esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Passive Phased Arrays with Integrated Metallic and Dielectric Waveguide Phase Shifters
At both ends of the phase shifter section, there is a taper section to reduce reflections. where \(\Delta\varphi_{\text{max}}\) is the maximum differential phase shift and \(II_{\text{max}}\) is the maximum insertion loss in all tuning states. The insertion losses depend on the topology, material, and operational frequency. The FoM in dependence on the voltage is [47; 186; 74]:  
\[FoM(V)=\frac{360}{\pi\cdot 1\,\text{NP}}\cdot\frac{\sqrt{\tau_{L}(V)}-\sqrt{ \tau_{L,}}}{\sqrt{\tau_{L}(V)}\cdot\tan\delta_{LC,max}}=\frac{360}{\pi\cdot 1\, \text{NP}}\cdot\frac{1-\sqrt{1-\tau_{LC}(V)}}{\tan\delta_{LC,max}}\,\text{with}\ \tau_{LC}(V)=\frac{ \epsilon_{L}(V)-\epsilon_{r,h}}{\epsilon_{r}(V)} \tag{9}\]  
For the maximal tunability \(\tau_{LC}(V=V_{sat})=\tau_{LC}\) and by using the approximation \(\sqrt{1-\tau_{LC}}\approx 1-\frac{1}{2}\tau_{LC}\) for small \(\tau_{LC}\), the maximal possible or optimal FoM is  
\[FoM_{opt}=\frac{180}{\pi\cdot 8.686\,\text{dB}}\cdot\frac{\tau_{LC}}{\tan \delta_{LC,max}}\ \approx 6.6\cdot\eta_{LC}\ \text{in}\ ^{\circ}/\text{dB} \tag{10}\]  
where \(\tan\delta_{LC,max}\) is the maximal loss factor of the LC and \(\eta_{LC}\) is the material's figure-of-merit. This equation is only valid by presuming a constant dielectric loss of the LC for all tuning states, while all other materials are assumed to be loss-free. \(FoM_{opt}\) would be \(113.5^{\circ}/\text{dB}\), \(306.2^{\circ}/\text{dB}\), and \(172.3^{\circ}/\text{dB}\) for GT3, TUD-566, and GT7, respectively. In practice, the FoM is typically in the range of 60 to 80% of \(FoM_{opt}\). Voltage-controlled MLC-phase shifters are aimed for adjustments and flexible operation, i.e., to reconfigure their characteristics in operation. The requirements are generally a high FoM or high FoM per unit length, low power consumption to tune the device, and high power-handling capabilities with low intermodulation distortion. They can be built up in different topologies, including various tunable low-profile planar transmission delay lines as well as high-performance metallic and dielectric waveguides. While planar line topologies are inherently flat and compact, volumetric waveguide-based structures and devices are bulky, more complex and usually more expensive to implement. However, the electrical losses of metallic and dielectric waveguide structures, particularly at higher frequencies \(f>30\) GHz, are significantly lower than for corresponding planar structures. However, these bulky structures require complex biasing electrode configurations with higher biasing voltages than planar structures. Due to their trade-off properties between conventional planar and waveguide structures, Substrate Integrated Waveguide (SIW) structures have attracted many researchers in recent years.
## 3 Global Positioning System (GPS) L1 Receiver: 2D Pattern vs. 3D Pattern  
The transmitters' antenna patterns vary not only among the elevation, but also among the azimuth angles. This is also confirmed by the data made public in [3]. On one side the gain variation for the elevations used for terrestrial services is less than 1.5 dB, but, on the other side, this variation becomes very significant for the remaining part of the main lobe, and moreover it reaches values larger than 10 dB for the side lobes. Hence, the resultant performance when a signal is tracked at same elevation but at different azimuth would be different, especially for signals processed from the side lobes. However, due to a lack of precise antenna directivity data, in many research studies, this element is neglected. In most studies, a 2D antenna pattern is assumed, where the gain value is a function of the different elevations, but not of the different azimuth, for which an average value is assumed. In this case, the term L\({}_{\text{TX}}\) in Equation (1) will be only function of the elevation \(\varphi\).  
In this context, this section aims at comparing quantitatively and qualitatively the GNSS performance obtained when assuming 3D antenna patterns with the one when 2D antenna patterns are considered.
## 1 Introduction
Then, the projection images of test rod A and B are recorded, while the position of the central axis of the transit axis can be recorded and identified by a telecentric measurement system. Finally, the transit offset and tilt errors can be obtained by comparing the central axis positions of test rod A and B before and after the standing axis is rotated 180 degrees. In general, the test rods need to be manually adjusted to make them coaxial with the transit axis, which is time-consuming. Therefore, we also proposed a numerical analysis based on the least square circle fitting method to calibrate the installation eccentricity errors of test rods, which can improve the accuracy of our proposed calibration method. The length measurement tests using our laser tracker qualify the reliability of the calibration method of transit offset and tilt errors.
## 3 Results and Discussion
### Analysis of Aroma-Active Compounds Based on AEDA and MF
#### 3.4.1 AEDA Results  
FD factor was employed to screen out key aroma-active compounds and a large FD indicates the compound contributes significantly to the overall aroma. In the present study, aroma-active compounds with FD \(>\) 16 were considered as key odorants and a total of 29 compounds were selected. The FD factor olfactograms of them are shown in Figure 3A. \(\beta\)-Myrcene (FD = 128, balsamic/fruity) and \(D\)-limonene (FD = 256, citrus/lemon/minty) were key aroma-active compounds with a high FD in both HY EO and GF EO. This suggested their crucial role in the formation of citrus flavor, which was consistent with previous research [1; 21]. Specifically, among the compounds with FD \(>\) 100, \(\alpha\)-cadin (FD = 256, pungent), nootkatone (FD = 256, woody), carvyl acetate (FD = 128, woody/fruity), germacrene D (FD = 128, paper/ink), germacrene B (FD = 128, woody) and unknown 12 (FD = 128, grass/fruity) were found to be unique key odorants in HY EO. This explained why HY EO exhibited a strongly woody and green scent distinctly different from GF EO. On the other hand, geraniol (FD = 512, citrus/floral), octanal (FD = 256, fatty/citrus) and linalool (FD = 256, floral) were identified as unique key odorants in GF EO, which contributed to a stronger fruity and floral aroma in GF EO as compared to HY EO. In
## 4 Glyphosate and AMPA in Foods  
As previously discussed, the extensive use of GLYP for weed control has resulted in the presence of residues of this pesticide and its by-products in water and food products; therefore, consuming contaminated foods is the main pathway through which humans are exposed in a chronic and ongoing manner (directly or indirectly) to herbicides [16], which is considered a significant global health issue today [35]. The overuse and misuse of GLYP, particularly in less-developed nations, can result in environmental contamination and negatively impact human health over time. To ensure compliance with regulations and guarantee food safety, it is crucial to systematically track pesticide residues in the environment and measure human exposure across the entire food production process, including vegetal and animal-based foods. In this context, the regulation of GLYP varies considerably among countries and depends on the foodstuff. Moreover, each country defines acceptable concentrations of GLYP in foods and restricts or prohibits its use due to adverse effects on health and environment [35]. In the United States of America, the Environmental Protection Agency (EPA) is responsible for regulating pesticides, including GLYP. The EPA has established the maximum residue limit (MRL) of 30 mg/kg for most grains and 40 mg/kg for most oilseeds [56]. The European Commission (EC) has set an MRL of 20 mg/kg for barley, oats, sorghum, soybeans, and sunflower seeds; 10 mg/kg for wheat, rye, mustard seeds, lentils, and peas; 2.0 mg/kg for beans; 1.0 mg/kg for corn; and 0.1 mg/kg for cereals and grains if not specified [56]. Similarly, the Food and Agriculture Organization of the United Nations (FAO) and WHO Codex established the MRL at 30 mg/kg for most cereals, 5 mg/kg for beans, and 2 mg/kg for dry peas [57]. The establishment of the MRL aims to maintain a substantial safety margin in food production through the implementation of effective agricultural methodologies; however, prolonged exposure to GLYP and AMPA, particularly when MRLs are exceeded, poses a significant risk to human health [35]. In 2020, a decree was established in Mexico mandating the gradual elimination of GLYP across the country [58]. Although various regulatory agencies have established MRL values for GLYP and AMPA in diverse food matrices, the presence of GLYP and AMPA has been reported in foods globally (Table 3). Figure 1: Potential sources of glyphosate and AMPA human exposure. GLYP and AMPA have been detected in a wide range of plant- and animal-based food products. Studies conducted in Canada, Switzerland, Estonia, the USA, and Brazil have identified the presence of GLYP and AMPA in honey samples, with detection frequencies ranging from 12 to 100% and concentrations ranging from \(<\)1 to 342 \(\upmu\)g/kg [59, 60, 61, 62, 64]. A honey sample from the USA exhibited the highest GLYP content [63]; however, some honey samples from European countries contained GLYP levels exceeding the legally permitted
# Beamforming Based on a SSS Angle Estimation Algorithm for 5G NR Networks
## 3 Proposed Beamforming Based on SSS Angle Estimation Algorithm
First, in line 2, we start by generating the weights for every transmitted beam directions, with angles \(\varphi_{b}=\varphi_{\min}+b\lambda\varphi\), \(\Delta\varphi=(\varphi_{\max}-\varphi_{\min})/B_{\varphi}\), where \(\varphi_{\min}\) and \(\varphi_{\max}\) are the minimum and maximum azimuth angles, respectively; and \(\omega_{b}=\omega_{\min}+b\lambda\omega\), \(\Delta\omega=(\omega_{\max}-\omega_{\min})/B_{\omega}\), where \(\omega_{\min}\) and \(\omega_{\max}\) are the minimum and maximum elevation angles, respectively, with \(B=B_{\varphi}+B_{\omega}\). The indexes \(0\leq m\leq N_{y}\) and \(0\leq n\leq N_{z}\) are the antenna indexes such that \(N_{tx}=N_{y}N_{z}\). Then, in line 5, we generate the weights for every received steam direction, with angle \(\theta_{s}=\theta_{\min}+s\Delta\theta\), \(\Delta\theta=(\theta_{\max}-\theta_{\min})/S\), where \(\theta_{\min}\) and \(\theta_{\max}\) are the minimum and maximum azimuth angles, respectively. Next, in line 6, we extract the received SSS and, in line 7, we obtain the SSSs symbols for the computed PCI. Afterwards, in line 8, we perform the channel propagation matrix estimation in the digital part based on SSS, which comprises the effects of the analog processing. In line 10, we perform the channel propagation matrix estimation in the analog part to remove the analog equalizer processing effects. Finally, this matrix is then used for metric evaluation of the different steering angles to determine the one that maximizes the RSRP and compute a best suitable adjusted beam at the UE end. ```
1 for\(b=0,\ldots,B-1\)do
2\(\mathbf{f}_{a,b}(\varphi_{b},\omega_{b})=\frac{1}{\sqrt{N_{s}N_{k}}}\left[1,e^{ i1 kd\{m\sin(\varphi_{b})\sin(\omega_{b})+n\cos(\omega_{b})\}},\ldots,\right.\) \(\left. \begin{array}{c}e^{i(N_{b}-1)\sin(\varphi_{b})\sin(\omega_{b})+(N_{i}- 1)\cos(\omega_{b})}\end{array} \right]^{T}\)
3 end for
4 for\(s=0,\ldots,S-1\)do
5\(\mathbf{w}_{a,s}(\theta_{s})=\frac{1}{\sqrt{N_{s}}}\left[1,e^{i1 kd\sin(\theta _{s})},e^{i2 kd\sin(\theta_{s})},\ldots,e^{i(N_{s}-1)kd\sin(\theta_{s})}\right]^ {T}\)
6\(\tilde{d}_{k,s}=\mathbf{w}_{a,s}^{H}\mathbf{H}_{k}\tilde{\mathbf{r}}_{a,b} \tilde{d}_{k}+\mathbf{w}_{a,s}^{H}\mathbf{n}_{k,s}\)
7 Get \(d_{k}\) for computed PCI
8\(\tilde{h}_{a,b,s}=\tilde{d}_{k,s}d_{k}^{-1}\)
9 end for
10\(\tilde{\mathbf{h}}_{a,k}=\left(\mathbf{W}_{a}\mathbf{W}_{a}^{H}\right)^{-1} \mathbf{W}_{a}\tilde{\mathbf{h}}_{a,b}\), \(-H\) \(\mathbf{w}_{a}=\arg_{\theta}\max\mathbf{w}_{a}^{H}(\theta)\tilde{\mathbf{h}}_{ a,k}\), \(\mathbf{w}_{a}^{-H}\in\Omega_{a}\)
```  
**Algorithm 1**Proposed BSAE algorithm
## 5 Numerical Examples and Results
### Free Vibration Analysis
On the other hand, when you introduce the coefficient k\({}_{1}\), there is an evident change in the value of natural frequencies \(\widetilde{\omega}\). The effect of the geometry changes of the plate (a/b ratio) and the index p is shown in the diagram 9b. With the increase of a/b ratio the curves become further away from each other, i.e., the fastest change in the \(\widetilde{\omega}\) value occurs in a ceramic plate, the slowest change occurs in a metal plate, and in FGM the rate of change depends on the ratio of constituents. Figure 9c shows the effect of different a/h ratios on the values \(\widetilde{\omega}\). For smaller values of a/h ratios, the changes in values \(\widetilde{\omega}\) are greater, while at values of a/h \(>\) 5, the effect on the values \(\widetilde{\omega}\) is decreasing. Figure 10 shows 3D diagrams of the non-dimensional natural frequencies \(\widetilde{\omega}\) of the plates resting on an elastic foundation for different ratios a/h, a/b, and values k\({}_{0}\), k\({}_{1}\), and the index p. This visualization provides a more transparent insight into the previously described effects of certain parameters on the natural frequency values and the conclusions reached. For example, at Figure 10c it can be clearly  
seen that the impact of the coefficient \(\mathrm{k}_{1}\) on the frequency values is far greater than the impact of the coefficient \(\mathrm{k}_{0}\).
# A Wi-Fi FTM-Based Indoor Positioning Method with LOS/NLOS Identification
###### Abstract  
In recent years, many new technologies have been used in indoor positioning. In 2016, IEEE 802.11-2016 created a Wi-Fi fine timing measurement (FTM) protocol, making Wi-Fi ranging more robust and accurate, and providing meter-level positioning accuracy. However, the accuracy of positioning methods based on the new ranging technology is influenced by non-line-of-sight (NLOS) errors. To enhance the accuracy, a positioning method with LOS (line-of-sight)/NLOS identification is proposed in this paper. A Gaussian model has been established to identify NLOS signals. After identifying and discarding NLOS signals, the least square (LS) algorithm is used to calculate the location. The results of the numerical experiments indicate that our algorithm can identify and discard NLOS signals with a precision of 83.01% and a recall of 74.97%. Moreover, compared with the traditional algorithms, by all ranging results, the proposed method features more accurate and stable results for indoor positioning.  
a _Article_  
A Wi-Fi FTM-Based Indoor Positioning Method with LOS/NLOS Identification  
[PERSON] 1,2, [PERSON] 1,2,*, [PERSON] 2, [PERSON] 2 and [PERSON] 2
## 7 Results and Discussion: Mean Torque
### Summary: SE and DE on the Mean Torque of PMSMs and MPMSMs  
In summary, it was found that eccentricity has a negligible impact on the mean torque of the evaluated slot/pole combinations. Modular machines may develop a lower mean torque than monolithic machines depending on the number of slots per pole per phase.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \(Q_{s}\)/\(2p\)ECC & \multicolumn{1}{c}{**0 mm (Faultless)**} & \multicolumn{2}{c}{**0.25 mm**} & \multicolumn{2}{c}{**0.50 mm**} & \multicolumn{2}{c}{**0.75 mm**} & \multicolumn{2}{c}{**1.00 mm**} \\ \hline  & Mod & Mon & Mod & Mon & Mod & Mon & Mod & Mon & Mod & Mon \\  & [Nm] & [Nm] & [Nm] & [Nm] & [Nm] & [Nm] & [Nm] & [Nm] & [Nm] \\
185 12P & 147.8 & 171.8 & 148.0 & 172.0 & 147.8 & 171.8 & 147.9 & 171.8 & 148.0 & 171.9 \\
185 20P & 218.9 & 226.1 & 218.7 & 225.8 & 218.9 & 226.0 & 218.9 & 226.0 & 219.0 & 226.2 \\
245 20P & 208.4 & 215.4 & 207.6 & 214.6 & 207.6 & 214.6 & 208.1 & 215.1 & 208.3 & 215.3 \\
245 22P & 216.2 & 218.4 & 216.2 & 218.4 & 216.3 & 218.5 & 216.4 & 218.6 & 216.6 & 218.8 \\
245 28P & 207.4 & 232.4 & 207.2 & 232.2 & 207.4 & 232.4 & 207.8 & 232.7 & 208.5 & 233.4 \\ \hline \hline \end{tabular}
\end{table}
Table 18: Mean torque of different PMSM and MPMSM slot/pole combinations in the presence of DE.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \(Q_{s}\)/\(2p\)ECC & \multicolumn{1}{c}{**0 mm (Faultless)**} & \multicolumn{2}{c}{**0.25 mm**} & \multicolumn{2}{c}{**0.50 mm**} & \multicolumn{2}{c}{**0.75 mm**} & \multicolumn{2}{c}{**1.00 mm**} \\ \hline  & Mod & Mon & Mod & Mon & Mod & Mon & Mod & Mon & Mod & Mon \\  & [Nm] & [Nm] & [Nm] & [Nm] & [Nm] & [Nm] & [Nm] & [Nm] & [Nm] & [Nm] \\
185 12P & 147.7 & 171.7 & 147.7 & 171.8 & 147.7 & 171.7 & 147.7 & 171.8 & 147.8 & 171.8 \\
185 20P & 218.2 & 225.3 & 218.4 & 225.5 & 218.4 & 225.5 & 218.6 & 225.7 & 218.7 & 225.9 \\
245 20P & 207.9 & 214.9 & 208.0 & 215.0 & 208.0 & 215.0 & 208.3 & 215.3 & 208.4 & 215.3 \\
245 22P & 216.2 & 218.4 & 216.2 & 218.4 & 216.3 & 218.5 & 216.4 & 218.6 & 216.6 & 218.8 \\
245 28P & 207.3 & 232.2 & 207.2 & 232.2 & 207.4 & 232.4 & 207.8 & 232.7 & 208.1 & 232.9 \\ \hline \hline \end{tabular}
\end{table}
Table 19: Mean torque of different PMSM and MPMSM slot/pole combinations in the presence of SE.
# Method of Calculating Desynchronization of DVB-T Transmitters Working in SFN for PCL Applications
## 11 Conclusions  
As shown, the synchronization of SFN DVB-T transmitters (and possibly other commercial transmitters) is not precise. While it is not a problem for a commercial user, it may be a source of significant localization error for a passive radar system.  
The described experiment proved not only that the chosen DVB-T transmitters were desynchronized by 3.4 \(\upmu\)s, but also that the synchronization error of two transmitters working in an SFN can be calculated using only passive radar receivers, without any additional hardware systems. Such a method is recommended, especially when a priori knowledge of transmitters' localizations is not reliable or inaccurate. This additional information about desynchronization can be used to alter the localization algorithms of passive radars, and possibly further improve their precision and detection probability, which the authors intend to implement in a future PCL processing chain.  
Conceptualization, K.K. and P.S.; methodology, K.K. and P.S.; software, K.K.; validation, K.K.; formal analysis, P.S.; data curation, K.K.; writing--original draft preparation, K.K.; writing--review and editing, P.S.; visualization, K.K.; supervision, P.S.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Proposed Circuit Implementation
### MNMC and Frequency Compensation Method  
The circuit adopts a multipath nested Miller compensation op-amp circuit structure, which consists of a low-speed channel composed of g\({}_{\text{m1}}\), g\({}_{\text{m2}}\) and g\({}_{\text{m3}}\) op amp, a chopper modulation circuit, and a notch filter in the blue area, as well as an NMC (nested Miller compensation) op amp and a high-speed channel SMC (single Miller compensation) op amp composed of g\({}_{\text{m3}}\) and g\({}_{\text{m4}}\). When the input signal is at a low frequency, the overall gain is mainly provided by the low-speed channel. At high frequencies, the gain is mainly provided by the high-speed channel. The overall circuit structure is shown in Figure 6.  
The low-speed channel is actually a three-stage op amp using NMC compensation, which can be regarded as two nested two-level Miller compensation structures. The difference from the conventional NMC circuit is that the Miller capacitor of the outer loop here is divided into two parts before and after the notch filter. This design choice is based on the fact that the output signal of the post-stage notch filter lags behind the input signal by one clock cycle. By feeding back the output to the notch filter both before and after, the output waveform becomes more continuous.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Auto-Zeroing** & **Chopper Stabilized** \\ \hline Very low offset & Very low offset \\ Sample-and-hold & Modulation/demodulation \\ Higher low-frequency noise & Similar noise to flat band \\ due to aliasing & (no aliasing) \\ Higher power consumption & Lower power consumption \\ Wide bandwidth & Narrow bandwidth \\ Lowest ripple & Higher ripple \\ Little energy at auto-zero frequency & Lots of energy at chopping frequency \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between chopping and auto-zeroing.  
The high-speed channel g\({}_{m4}\) will produce a left-half-plane zero LHP [40]:  
\[z_{1}=\frac{g_{m1}\cdot g_{m1}}{(C_{m2}+C_{m3})g_{m4}} \tag{4}\]  
In order to maximize the bandwidth of the op amp, it can be used to offset the second pole of the low-speed channel NMC (\(z_{1}\) = \(p_{2}\)). Finally, the third pole \(p_{3}\) is designed at a high frequency outside the bandwidth to increase the phase margin [40].  
\[p_{2}=\frac{g_{m3}}{2C_{L}}(1-\sqrt{1-\frac{4g_{m2}/C_{m2}}{g_{m3}/C_{L}}}) \tag{5}\]  
The main pole and the secondary main pole are located at very low frequencies, and the third pole \(p_{3}\) is outside the bandwidth. The final system function is similar to a two-pole system. The Bode plot will form a frequency response with a 20 dB/dec roll-off starting from the low-frequency part.
# PV Sizing for EV Workplace Charging Stations--An Empirical Study in France
## 3 Context and Methodology  
### Use-Case: CEA Cadarache EV Charging Infrastructure (EVCI) and PV Plant  
The data collection took place in the Cadarache research center of the French Alternative Energies and Atomic Energy Commission (otherwise known as CEA, the French acronym for \"Commissariat a l'Energie Atomique et aux Energies Alternatives\") locatednear Aix-en-Provence. This research center disposes its own water, heating, lighting and electricity distribution network in which it operates as the DSO. A private bus service is offered to its employees for commuting and for trips between laboratories and canteens. A taxi service is also offered for intra-site trips. Thus, the Cadarache center can be seen as a town with a population of approximately 5000, privately owned and managed by CEA.  
The CEA also set-up an EVCI during the summer of 2016. It involves 40 Diva-type terminals, produced and installed by G\({}^{2}\)Mobility, which was bought out by TotalEnergies in 2018. Each Diva terminal has two 22-kW AC charging points. Each of these charging points has a type 2 socket for mode 3 connection and a type E socket for mode 1 and 2 connections. These charging stations have been installed alone or in groups of up to four Diva terminals to create 30 charging stations spread throughout the center. Each charging station has an embedded IoT gateway that enables communication through 3G networks by using Open Charge Point Protocol (OCPP) commands. As soon as the EV is plugged in, it is charged at its nominal power. In other words, the EVCI lets the EV charge as rapidly as possible from the moment the vehicles are plugged in. This charging strategy, further named \"Plug and Charge\" strategy in this work, is generally the default strategy of most commercial charging points.  
CEA also conducts research at Cadarache with INES on solar thermal energy and photovoltaics. In particular, CEA tests and evaluates innovative PV systems (such as Tandem Perovskite-Silicium PV, bifacial PV, single- and dual-axis solar trackers) ranging in size from modules to a few tens of kW systems. The energy produced from all these pieces of equipment (which aggregates up to 50 kWp) is injected into the CEA private power network. Furthermore, the CEA plans to install other PV plants, either ground-mounted or on buildings, and to install PV solar shade. In this latter case, these PV plants will be sized for a self-consumption scheme, described in the following section.