esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Chip Architecture
### Light Spot Position Detection  
To find the position of the center of the light beam spot for optical communication, the image output can be used. If a LED light beam spot irradiates the focal plane as shown in Figure 8(a), the position of the center pixel of the light beam spot must be (i, j). As described in the next section, the photo current intensity due to the direct LED light beam spot for communication is much larger than that induced by the standard illumination level. Therefore, by choosing a very small accumulation time, the signal level of the image other than the region of light beam spot becomes very small and the resulting image contains light beam spot only. The region of the light beam spot can be extracted as shown in Figure 8(b) using a binarization algorithm given by:  
Figure 6: Waveforms in a readout channel.  
Figure 7: Frequency Response of Bandpass Amplifier.  
\[G(x,y)=\begin{cases}1&(\text{if}f(x,y)\geq T)\\ 0&(\text{if}f(x,y)<T)\end{cases}\text{ (for x=1,..., N, y=1,...,M)} \tag{1}\]  
where f(x,y) is the input gray-scale image at (x,y), G(x,y) is the binary image at (x,y), T is the threshold of binarization, N is the number of horizontal pixels, M is the number of vertical pixels. To know the address of the center pixel, an algorithm to find the center-of-gravity given by:  
\[i=\sum_{x=1}^{N}\sum_{y=1}^{M}xG(x,y)\bigg{/}\sum_{x=1}^{N}\sum_{y=1}^{M}G(x,y) \tag{2}\]  
\[j=\sum_{x=1}^{N}\sum_{y=1}^{M}yG(x,y)\bigg{/}\sum_{x=1}^{N}\sum_{y=1}^{M}G(x,y) \tag{3}\]  
If the addresses \(i\) and \(j\) are not an integer, they are rounded to the nearest integer. The addresses \(i\) and \(j\) are sent to the address decoders for the vertical and horizontal directions, respectively, and then the 5 \(\times\) 5 outputs of the \(i-2\) through \(i+2\) cells for horizontal direction and the \(j-2\) through \(j+2\) cells for vertical direction as shown in the shaded R cells in Figure 8(a) are selected and connected to the 25-channel bandpass amplifiers. The 25-channel parallel outputs can be used for tracking a moving target light source. Figure 9 shows a typical timing for light source tracking. While reading the image signal of the \(k\)-th frame, the V- and H- addresses of the center of the light beam slot are calculated. In the Vertical blanking time, or right after finishing the image signal readout of the \(k\)-th frame, the address data of the \(k\)-th frame are transferred to the ISC chip. If the V- and H- addresses of the \(k\)-th frame is different from that of the (\(k\)-\(1\))-th frame, the V- and H- addresses for the address decoder in the ISC chip is renewed. Then the communication signals are readout from the new position.  
Figure 8: **(a)** Light beam spot and **(b)** Binary image of the light beam spot.
## 2 History and Chemistry of Xylazine  
Xylazine (Figure 2) was synthesized in 1962 and identified by the code Bay Va 1470 [59]. Its use as an antihypertensive in animals was promoted by Farbenfabriken Bayer Ltd. (Leverkusen, Germany). However, it was not approved for human use due to severe hypotension and high CNS depression. Hence, it was registered with the name Rompun(r), and it was only for veterinary use in farm animals such as cattle and horses (European Medicines Evaluation Agency, EMEA) [60]). Later, its use was expanded to other animals, including sheep, goats, Ilamas, and buffalo [61,62].  
Xylazine [\(N\)-(2,6-dimethylphenylamino)-4\(H\)-5,6-dihydro-1,3-thiazine or \(N\)-(2,6-dimethylphenyl)-5,6-dihydro-4\(H\)-1,3-thiazine or 2-(2,6-dimethylphenylamino)-4\(H\)-5,6-dihydro-1,3-thiazine or 2-(2,6-xylidine)-5,6-dihydro-4\(H\)-1,3-thiazine, C\({}_{12}\)H\({}_{16}\)N\({}_{2}\)S, CAS Number: 7361-61-7] is a white crystalline solid, developed as a clonidine analog acting as an \(\alpha\)2-adrenoacceptor agonist [63]. It bears a 2,6-xylly moiety and a thiazine one, so it belongs specifically to the group of 1,3-thiazines, which are structurally similar to 1,4-thiazines (phenothiazazines) [64]. The 2,6-xylly! (or 2,6-dimethylphenyl) moiety can be found in numerous clinically relevant drugs [65], whereas the dihydro-1,4-thiazine cycle is related through cyclomethylene homology and isotopy to the 2-imidazoline ring, a well-known privileged scaffold (Figure 3) present in the structure of the \(\alpha_{2}\)-adrenergic agonist clonidine.  
The synthesis of xylazine is relatively simple and has been reported in several papers, mostly patents [66]. It is commonly used as hydrochloride salt and has four well-known polymorphic conformations (A, M, Z, and X) and a monohydrate form (H), which is the most stable [67]. A novel polymorph (Y) was recently reported by [PERSON] et al. (2023) [68]. Different partition coefficients (log\(P\)) have been reported for xylazine: log\(P\) = 2.37 [69], Xlog\(P\) = 2.8 [70], Calc log\(P\) = 1.34 [71,72]. Xylazine as a free base is not water-soluble, whereas xylazine hydrochloride is very soluble both in water and MeOH, while it is by far less soluble in  
Figure 3: Structure of 1,3-thiazine and 2-imidazoline derivatives.  
Figure 2: Structure of xylazine and its hydrochloride salt.  
hexane and Et2O [73]. The pK\({}_{\text{A}}\) value reported for xylazine is 6.94 [74; 75]. The melting point depends on the considered xylazine hydrochloride polymorphic form. Specifically, the xylazine hydrochloride form A melting point was reported to be 165.3 \({}^{\circ}\)C by [PERSON] et al. [76]. In his Master's thesis, [PERSON] [77] reported the melting points for all the forms of xylazine. Polymorphs X and Z have lower melting points (105-109 \({}^{\circ}\)C), whereas the other forms have melting points varying between 165 and 169 \({}^{\circ}\)C. The monohydrate melting point was 166-167 \({}^{\circ}\)C.
# Fully Superconducting Josephson Bolometers for Gigahertz Astronomy
## 4 Experimental Realization of JES and Nano-TES
### Fabrication Procedure  
Both devices were fabricated during the same evaporation process, ensuring the homogeneity of the properties of \(A\). In particular, they were fabricated by electron-beam lithography (EBL) and 3-angles resist shadow mask evaporation onto a silicon wafer covered with 300 nm of thermally grown SiO\({}_{2}\). To obtain a resist suspended mask, a bilayer composed of a 950-nm-thick MMA(8.5)MMA layer and a PMMA (A4, 950k) film of thickness of about 300 nm was employed. The ratio between the electron irradiation doses to make the resists soluble is about 1 to 4. The evaporation was performed in an ultra-high vacuum electron-beam evaporator with a base pressure of about \(10^{-11}\) Torr by keeping the target substrate at room temperature. The first step was the evaporation of a 13-nm-thick Al layer at an angle of \(-40^{\circ}\) and then oxidized by exposition to 200 mTorr of O\({}_{2}\) for 5 min to obtain the tunnel probes of the auxiliary device. In a second step, the Al/Cu bilayer (\(t_{Al}=10.5\) nm and \(t_{Cu}=15\) nm) forming the active region is evaporated at an angle of \(0^{\circ}\). Finally, a second 40-nm-thick Al film was evaporated at an angle of \(+40^{\circ}\) to obtain the lateral electrodes. The angle resolution of the evaporation was about \(1^{\circ}\). The average film thickness can be controlled with the precision of 0.1 nm at the evaporation rate of about 1.5 angstrom/s. This evaporation rate allows good thickness control and films with state-of-the-art diffusion constants and superconducting properties. Finally, the tunnel barriers present in the auxiliary device were reproducible and almost free from pin-holes.
# Operational Risk Assessment of Engineering Vehicles Considering Driver Characteristics
## 1 Introduction
They found that the speed limit values of trucks and the difference between the speed limits of the two types of vehicles play a decisive role in the frequency of accidents. [PERSON] [15] explored the traffic safety effect of heavy truck traffic flow. The results show that the increase in heavy truck traffic flow leads to a remarkable increase in the frequency of accidents. [PERSON] et al. [16] used the empirical Bayesian method to evaluate the level of traffic safety on the curved sections of two-lane rural highways in Pennsylvania, U.S.A. The crash severity was categorized into fatal and injury levels, and the period of the collision was categorized into daytime and nighttime. Six crash modification factors were constructed for a comprehensive assessment. [PERSON] et al. [17] integrated traffic accident data characteristics (e.g., accident severity, traffic accident rate, and traffic incident rate) to assess road accident prediction performance using decision trees, random forests, polynomial logistic regression, and plain Bayes. The findings demonstrate that all methods, except plain Bayesian, attain an acceptable level of accuracy in traffic accident prediction. The existing research still lacks any in-depth studies on engineering vehicle drivers, leading to inaccurate results in evaluating the characteristics of engineering vehicle drivers. There is a scarcity of studies specifically focusing on engineering vehicles for road traffic risk identification or car risk assessment. The analysis of engineering vehicle operation risk sources and their risk elements indicates that the operation system is complex and consists of human beings, vehicles, roads, environment, and management, affected by multiple factors. Furthermore, it is imperative to consider driver characteristics, select an appropriate method for risk assessment, and suggest targeted management measures.
## 4 Clinical Applications of AI to CHF Management
### Internet of Things (IoT)
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type of Wearable Device** & **Sensors** & **Measurements Available** & **Clinical Application** \\ \hline \multirow{4}{*}{Ear buds} & \multirow{4}{*}{PPG} & HR; BP; SaPO2; cardiac output; & Risk assessment and prediction; \\  & & HR; BP; SaPO2; cardiac output; & Cardiac telerehabilitation; Arrhythmia \\  & & stroke volume; rhythm and sleep & detection Long QT diagnosis; HF \\  & & evaluation & management; Hypertension screening \\  & & & and management \\ \hline \multirow{4}{*}{Smart ring} & \multirow{4}{*}{PPG} & HR; BP; SaPO2; cardiac output; & Cardiac telerehabilitation; Arrhythmia \\  & & stroke volume; rhythm and sleep & detection; Long QT diagnosis; HF \\  & & & evaluation & management; Hypertension screening \\  & & & & and management \\ \hline \multirow{4}{*}{Patch} & \multirow{4}{*}{ECG} & Single-lead and multi-lead ECG; & Cardiac telerehabilitation; Arrhythmia \\  & & continuous ECG-monitoring; QTc & detection; Long QT diagnosis; HF \\  & & measurement; arrhythmia detection & management; Hypertension screening \\  & & & & and management \\ \hline \multirow{4}{*}{Chest strap} & \multirow{4}{*}{ECG} & Single-lead and multi-lead ECG; & Risk assessment and prediction; \\  & & continuous ECG-monitoring; QTc & Cardiac telerehabilitation; Arrhythmia \\  & & measurement; arrhythmia detection & detection; Long QT diagnosis; HF \\  & & & & detection; Long QT diagnosis; HF \\  & & & & management; Hypertension screening \\  & & & & and management \\ \hline \multirow{4}{*}{Clothing and shoe sensors} & \multirow{4}{*}{ECG} & HR; BP; SaPO2; cardiac output; & Risk assessment and prediction; \\  & & stroke volume; rhythm and sleep & Cardiac telerehabilitation; Arrhythmia \\  & & evaluation. Single-lead and & detection; Long QT diagnosis; HF \\  & & multi-lead ECG; continuous & management; Hypertension screening \\  & & measurement; arrhythmia detection & and management \\ \hline \multirow{4}{*}{Smart watch} & \multirow{4}{*}{PPG; ECG} & HR; BP; SaPO2; cardiac output; & Risk assessment and prediction; \\  & & stroke volume; rhythm and sleep & Cardiac telerehabilitation; Arrhythmia \\  & & evaluation. Single-lead and & detection; Long QT diagnosis; HF \\  & & ECG-monitoring; QTc & management; Hypertension screening \\  & & measurement; arrhythmia detection & and management \\ \hline \multirow{4}{*}{Smart bang} & \multirow{4}{*}{PPG; ECG} & HR; BP; SaPO2; cardiac output; & Risk assessment and prediction; \\  & & stroke volume; rhythm and sleep & Cardiac telerehabilitation; Arrhythmia \\  & & multi-lead ECG; continuous & detection; Long QT diagnosis; HF \\  & & ECG-monitoring; QTc & management; Hypertension screening \\  & & measurement; arrhythmia detection & and management \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of common commercial smart wearables devices and their various cardiovascular clinical applications. BP, blood pressure; ECG, electrocardiogram; HR, heart rate; PPG, photoplethys-mography; SaO2, oxygen saturation.
###### Abstract  
The emergence of multiple global navigation satellite systems (multi-GNSS), including global positioning system (GPS), global navigation satellite system (GLONASS), Beidou navigation satellite system (BDS), and Galileo, brings not only great opportunities for real-time precise point positioning (PPP), but also challenges in quality control because of inevitable data anomalies. This research aims at achieving the real-time quality control of the multi-GNSS combined PPP using additional observations with opposite weight. A robust multiple-system combined PPP estimation is developed to simultaneously process observations from all the four GNSS systems as well as single, dual, or triple systems. The experiment indicates that the proposed quality control can effectively eliminate the influence of outliers on the single GPS and the multiple-system combined PPP. The analysis on the positioning accuracy and the convergence time of the proposed robust PPP is conducted based on one week's data from 32 globally distributed stations. The positioning root mean square (RMS) error of the quad-system combined PPP is 1.2 cm, 1.0 cm, and 3.0 cm in the east, north, and upward components, respectively, with the improvements of 62.5%, 63.0%, and 55.2% compared to those of single GPS. The average convergence time of the quad-system combined PPP in the horizontal and vertical components is 12.8 min and 12.2 min, respectively, while it is 26.5 min and 23.7 min when only using single-GPS PPP. The positioning performance of the GPS, GLONASS, and BDS (GRC) combination and the GPS, GLONASS, and Galileo (GRE) combination is comparable to the GPS, GLONASS, BDS and Galileo (GRCE) combination and it is better than that of the GPS, BDS, and Galileo (GCE) combination. Compared to GPS, the improvements of the positioning accuracy of the GPS and GLONASS (GR) combination, the GPS and Galileo (GE) combination, the GPS and BDS (GC) combination in the east component are 53.1%, 43.8%, and 40.6%, respectively, while they are 55.6%, 48.1%, and 40.7% in the north component, and 47.8%, 40.3%, and 34.3% in the upward component.  
multi-GNSS; precise point positioning; quality control; combined positioning 2019 11 311 3390/rs11030311 10.3390/rs11030311 10.3390/rs11030
# A Cost-Efficient Approach towards Computational Fluid Dynamics Simulations on Quantum Devices
###### Abstract  
Numerical simulations of physical systems are found in many industries, as they currently play a crucial role in product development. There are many numerical methods for solving differential equations that describe the underlying physics behind the mathematical models in the simulation, among which, the finite element method (FEM) is one of the most commonly used. Although in many applications the FEM seems to provide an acceptable solution to the problem, there are still many complex real-life processes that can be challenging to simulate numerically due to their complexity and large size. Recently, there has been a shift in research towards efficiently applying quantum algorithms in finite element analysis (FEA), as the potential and speedup that they could offer have been shown, but little to no effort has been made towards the applicability and cost efficiency of these algorithms in real-world quantum devices. In this paper, we propose a cost-efficient method for applying quantum algorithms in FEA for industrial problems post-processed by classical algorithms in order to address the limitations of available quantum hardware and their cost when accessing them through different cloud-based services. We carry this out by approximating the solution of the initially large system with a suitable quantum algorithm and using the obtained solutions to generate a set of reduced-order models (ROMs) that are much smaller in complexity and size than the original model. This allows the simulation of the original model with different parameter sets and excitations to be run efficiently on classical computers without having the need to access quantum subroutines again. This way, we have reduced the usage of quantum hardware (and thus the development cost) while still taking advantage of its quantum speedup.  
quantum equation solver; computational fluid dynamics; finite element method; model order reduction; proper orthogonal decomposition; quantum numerical simulation +
Footnote †: journal: Applied  
Article
# Visualizing Small Objects Using Amplitude-Modulated Laser Light at Microwave Frequencies
## 3 Experimental Set-Up  
### LADAR System  
Digital waveform synthesizers [14] generate identical waveforms of frequency-chirped microwaves repeatedly. The electro-optic modulator amplitude-modulated the laser light at the wavelength of 1.55 \(\upmu\)m. The microwaves were frequency-chirped in the range of 11.1-15.1 GHz, which is much wider than the range allowed in the radar band. The chirped pulse width was typically 1.5 ms. The modulated light was amplified and sent out through a beam splitter, which consisted of a half mirror. The target-scattered lights went back to the same beam splitter and were detected, i.e., demodulated, to the microwave signals. Mixing with the delayed modulation microwaves resulted in complex coherent signals that were sampled to generate digital data.  
In the first type of experiment (LADAR), the laser light was spotted on a metal sphere having diameter of \(D\) = 94 mm and that had been painted with retroreflective spray to increase its backward scattering power. The sphere surface scattered the laser light instead of mirror-reflecting. The spot position was scanned on the target in 2D by rotating the half mirror in the beam splitter. Here, 50 mm in horizontal and 50 mm in vertical area was scanned by 1 mm step, thus, there were 2500 measurement points. In this experiment, range was measured 100 times repeatedly at the same position to enhance the signal to noise ratio. Total measurement time was about 6 min. The size of the beam spot on the target was 7 mm. The range was set to 5.3 m. The set-up is depicted in Figure 3.  
Figure 2: SALE measurement coordinate system.
## 5 Strategies to Enhance the Delivery of Therapeutic Agents across the BBB  
The delivery of drugs to the brain is mandatory for the treatment of brain-associated diseases, but it is compromised by the presence of the BBB [59].  
Despite the multiple BBB crossing pathways, approximately 98% of small molecules, and most large molecules (e.g., antibodies, recombinant proteins and peptides, viral vectors), are unable to reach the brain through the BBB [60; 61], resulting in a very low bioavailability in the brain [62].  
All this has slowed down the exploitation of immunotherapy in brain diseases. In fact, intravenously administered antibodies rapidly enter the brain and subsequently are quickly expelled from it. Thus, only about 0.1% of therapeutic antibodies reach the targeting site, resulting in a demand of significantly higher antibody concentrations in systemic circulation, which, in turn, is associated with an increased risk of systemic toxicity [63].  
A common noninvasive strategy to enhance the BBB permeation of small drugs is to increase the lipophilic character of the molecule by chemical modification [45]. However, this approach may promote a faster clearance of the drug from the circulatory system through efflux transporters, thereby compromising the drug distribution and effectiveness. Thus, structural modification of the drug to increase its affinity to endogenous transport proteins on the cerebellar endothelium has been proposed [64].  
In addition to drug modification, increase of lipophilicity, or reduction of the molecular size, may also contribute to increase BBB permeation by focusing on the reduction of efflux transport, thus enhancing the transcellular diffusion permeability or disrupting the tight junction complexes [47]. Furthermore, the use of nanoparticles and other molecular Trojan horses may also be exploited to enhance the delivery of drug molecules across the BBB [65].
# ARAAC: A Rational Allocation Approach in Cloud Data Center Networks
## 2 ARAAC: A Rational Allocation Approach in Cloud-Service Networks
### Definitions and Mathematical Modeling
Being a candidate for allocation, and besides the service granted by the allocation itself, the _financial-utility_ is calculated in Equation (5) below as the difference between the sum of the offered price unit \(p_{i}\) and the charged price \({p_{i}}^{*}\) for the required service resource units \(s_{i}\). Intuitively, the objective for each tenant \(i\) is to maximize the value of \(U_{i}\) given below: \[U_{i}=\max\ \left(\sum p_{i}-\sum p_{i}^{*}\right)\] (5) The calculation of the tenants' utility is also bounded by the constraint discussed next: **Partial Allocation is Not Accepted:** To ease the presentation of such constraint, we define \(s_{i}\) as a set of entities referred to by \(s_{i_{j}}\) where \(j\in J\). It is worth to highlighting that allocation in this work is for _service instances_, and such instances require various types of network and computing resources. This includes resources such as bandwidth, switching capacities, memory space, and CPU cycles. Hence, the allocation of \(s_{i}\) is met only when all the \(j\) entities are granted, otherwise the allocation is not considered. Such constraint is described by: \[\sum_{j\in J}s_{i_{j}}=1\qquad;\ s_{i_{j}}\in\{0,1\},\ i\in C\] (6)
* **Definition 3:** (Incentive Compatible Approach). The proposal of ARAAC provides an incentive compatible [6] approach that _motivates_ the _rational_ service-tenants to reveal their _truthful valuation_ of the required services (i.e., resource units). Indeed, bids with low price units \(p_{i}\) may be excluded from the auction, as they do not satisfy a predefined price-unit threshold \(P^{*}\), while others with reasonable offered price units are rewarded by having the required resources allocated, and only pay the next highest price (i.e., the SBP in row). This allows for positive utilities. Moreover, ARAAC also motivates efficient resource utilization. True, bidders with bad utilization behavior records \(e_{i}\) may find themselves deprived of being candidates for allocation in this round and others, regardless of their offered prices units. * **Definition 4:** (Individual Rationality [10]). ARAAC can be considered as an approach that provides for rational allocation. Regardless of the offered price-units, it excludes those bidders with low reputation records, and instead, it allows others with potentially lower offered prices for allocation, as they have attained certain levels of reputation. Accordingly, we can clearly state that ARAAC maximizes the providers' profits while not being greedy. Indeed, among the bids with high offered price units, only those with acceptable reputation records remain; others are excluded regardless of their offered bids.