esa-sceva/satcom-chunk-collection huggingface dataset:
# Along-Track Multistatic Synthetic Aperture Radar Formations of Miniatellites
## 2 SIMO SAR Formation
### The Model of the Received Signal  
Without loss of generality, let us suppose the formation to illuminate a single point target on ground. We neglect radiometric considerations (i.e., the radar equation and the scattering of the target) and just focus on antenna amplitude and phase response due to the path. Moreover, we neglect synchronization errors between receivers, i.e., the range window alignment among the different response is supposed to be already solved with residual error to be included into a sensitivity analysis (which is beyond the purpose of the paper). The signal transmitted by the master sensor (in the \(j\)-th position) bounces on a ground target and is received by each antenna of the formation. After the carrier is removed and the pulse compressed, we have:  
\[\begin{split} h_{b,j}(t,\eta)=A_{i}(\eta)A_{j}(\eta)\exp\left\{- J\frac{2\pi}{\lambda}\left(r_{i}(\eta)+r_{j}(\eta)\right)\right\}\cdot\\ \text{sinc}\left\{B_{ch}\left[\frac{1}{c}\left(r_{i}(\eta)+r_{j} (\eta)\right)\right]\right\},\end{split} \tag{9}\]  
where \(A_{i}(\eta)\) and \(A_{j}(\eta)\) are the antenna patterns of the \(i\)-th receiver and the transmitter, function of the slow time \(\eta\); \(\lambda\) is the wavelength; \(c\) the light speed, and \(B_{ch}\) the chirp bandwidth. \(r_{i}\) and \(r_{j}\) are the sensor-target distances, function of the slow time and the target position \(\xi\). In case of simple geometry, the slant range distances have a simple formulation:  
\[r_{i}(\eta;\xi)=\sqrt{r_{0}^{2}+\left[v\eta-dx_{i}-\xi\right]^{2}} \tag{10}\]  
and similarly for \(r_{j}(\eta;\xi)\), with \(r_{0}\) the minimum approach of the formation center to the target position (We suppose to focus with respect to the formation center, which is also the relative zero for the sensors' distances and slow time, regardless of the transmitter or any other sensor position.) and \(dx_{i}\) the distance of the \(i\)-th sensor from the formation center.  
In the previous formulation, the rank extent, i.e., the movement of the receiver sensor during the flight of the sequence from transmitter to the target and back to the receiver, has not been considered. It has little effect and shall not be included to avoid useless confusion. The received signals, as expected, are a non separable function of \((t,\eta)\) because SAR impulse response for curved trajectories is range variant.  
Likewise, for the traditional monostatic SAR systems, the range migration effect can be efficiently corrected in the two-dimensional wavenumber domain [20], where the convolution of the system function by the target backscattering response is transformed into a multiplication and the reconstruction of the unambiguous spectrum becomes an inversion problem. This is the reason why we perform spectrum reconstruction in a 2D transformed domain.
## 2 Materials and Methods
### Chlorophylll a and Nutrient Analyses  
Chlorophyll \(a\) analysis was carried out on 2 L triplicate samples from the integrated water sample filtered through GF/F filters and extracted with ethanol, with absorbance read in a spectrophotometer (UV-1800, Shimadzu, Germany) at 665 and 750 nm, with a 1 cm cuvette [76]. Total nitrogen samples were analysed in a certified external laboratory (Aquatic Services Unit, University College Cork, Ireland) with a flow injection analysis system (Quickchem(r) 8500, Lachat Instruments, Germany) on unfiltered water after a persulfate oxidation digestion step (method detection limit of 0.1 mg N-L-1) [77]. Total phosphorus was analysed on unfiltered water samples using the molybdenum blue method, with absorbance read in a spectrophotometer (UV-1800, Shimadzu, Germany) at 882 nm, with a 5 cm cuvette, following a digestion step with potassium persulfate (K2S2O8) under pressure and temperature (autoclave for 30 min at 15 psi and 121 \({}^{\circ}\)C) (method detection limit of 1 ug P-L-1) [76; 78]. Total phosphorus analysis was carried out in triplicate using standard curves with certified reagents and quality controls using separate reagents to assess the performance of the analytical method.  
Seston carbon (C), nitrogen (N) and phosphorus (P) content was analysed from lake water passed through a 53-um mesh to remove zooplankton and filtered onto pre-combusted and weighted GF/F filters. For zooplankton community nutrient content analyses, the fourth un-preserved zooplankton sample was transferred to 2 L of filtered (0.45 um) lake water for approximately four hours to evacuate their gut. After gut evacuation, zooplankton fraction was rinsed with Milli-Q water and divided into six equal, well-mixed sub-samples and filtered onto pre-combusted and weighted GF/F filters. All GF/F filters (esston and zooplankton fractions) were immediately dried at 60 \({}^{\circ}\)C for 24 h. Triplicate filters for C and N analyses were measured using an elemental analyser (vario EL cube, Elementar(r), Germany). The standard reagent used for this analysis was Acetanilide (C8H9 NO) with a composition of 10.36% of N and 71.09% of C. Triplicate filters for P content were determined as specified for TP water analysis with an additional pre-step of combusting the filters at 550 \({}^{\circ}\)C for 2 h to ensure the seston and zooplankton organic matter would break down during the persulfate digestion step [78; 79]. A blank filter per replicate was considered for all C, N and P analyses.
## 4 Discussion  
The main objective of the current study was to analyze the neuromuscular response through electromyography (EMG) during open and closed directional changes, with a sex-based comparison. Several novel findings emerged from this study. Firstly, we assessed muscle coactivation of the dominant limb using the H/Q ratio, both traditionally (relative to MVC) and in relation to the capacity for rapid or explosive force development, specifically within the first 50, 100, 150, and 200 ms of contraction. This innovative approach has been recently proposed in the scientific literature as a more effective means of determining an increased risk of lower-limb injuries [19; 20]. Unlike the traditional MVC protocol, which shows peak force values around 5 s, analyzing the H/Q ratio with respect to the first 200 ms aligns with the time period when the risk of injury is at its peak [21]. In this regard, the study provides a novel perspective on the neuromuscular response using EMG with traditional H/QVC and explosive H/Q ratios. To analyze participants' EMG response, we assessed the root mean square (RMS) of the rectus femoris as a representative of the quadriceps and the midline of the semimem-bransous biceps fromoris as a representative of the hamstrings, following the protocol of [PERSON] et al. [22]. The coactivation ratio was obtained by dividing the H/Q activation [23]. As indicated by [PERSON] et al. [24], the H/Q ratio stands out as one of the best indicators of potential knee injuries. Typically, an imbalance in the activation of the posterior musculature relative to the anterior is a decisive factor in injury onset [25]. Detailed analysis of anterior and posterior musculature activation revealed that RMSQ was generally higher than RMSH in all directional changes, except for RMS50 and RMS100, where posterior musculature activation was slightly higher than anterior during SC30. Increased activation of the anterior musculature may signify greater issues with the anterior cruciate ligament (ACL), especially at knee flexion angles ranging from total extension to 30-45\({}^{\circ}\)[26]. Our kinematic results indicated knee flexion ranging from 0\({}^{\circ}\) (total extension) to 45\({}^{\circ}\) in all directional changes, suggesting that this anterior musculature activation, accompanied by these knee flexion angles, could lead to a notable increase in the risk of knee joint injuries. However, superior activation of the anterior musculature over the posterior seems insufficient [27]. Undoubtedly, the phenomenon best describing the risk of injury is the timing of muscle
# Low-Cost RSAC and Adsorption Characteristics in the Removal of Copper Ions from Wastewater
###### Abstract  
Adsorption is a typical method for treating copper-containing wastewater. Fly ash and steel slag both have a good adsorption performance, and activated clay is added in this study, too. In this study, the performance of residue and soil adsorption composite (RSAC) particles for copper ion adsorption was discussed through the substrate ratio and the influence mechanism, to achieve the win-win effect of industrial waste reuse and copper ion wastewater treatment. The results indicated that adsorption time, dosage, initial copper ion concentration, coexisting ions, and temperature showed different effects on the adsorption, respectively. Additionally, the adsorption kinetic study showed the removal of copper ions by adsorption of RSAC particles was in accordance with quasi-primary kinetic model and quasi-secondary kinetic model. The adsorption thermodynamics study shows the adsorption process of \(\Delta\)C\({}^{0}\) < 0, \(\Delta\)H\({}^{0}\) > 0 and \(\Delta\)S\({}^{0}\) > 0, indicating that the process of copper ion adsorption by RSAC particles was spontaneous, heat-absorbing, and entropy-increasing. The research demonstrates that RSAC particles have a certain adsorption capacity for copper ion.  
water pollution; adsorption; copper ions; adsorption mechanism; adsorption kinetics; thermodynamics +
Footnote †: journal: Applied  
1
Footnote 1: email: _Applied_  
Received: 29 April 2022  
Accepted: 28 May 2022  
Published: 1 June 2022  
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# A Bearing Fault Diagnosis Method Based on Wavelet Denoising and Machine Learning
## 2 Methods
### Clustering Evaluation Index  
When evaluating the prediction results, select the following six indicators: accuracy rate, recall rate, time-consuming, contour coefficient, F value and entropy value. The calculation formulas of precision rate, recall rate and contour coefficient index are as follows:  
There are four situations in the data test results: _TP_: predicted positive, actually positive; _TN_: predicted negative, actually negative; _FP_: predicted positive, actually negative; _FN_: predicted negative, actually positive.  
The calculation formula of the indicator is as follows:  
* Accuracy: \[Accuracy=\frac{TP+TN}{TP+TN+FN+FP}\] (12)
* Recall rate: \[R=\frac{TP}{TP+FN}\] (13)Silhouette coefficient: \[S(i)=\begin{cases}1-\frac{a(i)}{b(i)}&a(i)<b(i)\\ 0&a(i)=b(i)\\ \frac{b(i)}{a(i)}-1&a(i)>b(i)\end{cases}\] (14) where \(a(i)\) represents the cohesion of the sample point, and the calculation method is as follows: \[a(i)=\frac{1}{n-1}\sum_{j\
eq i}^{n}\text{distance}(i,j)\] (15) where \(j\) represents other sample points in the same class as sample \(i\), and distance represents the distance between \(i\) and \(j\). So the smaller \(a(i)\) is, the closer the class is. \(b(i)\) is calculated similarly to \(a(i)\). However, it is necessary to traverse other clusters to obtain multiple values \(\{b_{1}(i),b_{2}(i),b_{3}(i)\dots b_{m}(i)\}\)and select the smallest value as the final result. * The entropy value of the entire cluster division is: \[e=\sum_{i=1}^{K}\frac{m_{i}}{m}e_{i}\] (16) where \(K\) is the number of clusters, \(m\) is the number of members involved in the overall cluster division, \(m_{i}\) is the number of all members in cluster \(i\), and \(e_{i}\) is the entropy value of each cluster: \[e_{i}=-\sum_{j=1}^{L}P_{ij}\log_{2}P_{ij}\] (17) In this formula, \(P_{ij}\) refers to the probability that a member in cluster \(i\) belongs to class \(j\), \[P_{ij}=\frac{m_{ij}}{m_{i}}\] (18) \(m_{ij}\) is the number of members in cluster \(i\) belonging to class \(j\). * The \(F\) value is the weighted proportion of different categories of data in the total number of samples. Its formula is as follows: \[F=\frac{n_{i}}{n}F_{i}+\frac{n_{j}}{n}F_{j}\] (19) where \(n_{i},n_{j}\) is the number of samples of each category, and \(F_{i},F_{j}\) is the \(F\) value of each type of sample: \[F_{i}=\frac{2\cdot P_{i}\cdot R_{i}}{P_{i}+R_{i}}\] (20) \(P_{i}\) is the precision rate, and its calculation formula is: \[P=\frac{TP}{TP+FP}\] (21) Use the above formula to calculate each evaluation index.
## 4 Performance Analysis of the Generated Models in Virtual Environments
### Performance Analysis of Models
#### 4.2.3 GPU U (GPU Utilization)  
A GPU utilisation percentage equal to or greater than 99% can be one of the reasons indicating that the application is GPU-limited, with performance issues potentially starting to appear from 90% utilisation. Table 5 shows the percentage of application's execution time where the GPU U percentage exceeded or equalled 99%.  
In scenes with LP objects, up to 24 objects, the application does not reach 99% utilisation at any point, just like scenes with 4 MP objects, except in the Quest Pro in this last case. From 48 LP objects onwards, the percentage of time varies between the Quest 2 and Pro, as the Quest 3 does not maximise GPU usage until the scene with 96 LP objects, for which it only does so 5% of the total execution time. While in the Quest 2, nearly a third of the execution time of the two scenes with the most LP objects maximised GPU usage, in the Quest Pro, it only happened in the scene with the highest number of these objects (30% of the time).  
For scenes with 12 or more MP objects and those with HP objects, there is an upward trend. While in scenes with 12 MP objects, the percentage remains between 20 and 30% in all headsets; again, the difference in power of the Quest 3 is noticeable in the scene with 24 MP objects, where the Quest 2 and Pro reached nearly 60% of the time with the GPU at maximum use, against 34.43% in the Quest 3. However, all headsets remained near 90% of the time with the GPU at maximum use in all HP scenes, demonstrating how demanding these models are in terms of power when running applications as standalone VR.  
With these results, we can observe once again that it is advisable to use LP objects up to a reasonable amount based on the number of polygons or a few MP objects. Thus, the application will avoid maximising GPU usage for an extended period. This can lead to overheating in the headset, causing discomfort or loss of stability and fluidity in the application when run for a longer period of time.
## 2 Experimental Program
### Test Setup, Instrumentation, and Loading Procedure  
Figure 6 shows the setup of the quasi-static test. The lateral cyclic loading was exerted by a 1000-kN-capacity MTS hydraulic actuator. A 2000-kN-capacity jack was applied to exert the vertical load on the specimen and a pressure sensor was used to monitor the exporting axial force. A set of steel rollers was used to reduce the friction in the cyclic test and a spherical joint support was utilized to eliminate the extra bending moment produced by axial pressure due to the specimen's lateral movement. A reusable steel beam was placed at the top of each specimen to transfer the axial and horizontal load to the specimen and prevent local compression failure. The bottom reinforced concrete beam simulated a rigid foundation and was anchored to the laboratory floor to avoid the uplift and slippage of the base. Since the specimen had a high aspect ratio of 2, two parallel lateral bracings were used to avoid the out-of-plane movement and keep the wall stabilized.  
The lateral deflection, base slippage, and uplift of the specimens during the test were monitored by a set of eight displacement meters, which is shown in Figure 6. Figure 2 illustrates the wall internal instrumentation. The strain gauges were attached to the horizontal and vertical rebars to monitor their contribution to the seismic performance of the specimen and to ensure the yield range of the vertical rebar.  
All traditional and precast shear wall specimens were subjected to similar loading histories. The quasi-static tests were carried out under a mixed force and displacement-control loading scheme. First, the target vertical load was exerted to the full amount and a horizontal load, which corresponded to approximately 20% of the predicted cracking load, was applied to the specimen to examine the test setup and the instruments. Second, the specimen was unloaded and reloaded up to the designed axial load. Lateral loading displacements were imposed on the specimens according to the following loading scheme, as shown in Figure 7.  
Figure 6: Setup of the cyclic loading test: 1 wall; 2 hydraulic actuator; 3 roller bearing; 4 pressure sensor; 5 spherical joint; 6 hydraulic jack; 7 steel loading beam; 8 displacement sensor; 9 anchoring bolt; 10 wall foundation beam; 11 reaction wall; 12 reaction slab; 13 lateral bracing; and 14 steel reaction frame.
# Carbon Nanotubes-Based Hydrogels for Bacterial Eradiation and Wound-Healing Applications
## 1 Introduction
The combination of intelligent hydrogels and CNTs was very effective in wound dressing and antibacterial treatment [21; 22]. Hydrogels belong to the group of biomaterials composed of a cross-linked polymer network to form 3D structures that can hold large proportions of water and retain their primary system even after swelling of the wound [21; 29; 30]. Nanomaterials-based hydrogels are considered an attractive platform for wound-healing applications [13]. These hydrogels demonstrated superior mechanical strength, antioxidant, antibacterial, electrical, and tissue \begin{table}
\begin{tabular}{p{108.4 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**CNT Composite** & **Highlight** & **Biological Application** & **Reference** \\ \hline \hline \multirow{2}{*}{CP@CNT} & Examine CNT as a nanocarrier of drug CP & Cyclophosphamide, anticancer drug delivery, reduced side effects & [23] \\ \hline \multirow{2}{*}{CNT-Alg-Ch-FA} & Penetration of functionalized CNT through cell membrane & Doxorubicin hydrochloride delivery for cancer treatment & [24] \\ \hline \multirow{2}{*}{CNT-UHMWPE} & CNT incorporation showed high mechanical and tribological properties & Sustained release (up to 429 h) of gentamicin & [25] \\ \hline \multirow{2}{*}{CNT-3H\({}_{2}\)PO\({}_{4}\)} & Binding energy of drug to CNT increased with more H\({}_{2}\)PO\({}_{4}\)moeities & Delivery for anti-osteoporosis zolendronate and risedronate drugs & [26] \\ \hline \multirow{2}{*}{PLGA-CNT-PDA-lam} & PDA modified scaffold can adhere laminin for longer time and promote neurite outgrowth & Enhancement of PC12 cells for nerve tissue engineering & [27] \\ \hline \multirow{2}{*}{Rh-CNT} & Analysis of gases of lung cancer & Biosensor for prediagnosis of lung cancer & [28] \\ \hline \hline \multicolumn{3}{p{108.4 pt}}{CP-cyclophosphamide; Alg-alginate; Ch-chitosan; FA-folic acid; UHMWPE-ultra-high molecular weight polyethylene; PLGA-polylicide-co-glycolide; PDA-polydopamine; lam-laminin; PC12-adrenal phaeochromocytoma cell line; Rh-medium.} \\ \hline \end{tabular}
\end{table} 
Table 1: List of CNTs-based composites and their biological applications. -regeneration potentials [31; 32; 33]. The physicochemical properties of nanomaterials-based hydrogels are profoundly affected by the surface functionalities [22]. The appealing properties of multifunctional hydrogel make it a promising candidate for wound healing, antibacterial treatment, and other biomedical applications. This review describes the preparation of CNTs-based hydrogels and their potential applications for skin dressing and antibacterial. We briefly discussed the functionalizations and conductivity of CNTs. The perspective for the possible future directions in developing CNTs-based hydrogels for wound-healing treatment is also summarized. Figure 1 shows the overview of the review article, which includes types of CNT, properties of CNT hydrogels, and their applications.
## 3 Control Strategy Design
### Valve Control
#### 3.1.2 Fuzzy Logic Controller  
During actual operation, the noise and throttling loss can affect the valve control signal, leading to errors in the system, especially the displacement accuracy of the cylinder. For this reason, the FLC with the position feedback control is added to eliminate the noise and throttling loss in the valve during operation, as shown in detail in Figure 4. Two input variables are used in this FLC, namely the displacement error of cylinder \(e(t)\) and the derivative of the error \(de(t)\), while the voltage \(U_{FLC}\) is the output variable. Figure 5 depicts the specifics of the input and output in the FLC with triangular-type membership functions which are characterized by different colors. Moreover, the inputs and output membership functions are divided into seven groups and considered as: \"neg-big\" (NB) as an acronym for \"negative small in size\", \"neg-middle\" (NM), \"neg-small\" (NS), \"zero\" (Z), \"pos-small\" (PS) is the \"positive small in size\", \"pos-middle\" (PM), and \"pos-big\" (PB). According to linguistic variables, the logic rules related to the inputs and outputs are developed and shown in Table 2.  
The output range and logic rule of FLC is flexibly designed for each metering mode, this means that one controller is needed for one mode. However, using multiple FLC complicates the control system and makes the system take longer to process. For this reason, one FLC for all metering modes is designed. Moreover, the gains of output \(g_{i}\) (\(i\)  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**R\({}_{\text{FCL}}\) for Valve**} & \multicolumn{8}{c}{\(\mathbf{e}\)} \\ \cline{2-9}  & **NB** & **NM** & **NS** & **Z** & **PS** & **PM** & **PB** \\ \hline \multirow{6}{*}{_de_} & **NB** & NB & NM & NM & NS & NS & NS & Z \\  & **NM** & NM & NM & NS & NS & NS & Z & PS \\  & **NS** & NM & NS & NS & NS & Z & PS & PS \\  & **Z** & NS & NS & NS & Z & PS & PS & PS \\  & **PS** & NS & NS & Z & PS & PS & PS & PM \\  & **PM** & NS & Z & PS & PS & PS & PM & PM \\  & **PB** & Z & PS & PS & PS & PM & PM & PN \\ \hline \hline \end{tabular}
\end{table}
Table 2: Rule table of FLC for valve.  
Figure 4: The fuzzy logic controller for valve control.  
Figure 5: Membership functions of FLC for valve control.  
PE, PR, HSRE or LSRR) for each mode are added to adjust the range for flexibility in each mode. Therefore, the \(U_{K}^{PB}\) signal can be obtained as:  
\[U_{K}^{PB}=g_{i}U_{FLC} \tag{13}\]  
where \(U_{FLC}\) is the output of FLC, \(g_{i}\) are chosen from the experiments.