esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Among those algorithms, the method proposed by [PERSON] in 1971 is the most widely cited in the solar literature, with a maximum error of 0.28\({}^{\circ}\)[7]. The second consists of more complex algorithms, considering nutation and other effects, and requires the time and geographical location of the observer to calculate the ecliptic coordinates, celestial coordinates and local horizontal coordinates of the sun. These algorithms usually have high accuracy, but can generally only be used within a certain time range. The PSA algorithm proposed by [PERSON] et al. is limited to the period from 1999 to 2015 with uncertainty of 0.01 degree. It has been revisited to improve its accuracy in the period from 2020 to 2050 and achieves a 25% decrease in the average error of the angular deviation with respect to the true solar vector [3; 8]. [PERSON] proposed five algorithms with maximum errors in the solar position spanning from 0.19 to 0.0027 degree, and is limited in the period from 2010 to 2110 [9]. The ideal SPA is supposed to be both accurate and efficient at the same time. However, those SPAs with low uncertainties always require a lot of complex corrections, which increase their computation cost. NREL have proposed a SPA in the period from the year \(-\)2000 to 6000 with uncertainties of \(\pm\)0.0003\({}^{\circ}\)[10]. Compared with other methods, its errors can be neglected, which can be regarded as an accurate algorithm. In this paper, we assume that the solar position calculated by SPA of NREL is the real solar position. The calculation error of other algorithms is obtained by comparing with the NREL algorithm. The error generated by SPA changes greatly with the change of the solar position in one year. For annual performance of solar trough system, accurate calculation needs to firstly calculate the solar position error from error of SPA at every moment, and then calculate the impact of these errors on the optical effect of the system. Whether using a calculation method or measurement method, it requires lengthy calculation and measurement. In order to evaluate the impact of SPA calculation error more simply, we try to use average tracking error to represent constantly changing tracking error. However, the relation between efficiency and tracking error is not linearity. When evaluating the impact of tracking error over a period of time, using the average tracking error to represent the changing error will lead to error. The main aim of this study is to evaluate the error when average tracking error generated from SPA error is applied to characterize its impact on trough solar system. In this paper, the numerical simulation experiment is used to check and estimate the error of this method. The error influence of SPA on the performance of parabolic trough solar system is also evaluated.
## 2 Kinematics, Dynamics, and Control of the Haptic CDPR  
### Kinematic and Wrench Force of the CDPR  
The kinematic geometry of the \(i^{th}\) cable in a CDPR is depicted in Figure 1. The geometric parameter \(A_{i}\) represents the connecting point between the cable and the base frame, and \(B_{i}\) is the connecting point between the cable and the end-effector. Variables \(a_{i}\) and \(b_{i}\) are two constant vectors on the base coordinate \(\{G\}\) and the end-effector coordinate \(\{P\}\), respectively.  
The inverse kinematics of the CDPR used in this study compute the cable length in the joint space from the given end-effector posture in the Cartesian space. Based on the closure vector loop of the CDPR, the cable length vector can be calculated, where \(l_{i}\) represents the vector for the \(i^{th}\) cable, and \(r_{p}\) and \(R\) are the position vector and rotation matrix of the end-effector with respect to the base coordinate \(\{G\}\), respectively:  
\[l_{i}=a_{i}-r_{p}-Rb_{i} \tag{1}\]  
The length of each cable \(l_{i}\) is obtained by the 2-norm of (1) as follows:  
\[l_{i}=\sqrt{\left[a_{i}-r_{p}-Rb_{i}\right]^{T}\cdot\left[a_{i}-r_{p}-Rb_{i} \right]} \tag{2}\]  
To calculate the wrench force applied to the end-effector from the measured tension of each cable, the force and torque equilibria are applied as follows:  
\[\sum_{i=1}^{N}f_{i}+f_{p}=0,\text{ and }\sum_{i=1}^{N}(Rb_{i}\times f_{i})+ \tau_{p}=0 \tag{3}\]  
where \(N\) is the number of cables in CDPR, \(f_{i}\) is the tension vector of the \(i^{th}\) cable, and \(f_{p}\) and \(\tau_{p}\) are the external force and torque applied to the end-effector, respectively. Then, by considering the unit vector that represents the direction of the tension \(u_{i}=l_{i}/\|l_{i}\|\), we can obtain a closed form of dynamics for the CDPR as follows:  
\[\underbrace{\left(\begin{array}{ccc}u_{i}&\cdots&u_{N}\\ Rb_{i}\times u_{i}&\cdots&Rb_{N}\times u_{N}\\ \end{array}\right)}_{A^{T}}\left(\begin{array}{ccc}f_{1}\\ \vdots\\ f_{N}\\ \end{array}\right)+\underbrace{\left(\begin{array}{c}f_{p}\\ \tau_{p}\\ \end{array}\right)}_{w}=0 \tag{4}\]  
Finally, the simplified form of (4) and the force limitation boundary can be written as follows:  
\[A^{T}f+w=0,f_{max}\geq f\geq f_{min}>0 \tag{5}\]  
where \(w\) is the external wrench force, \(A^{T}\) is the structure matrix, and \(f\) is the cable tension.
# Compact Dual-Band Tapered Open-Ended Slot-Loop Antenna For Energy Harvesting Systems
## 2 Antenna Design Architecture  
In this section, the operating principle of the antenna and parametric sweep analysis is illustrated. The dual-band characteristic is realized using two different type resonant modes of the loop-slot antenna, namely, a loop basic mode in the lower band and a slot basic mode in the higher band. It is wise to state the antenna design polarization characteristics whether it is preferred for the EH system design or not. For the ambient energy harvesting, a high-efficient rectennas are desirable for energy harvesting to intercept more power from the radiated electromagnetic energy and hence maximize the output DCpower. This feature can be achieved using different methods. Solutions to high conversion efficiency rectenna is mainly dependent on the receiving antenna design where the influence of receiving antenna on system performance (in terms of conversion efficiency and working distance) is of significance. The receiving antenna can either be designed to have high-gain, multi-polarized, multi-beam,... etc. A high-gain antenna is preferred for the longest distance applications. Furthermore, a circularly polarized (CP) antenna is desirable to receive electromagnetic energy from different polarization to improve the total conversion efficiency. Therefore, a wide-band and high-gain CP antenna is a good candidate to collect energy from random polarization at different operating frequencies for RF energy harvesting.  
On the other hand, in case of dedicated RF energy harvesting where the transmitting source features are well-known to the receiver, the aforementioned characteristics are not critically important. The receiving antenna will be designed to have the typical polarization of the transmitting antenna. In our case, the transmitting antenna is a high gain linear polarized Yagi antenna. Therefore, the receiving antenna was designed to also have linear polarization. This ensures a large power conversion between the Tx and Rx side.
## 4 Recommendations and Future Research Directions
The authors of [37], pointed out that none of the different information filtering approaches uniformly dominates the others across all factors. Therefore, there is a need for extensive research to compare performance by considering different performance measures other than accuracy, like novelty, diversity, and serendipity etc. Hybridizing the three filtering approaches may also be a good area for future work. Another good line of future research is to determine the proper combination of existing recommendation approaches in order to explore their full potentials.
## 2 Data and Methodology
### Spire Signal-to-Noise (SNR) Latitudinal Distribution  
We usually use the SNR to indicate the strength of RO signals in penetrating the lower troposphere (see Section 3.1). Note that many factors affect the magnitude of SNRs and their spatial distribution. The most dominant factors include (i) the GNSS emitter's signal power, (ii) the receiver intermediate frequency bandwidth, (iii) the RO antenna design, (iv) the antenna gain pattern related to the viewing geometry, and (v) the azimuth angle (the angle between the occultation plane and the direction to the true north). Each Spire flight module is equipped with a STRATOS GNSS RO receiver with a high-gain side-mounted antenna for both the L1 and L2 frequencies. With the antenna beam peak at a 20\({}^{\circ}\) electronic beam tilt and about a 36\({}^{\circ}\) vertical beamwidth [51], the Spire SNRs for GPS, GLONASS, and GALILEO are uniformly distributed at all latitudes (Figure 4), where the Spire SNR from GLONASS is higher than those from GPS and GALILEO. The mean Spire SNRs for the tracking from GPS, GLONASS, and GALILEO are equal to around 300 \(v/v\), 850 \(v/v\), and 250 \(v/v\), respectively.  
However, with different antenna geometry, the spatial distribution of the COSMIC-2 SNR differs from that of Spire. Each of the COSMIC-2 flighting modules equip two TGRS RO antennas, one forward antenna (+X), and one backward antenna (-X). Unlike Spire's side-mounted RO antenna, the COSMIC-2 RO antenna points to the nadir, and the position-tracking antenna points to the sideway. Because the COSMIC-2 forward and backward antennas are within [23\({}^{\circ}\), 66\({}^{\circ}\)] and [115\({}^{\circ}\), 158\({}^{\circ}\)], respectively [32], the RO sample distribution has a maximum RO event at 24\({}^{\circ}\) for the forward antenna and 157\({}^{\circ}\) for the backward. As a result, the COSMIC-2 SNR is smaller in the mid-latitude (see Figure 5) and it has a broader distribution (from 200 \(v/v\) to 2000 \(v/v\)) from 30\({}^{\circ}\)N-30\({}^{\circ}\)S. The COSMIC-2  
Figure 4: Latitudinal distribution for Spire L1 SNR from 15 February to 15 March 2022 for (**a**) GPS, (**b**) GLONASS, and (**c**) GALILEO.  
Figure 3: Same as Figure 2, but for the hourly local time distribution binned at 5\({}^{\circ}\) latitude bin for (**a**) Spire, (**b**) COSMIC-2, and (**c**) KOMPSAT-5 for the DO3 period. The observation numbers at each box are indicated by the color bar.  
event distribution as a function of the antenna view angle was also shown by [32]. The geolocation distribution of the COSMIC-2 SNR may affect the COSMIC-2 penetration depth at different latitudes (see Section 3.1).
## 3 Results  
### Preliminary Statistical Analysis  
Preliminary statistical analysis carried out on grain yield and protein content, response variables in PLSR, showed close mean and median values and coefficient of skewness and kurtosis equal or lower than 0.5. These results were confirmed by normality tests indicating for both variables a not significant deviation from normal distribution (P = 0.5198 and P = 0.0970 for the Shapiro-Wilk test, for grain yield and protein content, respectively). Variances were homogeneous over management treatments for both variables according to Levene test (P = 0.4128 for grain yield and P = 0.2432 for protein content). Average values of grain yield and protein content were respectively 5.247 Mg ha\({}^{-1}\) and 13.39 g 100 g\({}^{-1}\).  
Soil variable distributions did not significantly deviate (microbial biomass C, TOC, TEC, HA\({}_{-}\)FA, N, Olsen P, pH, K\({}^{+}\), Mg\({}^{2+}\), Na\({}^{+}\), BD, AC, RFC, clay and sand) or only slightly deviated (EC, Ca\({}^{2+}\), P\({}_{\rm MAC}\)) from normal distribution, except for WEOC and WEN. Homoscedasticity was also observed in the larger part of the cases (except for WEN and P\({}_{\rm MAC}\)). For this reason, data were analyzed for all the variables in the original scale.
## 3 Results and Discussion  
### Bacterial Characterization  
#### 3.1.1 Gram Staining  
One final colony of biohydrogen-producing bacterium was isolated from UKM's lake soil sample after several batches of repeated sub-culturing and isolation. The bacterium was first identified as KBH 6958. The isolate was facultatively anaerobic, so it could grow on RCM agar in normal atmospheric conditions [8]. However, biohydrogen production was only observed in anaerobic conditions, wherein the bacterial colony on RCM agar was mildly yellow, non-spore-forming, and mucoid after prolonged growth on the agar plate. A negative Gram-stained isolate KBH 6958 under 10X magnification using light microscope was observed (Figure S1). The isolate KBH 6958 appeared to be somewhat rod shaped and pink after Gram staining due to the counter stain safranin, and it was deduced that the isolate is a Gram-negative bacterium [8; 22]. The bacterium did not retain the primary crystal violet color (purple) after the ethanol washing. Gram-negative bacterium typically has a thinner peptidoglycan layer on its cell wall [22].
## 3 Using ML for Water Activities
### Water Quality and Streamflow Management  
#### 3.2.1 ML Applications in Water Resource Management  
* **Streamflow and water quality management[250]**: A novel approach was introduced for water resource management, addressing the nonlinearity and uncertainty of streamflow. The proposed hybrid model effectively predicted water quality and quantity, offering improved accuracy in capturing nonlinear characteristics.
* **Enhanced streamflow forecasting[251]**: With LSTM and metaheuristic optimization, we can improve streamflow forecasting. The results demonstrated significant enhancements in model performance, with the potential to support more effective flood management.
* **Smart farming in India[252]**: India's smart farming endeavors employed the Internet of Things (IoT) and ML classifiers, particularly the binary support vector machine (SVM). These technologies assisted farmers in optimizing crop irrigation, enhancing sustainability.
* **Urban water demand forecasting[253]**: Adaptive urban water demand forecasting was proposed, utilizing ML models to cater to changing consumption patterns and improving water resource management.
* **Cybersecurity in water infrastructure[254]**: Combining ML with operational metrics, this study enhanced cybersecurity measures in critical water infrastructure, addressing the increasing risk of cyberattacks in modernized water plants.
###### Abstract  
The addition of ionic liquids with MXene nanofluid has a substantial impact on the solar thermal collectors' working fluid's optical properties that effectively absorb and distribute solar radiation. Increased solar radiation absorption potential ensures that heats are transported more rapidly and effectively. This research endeavors to investigate the concept of accumulating solar energy via the usage of ionic liquid-based 2D MXene nanofluid (Ionanofluids) for solar applications. In this study, the optical potential of Diethylene Glycol/MXene nanofluid incorporated with 1-ethyl-3-methyl imidazolium octyl sulfate ([Emim][OSO4]) ionic liquid was extensively investigated with respect to MXene concentration (0.1 to 0.4 wt%) and time (first day and seventh day) through UV-Vis Spectroscopy. A two-step approach was employed to synthesize the proposed ionanofluids with nanoparticle concentrations from 0.1 to 0.4 wt%. In wavelengths between 240 to 790 nm, the effect of ionic liquids, MXene concentration, and dispersion stability played a significant part in enhancing the absorbance capacity of the formulated MXene based Ionanofluid. Furthermore, the increase in the concentration of MXene nanoparticles resulted in more absorbance peaks facilitating high light absorption. Finally, the electrical conductivity of the ionanofluids is also analyzed as MXene renders them promising for solar cell applications. The utmost electrical conductivity of the formulated fluids of 571 \(\upmu\)S/cm (micro siemens per centimeter) was achieved at 0.4 wt% concentration.  
2D MXene; ionic liquids; nanofluids; electrical; optical; stability; spectroscopy +
Footnote †: journal: Applied Physics Letters
# A Cross-Medium Uplink Communication Model Merging Acoustic and Millimeter Waves
## 2 Model of Cross-Medium Communication Channel
### Physical Process
#### 2.1.3 Detection of Water Surface Wave by MMWR  
Above the water surface, a radar transmits frequency modulated continuous waves (FMCW) for measuring the amplitudes of the surface wave. The transmitted signal from the radar is mixed with the received signal to generate an intermediate frequency signal (IF Signal). The phase of the IF signal is utilized to reconstruct the vibration waveform of the surface wave. The relationship between the received phase of the radar and the amplitude of a surface wave is described by the following equation [22]:  
\[\Delta\varphi_{MMWR}(t)=4\pi\frac{\delta(t)}{\lambda} \tag{6}\]  
In this equation, \(\delta(t)\) represents the microamplitude of a surface wave, and \(\lambda\) denotes the wavelength of the radar transmitted signal. By performing the unwrapping process, the waveform and the true amplitude of the water surface can be restored.
## 5 Discussion
### The Initial MECO Phase (142-155 m): Rising of Mesotrophic Conditions  
During the INITIAL MECO Phase, which corresponds to the initial warming trend, as indicated by the \(\delta^{18}\)O curves (Figure 2), the variations in all the groups analyzed record the surface water temperature increase and a shift towards mesotrophic conditions throughout the water column. This is possibly related to the enhanced hydrological cycle and terrigenous flux as documented by geochemical proxies [32].  
Benthic foraminiferal assemblages are characterized by a slight increase in bi-triserial taxa and E/I values and decrease of diversity (Figure 3). Although the increase in E/I values might suggest an overall decrease, in terms of quantity, of nutrient supply inthe bottom, the increase in bi-triserial forms likely points to a change in the quality of organic matter (i.e., more labile) reaching the seafloor. This might also be expressed by the slight divergence of the \(\delta^{13}\)C curve of the _Cibicidoides_ from those of the planktic genera of foraminifera in this interval.  
Similar findings were reported for the initial phase of MECO at Alano section and interpreted as a shift from more oligotrophic to fully mesotrophic conditions [9]. The drastic decrease of _Nuttalides_ and increase in _Uvigerina_ spp. (Figure 4) at the Baskil section in this phase might suggest a reduction in oxygen availability at the sea floor. The decrease in the P/B, C/A and diversity indices (Figure 3) can be related with increase of carbonate dissolution.  
A major change is recorded within planktic foraminiferal assemblages as the large _Azarinina_ markedly decreased in abundance within this phase (Figure 5). Although this group is a warm index that should benefit of the warming increase, the drop in abundance is likely related to the arising mesotrophic conditions. This new trophic state may explain the apparently contradictory increase in abundance of the cold eutrophic index _Subbotina_.  
Calcareous nanofossils assemblages record increase in abundance of the eutrophic taxa, namely _C. pelagicus_, _Reticulofenestra_, _R. umbilicus_ and _Helicosphaena_ and a concurrent decrease of the oligotrophic ones (i.e., _Sphenolithus_ and _Z. bijugatus_) (Figure 6) thus confirming the transition towards mesotrophic conditions. These conditions are even more intensified in the upper part of this phase as suggested by the peak of the eutrophic _D. scrippsae_. The high abundance of the oligotrophic warm index _Discoaster_ in this phase can be explained with a major sensitivity to the temperature increase of this genus.  
The statistical analysis shown in Figure 7 clearly demonstrate as our biotic proxies reflect an increase in temperature along the water column, the transition to a mesotrophic state of surface water and a change in the quality of organic matter (i.e., more labile) reaching the seafloor.
## 3 Results and Discussion  
### Compact Model of GFET  
We derived a semi-empirical compact model useful for GFET design and analysis. This model shown in Figure 2 has good agreement with DC and RF measurements [15]. In this model we have taken into account the quantum capacitance (C\({}_{\text{q}}\)), velocity saturation (v\({}_{\text{sat}}\)), contact resistance (R\({}_{\text{c}}\)) and access resistance (R\({}_{\text{a}}\)). The derived drain current I\({}_{\text{DS}}\) (see Equation (1)) depends on the graphene channel width (W) and length (L), carrier density (ni), effective mobility(ueff), drain voltage (V\({}_{\text{DS}}\)), gate voltage (V\({}_{\text{GS}}\)), oxide capacitance (C\({}_{\text{OX}}\)), quantum capacitance (C\({}_{\text{q}}\)) and temperature (T). This model is described in more detail in our earlier work [15,16]. This model is implemented in Agilent ADS for linear and nonlinear RF circuit simulation.  
\[{}_{\text{{{{I}}}os}}=\underset{q\,\frac{W}{L}\text{{{{I}}}w_{\text{eff}}}}{W} \Bigg{(}\underset{u\,V_{\text{{{{I}}}os}}+\Bigg{(}\frac{\alpha+1}{4\pi\text{ h}^{2}v_{T}^{2}(\alpha\ln 2+1)}\Big{(}-2V_{\text{{{{O}}}os}+V_{\text{{{{O}}}os}}\Big{[}1+ \delta^{2}V_{\text{{{{O}}}os}}^{2}+V_{\text{{{{G}}}os}}\Big{(}1+\delta^{2}V_{ \text{{{{O}}}os}}^{2}+\frac{\text{asinh}\,\delta V_{\text{{{{O}}}os}}+\text{asinh} \,\delta V_{\text{{{{G}}}os}}}{\delta}\Big{)}\Bigg{)}\Bigg{)}}} \tag{1}\]  
where \(\mu_{eff}=\frac{\mu}{\Bigg{[}\Big{(}1+\frac{\mu W_{\text{DS}}}{Lv_{\text{sat} }}\Big{)}^{2}\Bigg{)}};\beta=\frac{q^{2}}{\pi\text{h}^{2}v_{T}^{2}};\delta= \frac{2\alpha\sqrt{(\alpha\ln 2+1)}}{(\alpha+1)^{2}}\frac{q}{k_{B}T\ln 4}\).  
Figure 1: **(a)** 3D view of multi-fingered GFET on flexible polyimide film; **(b)** Enlarged optical image of a representative GFET transistor.  
Figure 2: Compact model of GFET (adapted from [17]. Copyright 2013, permission granted by [PERSON]).
## 2 Results and Discussion
### Cd\({}^{2+}\) Adsorption with Biochar  
#### 2.3.1 Effect of the Addition of Various Amounts of Biochar on the Cd\({}^{2+}\) Removal  
From Figure 3, we can see that, at pH 5, the Cd\({}^{2+}\) removal of biochar obtained from both materials significantly increased with additive content. A larger biochar amount provided a larger adsorption surface area and a greater number of Cd\({}^{2+}\) adsorption sites. Therefore, the removal increased. The highest Cd\({}^{2+}\) removal was obtained with 200 mg of the rice-husk biochar, with RH300 affording the lowest (54.63%) removal, and RH700 the highest (96.12%) removal. For the cotton-straw biochar, the Cd\({}^{2+}\) removal was highest for 200 mg of CS300 (94.22%). The Cd\({}^{2+}\) removal reached 95% when 100 mg of CS400 and 50 mg of CS 500 were employed. Only 50 mg of CS600 was required to remove nearly 100% of Cd\({}^{2+}\) ions, while only 25 mg of CS700 was required to achieve a Cd\({}^{2+}\) removal of 99.67%. Therefore, huge differences existed in the Cd\({}^{2+}\) adsorption by biochar obtained from both materials pyrolysed at various temperatures [30; 31]. Upon comparing the two materials, we can see that a smaller amount of the cotton-straw biochar was required to achieve the same removal in a shorter time. Therefore, from an energy-saving perspective, the conditions to obtain the best adsorption performance involve the use of 150 mg of rice husk biochar pyrolysed at 500 \({}^{\circ}\)C.