esa-sceva/satcom-chunk-collection huggingface dataset:
### Scope Limitations and Extended Application  
The evaluation process presented in the following paragraphs focuses on a specific application (heat transfer through masonry wall elements exposed to fire on one side) and, as such, it is optimised for the parameters describing this particular case. The parameters used as part of this study could be extended to capture additional features of the experimental and/or modelling data. This would provide a wider scope for the application of the evaluation methodology within the context of heat transfer and fire engineering. The additional parameters could include, but not be limited to, the moisture content of the wall assembly materials, the different types and positions of insulation, a range of different fire loads, ventilation parameters, the different compositions of masonry units with the associated variations in their thermal properties, and the different qualities of bedding and render mortar. The inclusion of a wider array of material properties, geometries, and thermal loading could greatly increase the scope of the modelling process and evaluation method.  
Another aspect that could enhance the flexibility of the current proposal would be the integration of other Machine Learning (ML) techniques. Although an extensive comparative study is beyond the scope of the present work, a replication of the proposed process on the output generated by other ML algorithms and models would help build a more holistic understanding of the method's transferability.  
Although there is wide scope for the further expansion of the proposed methodology, a conscious effort has been made to primarily focus on its underpinning principles. This provides an overview of the cornerstones of the evaluation concept and enables a straightforward transfer of its universally applicable values. The aim of the final conclusions and recommendations is to furnish fellow researchers with a guideline on how to assess their ANN output regardless of their specific scientific field and practical application.
# A Resilient Large-Scale Trajectory Index for Cloud-Based Moving Object Applications
## 4 Query Processing
### Range Query  
Given a range query \(RQ=<P_{bl},P_{ur}>\), where \(P_{bl}\) is the bottom left point of the spatial range and \(P_{ur}\) is the upper right point, on trajectory dataset \(T\), RMOI needs to find any \(Segs\in T\) such that \(Seg_{space}\cap RQ_{space}\). It first determines the involved RDD partitions by traversing the GlobalRMOI based on the global nodes' MBRs. It returns the _Pids_, which are contained by the corresponding leaf nodes. After that, a Spark Job is initialized and targets only the required partitions. During the Job execution, engaged worker nodes traverse their own LocalRMOI. For range queries, LocalRMOI uses only the STRtree without the need for the interval B-tree as it only searches for spatial overlapping. The result is returned as a new RDD, which only contains the segments covered by \(RQ\).
# Transformative Technology for FLASH Radiation Therapy
## FLASH Studies with Laser-Driven Particle Sources Developed at Lawrence Berkeley National Laboratory  
### Status of Laser-Driven Particle Sources  
Novel laser-driven particle sources are receiving increasing attention due to their potential of providing particle beams for applications on a relatively small footprint and at a potentially lower cost than radio-frequency (RF)-driven accelerators [53; 54]. Efficient laser-particle acceleration has become feasible with the advent of ultra-short-pulse high-power lasers enabled by chirped pulse amplification [55], a technology that was awarded the Nobel Prize in Physics in 2018, which yielded peak laser powers exceeding 1 petawatt (PW) [56]. The most prominent acceleration schemes are the laser-wakefield acceleration (LWFA) of electrons [57] and target normal sheath acceleration (TNSA) of protons and ions [58]. LWFA is conducted with gas targets that are quickly ionized by the leading laser pulse edge, followed by the formation of collective plasma oscillations in the wake of the pulse as it propagates through the transparent plasma. Free electrons are accelerated up to several GeV energies in dynamic electric fields associated with the resulting plasma wave [59; 60]. If optimized, monoenergetic electron bunches with an nC charge can be generated [61]. At the time of writing, the LWFA electron energy record was held by Lawrence Berkeley National Laboratory, with 8 GeV and few pC charge [62]. LWFA sources can be used to drive compact light sources from the high-field THz [63] over the high-brightness X-ray [64] to the gamma-ray range [65].  
TNSA is generally pursued with solid targets, most commonly in the form of few \(\mu\)m-thick metal or plastic foils that are ionized by the lead edge of the laser pulse. The laser peak intensity interacts with free electrons in a preformed plasma layer at the target surface. Electrons gain energy in the laser field, circulate through the target bulk, and expand beyond the predominantly fixed ion distribution at the target surfaces. The resulting quasi-static charge separation fields are in the order of TV/m and lead to the acceleration of protons and ions to >10 MeV energies, emitted along the target normal with a beam divergence of roughly \(\pm\) 20\({}^{\circ}\)[23; 24; 66; 67]. The generated proton beams are of high flux (up to 10\({}^{13}\) particles per pulse [68]) and feature broad exponential energy spectra up to the characteristic cutoff energy, approaching 100 MeV [69; 70; 71; 72].  
At the current PW laser pulse repetition rates of at most 1-10 Hz, directing laser-driven (LD) particle beams to biological samples results in a moderate mean dose rate. However, due to their generation mechanism, resulting in ultra-short particle pulse lengths of less than a picosecond at the source, LD particle beams naturally feature ultra-high IDR, exceeding 10\({}^{9}\) Gy/s. This IDR is several orders of magnitude higher than dose rates typically delivered with RF-driven accelerator technology [73].
## 2 Materials and Methods
### Spectral Signal Processing
#### 2.3.3 Wind Field Retrieval  
The estimation of radial wind from the power spectrum is based on wind spatial continuity [37]. The distance resolution between adjacent distance gates is smaller than the scale of most wind shear [41]. The measurement is approximately valid during the time needed to measure along the four lines of sight. Radial wind can be estimated by the Doppler principle. According to the principle of Doppler frequency shift, the echo signal received by the telescope through the atmosphere will generate a frequency shift with respect to the signal transmitted by the telescope [42]. Fundamentally, the relationship between Doppler frequency shift and radial wind speed can be described as follows [43]:  
\[f_{d}=f_{r}-f_{0}=(2V\cos\theta_{0})/\lambda, \tag{12}\]where \(f_{r}\) is the frequency of echo signal, \(f_{0}\) is the frequency of output laser, \(\theta_{0}\) is the included angle between emitting laser and motion direction of the particle to be measured, \(\lambda\) is the central wavelength of the emitting laser, and \(V\) is the movement speed of the particle. Accordingly, CWML can measure the radial wind field information by detecting the Doppler frequency shift in the echo signal. Moreover, the four-beam scanning mode is demonstrated in Figure 5. Here, the zenith angle of laser emission is \(\varphi\), and the radial wind fields with azimuth angles of \(0^{\circ}\), \(90^{\circ}\), \(180^{\circ}\), and \(270^{\circ}\) are measured successively. The radial wind speeds in the four measurement directions are as follows [18]:  
\[V_{rn}=V_{y}\sin\varphi+V_{z}\cos\varphi, \tag{13}\]  
\[V_{re}=V_{x}\sin\varphi+V_{z}\cos\varphi \tag{14}\]  
\[V_{rs}=-V_{y}\sin\varphi+V_{z}\cos\varphi \tag{15}\]  
\[V_{rw}=-V_{x}\sin\varphi+V_{z}\cos\varphi \tag{16}\]  
Then, \(V_{x}\), \(V_{y}\), and \(V_{z}\) are given as  
\[V_{x}=(V_{re}-V_{rw})/2\sin\varphi, \tag{17}\]  
\[V_{y}=(V_{rn}-V_{rs})/2\sin\varphi \tag{18}\]  
\[V_{z}=(V_{re}+V_{rw}+V_{rn}+V_{rs})/4\cos\varphi \tag{19}\]  
Additionally, the magnitude and direction of horizontal wind speed are  
\[V_{h}=[(V_{re}-V_{rw})^{2}+(V_{rn}-V_{rs})^{2}]^{1/2}/(2\sin\varphi), \tag{20}\]  
\[\gamma=\arctan(V_{x}/V_{y})+\pi\big{\{}1-sign[(V_{y}+\big{|}V_{y}\big{|})\cdot V _{x}\big{\}},V_{y}\
eq 0 \tag{21}\]
## 2 Materials and Methods
### Sample Fabrication Process
#### 2.2.3 Perovskite Film Deposition  
Unlike in conventional solution-processed perovskites, a two-step post-treatment method was used to improve the quality and stability of the perovskite films. First, a precursor solution containing Cs, FA, and Pb was deposited to form a (Cs\({}_{0.05}\)FA\({}_{0.95}\))PbI\({}_{3}\) perovskite layer.  
The CsFAPbI\({}_{3}\) precursor solution was prepared by dissolving PbI\({}_{2}\) (1.4 M), CsI (0.07 M), and FAI (1.33 M) in a DMSO:DMF (10:1, \(v/v\)) solvent system, corresponding to a molar ratio of Cs:FA = 0.05:0.95. This ensured that the total concentration of the A-site cation was equal to that of the B-site cation (Pb\({}^{2+}\)), maintaining the stoichiometry of Cs\({}_{0.05}\)FA\({}_{0.95}\)PbI\({}_{3}\). MACI (0.1 M) was added to the precursor solution to promote grain growth and improve crystallinity, and the mixture was stirred for 30 min. The solution was filtered through a 0.45 \(\upmu\)m syringe filter immediately before spin-coating. The precursor solution was deposited onto the substrates at 4000 rpm for 25 s. To induce rapid crystallization, 100 \(\upmu\)L of toluene was dropped onto the spinning substrate 5 s after the start of spinning as an anti-solvent. This step promoted uniform nucleation and facilitated the formation of dense and pinhole-free perovskite films. After spin-coating, a post-treatment was performed using a solution of MAI (20 mg) in IPA under the same spin-coating conditions. The films were then annealed at 150 \({}^{\circ}\)C for 15 min to ensure complete crystallization.  
The manufacturing process for the triple mixed-cation perovskite (CsFAMAPbI\({}_{3}\)) and the schematic illustrating the device architecture for the perovskite-based PD are shown in Figure 1.  
After the deposition of the initial perovskite layer, a post-treatment was applied to enhance crystallinity and reduce surface defects. A solution of MAI (20 mg) in IPA (1 mL) was spin-coated onto the perovskite layer under the same spin conditions (4000 rpm, 25 s). This post-treatment is known to passivate surface traps and improve film uniformity. The treated films were subsequently annealed at 150 \({}^{\circ}\)C for 15 min to promote complete crystallization.  
Previous studies have shown that such an MAI-IPA treatment can induce surface recrystallization and passivate shallow trap states, thereby enhancing the optoelectronic quality of perovskite films [19]. Although trap density and carrier lifetime were not directly measured in this work, improvements in grain size, optical absorption, and charge transport characteristics provided indirect evidence of the treatment's effectiveness.
## 3 Controller Design
### Controller Design and Stability Analysis
\begin{array}{c}F_{11}q_{1}\bar{x}_{1}+F_{12}q_{2} \bar{x}_{2}+F_{13}q_{3}\bar{x}_{3}\\ F_{21}q_{1}\bar{x}_{1}+F_{22}q_{2}\bar{x}_{2}+F_{23}q_{3}\bar{x}_{3}\end{array}  \tag{50}\]  
then,  
\[\left[ \begin{array}{c}\dot{\bar{x}}_{1}\\ \dot{\bar{x}}_{2}\end{array} \right]\equiv\left[ \begin{array}{c}0\\ 0\end{array} \right]\equiv\left[ \begin{array}{c}0\\ K_{m}K_{c}\end{array} \right]q_{3}\Rightarrow\bar{x}_{3}\equiv 0 \tag{51}\]  
Therefore, the equilibrium point \(\bar{\mathbf{x}}^{*}=\mathbf{0}\) is asymptotically stable. The following remarks are in order about the proposed control scheme:  
**Remark 5**. : _Regarding the structure of (26), notice that it depends on the value of the motor parameters. When this scheme is evaluated, specially in an experimental setup, this condition imposes a robustness challenge since these values are not known in a precise way. Thus, the closest available value of them must be considered but the controller must be able to deal with this uncertainty._  
**Remark 6**. : _Concerning also with the robustness properties of the presented controller, an important feature of this scheme comes from (47). In order to achieve the desired stability properties, this expression must be semi-definite negative, which holds by only requiring that the parameters involved in this expression are positive, i.e., it is not necessary to impose to them an specific value. This is an evident robustness advantage of the proposed control._  
**Remark 7**. : _With respect to Remark 1, it is interesting to mention that if the viscous friction coefficient is explicitly considered, then the entry \((3,3)\) of matrix (41) is filled with a positive constant that denotes the value of this parameter. Under this condition, the stability proof is at some extent simplified since this matrix becomes definite positive. Thus, the case considered during the developed stability proof describes the worst case that must be considered for the controller implementation._  
**Remark 8**. : _The final structure of the proposed scheme is obtained by taking the equations_  
\[\bar{x}_{1}(t)=x_{1}(t)-x_{1}^{*}(t)=L_{s}i_{d}(t)-L_{s}i_{d}^{*}(t), \tag{52}\]  
\[\bar{x}_{2}(t)=x_{2}(t)-x_{2}^{*}(t)=L_{s}i_{d}(t)-L_{s}i_{d}^{*}(t), \tag{53}\]  
\[\bar{x}_{3}(t)=x_{3}(t)-x_{3}^{*}(t)=J\omega(t)-J\omega^{*}(t), \tag{54}\]  
_and substituting them in (26). Hence, the controller becomes_  
\[\mathbf{u}= \begin{bmatrix}-R_{s}(K_{c}-1)(i_{d}-i_{d}^{*})+n_{p}K_{d}\Big{(}i _{q}-i_{q}^{*}\Big{)}(\omega-\omega^{*})+n_{p}\eta_{1}\\ -R_{s}(K_{d}-1)\Big{(}i_{q}-i_{q}^{*}\Big{)}-n_{p}K_{d}(i_{d}-i_{d}^{*})( \omega-\omega^{*})+n_{p}\eta_{2}+K_{m}(\omega-\omega^{*})\end{bmatrix} . \tag{55}\]
# Non-Local Effects in Brillouin Optical Time-Domain Analysis Sensors
## 1 Introduction  
In the last two decades, distributed fiber optic sensors [1] have been the subject of an intensive research effort due to their unique capability to provide a multitude of measurement locations along an optical fiber. Among the different distributed sensor types, Brillouin-based distributed optical fiber sensing systems have been one of the most widely studied, particularly those based on the Brillouin optical time-domain analysis (BOTDA) measurement principle. These sensors provide high precision distributed temperature and strain measurements along hundreds of thousands of measurement positions over large structures. This renders BOTDA sensors highly useful for a number of structural health applications in several industrial sectors, such as ensuring the integrity of oil and gas pipelines, the structural health of bridges, tunnels, roads, railways and dams, the detection of fire in tunnels and industrial facilities, or assessing the temperature of sub-sea and underground electric power cables [2].  
Owing to their importance, many research advancements have been made in recent years on BOTDA sensors. Indeed, different works have been focused on the improvement of the spatial resolution of the sensor [3], the enhancement of the signal-to-noise ratio (SNR) of the analyzer for long-range measurement [4] and on the simplification and reduction of the final cost of the setup [5], among other research lines.  
We focus here on techniques to improve the SNR of long-range BOTDA sensors, which directly determines their performance in terms of measurement time and precision. The SNR of the probe wavedetected in a BOTDA depends on the power of the pump and probe waves that can be injected in the fiber under test. The pump pulse power is fundamentally limited by the onset of nonlinear effects such as modulation instability (MI), self-phase modulation and Raman scattering [6; 7]. As for the probe wave, its power is first limited by the onset of so-called non-local effects. These are due to the transfer of energy from the pump pulse to the probe wave during their interaction along the sensing fiber. This transfer has been shown to introduce an additional transfer function in the measurement due to optical frequency-dependent pump depletion, which induces a systematic error in the measurement of the Brillouin frequency shift (BFS) [8; 9]. A second higher-power limit for the probe power that can be injected in the sensing fiber link would be the Brillouin threshold of the fiber, over which significant noise is added to the detected probe signal due to spontaneous Brillouin scattering [10].  
In this paper, we carry out a review of the physical origins of non-local effects and their consequences to BOTDA measurements. In addition, the different techniques that have been presented in recent years to mitigate the appearance of non-local effects are introduced, with special emphasis on a technique that we have introduced based on the modulation or dithering of the optical frequency of the probe wave.
# Rare Earth Elements Uptake by Synthetic Polymeric and Cellulose-Based Materials: A Review
## 4 Sources and Recovery of REEs
### Secondary Sources of REEs  
Processes for REE recovery from secondary sources, such as mining wastewaters, AMD, and regolith, are under development in pilot scales [5; 6; 7; 8; 9; 10; 11; 12]. Mining wastewaters are acidic and are produced due to the active exploitation of mines that contain economically relevant minerals, and in some cases, they could contain REEs [7; 8]. AMD is a phenomenon that occurs when heavy metal- and sulfur-containing rocks and minerals are exposed to air and humidity. Sulfur oxidation due to the exposure of oxygen in the air produces sulfuric acid, which corrodes minerals and rocks. When REEs are present near AMD sites, the acidifying nature of AMD increases the mobility of REEs in soils and waters, which also happens with mining wastewaters [9; 10]. On the other hand, REEs' regolith formation is associated to the mobility of REEs from mining wastewaters or AMD. When REEs are liberated from acidic waters to groundwaters, the presence of clay minerals allows the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Application Field** & **REEs Involved** & **Uses** \\ \hline Electronics & La, Eu, Gd & Screen, personal computers, mobile phones, silicon chips, LED diodes, fluorescent lamps \\ Technology & La, Pr, Nd, Sm, Eu, Tb, Dy, Ho, Er, Yb & Lasers, masers, fiber optics, radars, nuclear reactors, lamps, high-reflectance glass, high-temperature superconductors \\ Medical sciences & Y, Eu, Gd, Tm, Lu, Sm & X-ray apparatus, nuclear resonance imaging, cancer treatments \\ Renewable energies & La, Pr, Nd, Eu, Tb & Electric cars, turbines, batteries \\ Chemical and industrial processes & La, Ce, Pr, Nd, Yb, Er, Lu & Catalysts, steel manufacture, magnets, pigments, colorants, oxidizers, plastics, catalytic converters, catalytic columns \\ Agriculture & La, Ce, Pr, Nd & Fertilizers, crop yield and crop quality enhancers \\ \hline \hline \end{tabular}
\end{table}
Table 1: Applications of REEs.  
sorption of REEs present, which accumulate over time, although this process is still not well understood [11; 12]. REEs have been found in concentrations up to 100 times more than naturally found amounts in soils and waters surrounding REE mines and processing plants [5; 6; 7; 8; 9; 10; 11; 12].
# A 28 nm Bulk CMOS Fully Digital BPSK Demodulator for US-Powered IMDs Downlink Communications
## 4 The Proposed Fully Digital BPSK Demodulator
### D-Flip-Flops (DFFs) and XOR Gate  
The frequency dividers used in the block diagram in Figure 6 were designed using True Single-Phase Clocking (TSPC) D-flip-flops (D-FFs) [41]. The single TSPC D-FF operates with only one clock signal and offers advantages, such as small silicon area occupation for clock lines, reduced clock skew and high-speed operations. Figure 12a shows the schematic block diagram of the TSPC D-FF in the version rising-edge. It is based on three clocked inverting stages, a p-type MOSFET, \(M_{10}\) for the reset and the last CMOS inverter to provide the direct output starting from the negated one. The only disadvantage of this topology is related to the clock slope; indeed, if it is not sufficiently steep, both NMOS and PMOS transistors of the clocked inverter could simultaneously be turned on during the clock transition. As a result, the logic level of internal signals may become undefined, and a race condition may occur. However, as compared with the conventional master-slave flip-flop, TSPC D-FF has the advantages of reducing the load of the clock distribution network and the switching power dissipation. Moreover, by requiring only a single-phase clock signal, they are less affected by clock skews caused by process variations [42]. By inspection of Figure 6, the reset (RST) inverted signal is driven, for the 2-bit frequency divider, by the modulating phase signal \(V_{P2}\), while for the RX-BPSK Data Reconstructor (consisting of a 1-bit frequency divider), the RST terminal is pulled up to the power supply.  
Regarding the XOR gate, its scheme is depicted in Figure 12b). The inputs are both complementary signals outputted from the 2-bit frequency divider. For the XOR gate, the pull-up transistors (\(M_{5}\)-\(M_{8}\)) are configured based on the function \(Q_{O}\oplus Q_{1}\), while the structure of the pull-down transistors is determined by the function \(\overline{Q_{O}\oplus Q_{1}}\). The output is at the logic high level when only one of the inputs is set to the logic high level. When the logic level of both inputs is either high or low, that of the output is low. Aspect ratios of transistors in the TSPC D-FF and XOR gate are summarized in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Corners @**\(V_{DD}=0.9\) V, \(I_{REF}=325\) nA and \(T=37\)\({}^{\circ}\)C & & \\ \hline
**Parameter** & **TT** & **FF** & **FS** & **SF** & **SS** \\ \hline \(f_{OSC}\) (MHz) & 4.07 & 3.72 & 4.16 & 3.95 & 3.93 \\ \(P_{DD}\) (\(\mu\)W) & 1.0 & 1.37 & 1.0 & 1.0 & 0.87 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Corner simulations of CSGRO at body temperature (37 \({}^{\circ}\)C).  
Figure 12: (**a**) Schematic of the TSPC D-FF and (**b**) schematic of the XOR Gate used for the proposed demodulator.