esa-sceva/satcom-chunk-collection huggingface dataset:
# Tripartite Quantum Key Distribution Implemented with Imperfect Sources
## 3 Operation of Proposed Reference-Frame Independent (RFI) Protocol
### State Preparation
This is followed by manipulation of the two photons into quantum states belonging to the bases \(\{X,Y,Z\}\) using a phase modulator (PM). Next, the states are transmitted to [PERSON] and [PERSON] through the insecure quantum channel. In this figure, IM: intensity modulator, BS: beam splitter, FM: Faraday Mirror, WDM: Wavelength Division Multiplexing, CIRC: Circulator, SPD: single-photon detector. Bottom left: A Bloch sphere representation of the four states used in the three-party RFI protocol. The \(Z\) basis states lie on the poles of the Bloch sphere and the \(X,Y\) basis states lie on the equator of the Bloch sphere. \(|0_{X}\rangle=\frac{1}{\sqrt{2}}(|0_{Z}\rangle+|1_{Z}\rangle)\), \(|1_{X}\rangle=\frac{1}{\sqrt{2}}(|0_{Z}\rangle-|1_{Z}\rangle)\), \(|0_{Y}\rangle=\frac{1}{\sqrt{2}}(|0_{Z}\rangle+i|1_{Z}\rangle)\) and \(|1_{Y}\rangle=\frac{1}{\sqrt{2}}(|0_{Z}\rangle-i|1_{Z}\rangle)\). Therefore, Equation (4) can equivalently be expressed as  
\[e^{i\big{(}\frac{\theta_{B(C)}+i\theta_{B(C)}/\pi}{2}\big{)}}\Big{[}\cos\Big{(} \frac{\theta_{B(C)}}{2}+\frac{\delta}{2}\Big{)}|0_{X}\rangle+i\sin\Big{(} \frac{\theta_{B(C)}}{2}+\frac{\delta}{2}\Big{)}|1_{X}\rangle\Big{]}. \tag{5}\]  
Moreover, it is possible to obtain an expression that is similar to the above for the eigenstates \(|0_{Y}\rangle\),\(|1_{Y}\rangle\). When the overall phase factor in Equation (5) is ignored, the following expressions are obtained for the four states:  
\[|\phi_{0Z}\rangle =\cos\tfrac{\delta_{1}}{2}|0_{Z}\rangle+\sin\tfrac{\delta_{1}}{2 }|1_{Z}\rangle, \tag{6}\] \[|\phi_{1Z}\rangle =\sin\tfrac{\delta_{2}}{2}|0_{Z}\rangle+\cos\tfrac{\delta_{1}}{2 }|1_{Z}\rangle,\] (7) \[|\phi_{0X}\rangle =\cos\Big{(}\tfrac{\delta_{1}}{4}\Big{)}|0_{X}\rangle+\sin\Big{(} \tfrac{\delta_{1}}{4}\Big{)}|1_{X}\rangle,\] (8) \[|\phi_{0Y}\rangle =\cos\Big{(}\tfrac{\pi}{2}+\tfrac{\delta_{1}}{2}\Big{)}|0_{Y} \rangle+i\sin\Big{(}\tfrac{\pi}{2}+\tfrac{\delta_{1}}{2}\Big{)}|1_{Y}\rangle. \tag{9}\]  
Note that for signal states belonging to the \(Z\) basis, due to the extinction ratio of the optical attenuator resulting in source flaws Alice prepares \(|0_{Z}\rangle\) and \(|1_{Z}\rangle\) with probabilities \(\cos^{2}(\delta_{1}/2)\) and \(\cos^{2}(\delta_{2}/2)\), respectively. The signal states from the three bases can be expressed in terms of an identity matrix and a Pauli matrix. Thus, they are represented using density matrix notation, as follows:  
\[\sum_{n=0}^{\infty}\sqrt{\frac{\mu^{n}}{(1+\mu)^{n+1}}}e^{in\chi}|n,n\rangle. \tag{2}\]0  
with \(\mathbf{n}_{\alpha}^{j\alpha}\) denoting the coefficients of the Bloch vector of \(\rho_{j\alpha}\), where \(\alpha\in\{X,Y,Z\}\) and \(j\in\{0,1\}\).
## 3 Experiment
### Ablation Experiment Result Analysis  
To validate the impact of individual components in TGANs, we conduct controlled experiments with five configurations:  
* Standard GAN: basic GAN without Transformer or conditioning.
* TGAN w/o Self-Attention: remove self-attention modules.
* TGAN w/o Conditioning: remove conditional inputs (labels).
* Fixed-Weight: TGAN with fixed \(\omega_{i}\) weights.
* Adaptive-Weight (Full): Our complete model with dynamic \(\omega_{i}\).  
The ablation study results (Table 1) reveal the following key insights:  
* Self-attention impact: Removing self-attention (TGAN w/o SA) degrades alignment by 23.6% (0.89\(\rightarrow\)0.68) and increases overlap by 257% (0.07\(\rightarrow\)0.25), confirming its critical role in modeling spatial dependencies.
* Conditioning necessity: disabling conditional inputs (TGAN w/o Cond) reduces balance by 16.5% (0.85\(\rightarrow\)0.71), indicating that label guidance stabilizes layout semantics.
* Adaptive-Weight superiority: Adaptive-Weight outperforms Fixed-Weight in all metrics (e.g., +8.5% alignment), demonstrating the necessity for dynamic constraint balancing.
* Standard GAN limitation: the basic GAN achieves the lowest scores, proving that vanilla architectures struggle with layout generation.
## 3 The Proposed EGR Approach for Highway Traffic Environment
### Geometrical Localization for GPS Outage  
The majority of the GPS-assisted localization algorithms assume the availability of a sufficient number of location-aware neighboring vehicles for the purpose of routing. In a realistic highway traffic environment, the assumption, which include GPS-capable surrounding vehicles is not feasible for routing purposes. When a vehicle's GPS signal is blocked in certain highway traffic environments, how are its neighbors in those environments supposed to know where they are relative to their own GPS signals? To solve this problem, we present geometrical localization to compute the locations of moving vehicles that are facing a GPS outage problem.  
Every vehicle is capable of measuring speed, direction, and time with a degree of precision that is adequate. In the event that the GPS in a vehicle fails to work, an EGR system will allow the vehicle to estimate its location using geometrical localization. We consider two vehicles \(N_{i}\) and \(N_{j}\) that are moving on a straight road segment (see Figure 2). The maximum transmission radius of a vehicle in a perfect environment is assumed to be \(R\). Its values for different types of vehicles is not equal. Let \((x_{i},y_{i})\) and \((x_{j},y_{j})\) represent the position of the vehicles \(N_{i}\) and \(N_{j}\) at time \(t\) respectively. In addition, let \(v_{i}\) and \(v_{j}\) represent the velocity of the vehicles and the direction of movement for both the vehicles \(N_{i}\) and \(N_{j}\) are \(\theta_{i}\) and \(\theta_{j}\) respectively. After a short while, if the vehicle experiences a GPS outage issue on the same road segment, the unknown position vectors of the vehicles \(N_{i}\) and \(N_{j}\) at time \(t^{\prime}\) are \(\left(x_{i}^{\prime},y_{i}^{\prime}\right)\) and \(\left(x_{j}^{\prime},y_{j}^{\prime}\right)\), respectively, and the movement direction and velocities for both vehicles may change when needed. Using the geometrical rule, the unknown positions of the vehicles in case of GPS outage can calculated as follows. The position for both the vehicles at time \(t^{\prime}\) can be expressed as  
\[\left.\begin{array}{l}x_{j}^{\prime}=x_{k}+\ v_{k}(t^{\prime}-t\ )\cos\theta_{k}\\ y_{k}^{\prime}=y_{k}+\ v_{k}(t^{\prime}-t\ )\sin\theta_{k}\end{array}\right\} \tag{1}\]  
where \(k=\{i,\ j\}\). The temporal distance between both vehicles at time \(t^{\prime}\) can be expressed  
\[d\big{(}t^{\prime}\big{)}=\sqrt{\left(\Delta x+\Delta v\Delta t\Delta\cos \theta\right)^{2}+\left(\Delta y+\Delta v\Delta t\Delta\sin\theta\right)^{2}} \tag{2}\]  
where \(\Delta x=\left(x_{i}-x_{j}\right)\), \(\Delta v=\left(v_{i}-v_{j}\right)\), \(\Delta\cos\theta=\left(\cos\theta_{i}-\cos\theta_{j}\ \right)\), \(\Delta\sin\theta=\left(\sin\theta_{i}-\sin\theta_{j}\right)\), and \(\Delta t=(t^{\prime}-t)\).  
Figure 2: A vehicle’s localization scenario.
# Estimating Position from Millisecond Samples of GPS Signals (the \"FastFix\" Algorithm)
## 2 Satellite Signal Acquisition  
The GPS L1-band civilian signal is transmitted in a 2.5 MHz bandwidth radio channel centered at 1575.42 MHz. The satellite signals are modulated using Code Division Multiple Access (CDMA) [10]. A GPS receiver digitizes this channel, and signals from all visible satellites are contained within the received data. The individual satellite signals are recovered by correlating the received data against a pseudorandom sequence unique to each satellite called a C/A (Coarse Acquisition) code. In the case of GPS, the C/A code repeats every millisecond and contains 1023 binary numbers in a pseudorandom sequence. The motion of each satellite relative to the observer causes the signal from that satellite to be Doppler-shifted, and this Doppler-shift must be compensated for in the receiver. The process of _signal acquisition_ recovers the signal, and its Doppler-shift from each visible satellite.  
There are several well-established methods for signal acquisition; see, for example, [PERSON] [10] or [PERSON] et al. [22]. In this work, parallel codephase recovery [22] using Fourier transforms was used. For the \(i\)th satellite, the acquisition process results in three measurements:  
* \(X_{max}/\sigma_{X}\). The maximum correlation divided by the standard-deviation of the correlation--a measure of the ratio of correlation amplitude to the noise. This is similar to the carrier-to-noisedensity ratio, or the correlation amplitude relative to the noise level in the data. A satellite is considered _visible_ if its \(X_{max}/\sigma_{X}\) exceeds a threshold (for the data shown in Figure 1 and Table 1, a threshold of 7 is used).
* \(\delta^{i}_{\phi}\), the _code-phase_--a number between zero and 1 that indicates the phase of the C/A code. The starting time of the C/A code is given by \(t_{0}+\delta^{i}_{\phi}\tau\), where \(t_{0}\) is the time of the start of the data, and \(\tau\) is the C/A code sequence repeat time (one millisecond in the case of GPS).
* \(\Delta f^{i}_{m}\), the Doppler-shift of the signal from the satellite.  
Table 1 shows a typical set of acquisition results from the data, which are shown in Figure 1. Some of the correlation plots from this data are shown in Figure 1.  
Figure 1: Some results showing typical correlation vs fractional-codephase for a sample of GPS L1 data, sampled at 16.368 MHz and down-sampled to 4.092 MHz. (**a**) baseband spectrum of the L1-band sampled GPS data. The center frequency is 1575.42 MHz downsampled to an intermediate frequency (IF) of 4.092 MHz. (**b**) correlation amplitude as a function of fractional codephase for the satellite with Pseudorandom Noise (PRN) code PRN = 4. The measured codephase is \(\delta^{4}_{\phi}=0.4398635\); (**c**) correlation amplitude as a function of fractional codephase for satellite with PRN = 16. The measured codephase is \(\delta^{16}_{\phi}=~{}0.503052\); (**d**) correlation amplitude as a function of fractional codephase for satellite with PRN = 20. The measured codephase is \(\delta^{20}_{\phi}=0.344973\).
## 2 Materials and Methods  
### Test Materials  
Two soils (A and B) were used in the current pullout testing program. Both materials were collected close to Sao Carlos (Sao Paulo, Brazil). Soil A consists of tropical soil, classified as high-plasticity silt (MH), as per the Unified Soil Classification System (USCS). This material was used in the first three layers of compacted material inside each pullout box. The other soil (soil B) consists of a clayey sand (SC) and was used above the geogrid in the top layers. The particle size distribution of these soils is presented in Figure 1. It is noteworthy that soil A includes about 65% fines, with D\({}_{50}\) = 0.007 mm and D\({}_{85}\) = 0.25 mm. On the other hand, soil B has less fines (20%), with D\({}_{50}\) = 0.15 mm and D\({}_{85}\) = 0.4 mm.  
The shear strength parameters of soils A and B were determined using triaxial load tests conducted at identical relative compaction and moisture conditions as the specimens used for the pullout tests. Consolidated drained (CD) triaxial tests were performed to characterize soil B, whereas consolidated undrained (CU) triaxial tests were conducted on soil A. These tests followed the recommendations of the ASTM D4767 and ASTM D7181 standards [41; 42]. Table 1 summarizes the main physical properties of these soils.  
The choice of different materials was made taking into account the distribution of induced interfacial shear stresses in the soil mass around the geosynthetic, and the study presented by [PERSON] et al. [43] and [PERSON] et al. [44], which reported that the inclusion of high-quality sand around the reinforcement can effectively improve the strength and deformative response of reinforced clay. With this in mind, while the bottom layer (soil A) has low mobilization of interface shearing resistance, the upper layer (soil B) provides a layer of frictional material above the geosynthetic reinforcement, potentially contributing to enhanced interface shear strength.  
The reinforcement tested was an extruded flexible biaxial polypropylene (PP) geogrid (GGR) coated with polyvinyl chloride (PVC). Table 2 summarizes the main properties of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Property** & **Unit** & **Soil A** & **Soil B** \\ \hline Classification (USCS) & - & MH & SC \\ Specific gravity, GS & - & 2.913 & 2.646 \\ Liquid limit, w\({}_{\text{L}}\) & \% & 55 & 16 \\ Plastic limit, w\({}_{\text{P}}\) & \% & 32 & - \\ Maximum dry unit weight, \(\gamma_{\text{d,max}}\) & g/cm\({}^{3}\) & 1.626 & 1.983 \\ Optimum water content, w\({}_{\text{GP}}\) & \% & 22.75 & 9.24 \\ Friction angle, \(\phi\) & \(\circ\) & 27 & 31 \\ Cohesion, c & kPa & 66.27 & 34.64 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Soil characteristics.  
Figure 1: Particle size distribution curves of soils A and B.  
the geogrid. The aperture size of this particular geogrid takes into account the size of the small pullout box.
## Glossary  
\(A_{g}\)  
Gross cross-section area of chord:  
D  
Diameter of chord (=\(d_{0}\));  
E  
Elastic modulus;  
\(F_{c}\)  
Available stress of chord;  
\(F_{pl,0}\)  
Chord axial capacity;  
\(F_{u}\)  
Nominal ultimate strength of chord (=\(\sigma_{u}\));  
\(F_{y}\)  
Nominal yield strength of chord (=\(\sigma_{y0}\) and \(\sigma_{y0}\));  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Nominal Yield Strength** & **AISC (2016) [2]** & **Eurocode (2005) [3]** & **ISO 14346 (2013) [4]** \\ \hline
460 & 1.0 & 0.8 1 & 0.9 1 \\
650 & 0.9 & 0.8 1 & 0.9 \\
900 & 0.75 & 0.8 & 0.75 \\
1100 & 0.62 & 0.67 & 0.62 \\ \hline \hline \end{tabular}  
* Design equations have reduction factors for high-strength steel in each code (See Table 1).  
\end{table}
Table 13: Reduction factors for high-strength steel.  
\begin{tabular}{l l} \(l_{b}\) & Width of longitudinal plate (=\(h_{1}\)); \\ \(l_{0}\) & Length of chord excluding plate width; \\ \(M_{pl,0}\) & Chord plastic moment capacity; \\ \(M_{no}\) & Required flexural strength in chord (=\(M_{0}\)); \\ \(P_{no}\) & Required axial strength in chord (=\(F_{0}\)); \\ \(Q_{f}\) & Chord stress interaction parameter (=\(k_{p}\)); \\ \(Q_{u}\) & Partial design strength function; \\ \(R_{FE}\) & Joint strength in finite element analysis; \\ \(R_{max}\) & Joint strength determined by strength limit state; \\ \(R_{n}\) & Nominal strength of joint (=\(N_{1,Rd}\) and \(F_{1}\)); \\ \(R_{test}\) & Joint strength in test; \\ \(R_{1\%D}\) & Joint strength in serviceability deformation limit; \\ \(R_{3\%D}\) & Joint strength in ultimate deformation limit; \\ \(S\) & Elastic section modulus about the bending axis; \\ \(s\) & Weld size; \\ \(t\) & Thickness of chord (=\(l_{0}\)); \\ \(t_{b}\) & Thickness of longitudinal plate (=\(t_{1}\)); \\ \(U\) & Utilization ratio (=\(n_{p}\) and \(n\)); \\ \(\alpha\) & Chord length-to-diameter ratio (=\(l_{0}/D\)); \\ \(\beta\) & Reduction factor for high-strength steel; \\ \(2y\) & Chord diameter-to-thickness ratio (=\(D\)/\(t\) or \(d_{0}\)/\(t_{0}\)); \\ \(\varepsilon^{pl}\)\({}_{\rm In}\) & Log strain; \\ \(\varepsilon_{nom}\) & Nominal strain; \\ \(\eta\) & Plate width-to-chord diameter ratio (=\(l_{b}\)/\(D\) or \(h_{1}\)/\(d_{0}\)); \\ \(\sigma_{nom}\) & Nominal stress; \\ \(\sigma_{p,Ed}\) & Maximum compressive stress in the chord; \\ \(\sigma_{true}\) & True stress; \\ \(0.3\%D\) & Chord indentation of joint strength by deformation limit state. \\ \end{tabular}
## 4 Discussion
### Elicited Constructs
#### 4.3.2 Multi-Dimensionality of Apparent Source Width (ASW)  
It is considered that the ASD and PSL attributes might also contribute to the perception of Apparent Source Width (ASW), which was rated as the third most salient attribute in the current study. The basis for this argument is as follows. [PERSON] [20] found that the perceived magnitude of ASW decreased linearly per doubling the distance between 3 m, 6 m and 12 m. It was also reported that the perceived result was not predicted accurately by conventional objective measures for ASW such as IACC Early [48] and Early Lateral Energy Fraction (Lf) [4], whereas it had a high positive correlation with Sound Strength (G) [49] The importance of G on ASW was also claimed by [PERSON] and [PERSON] [50], who proposed an objective measure Degree of Source Broadening (DSB) employing both G and Lf. Similarly, a relationship between ASW and the sound pressure level was also acknowledged by [PERSON] et.al [51]. A correlation between width, loudness and the source size was also reported by [PERSON] et al. [15], supporting the hypothesis that ASW perception is indeed influenced by the sound strength. However, other studies in the context of concert hall acoustics confirmed the IACC Early and Lf as well as [1-IACC E3] (a.k.a. Binaural Quality Index) by [PERSON] et al. [9] to be successful measures for evaluating perceptual differences between different concert halls [9; 48; 52; 53]. It should be noted that these studies used source-receiver distances larger than around 10 m, where reflections and reverberation energies are more dominant than the direct sound energy.  
From the above, it is hypothesised that the ASW elicited in the present study might have had two perceptual dimensions: one that is related to G, which would determine the PSL perception, and the other associated with IACC or/and Lf, which would lead to a width perception influenced by the decorrelation and lateral energies of ear signals resulting from the combination of direct sound and early reflections. This is supported by the elicited terms from Subject no. 2, who mentioned both \"sharp/focused direct sound\" and \"wide/narrow source\" (Figure 6). From a personal communication with the subject after the audibility grading experiment, it was identified that he indeed meant two different types of auditory sensations: direct-sound-related and reflection-related ones. As implied from [20] and previous concert hall research mentioned above, ASW would likely be determined predominantly by G when the listener is positioned within the critical distance of a space, whereas IACC or/and Lf might be a better predictor for ASW beyond that point. The critical distance of the concert hall used in our study was around 7 m for the given source position, meaning that the two back positions (P2 and P3) were outside the critical distance. Therefore, it can be considered that the balance between the properties of ASW might have varied depending on their position.
## 4 Conclusions  
We have implemented a simple, robust and efficient scheme that combines single-pass SHG at 780.2 nm with single-pass SFG at 852.3 nm based on diode-laser-seeded fiber amplifiers. A power of more than 630 mW of the 780 nm single-frequency continuous-wave laser is demonstrated through  
Figure 8: Beam quality factor \(M^{2}\) of the SFG laser beam at 852.3 nm. Typical values for the horizontal and vertical directions are \(M_{X}^{2}=1.07\) and \(M_{Y}^{2}=1.13\). Inset shows the typical intensity profiles of the SFG laser beam spot.  
Figure 9: The trace (1) shows the D\({}_{2}\) line absorption spectra of 6S\({}_{1/2}\)–6P\({}_{3/2}\) transition of cesium atoms by using SFG laser at 852.3 nm, while the 1560.5 nm fundamental-wave laser is frequency-scanned linearly, but the 1878.0 nm fundamental-wave laser’s frequency is fixed. Frequency scanned 852.3 nm laser beam is monitored using a confocal Fabry–Perot cavity with free spectral range (FSR) of 0.75 GHz as shown by the trace (2). Clearly the continuously tunable range of the 852.3 nm laser’s frequency is more than 9.2 GHz.  
two PPMgO:LN cascaded bulk crystals, and the SHG efficiency obtained is 12.0%. At the same time, a power of more than 270 mW of the 852 nm single-frequency continuous-wave laser is reported with the PPMgO:LN bulk crystal, and the SFG efficiency is 4.1%. In addition, the continuously tunable ranges of the 780 nm and 852 nm laser beams are at least 6.8 GHz and 9.2 GHz. Although, the size of our device is large, its mechanical stability can be further improved by careful design with a much more compact structure.  
Most importantly, the output lasers have a high beam quality, which cannot be addressed by using a diode laser combined with a semiconductor tapered amplifier. In addition, we can confine Rb atoms by using an 852 nm optical dipole trap on the basis of a 780 nm magneto-optical trap. Previously, we required 852 nm and 780 nm diode lasers to perform such an experiment, but our current system can produce both lasers at high power. Finally, the system has great potential for application in the formation of RbCs dimers via photoassociation of cold Rb and Cs atoms and an Rb-Cs two-component atomic interferemeter.  
The experiment and data were completed by [PERSON] [PERSON] undertaked guidance during the experiment. And [PERSON] contributions included coordination, guidance and analysis in the experiment.  
This research was funded by [the National Key R&D Program of China] grant number [2017 YFA0304502], [the National Natural Science Foundation of China] grant number [11774210 and 61875111], and [the Shanxi Provincial 1331 Project for Key Subject Construction] grant number [201542033].  
There is no conflict of interest in this article.