esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of XOR Photonic Gate Using Highly Nonlinear Fiber
## 4 Band Pass Filter Stages for the XOR Gate  
In the time domain, the output binary sequence after BPF1 is shown in Figure 3 as 01001010 at P = 1.2 mw. This sequence is generated upon the convolution process between the input sequence to BPF1 (11011010) and the impulse response of the optical filter expressed by a'since' function. This resulting BPF1 is to be coupled with BPF2 at later stages in order to generate the XOR gate). Similarly, a variable optical attenuator was connected after BPF1 to maintain the signal synchronization.  
The BPF2 input port contains the original probe B wavelength spectrum at \(\lambda_{\text{B}}\)\(=1541.3\) nm and the identical shifted XPM replica at \(\lambda^{\prime}_{\text{B}}\)\(=1538.32\) nm as shown in Figure 4. BPF2 clips \(\lambda^{\prime}_{\text{B}}\) and keeps \(\lambda_{\text{B}}\) in order to generate the function A\({}^{\prime}\).B. As a result, the cut-off wavelength of BPF1 will be selected as \(\lambda_{\text{BPF2}}\)\(=1541.3\) nm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**BPF Port** & **Filter Logical Output** & **Filter Cut-Off Wavelength** \\ \hline BPF1 & A \(\times\) B\({}^{\prime}\) & \(\lambda_{\text{BPF1}}\)\(=1555.08\) nm \\ \hline BPF2 & A\({}^{\prime}\times\) B & \(\lambda_{\text{BPF2}}\)\(=1541.3\) nm \\ \hline \hline \end{tabular}
\end{table}
Table 1: The cut-off wavelengths for BPF1 and BPF2.  
Figure 3: Time domain output for BPF1.  
The optical time domain visualizer in Figure 5 illustrates the binary output from the convolution between the impulse response of the filter and the binary sequence 01101010 (output of OC2). The resultant logic is 10010000 which is the second input arm required to generate the XOR gate by the optical coupler. The output spectrum is composed of the filtered spectra at \(\lambda_{\text{BPF2}}\)\(=\) 1541.3 nm and the adjacent low power side band spectrum between 1550-1570 nm, as per Figure 6. This is attributed to the available noise introduced by the EDFA amplifier in the earlier stage of the design. However, this side band have negligible effect to the performance of the logic gate due to the low power level compared to that of the main spectrum (\(-\)12.079 dBm), as gauged in Figure 7.  
Figure 4: Output spectrum for BPF1.  
Figure 5: Time domain output for BPF2.  
Note that an optical delay line is adjusted after BPF2 in order to equalize the power values and optical pulses before the combination process.  
For the output at the optical coupler stage, both branches which output from BPF1 and BPF2 are coupled together by employing a 50:50 coupler with coupling coefficient = 0.5. As a result, the XOR operation for the photonic logical gate is performed between the two binary sequences, as shown in Figure 7 with a bit sequence of 11011010.  
The optical spectrum analyzer in Figure 8 shows the coupled wavelengths at 1541.3 nm and 1555.08 nm, respectively, which corresponds to the logical XOR operation of both input signals at \(-11.590\) dBm as recorded from the  
Figure 6: Output spectrum for BPF2.  
Figure 7: XOR gate output in the time domain.
## 4 Experiment Results and Discussion  
The experiments were carried out based on the above-mentioned procedure and the focused signals of two specimens under the same conditions were obtained. The relationship between the bolt preload and the peak of the focused signal extracted from the experimental data are shown in Figures 8 and 9. According to the relevant theory of bolt connection, only a very small part of the external load, which is applied on the bolt joint structure, works on the bolt when the bolt preload is enough, but once the bolt preload is insufficient, the external load that works on the bolt will be greatly increased and will most likely lead to the failure of the bolt. For the bolt joint structure, it is important to ensure that the bolt preload is at a reasonable level. Therefore, for the bolt preload monitoring, the prominent task is to monitor the axial force changes around its rated preload. Thus, it is unnecessary to monitor the force which is much smaller than the rated preload, and for this reason, the starting value of the abscissa is 20 KN in the experiments.  
Figure 8 shows the experimental result of the first specimen with its center distance of the convex and concave being 25 mm from the washer center. It can be found that, with the increase of the bolt preload, the focused signal peak increases approximately linearly. This agrees with the analysis in Section 2 which shows that the peak amplitude increases with the increase of the axial force. The slope of the curve is steep enough to interrogate the change of the bolt axial force. Even at the 60 KN preload, the focused signal peak still kept increasing, thus the designed smart washer can monitor the full range of the M16 bolt. The effectiveness of the proposed design is validated by this experiment.  
Figure 9 shows the result of specimen 2 (the center distance is 29 mm); the focused signal peak amplitude keeps increasing and does not reach saturation. Nevertheless, the curve has gently changed between 30 and 40 kN, which may be caused by the machining errors of the specimen.  
Figure 7: Experimental signals. (**a**) Pulse signal; (**b**) Response signal; (**c**) Time reversed signal; (**d**) Focused signal.  
The experimental results show that the amplitude of the focused signal can indicate the bolt preload obviously, that is to say that the smart washers, based on the piezoelectric active sensing method and time reversal technique, can effectively monitor the bolt looseness in the related preload range. By comparing the experimental results of the two specimens, it is shown that a small change in the contact location will not obviously affect the contact area change under the applied preload range and thus will not affect the transmitted wave energy change. For the M16 bolt, the designed smart washer is sufficient to measure 20 kN\(\sim\)60 kN axial force which covers the rated preload range of the bolt, and the change of amplitude with obvious axial force. Hence, for the different types of bolt, by designing a washer that is different in size, the entire rated preload measurement of the bolt can be fulfilled successfully.
## 3 PAPR Reduction with Clipping and Modified Iterative Detection Scheme  
Consider the OvTDM transmitter scheme depicted in Figure 1, where the signal is clipped before transmission in order to reduce the PAPR. The clipping function \(c(x)\) can be written as  
\[c(x)=\left\{\begin{array}{l}x\ldots\ldots\ldots\left(|x|\leq A\right)\\ \frac{Ax}{|x|}\ldots\ldots\ldots\left(|x|\geq A\right),\end{array}\right. \tag{8}\]  
where \(x\) represents the input signal. The clipping threshold, denoted by \(A\), serves as a limit amplitude threshold in signal processing. This threshold defines the maximum amplitude value beyond which a signal will undergo amplitude clipping. In other words, when the signal's amplitude surpasses the specified threshold \(A\), the signal is restricted or \"clipped\" to the predefined maximum value.  
For convenience, the parameter clipping ratio (CR) is usually used as an alternative representation of the clipping threshold with the expression as  
\[CR=\frac{A^{2}}{E\Big{[}|s(t)|^{2}\Big{]}}. \tag{9}\]  
Note that clipping is a nonlinear process that leads to severe in-band interference and out-of-band noise, so as to degrade the performance of the entire system. Therefore, this paper focuses on the in-band signal effects to minimize the BER performance loss at the receiver side.  
The assumptions in this paper are firstly described as follows.  
* The signal processing at the receiver side uses the Forney model, i.e., after down-sampling the signal passes through a whitening filter. Thus, the total noise can be written as an additive Gaussian white noise.
* Since multiple layers are superimposed, the distribution of the signal in the time domain should asymptotically obey a Gaussian distribution. Here, it is assumed that the signal time domain obeys Gaussian distribution. Also, since BPSK is used as the baseband modulation, the transmitted signal is a real signal and its amplitude in the time domain obeys the following distribution as \[f(x)=\frac{1}{\sqrt{2\pi}\sigma_{x}}e^{-\frac{x^{2}}{2\sigma_{x}^{2}}}.\] (10)
# Four-Channel Polarimetric-Spectral Intensity Modulation Imager
## 2 Optical Layout and Principle
Through R1, R2, R3, and R4 and the polarizers P1, P2, P3, and P4, the output Stokes vector can be expressed as Equations (4) to (7):  
\[I_{2:1}^{0}(\lambda) =M_{P4}M_{R2}M_{R1}S_{in}(\lambda) \tag{4}\] \[I_{1:2}^{\varphi 0}(\lambda) =M_{P3}M_{R4}M_{R3}S_{in}(\lambda)\] (5) \[I_{1:2}^{\varphi 15}(\lambda) =M_{P2}M_{R4}M_{R3}S_{in}(\lambda)\] (6) \[I_{1:2}^{0}(\lambda) =M_{P1}M_{R4}M_{R3}S_{in}(\lambda) \tag{7}\]  
where \(S_{in}\) denotes the input Stokes. \(M_{Pi}\) represents the Muller matrix of linear polarizers P1, P2, P3, and P4, and \(M_{Ri}\) denotes the Muller matrix of phase retarders R1, R2, R3, and R4. Equations (3) and (2) into Equations (4) to (7) yields the following:  
\[I_{2:1}^{0}(\lambda) =\frac{1}{2}S_{0}(\lambda)+\frac{1}{2}S_{1}(\lambda)\cos(\varphi( \lambda))+\frac{1}{2}S_{2}(\lambda)\sin(\varphi(\lambda))\sin(2\varphi(\lambda ))-\frac{1}{2}S_{3}(\lambda)\sin(\varphi(\lambda))\cos(2\varphi(\lambda))\] (8) \[I_{  
The upper right part of \(I\) represents the angle of the linear polarizer and the lower right part of \(I\) represents the thickness ratio of the phase retarders. Equations (4) to (7) can change to Equation (12):  
\[I=PS_{in} \tag{12}\]  
\[P(\lambda)= \begin{bmatrix}\frac{1}{2}&\frac{1}{2}\cos(2\varphi(\lambda))&\frac {1}{2}\sin(2\varphi(\lambda))\sin(\varphi(\lambda))&-\frac{1}{2}\sin(2\varphi (\lambda))\cos(\varphi(\lambda))\\ \frac{1}{2}&-\frac{1}{2}\cos(2\varphi(\lambda))&-\frac{1}{2}\sin(2\varphi( \lambda))\sin(\varphi(\lambda))&\frac{1}{2}\sin(2\varphi(\lambda))\cos( \varphi(\lambda))\\ \frac{1}{2}&0&\frac{1}{2}\cos(\varphi(\lambda))&\frac{1}{2}\sin(\varphi( \lambda))\\ \frac{1}{2}&\frac{1}{2}\cos(\varphi(\lambda))&\frac{1}{2}\sin(\varphi( \lambda))\sin(2\varphi(\lambda))&-\frac{1}{2}\sin(\varphi(\lambda))\cos(2 \varphi(\lambda))\end{bmatrix}  \tag{13}\]  
\(P\) is referred to as the system matrix. Since \(P\) has a full rank, the full Stokes can be solved as follows:  
\[P(\lambda)^{-1} \begin{bmatrix}S_{out}^{1}(\lambda)\\ S_{out}^{2}(\lambda)\\ S_{out}^{3}(\lambda)\\ S_{out}^{4}(\lambda)\end{bmatrix} = \begin{bmatrix}S_{0}(\lambda)\\ S_{1}(\lambda)\\ S_{2}(\lambda)\\ S_{3}(\lambda)\end{bmatrix}  \tag{14}\]  
By Equation (14), Stokes in space can be demodulated while avoiding channel crosstalk and resolution reduction.
# Automatic Fabric Defect Detection Method Using PRAN-Net
## 4 Conclusions  
This paper proposed a PRAN-Net method that improves the detection accuracy of fabric defects. Aiming at the problem that the initial anchor in the Faster R-CNN model is not suitable for tiny and extreme shape fabric defect detection, we propose Priori Anchor to effectively generate sparse anchors adaptively. On this basis, the FPN is used to obtain multi-scales feature maps to extract more detailed information of the defects. Two different fabric datasets were used to verify the performance of PRAN-Net by comparing it with RetinaNet, Mask R-CNN and GA-Faster R-CNN. Compared with the One-stage algorithm, the detection accuracy is improved 7.2% and 7.4% on the denim dataset and the plain fabric dataset separately, while the detection speed decreased less than 0.7f/s. Compared with the Two-stage algorithm, the mAP increases at least 2.1% and 2.4% separately on the two datasets. The PRAN-Net method increases detection and location accuracy for tiny and extreme fabric defects greatly, and the detection speed is also improved because of using fewer prior anchors, which satisfies the requirements of real-time detection. In the future, we will try PRAN-Net with other deep learning networks and apply to multiple tiny objects detection of other application fields.  
Conceptualization, methodology and software, P.P.; validation, P.P., Y.W. and C.H.; formal analysis, P.P. and Y.W.; investigation, P.P. and T.L.; resources, Z.Z. and W.Z.; writing--Original draft preparation, P.P.; writing--Review and editing, P.P. and Y.W.; funding acquisition, Y.W., Z.Z. and W.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the Science Technology Service program of Science Academy of China under Grant KFJ-STS-QYZX-098 and the Strategic Priority Research Program of Science Academy of China under Grant XDA13030404. The authors declare no conflict of interest.
# HTN: Hybrid Transformer Network for Curvature of Cervical Spine Estimation
## 3 Experiments
### Ablation Study  
Our model achieved a Pearson correlation coefficient of \(R\) = 0.9619 (\(p\) < 0.001), which means that the prediction of our method has high consistency with the ground truth. In other words, the Cobb angle estimated by our method highly correlates with that measured by experts. The SMAPE of our model is 11.06%. We also show the results of a student's paired \(t\)-test. The significance level in hypothesis testing was set to 0.05. No statistically significant differences were observed between the measurement of our method and the manual method since the \(p\)-value is greater than 0.05 (\(p\) = 0.876).  
To thoroughly validate the effectiveness of our model, various ablation studies were performed. We analyzed the effect of our encoder-decoder structure, transformer module, intermediate branch, and self-supervision learning. As shown in Table 1, with the simple encoder-decoder structure, our model achieved a SMAPE of 14.25%, an MAD (SD) of 2.26\({}^{\circ}\) (2.84\({}^{\circ}\)), a Pearson correlation coefficient of 0.9501 (\(p\) < 0.001), and an MSE of 6.01. When adding self-supervision, we obtained 12.58%, 2.14\({}^{\circ}\) (2.77\({}^{\circ}\)), 0.9527 (\(p\) < 0.001), and 5.84 in terms of SMAPE, MAD (SD), Pearson correlation coefficient, and MSE, respectively. Subsequently, an intermediate branch was added. All indicators exhibited an improving trend. We were able to obtain the lowest SMAPE of 11.06%, the smallest mean absolute difference, the narrowest confidence interval of the difference, a higher Pearson correlation coefficient of 0.9619 (\(p\) < 0.001), and the smallest MSE of 5.24 in our final model.  
In addition, we also used SE attention [28] instead of the transformer to determine how different attention mechanisms affected the model. Ablation studies about SE attention were also conducted (refer to the last three rows of Table 1). We observed that SE attention could also improve the model's performance. Nevertheless, compared with the transformer, this performance gain was slightly smaller.  
We explored the impact of introducing a transformer earlier in the encoder-decoder network on the final model. Table 2 shows the results of the encoder-decoder network with three residual modules and the encoder-decoder network with four residual modules. The results show that using a larger feature map as the transformer's input will reduce the model's performance.
# Investigating Models for the Transcription of Mathematical Formulas in Images
## 4 Experimental Setup
### Mathematical Expression Recognition Models
#### 4.2.1 LaTeX-OCR  
_LaTeX-OCR_ ([[https://github.com/lukas-blecher/LaTeX-OCR](https://github.com/lukas-blecher/LaTeX-OCR)]([https://github.com/lukas-blecher/LaTeX-OCR](https://github.com/lukas-blecher/LaTeX-OCR)), accessed on 21 January 2024), also known as _pix2 tex_, is a transformer-based model that generates LaTeX code from images of mathematical formulas [38]. The model uses a pre-trained vision transformer encoder [39] with a _ResNet_ backbone and a transformer decoder [40] trained on the _IM2 LATEX-100K_[5] and _CROHME_[41] datasets. [38] reported a BLEU score of 88% on the corresponding test set. An important part of the model is an additional neural network that predicts the optimal resolution for the input image as a pre-processing step.
## 1 Fiber Optical Sensing  
In the area of fiber optic sensing, five papers are presented in this Special Issue. For example, [PERSON] et al. [1] reported an investigation on the feasibility of utilizing Mode Division Multiplexing (MDM) for simultaneous measurement of Surrounding Refractive Index (SRI) and temperature using a single sensor element based on an etched OM4 Graded Index Multi Mode Fiber (GI-MMF) with an integrated fiber Bragg Grating (BG). In another research article by [PERSON] et al. [2], the durability of functionalized carbon structures (FCS) that are equipped with fiber optic sensors in a highly alkaline concrete environment are investigated. In their investigation, the suitability of optical fibers with different coatings as well as different integration techniques for the FCS were analyzed. In terms of fiber optic strain sensing, a new concept is presented by [PERSON] et al. [3], where, an intensity-modulated Sagnac loop sensor based on Polarization-Maintaining Photonic Crystal Fiber (PM-PCF) in a setup with a Dense Wavelength Division Multiplexer (DWDM) for strain measurement, is presented. The proposed setup uses an optical power measurement scheme, i.e., as opposed to measurement of wavelength, and thus would be relatively cheaper when compared to the utilization of complex optical spectrum analyzers for the target application. Moreover, two additional research papers presented by [PERSON] et al. [4] and [PERSON] et al. [5] have also been categorized under the rubric \"fiber optic sensing\". For instance, [PERSON] et al. [4] reported the application of an integrated optical electric-field sensor on the measurements of transient voltages in AC high-voltage power grids. Theydeveloped an integrated optical electric-field sensor based on the Pockels effect to measure the transient voltages of high-voltage conductors and achieved a response speed faster than 6 ns and a wide bandwidth ranging from 5 Hz to 100 MHz [4]. In contrast, [PERSON] et al. [5] present an electro-optic dual-comb Doppler velocimeter for high-accuracy velocity measurement by generating two optical combs using electro-optic phase modulators and tracing their repetition frequencies to a rubidium clock and demonstrating experimentally a high accuracy in the range of 100-300 mm/s with a maximum deviation of 0.44 mm/s.
## 4 Experiments and Analysis
### Pre-Processing and Data Augmentation  
To pre-process the data, we first extracted frames from the videos and resized all inputs to a uniform resolution of 224 by 224 pixels. Subsequently, we applied several data augmentation techniques to enhance the dataset and increased the robustness of our model. These techniques include translating the images by 6 pixels in each direction, applying a flip-left-to-right (flipLR), which necessitates a corresponding label change (e.g., a 'turning left' label becomes 'turning right', or the driver's behavior on the left side becomes that on the right side, which covers a broader range of usage scenarios.), and implementing cutout [39]--a method that randomly masks out square regions of the image. Additionally, we employed Augmix [40], which combines various augmentations such as auto contrast, equalization, posterization, and solarization to create a diverse set of training examples. This comprehensive augmentation approach not only amplified our dataset but also ensured broader scenario coverage. Further details of these techniques are discussed in the referenced literature.
# BIM Policy Trends in Europe: Insights from a Multi-Stage Analysis
## 3 Results
### BIM Policy in Europe
#### 3.2.7 Estonia  
By 2035, BIM requirements for public investment are planned to be introduced. More specific requirements will be published in the future, alongside the development of BIM implementation, the introduction of standards, and the ability to obtain building permits and usage approvals based on BIM models.  
In 2017, the Estonian Ministry of Economic Affairs and Communications (MEAC) (_Majandus-ja Kommunikatsioominitisteerium_) initiated and signed an agreement with several agencies to implement BIM in public procurement, as outlined in the Estonian Declaration of Intent on Digital Construction. This initiative aimed to introduce pilot projects and establish criteria for public procurement. Similarly, in 2022 (version 1.7 in 2021), The Long-Term View on Construction 2035 highlighted a vision of e-construction, introducing principles of sustainable construction and a circular economy, the development of digital twins of buildings, indicators for monitoring the construction sector's development (i.e., the share of building permits applied for on the basis of BIM), BIM model platforms, and the introduction of a BIM mandate in the public sector (Activity 6.2, led by the MEAC, with involvement from the Ministry of Finance, State Real Estate Ltd., and the private sector) [56]. The BIM Working Group of Public Contracting Authorities was also established to continue the implementation of pilot projects.  
Currently, the Building Register (BR, _Ehitisregister_) portal is available and under development, which aims to facilitate the exchange of information, including the digital representation of buildings known as digital twins [57]. The development of the e-construction platform, an extension of the existing Building Register, is implementing procedures for obtaining construction and usage permits online based on BIM models. There are also plans for the partial automation of BIM model inspections to expedite the permit process, necessitating the development of unified standards, which are currently under discussion.  
A 2024 report commissioned by the Ministry of Economic Affairs and Communications sets a goal to increase the level of digitalization by 15% by 2030 and an additional 15% by 2035 [58]. That same year, an action plan and roadmap for the further development of e-construction was published (available in English) [59]. This plan outlines actions for specific years to create a national model of the building environment in a common data environment (CDE), establishing standard data formatting and exchange, unified terminology, and classification systems and developing digital data templates. It also plans the implementation of pilot projects and the procedure for obtaining permits and controlling applications based on submitted BIM models, indicating that BIM is already widely used in design and construction.  
The international railway infrastructure project, Rail Baltic, coordinated by Estonia, Latvia, and Lithuania, also incorporates BIM.