identification [53] and crack identification in plates [54]. However, few applications of Kriging surrogate model have been reported in crack identification in rotors. In this work, a novel Kriging-based FEM updating method for crack identification for rotors with a breathing crack is proposed using nonlinear super-harmonic features from two \"measurement\" points, integrating with an improved intelligent optimization algorithm of Particle Swarm Optimization (PSO). The proposed method is effective, accurate and robust for breathing crack identification in rotors and can identify all the crack parameters at the same time, moreover, it is promising for practical engineering applications. The structure of rest of the paper is organized as follows. Section 2 establishes a model of a two-disc rotor-bearing system with a breathing crack and validates this model by experiment and previous published work. Section 3 carries out the sensitivity analysis of crack parameters to super-harmonic characteristics. Section 4 constructs the Kriging surrogate model that relates crack parameters and super-harmonic features. Section 5 describes the detail of the proposed crack identification method. Section 6 presents the numerical simulations and crack identification results. Finally, Section 7 concludes the paper.
# Regular Wave Effects on the Hydrodynamic Performance of Fine-Mesh Nettings in Sampling Nets
## 2 Material and Methods
### Numerical Simulation of the Netting Models
#### 2.4.3 Boundary Conditions and Solution Algorithm for the Numerical Wave Tank  
The numerical simulation of the wave tank was developed and solved using the finite volume method within ANSYS FLUENT 2022 R1. Figure 3a depicts the computational domain along with the specified boundary conditions. The netting is positioned 10 m downstream from the inlet, which is defined by a velocity-inlet boundary condition. Pressure-outlet boundary conditions are applied at both the outlet and the upper boundaries of the tank, while the bottom and sidewalls are treated as no-slip walls. To minimize wave reflections, a 6 m long damping zone was implemented at the downstream end of the domain. Figure 3b presents the mesh configuration, which employed poly-hexcore-structured grids to balance computational efficiency and accuracy. A mesh independence study was performed to assess wave surface elevation at \(T\) = 1.3 s and \(H\) = 0.10 m (see Table 4). The results demonstrated that Mesh 3 offered adequate accuracy and was thus selected for all subsequent simulations (see Figure 4). The computational domain consists of approximately 2.1 \(\times\) 10\({}^{6}\) grids, with a maximum grid size of 30 mm and a minimum grid size of 4 mm. An unsteady, pressure-based solver was employed to achieve a converged solution. Pressure-velocity coupling was accomplished using the PISO algorithm. Second-order upwind schemes were applied for discretizing pressure, momentum, turbulent kinetic energy, and turbulent dissipation rate.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Mesh**} & \multirow{2}{*}{**Mesh Quality**} & \multicolumn{2}{c}{**Maximum Grid Size**} & \multicolumn{2}{c}{**Minimum Grid Size**} & \multirow{2}{*}{**Total Elements**} \\  & & **(mm)** & & **(mm)** \\ \hline
1 & Coarse & 50 & 5 & 1,035,819 \\
2 & Medium & 40 & 5 & 1,265,828 \\
3 & Fine & 30 & 4 & 2,149,106 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mesh independence analysis for surface elevation at \(T\) = 1.3 s and \(H\) = 0.1 m.
## 3 Simplification of CDM
### Simplification and Validation of SDM  
Based on the analysis results in Section 3.1, CDM is simplified to obtain SDM as  
\[\dot{v}_{x}=\frac{F_{T}}{m}+\frac{2C_{yf}}{m}\cdot\left(\frac{v_{y}+l_{f}\omega}{ v_{x}}-\delta_{f}\right)\cdot\sin\left(\delta_{f}\right)+v_{y}\omega \tag{10}\]  
\[\dot{v}_{y}=-\frac{2C_{yf}}{m}\cdot\left(\frac{v_{y}+l_{f}\omega}{v_{x}}-\delta _{f}\right)\cdot\cos\left(\delta_{f}\right)-\frac{2C_{yf}}{m}\cdot\frac{v_{y} -l_{r}\omega}{v_{x}}-v_{x}\omega \tag{11}\]  
\[\dot{\omega}=-\frac{2C_{yf}}{l_{z}}\cdot\left(\frac{v_{y}+l_{f}\omega}{v_{x}}- \delta_{f}\right)\cdot\cos\left(\delta_{f}\right)\cdot l_{f}+\frac{2C_{yf}}{l_ {z}}\cdot\frac{v_{y}-l_{r}\omega}{v_{x}}\cdot l_{r} \tag{12}\]  
A typical driving condition, including acceleration, deceleration and steering operations, is selected from the natural driving data to validate the accuracy of SDM by comparing with CDM, the kinematic model and the single-track model. The comparative results are shown in Figure 9, where the single-track model and kinematic model are denoted by \"STM\" and \"KM\", respectively.  
In Figure 9a, the longitudinal speeds of CDM, SDM, KM, STM are almost consistent with the real vehicle. But the lateral speeds and yaw rates of KM and STM deviate from the real vehicle greatly during 20-30 s (See Figure 9c,d), because the kinematic and single-track models ignore the coupling effect on the longitudinal and lateral directions. As shown in Figure 9b, the coupling force along the longitudinal direction reaches 137 N. Comparatively, SDM describes vehicle lateral and longitudinal dynamics finely and is simpler than CDM. This is beneficial to reduce the computation burden of the motion planner, meanwhile ensuring the optimization performance.  
Figure 8: Comparative results of coupling effect on lateral dynamics: (**a**) amplitude-frequency characteristics from \(F_{T}\) to \(\omega\) and \(\delta_{f}\) to \(\omega\); (**b**) open-loop gains from \(F_{T}\) to \(\omega\).  
Figure 9: _Cont._
## 3 Results
### Psychometric Testing of Final FGAGR
#### 3.2.1 Reliability  
The scale yielded an excellent inter-rater (ICC = 0.984; 95% CI 0.963-0.993; SEM = 0.459, \(p\) < 0.001) and test-retest reliability (ICC = 0.976; 95% CI 0.945-0.989; SEM = 0.564, \(p\) < 0.001). Single items agreement between raters and repeated measures was strong to almost perfect for all items (weighted kappa above 0.75). Only items 6 and 7 presented moderate inter-rater agreement and weak to moderate test-retest agreement (Table 3). The scale demonstrated low internal consistency ([PERSON]'s alpha = 0.66) with the item to total correlation to be statistically significant for all, except for items 1 (\"gait level surface\"), 6 (\"step over obstacle\"), and 10 (\"steps\"), which yielded the weakest correlations with total FGA score (Table 3).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Characteristics** & **Mean (Standard Deviation)** \\ \hline Total Sample & n = 24 \\ Age (y) & 74 (7) \\ Range (min-max) & 66–94 \\ Sex (m/f) & 6/18 \\ Height (cm) & 161 (7) \\ Weight (kg) & 74 (13) \\ Body Mass Index (kg/cm\({}^{2}\)) & 28 (4) \\ Mini-Mental State (/30) & 28 (1) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sample characteristics.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Characteristics**} & **Men** & **Women** & **\multirow{2}{*}{**\(p\) Value**} \\  & **Mean (Standard Deviation)** & & \\ \hline Age (years) & 72 (5) & 80 (9) & 0.093 \\ Body Mass Index (kg/cm\({}^{2}\)) & 27 (2) & 29 (5) & 0.560 \\ FGAGR total score (/30) & 25 (3) & 28 (3) & 0.064 \\ Mini-BESTest total score (/28) & 20 (3) & 18 (3) & 0.167 \\ TUG (seconds) & 13.3 (3.4) & 13.5 (2.8) & 0.891 \\ FES-I total score (/64) & 24 (5) & 26 (4) & 0.297 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of the sample between men and women at baseline (significant differences between groups at value \(p\leq\) 0.05).
###### Abstract
In the literature, the separation of such positioning errors into a systematic and a random component is well-established [15; 16]: the former refers to fact that the patient's geometry at the time of sCT is not representative of anatomy displacement during treatment, while the latter represents the intra- or inter-fraction fluctuation around the systematic displacement. Specific protocols have been developed for photon RT that allow one to abolish in-room setup verification before selected treatment fractions by resetting the systematic component of positioning error by exploiting information from a few precedent fractions [16]. Although a similar approach alone is not easily replicable in HT due to the higher geometrical selectivity and hence increased risk of missing the target, a hybrid strategy that aims at diminishing the systematic positioning errors before image-based setup verification could be a game-changer in setup verification procedures, especially in pediatric patients. As a matter of fact, with smaller displacements, either automatic or semi-automatic registration procedures would be faster and more robust, and the need for a second imaging process to confirm setup would be reduced, with consequent benefits for the patients (decreased overall treatment time and imaging dose) and clinical throughput. Overall, faster setup procedures improve patients' experiences, reduce intra-fractional movement, increase patients' throughput and in principle reduce the frequency of imaging and related dose [17]. Given this rationale, the aim of this retrospective study was to evaluate the effectiveness of a patient pre-alignment protocol before planar kilovoltage (kV) imaging based on positioning information retrieved from previous fractions, in an attempt at reducing dose to patients and setup time. The envisaged benefit will be quantified in terms of reduction in the magnitude of the correction vectors, i.e., shifts of couch after imaging.
## 3 Results and Discussion
### DT-TG Results  
Figure 2 reports the DT results for the C_R, C_15, C_25, and C_35 samples cured at 4 h and 1, 28, and 56 days. From the data published in [99], the following endothermal effects (in order of the increasing temperatures) were assigned to ettrinige (E) and Al(OH)\({}_{3}\) (aluminium hydroxide (AH)).  
Except for the peak of the CaCO\({}_{3}\), no relevant thermal (either exo/endo) effects were present above 500 \({}^{\circ}\)C, and for the examined cement pastes, the endothermal dehydration peaks, attributed to E and AH (already detectable after 4 h of hydration), were observed at 164 \({}^{\circ}\)C \(\pm\) 5 \({}^{\circ}\)C and 287 \({}^{\circ}\)C \(\pm\) 4 \({}^{\circ}\)C, respectively.  
Overall, the thermogravimetric analyses revealed that the hydration behaviour was basically due to the reaction of the 4 CaO\(\cdot\)3 Al\({}_{2}\)O\({}_{3}\cdot\)SO\({}_{3}\) with the CaSO\({}_{4}\). The hydration behaviour of the systems was also assessed by means of the determination of the bound water content (CBW, Figure 3). It was evaluated considering the weight loss values up to 500 \({}^{\circ}\)C, normalised to 100 g of dry cement.  
From Figure 3, it can be argued that the four systems followed an analogous trend during the first 24 h of aging. For that period, the hydration rate was quite high because of the fast ettrinige formation; from 7 to 56 days of curing, the curves exhibited an almost constant value.  
Figure 2: DT curves for the cement pastes: (**a**) C_R, (**b**) C_15, (**c**) C_25, and (**d**) C_35 at 4 h and 1, 28, and 56 d of hydration. E, ettrinige; AH, aluminium hydroxide.
# A Study on the Properties of Recycled Aggregate Concrete and Its Production Facilities
## Mechanical Properties of the Recycled Aggregates Using Concrete and Durability Characteristics
### Physical and Mechanical Characteristics
#### 5.2.2 Slump  
The elapse time of the slump showed that the slump decreased, following the elapse time, with an increase in the replacement rate for the general strength territory and a high strength territory, and up to 60% of the replacement rate. The slump decline was 55-75 mm at 60 min of elapse time, which was not significantly different to that of ordinary concrete at 60 mm. However, at 100% of the replacement rate, the slump decline was 100 mm at 60 min of elapse time. In addition, from the replacement of the recycled fine aggregate before modification, the general strength territory and high strength territory showed slump declines, following the elapse time with an increase in the replacement rate. Up to a 30% replacement rate, the slump decline was 60-70 mm, at 60 min elapse time, which was similar to the 60 mm of the slump decline of the ordinary concrete. However, at a 60% replacement rate, the slump decline was 80-105 mm, and at a 100% replacement rate, it was 115-150 mm. For the replacement of the recycled fine aggregate, after modification, up to 60% of the replacement rate, the slump decline was 50-55 mm at 60 min of elapse time, similar to that of ordinary concrete. At a 100% replacement rate, the slump decline was 60-75 mm, corresponding to a slump reduction of up to 20 mm, from ordinary concrete.  
Thus, a replacement rate up to 60% for the recycled coarse aggregate, 30% for the recycled fine aggregate before modification, and 60% for the recycled fine aggregate after modification, were suitable. For any replacement of these limits, a slump reduction with elapse time occurred.  
Figure 9: Air volume versus the replacement rate for each type of recycled aggregate for the general strength (24 MPa) and high strength (35 MPa) territory.