esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Hybrid Photonic/Millimetre-Wave System and Simulation Details
### System Simulation
**LO-based Receiver**: A standard receiver structure consisting of a mm-wave mixer and local oscillator (LO) is depicted in the lower half of the receiver side in Figure 1. The incoming signal is passed through an electrical band-pass filter (EBPF), which serves to isolate the upper data sideband portion of the mm-wave spectrum, centred at 62 GHz. The filtered signal is mixed with a 60 GHz carrier generated by the receiver LO, which is phase locked to the transmitter side. The down-converted IF signal at 2 GHz is then passed to the IF OFDM/APSK demodulator. 2. **PNC Receiver**: The analog phase noise cancelling mm-wave receiver architecture is outlined by a dotted red line in Figure 1. This architecture allows phase noise and frequency offset cancellation at the IF down-conversion stage without the requirement for a standalone receiver LO, and this is described in detail in [11]. After photo detection, the electrical spectrum shown in Figure 2c is split into two paths. The PNC receiver contains an EBPF in each path; a signal filter and a carrier filter, which are used to isolate the upper data sideband (at 62 GHz) and mm-wave carrier term (at 60 GHz), respectively (these terms are represented by the purple illustrative spectra in Figure 1). The filtered signal and carrier terms are then used as the 'RF' and 'LO' inputs to a mm-wave mixer, respectively. These filtered components exhibit matching phase noise as a result of being generated through the same photo-mixing process. This allows the filtered carrier term to be used as a phase noise correlated 'LO', which, when mixed with the signal term, produces an IF data signal (at 2 GHz) free from phase noise. For both receiver stages, sharp band-pass filtering in the mm-wave domain is implemented in the model by using 10 th order Gaussian shaped frequency domain filter profiles, as given by the function:  
\[g(f)=\exp\left[\frac{(f-f_{o})^{n}}{2\sigma^{\ n}}\right]\ . \tag{3}\]  
where \(f_{o}\) is the filter center frequency, \(2\sigma\) is the 3-dB bandwidth of the filter and \(n\) in the filter order.
## 3 Results  
### Implant Description and Distribution  
The study group consisted of 111 individuals, including 48 (43%) men and 63 (57%) women whose age range was 19-77 years, mean \(54\pm 15\) years. Twenty-three (21%) were current smokers. Fifteen individuals had undergone both dental implant methods in different areas of the jaw. A total of 126 areas were included in the study: the two-stage surgical approach was used at 74 (59%) areas and the one-stage surgical approach was used at 52 (41%) areas. There was a total of 393 implants, of which 228 (58%) were located in the mandible and 165 (42%) were located in the maxilla. Four implant brands and five implant types were used. All implants had bone level insertion indication. Two types had external hex: Branemark system MK II 47/393 (12%) and 3I Osseotite implant 50/393 (13%). Three types had internal hex: Screw vent, Zimmer Dental 110/393 (28%); 3I Certain 49/393 (12%); Seven, MIS 137/393 (35%) The implant diameter ranged from 3.25 mm to 6 mm (average \(4.1\pm 0.4\) mm). The implant length ranged from 8 mm to 16 mm (average \(12.3\pm 1.5\) mm). Follow-up time ranged from 12 to 181 months (average \(36\pm 38\) months). The gingival index at the last follow-up was 0 for 279 (71%) implants and 1 for the remaining 114 (29%) implants. Crestal bone loss ranged from 0 mm to 8 mm, and the average crestal bone loss for the study was \(1\pm 0.9\) mm in both the mesial and distal implant areas.
## 5 Conclusions  
In this study, a comparative analysis among three different transfemoral osseointegrated implants was performed. A straight stem and two patient-specific implants were analyzed: the first one was designed by exactly following the medullary canal midline curve, and the second one was designed by projecting this curve on the sagittal plane. Specifically, equipping a residual femur with a stem designed following the medullary canal midline allows obtaining a lower value in micromotion (51.4 um) at the bone-implant interface for 1000 N, compared to a stem realized following a straight line (73.9 um) or a planar projection of the medullary canal midline (58.6 um). Moreover, the patient-specific implants have shown a wider contact area, with an increment, concerning the SD, of 11% and 6% for the PSD and PPSD, respectively. This results in a larger part of bone volume participating in the interference and so involved in the load transferred, leading to a relevant increase in the average contact pressure. These results show the effectiveness, in terms of primary stability, of a patient-specific approach and suggest that an implant designed from the bone morphology can reduce the risk of fibrous tissue formation and homogenize the stress distribution and consequently help to reduce the bone loosening.  
The approach used in this study could be effectively used for other anatomical districts [51], contributing to spreading the patient-specific prosthesis design, as suggested in different review works [2, 8, 13, 39]. In conclusion, the results obtained in this study provide some preliminary indications that could be useful in designing patient-specific intramedullary prostheses starting from CT images of an amputee patient.  
Conceptualization: A.V., T.L, and A.I.M.; data curation: A.V., G.M., and V.R.; methodology: A.V., T.L., and A.I.M.; software: A.V. and A.I.M.; supervision: [PERSON], T.I, G.M., and V.N.; writing--original draft: A.V. and A.I.M.; writing--review and editing: [PERSON], T.I., V.N., and V.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors thank the MeTACOs (Trattamento delle amputazioni mediante osteointegrazione) project for the support in the conceptualization of this work.  
The authors declare no conflicts of interest.
## 4 Fabrication and Evaluation of Multi-Functional Transmitarray
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{\begin{tabular}{c} **Phase** \\ **Resolution** \\ \end{tabular} } & \multirow{2}{*}{**Layers**} & \multirow{2}{*}{\begin{tabular}{c} **Size of** \\ **Unit Cell** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Gain** \\ **[dBi]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Ap.** \\ **Eff. [\%]** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Polarization** \\ **Consersion** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Beam** \\ **Scanning** \\ **Capability** \\ \end{tabular} } \\ \hline
[4] & 1 bit & 5 & 0.54\(\lambda\)\(\times\) 0.54\(\lambda\) & \
\begin{tabular}{c} 21.0 \\ (Sim.) \\ \end{tabular} &
\begin{tabular}{c} 24.2 \\ (Sim.) \\ \end{tabular} & Passive LP & \(\pm\)40\({}^{\circ}\) E, and \\  & 2 bit & 4 & 0.49\(\lambda\)\(\times\) 0.49\(\lambda\) & 18.3 & 9.5 & Active CP & \(\pm\)60\({}^{\circ}\) E, and \\  & 1 bit & 4 & 0.50\(\lambda\)\(\times\) 0.50\(\lambda\) & 22.8 & 15.9 & N/A & \(\pm\)40\({}^{\circ}\) E, and \\  & 1 bit & 4 & 0.51\(\lambda\)\(\times\) 0.51\(\lambda\) & N/A & N/A & Active LP & N/A \\  & 1 bit & 4 & 0.48\(\lambda\)\(\times\) 0.48\(\lambda\) & N/A & N/A & Active CP & N/A \\  & 1 bit & 4 & 0.33\(\lambda\)\(\times\) 0.33\(\lambda\) & 17.0 & 14.0 & Passive LP & \(\pm\)50\({}^{\circ}\) E, and \\  & 2 bit & 6 & 0.49\(\lambda\)\(\times\) 0.49\(\lambda\) & 19.8 & 15.9 & Passive LP & \(\pm\)60\({}^{\circ}\) E, and \\  & 1 bit & 4 & 0.54\(\lambda\)\(\times\) 0.54\(\lambda\) & 21.4 & 14.7 & Passive LP & \(\pm\)60\({}^{\circ}\) E-plane \\  & 1 bit & 5 & 0.29\(\lambda\)\(\times\) 0.29\(\lambda\) & 16.8 & 18.4 & Passive LP & \(\pm\)40\({}^{\circ}\) E, and \\  & 1 bit & 4 & 0.50\(\lambda\)\(\times\) 0.50\(\lambda\) & 14.6 & 16.4 & Active LP & \(\pm\)45\({}^{\circ}\) E, and \\  & & & & & & & \\ \end{tabular}
\end{table} 
Table 4: Performance comparison with previous similar studies. \begin{table}
\begin{tabular}{c c} \hline \hline Aperture Size & 120 mm \(\times\) 120 mm \\ \hline Measured Gain & 14.6 dBi \\ \hline Ideal Directivity & 22.4 dBi \\ \hline Spillover Loss & 0.4 dB \\ \hline Illumination Loss & 1.0 dB \\ \hline
1 bit Quantization Loss & 2.6 dB \\ \hline Element Insertion Loss & 1.3 dB \\ \hline Reflection and Cross-Polarization Loss & 0.6 dB \\ \hline PIN Failure Loss & 0.8 dB \\ \hline Losses from Fabrication, \\ Assembly and Measurement Errors & 1.2 dB \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Gain loss analysis for boresight beam.
# Saturation-Based Airlight Color Restoration of Hazy Images
## 3 Proposed Dehazing Method
### Dehazing  
We detected the airglow \(A(\infty)\) through Section 3.1 and restored the color of the image through Section 3.2, and we estimated the depth of the fog \(d(x)\) in Section 3.3. Thus, \(t(x)\) and \(J(x)\) can be obtained using the ASM equation.  
Fog images captured in bad weather are more affected by noise than normal images due to the influence of suspended particles. The resulting image \(J(x)\) is more susceptible to noise and more noise can occur when the transmission rate \(t(x)\) value approaches 0 or 1. To avoid this noise, we set \(t(x)\) in the range of 0.05 to 0.95 [5; 7; 8; 9; 10]. Using about 200 different fog image datasets, we measured the range from 0 to 1 on both sides in 0.01 decrements, with the best overall results obtained between 0.05 and 0.95.  
\[t(x)=max\Big{(}0.05,min\Big{(}0.95,e^{-fd(x)}\Big{)}\Big{)} \tag{18}\]  
The aforementioned output \(A(\infty)\) and \(t(x)\) in Equation (18) are entered into ASM Equation (1). To prevent color distortion, the L channel, which shows only luminance, is set to input image \(I(x)\) and used in the ASM equation.  
\[J(x)=\frac{L(x)-L_{A}}{t(x)}+L_{A} \tag{19}\]  
In Equation (19), \(L_{A}\) is the L value of the airlight matrix \(A\), \(A(1,1)\); \(J(x)\) is a grayscale image. To obtain an RGB image, the L, \(\widehat{a_{A}}\), and \(\widehat{b_{A}}\) values are substituted in the \(J\), a, and b values of the LAB color space conversion equation. The equation for converting the LAB color space to the RGB space is shown below [18; 19].  
\[\left\{\begin{array}{l}X=X_{n}f^{-1}\left(\begin{array}{l}\frac{l+1}{116} +\frac{\widehat{a_{A}}}{500}\\ \\ Y=Y_{n}f^{-1}\left(\frac{l+1}{116}\right)\\ \\ Z=Z_{n}200\Big{(}\frac{l+16}{116}-\frac{\widehat{b_{A}}}{200}\Big{)}\end{array}\right) \\ \begin{bmatrix}R\\ G\\ B\end{bmatrix}=\begin{bmatrix}3.2410&-1.5374&-0.4986\\ -0.9692&1.8760&0.0416\\ 0.0556&-0.2040&1.0570\end{bmatrix}\begin{bmatrix}X\\ Y\\ Z\end{bmatrix}\end{array}\right. \tag{20}\]  
Figure 7: Depth map of the proposed method: (**a**) estimated depth map and (**b**) depth map applied with guide map.  
In Equation (19), \(f^{-1}(q)\) is the inverse function of \(f(q)\) in Equation (9); \(X_{n}\), \(Y_{n}\), and \(Z_{n}\) show values identical to those in Equation (10).  
The guide filter applied in Figure 7 can reduce the contrast of the image somewhat because it smooths out the edge area based on the original image [5; 31]. To solve this issue, the well-known contrast improvement technique CLAHE [23] was applied to an RGB image previously produced for correction. In this study, to prevent supersaturation of the image, the contrast improvement limit of CLAHE was set to 0.03-0.05. CLAHE's contrast enhancement limit has a range of [0 1]. The proposed algorithm was simulated for various fog images in 0.01 increments, and the best overall characteristics were obtained at 0.03.
# Characterization of GNSS Signals Tracked by the iGMAS Network Considering Recent BDS-3 Satellites
###### Abstract  
The international GNSS monitoring and assessment system (iGMAS) tracking network has been established by China to track multi-GNSS satellites. A key feature of iGMAS stations is the capability to fully track new navigation signals from the recently deployed BDS-3 satellites. In addition to the B1I and B3I signals inherited from BDS-2 satellites, the BDS-3 satellites are capable of transmitting new open service signals, including BIC at 1575.42 MHz, B2a at 1176.45 MHz, and B2b at 1207.14 MHz. In this contribution, we present a comprehensive analysis and characterization of GNSS signals tracked by different receivers and antennas equipped in the iGMAS network, especially as they relate to BDS-3 signals. Signal characteristics are analyzed in terms of the carrier-to-noise density ratio for the different signals as measured by the receiver, as well as pseudo-range noise and multipath. Special attention is given to discussion of the satellite-induced code bias, which has been identified to exist in the code observations of BDS-2, and the inter-frequency clock bias (IFCB), which has been observed in the triple-frequency carrier phase combinations of GPS Block IIF and BDS-2 satellites. The results indicate that the satellite-induced code bias is negligible for all signals of BDS-3 satellites, while small IFCB variations with peak amplitudes of about 1 cm can be recognized in BDS-3 triple-carrier combinations.  
iGMAS; BDS-3; satellite-induced code bias; inter-frequency clock bias; multipath combination; triple-frequency carrier phase combination Article
# Transmit and Receive Diversity in MIMO Quantum Communication for High-Fidelity Video Transmission
## 3 Methodology
### Quantum Encoder  
The quantum encoder converts channel-encoded classical bits into quantum superposition states. First, if the classical bit is 0, it is mapped to the quantum state \(|0\rangle\). If the classical bit is 1, it is mapped to the quantum state \(|1\rangle\). After that, a Hadamard gate is applied to each qubit to convert it into a superposition state. Each step of the process is described in detail in the following subsections.
# Interaural Level Difference Optimization of Binaural Ambisonic Rendering
## 4 Perceptual Evaluation
### Results
#### 4.2.2 Complex Scenes  
The median scores of the complex-scenes test, conducted to determine whether AIO improves plausibility and spaciousness of binaural Ambisonic rendering, are shown in Figure 15 for each condition across all participants and test sound locations, with non-parametric 95% CI [57]. [PERSON] [PERSON]'s ANOVA confirmed that the test conditions produced statistically significantly different results (\(\chi^{2}(7)=264.4,p<0.01\)). An observation of Figure 15 indicates that ratings increase with Ambisonic order, tapering off as order increases, and AIO improves the ratings for all tested orders, though the improvement is greatest at \(M=1\) and \(M=2\). To test whether this improvement for each order is statistically significant, Wilcoxon signed-rank tests were conducted. Table 4 presents the significance results. For \(M=1\) and \(M=2\), AIO produces a statistically significant improvement. Though improvements are still observed for \(M=3\) and \(M=4\), they are not statistically significant at 95% confidence (\(p=0.1\) and \(p=0.07\), respectively).  
Figure 16 shows the median scores with non-parametric 95% CI across all participants for each individual soundscape. AIO produced a higher median score than without AIO for all soundscapes and tested orders, apart from the conditions of \(M=4\) soundscape 1 and \(M=3\) soundscape 3. To observe whether soundscape type had a statistically significant effect on results, a [PERSON]'s ANOVA was conducted, which showed no significance (\(\chi^{2}(3)=1.9,p=0.59\)).  
\begin{table}
\begin{tabular}{c
## 3 MPC Controller Design and Longitudinal Speed Compensation
### Longitudinal Speed Compensation
#### 3.4.2 Influence on Control System Stability  
We assume the feedback gain is \(\mathbf{g}(k)\) in prediction horizon at time \(\mathsf{t}\) according to Equation (7), and it is defined as:  
\[\mathbf{u}(k+1)=\mathbf{g}(k)\mathbf{x}(k) \tag{27}\] \[\mathbf{g}=[k_{1},k_{2},k_{3},k_{4}]\]  
The transfer function of the state feedback control system can be expressed as:  
\[\mathbf{W}_{\rm G}(s)=\mathbf{C}\left[s\mathbf{I}-(\mathbf{A}+\mathbf{B} \mathbf{g})\right]^{-1}\mathbf{B} \tag{28}\]  
Therefore, the state matrix of the closed-loop control system can be expressed as:  
\[\begin{array}{l}f(s)=\left|s\mathbf{I}-(\mathbf{A}_{\rm c}+\mathbf{B}_{\rm c }\mathbf{g})\right|\\ =s^{2}\left|\begin{array}{cc}s-(Ta_{33}+1+k_{3}Tb_{31})&Ta_{34}+k_{4}b_{31}\\ -Ta_{43}-Tk_{3}b_{41}&s-Ta_{44}+1+Tk_{4}b_{41}\end{array}\right|\end{array}\right| \tag{29}\]  
\[\begin{array}{l}f(s)=s^{2}(s^{2}+s\chi+\gamma)\\ \chi=Ta_{33}+Ta_{44}+Tk_{3}b_{31}+Tk_{4}b_{41}+2\\ \gamma=(Ta_{33}+Tk_{3}b_{31}+1)(Ta_{44}+Tk_{4}b_{41}+1)+(Ta_{43}+Tk_{3}b_{41})( Ta_{34}+Tk_{4}b_{31})\end{array}\]  
The closed-loop system is nominally progressively stable, and the characteristic roots are all within the unit circle [38]. Therefore, the system stability constraints are expressed as:  
\[\left\{\begin{array}{l}\chi^{2}-4\gamma>0\\ \left|\frac{-\chi\pm\sqrt{\chi^{2}-4\gamma}}{2}\right|<1\end{array}\right., \sigma\gamma\left\{\begin{array}{l}\chi^{2}-4\gamma<0\\ |\chi|<2\\ 0<4\gamma-\chi^{2}<4\\ 0<\gamma<1\end{array}\right. \tag{30}\]When \(v_{\rm x}\) becomes (\(v_{\rm x}+k\cdot\Delta v_{\rm x}\) ) (\(k=0,\,...\,\,k,\,\ldots\,\,N_{\rm P}-1\)) in the prediction horizon, the system stability constraints will produce difference.  
\[\left\{\begin{array}{l}\left(\chi+\Delta\chi\right)^{2}-4(\gamma+\Delta \gamma)>0\\ \left|\frac{-(x+\Delta\chi)\pm\sqrt{(x+\Delta\chi)^{2}-4(\gamma+\Delta\gamma)} }{2}\right|<1\end{array}\right.,\sigma\gamma\left\{\begin{array}{l}\left( \chi+\Delta\chi\right)^{2}-4(\gamma+\Delta\gamma)<0\\ \left|\left(\chi+\Delta\chi\right)\right|<2\\ 0<4(\gamma+\Delta\gamma)-\left(\chi+\Delta\chi\right)^{2}<4\\ 0<(\gamma+\Delta\gamma)<1\end{array}\right. \tag{31}\]  
where \(\Delta\chi\) and \(\Delta\gamma\) are the produced difference due to (\(k\cdot\Delta v_{\rm x}\)).
# Studies on Seismic Performance of Precast Concrete Columns with Ground Splice Sleeve
## 2 Test Setup  
In this section, pseudo-static tests on four precast columns and two cast-in-situ columns have been conducted. Through these tests, the seismic performance of the cast-in-situ columns has been compared with that of the precast columns. There are six specimens: four of them are prefabricated columns whose numbers are 2#, 3#, 5#, and 7#, while other two specimens, 1# and 6#, are cast-in-situ columns set as the control group. The size and reinforcement of these two types of columns are shown in Figure 2, where \"4D16\" means that the number of steel bars is four, and the diameter of the steel bars is 16 mm. \"D8@100\" means that the diameter of the stirrups is 8 mm, and the spacing of the stirrups is 100 mm.
# Long-Range Multispectral Thermal Imager for Simultaneous Measurement of Distance and Temperature
###### Abstract  
The multispectral thermal imager is a cutting-edge tool for the real-time measurement of high-temperature and transient-temperature fields. It can achieve high-precision temperature distribution measurements in complex scenes by acquiring multi-spectral radiation information. However, the existing research mainly focuses on the temperature measurement function, and there is little exploration of its potential application. In this paper, the method of measuring the distance of a high-temperature target based on a multispectral thermal imager is proposed and verified for the first time, which solves the problem that the traditional ranging model cannot be accurately measured in high-temperature environments. By constructing the theoretical model of the multi-aperture optical splitting system, the internal relationship between temperature field pixel migration and target distance is analyzed, and the feasibility and applicability of the method is verified by experiments. The results show that the multispectral thermal imager can overcome the interference of high-temperature radiation to traditional ranging technology, realize high-precision temperature measurement, and obtain the target distance information simultaneously. The measurement error of 300 m is 12.09%, which can be applied to the real-time measurement of the flame temperature field.  
radiation temperature measurement; multispectral temperature measurement; multispectral true temperature inversion; binocular ranging +
Footnote †: journal: Applied Sciences
# Temporal Instability Analysis of Injury Severities for Angle and Non-Angle Crashes at Roundabouts
## 6 Results and Discussion
### Analysis of the Characteristics' Effects
#### 6.1.7 Human Characteristics  
Crashes involving disabled drivers were less likely to result in severe injuries (0.0141, \(-\)0.0098, and \(-\)0.0043 for no injury, minor injury, and severe injury, respectively). Understandably, drivers with disabilities may be more conservative than normal drivers, especially in roundabouts with complex traffic situations. According to the characteristics of different collision modes, angle collisions also usually occur in complex traffic involving frequent interactions, such as entering or exiting roundabouts. Therefore, the disabled driver indicator is more prominent in reducing the injury severity of angle collisions.  
Regardless of the angle or non-angle collisions, the cyclist indicator steadily reduced the probability of no injury and increased the probability of a minor injury. In terms of severe injury, the effects of the cyclists showed slight temporal instability as the 2018-2019 angle collision model produced a negative effect (\(-\)0.0059, as shown in Table 11) that was different from other periods. In general, cyclist participation significantly increased the injury severity at roundabouts, as mentioned in previous studies [62]. This study further highlights that this variable is non-negligible for both angle and non-angle collisions.