esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
We design and fabricate meter-scale long connectorized paper-like flexible multimode polymer waveguide film with a large bandwidth-length product (BLP) for board-level optical interconnects application. The measured BLP of the multimode waveguide is greater than 57.3 GHz-m at a wavelength of 850 nm under the strictest overfilled launch condition with a maximum length of 2.1 m and 10-dB insertion loss. The fabricated waveguide films are as flexible as regular printing paper and can be conveniently interfaced with standard mechanically transferable (MT) fiber connectors with low loss. The average insertion loss of the connectorized waveguide is about 0.042 dB/cm with inter-channel crosstalk as low as \(-\)46.4 dB, and the bending loss is less than 1 dB at a bending radius of 1 mm under the overfilled launch condition. We also demonstrate a vertical-cavity surface-emitting laser (VCSEL)-based single-lane 100 Gbps PAM4 transmission. Our results show that the meter-scale long paper-like polymer waveguide film has both excellent optical properties and large bandwidth and is ideal for high-speed board-level optical interconnects application with a single-lane data rate of 100 Gbps and beyond, especially those that have a strict requirement on the length of connection and compactness.  
optical interconnections; optical waveguides; laser direct insertion +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
###### Abstract  
Intelligent Transport Systems (ITS) are anticipated to be one of the key technologies for the next decade and their deployment can benefit from the recent developments in the domain of Visible Light Communication (VLC). Light Emitting Diode (LED)-based low-cost VLC is considered in this work to provide a practical approach towards the implementation of an ITS by addressing the major issues of channel noise, free-space optical multipath reflections and interference from light sources. An analytical model is presented for the proposed Multiple-Input-Single-Output (MISO)-based VLC, and simulations are performed to analyze the performance of the system for various transmission distances. Results show that the proposed optimal receiver for \(4\times 1\) MISO can provide considerable improvement in the bit error rate for the forward error correction (FEC) threshold of \(3.8\times 10^{-3}\) in the presence of optical interference, and is suitable to support an ITS with an inter-vehicle transmission approach. The comparison of achieved performance with existing solutions for VLC-based ITS depicts that the proposed framework provides much higher data rates, three times longer transmission distance and improved receiver sensitivity.  
Keywords:intelligent transport systems (ITS); visible light communication (VLC); light emitting diode (LED); bit error rate (BER) +
Footnote †: journal: _articles
## 2 Background: Data and Autoregressive Models
### Time Series Data Sources  
Time series data can come from a multitude of sources and systems with different classes of characteristics, complexity, volume, and frequency of acquisition. Some categories of sources of this kind of data are industrial production, the financial sector, the consumer electronics industry, health system structures, retail trade, meteorology, and generally any measurable and quantifiable manifestation of human life and its environment. Especially in recent years, through the exponential growth of data sources, time series analysis and forecasting systems have been required to approach models of ever-increasing dimension and complexity.  
Time series from complex systems capture the dynamic behavior and causes of the underlying processes and provide a practical means of predicting and monitoring the evolution of the system state. However, the non-linearity and non-stationarity that often characterize the underlying processes of these systems pose a strong challenge to the process of accurate prediction. For most real-world systems, their dynamical state vector field is a nonlinear function of the state variables, meaning that the relationship connecting the system's intrinsic state variables to their autoregressive terms and exogenous variables is nonlinear. The time series resulting from such complex systems show aperiodic (chaotic) patterns even in steady states. Furthermore, since real-world systems often evolve under transient conditions, the signals obtained from them tend to exhibit various forms of non-stationarity. However, the methods that dominate the literature on the analysis and forecasting of time series derived from such systems focus mainly on the forecasting of linear and static processes. According to the work of [PERSON] et al. [12], conventional forecasting approaches, such as ARIMA techniques, cannot adequately capture the evolution of these systems from the perspectives of forecasting accuracy, computationalworkload, and sensitivity to the quantity and quality of a priori input information. This finding prompts us to assume that the success of the used prediction method, both in the category of classical algorithms and in the category of machine learning and deep learning techniques, depends to a large extent on the complexity of the system described by the data. From this point of view, artificial intelligence has an advantage over ARIMA techniques, as will be seen from the results of the studies presented later in the paper. At this point, it must be mentioned that a number of non-linear transformations exist for optimizing the ARIMA models in non-linear forecasting applications, which we will refer to in the ARIMA theoretical background of our study. On this note, there are a few notable exceptions where the performance of classical techniques exceeds that of artificial intelligence methods. These results will be commented on in the conclusions of our research.
# Golgi Metal Ion Homeostasis in Human Health and Diseases
## 4 Cu Ion (Cu\({}^{2+}\) and Cu\({}^{+}\)) Transporters
### Cu Ion Transporters in the Golgi  
Once transported from the blood circulation into the cell by the membrane-localized high affinity copper uptake protein 1 (hCtrl)/SLC31A1, the Cu ion associates with the copper transport protein ATOX1, which transfers it to copper-transporting ATPase-1 (ATP7A) and -2 (ATP7B) on the Golgi membrane. Both ATP7A and B are 8-transmembrane domain containing proteins with six metal-binding domains (MBD) on the cytoplasmic N-terminus. They also contain ATP-binding, phosphatase and phosphorylation domains that regulate their catalytic activity. These ATP-driven pumps acquire Cu ion from the copper chaperones on the cytoplasmic side of the TGN membrane and delivers it into the Golgi lumen to activate Cu-dependent enzymes. Similar to Ca\({}^{2+}\) ATPases, the Cu transporters belong to the P-type ATPase family and a complete Cu transport cycle involves ATP hydrolysis, catalytic phosphorylation/dephosphorylation, and consequential conformation change that facilitates the transmembrane transport of Cu ions. The import of Cu into Golgi lumen facilitates the incorporation of Cu into Cu-dependent enzymes in the secretory pathway; on the other hand, this avoids the potential toxic accumulation of Cu in the cytoplasm [148; 149].
# On-Chip Reconstructive Spectrometer Based on Parallel Cascaded Micro-Ring Resonators
## 3 Simulation Results and Analysis
### Discussion  
The performance of the proposed computational reconstruction spectrometers was compared with other filter-based integrated computational reconstruction spectrometers as shown in Table 1. The proposed on-chip spectrometer has a more compact footprint and faster computing time compared with the others with similar bandwidth.  
As shown in Table 1, the resolution of our on-chip spectrometer is indeed lower compared to some previous works, which is primarily determined by the spectral reconstruction algorithm we employed. The choice of different types of spectral reconstruction algorithms can affect the reconstruction error, resolution, reconstruction time, and noise tolerance. In our work, we adopted the ANN algorithm to perform computational spectral reconstruction. ANN models are capable of effectively fitting nonlinear relationships, and the output nodes of the model correspond to the wavelength points of the reconstructed spectrum. Theoretically, a higher number of output nodes could lead to a higher spectral resolution achievable by the reconstruction algorithm. However, it is important to note that the number of parameters in the ANN model grows approximately quadratically with the increase in the number of wavelength points. When the number of wavelength points reaches 20,001 (corresponding to a resolution of 0.01 nm in the 1450-1650 nm range), the model would contain 800 million parameters, posing significant computational challenges. Due to hardware limitations (NVIDIA GeForce RTX 4070), we were unable to train a model with such a vast number of parameters.  
Although the resolution of our on-chip spectrometer is limited by the employed spectral reconstruction algorithm, our design still possesses unique advantages. Firstly, using an ANN model as the spectral reconstruction algorithm enables faster computation of the incident spectrum from the obtained intensity vector. The CVX algorithm used in Refs. [25,26] to solve the regularized regression model achieves a resolution in the order of 10 pm, but it requires a relatively longer computational reconstruction time. In contrast, using an ANN model to perform spectral reconstruction with 2187 sampling channels (729 sampling states) takes approximately 1.93 s (including the time for computing the pseudo-inverse of the sampling matrix, based on i5-1135G7 CPU, Intel, Santa Clara, CA, USA). It is evident that the proposed ANN-based on-chip spectrometer can reconstruct the spectrum more rapidly, demonstrating a clear advantage in real-time performance compared to the CVX algorithm. Moreover, based on the discussion in Ref. [29], ANN-based spectral reconstruction algorithms exhibit stronger noise tolerance, which is another important factor to consider in spectral computational reconstruction.
# Experimental Study on the Current Pretreatment-Assisted Free Bulging of 304 Stainless Steel Sheets
## 3 Results and Discussion
### Influence of Material Properties on 304 Stainless Sheet Bulging  
Under the pressure condition of 0.5 Mpa and a holding time of 1.5 s, using a convex and concave mold with a groove width W of 1.6 mm and a depth H of 0.8 mm, a 25 A current was applied to the 304 stainless steel sheet and the TC4 titanium alloy to conduct bulging experiments. The single-channel bulging parts of different materials are shown in Figure 16. Under the same experimental conditions, it can be clearly seen that the bulging parts of the TC4 titanium alloy have obvious rebound phenomenon compared with the 304 stainless steel. This is mainly due to the fact that the elemental component of the TC4 titanium alloy is mainly Al, which is an \(\alpha+\beta\) type alloy that has high strength, toughness, and yield strength at room temperature. When subjected to a pressure load, its cold deformation is difficult, and the elastic modulus is small. When the pressure load is removed, large relaxation stress and strain are generated, resulting in obvious bending springback. The 304 stainless steel sample is an austenitic stainless steel with a soft structure, good plastic deformation ability, low yield strength, and large elastic modulus, so there is no obvious rebound phenomenon during the forming process.  
Figure 17 shows the bulging depths of different materials. It can be seen that the bulging depths of the stainless steel sheets and TC4 titanium alloy are 503 \(\upmu\)m and 364 \(\upmu\)m, respectively. This is mainly due to the lower yield limit of the stainless steel sheets. When performing bulging, the smaller the yield strength, the less energy required for the plastic deformation of the material and the easier it is to form. Under the same external load, the forming effect is better. Therefore, the bulging depth of the 304 stainless steel sheets is relatively high. Another reason is that the resistivities of the two materials are different. Compared with the TC4 titanium alloy, the 304 stainless steel sheet has a larger resistivity and is more sensitive to current, allowing it to generate a higher temperature rise and obtain more Joule heat under the same current. When the current is 25 A, its mechanical properties and microstructure are significantly improved, and the electroplastic effect is obvious.  
Figure 16: Physical diagram of the bulging parts under different materials: (**a**) 304 stainless steel; (**b**) TC4 titanium alloy.
## 2 Mobility Models in FANETs
### Time-Dependent Mobility Model
#### 2.3.2 Gauss Markov  
The Gauss Markov (GM) model [57] is a mobility model based on the time that adjusts for different levels of randomness using multiple parameters to minimize sudden movement changes. As shown in Figure 13, any node is first given a current direction and speed, and its coming travel is then updated and specified depending on its prior direction and speed. As a result, GM could avoid the abrupt turns and stops that were observed in models based on random patterns. The equations system connects previous directions and speed to upcoming ones, allows for seamless updating if the right settings are used for parameters. GM is adopted to communicate among UAVs in various applications scenarios, such as those found in [58; 59].
## 3 Results
### Results of Enrichment
#### 3.4.2 Physical Characteristics of Gluten-Free Cookies
The results presented in Table 5 show that there is a significant difference (<0.05) in the final moisture content between soy and other fortified cookies, but no significant difference \begin{table}
\begin{tabular}{c c c c c} \hline
**Cookies** & \begin{tabular}{c} **Specific Volume** \\ **(cm\({}^{3}\)/g)** \\ \end{tabular} & \
\begin{tabular}{c} **Moisture** \\ **Content (\%)** \\ \end{tabular} & **Spread Ratio** &
\begin{tabular}{c} **Expansion** \\ **Factor** \\ \end{tabular} \\ \hline Corn EGFC & 1.989 \(\pm\) 0.084 \({}^{\text{c}}\) & 6.534 \(\pm\) 0.294 \({}^{\text{b}}\) & 7.803 \(\pm\) 0.167 \({}^{\text{g}}\) & 1.038 \(\pm\) 0.025 \({}^{\text{a}}\) \\ \hline Chickpea EGFC & 2.405 \(\pm\) 0.033 \({}^{\text{a}}\) & 6.432 \(\pm\) 0.387 \({}^{\text{b}}\) & 8.736 \(\pm\) 0.488 \({}^{\text{f}}\) & 1.011 \(\pm\) 0.009 \({}^{\text{d}}\) \\ \hline Chestnut EGFC & 2.005 \(\pm\) 0.08 \({}^{\text{b}}\) & 6.432 \(\pm\) 0.387 \({}^{\text{b}}\) & 11.404 \(\pm\) 0.260 \({}^{\text{c}}\) & 1.006\(\pm\) 0.018 \({}^{\text{c}}\) \\ \hline Soy EGFC & 2.428 \(\pm\) 0.057 \({}^{\text{a}}\) & 10.323 \(\pm\) 0.534 \({}^{\text{a}}\) & 12.250 \(\pm\) 0.606 \({}^{\text{b}}\) & 0.979 \(\pm\) 0.018 \({}^{\text{f}}\) \\ \hline Acorn EGFC & 1.951 \(\pm\) 0.078 \({}^{\text{b}}\) & 6.8 \(\pm\) 1.385 \({}^{\text{b}}\) & 10.667 \(\pm\) 0.593 \({}^{\text{c}}\) & 1.025 \(\pm\) 0.018 \({}^{\text{c}}\) \\ \hline Quinoa EGFC & 2.081 \(\pm\) 0.032 \({}^{\text{b}}\) & 5.570 \(\pm\) 0.028 \({}^{\text{b}}\) & 12.588 \(\pm\) 0.539 \({}^{\text{a}}\) & 1.025 \(\pm\) 0.049 \({}^{\text{c}}\) \\ \hline Millet EGFC & 1.934 \(\pm\) 0.033 \({}^{\text{c}}\) & 6.551 \(\pm\) 0.462 \({}^{\text{b}}\) & 10.764 \(\pm\) 0.764 \({}^{\text{d}}\) & 1.032 \(\pm\) 0.016 \({}^{\text{a}}\) \\ \hline \end{tabular}
\end{table} 
Table 5: Physical properties of enriched gluten-free cookies. between the other enriched gluten-free cookies. Soy-enriched cookies had the highest moisture content (10.32%), which is comparable to the results of [64] on cookies made from soy flour. This is probably related to the hygroscopic property of soy flour, which readily absorbs water. Conversely, the gluten-free cookies enriched with quinoa displayed the lowest moisture content (5.570%) compared to other cookies. However, this value is higher than that of [30], who found a low moisture content in cookies enriched with quinoa flour (3.33%). The texture and shelf life of cookies could be affected by the observed changes in moisture content. According to previous studies, microbial development can be inhibited if a moisture content of less than 13% is maintained [63]. In this case, a satisfactory shelf life would be expected for all enriched cookies with a moisture content of less than 13%.
## 3 Biosynthetic Pathways  
Sesquiterpenenes are synthesized in plants through complex biosynthetic pathways, which involve several enzymatic reactions and intermediates. Sesquiterpene biosynthesis typically begins with the isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP) precursors, which are common to all terpenoids. These precursors are generated through the mevalonate (MVA) pathway or the 2-C-methyl-D-erythnitol 4-phosphate (MEP) pathway. Once IPP and DMAPP are synthesized, they serve as the building blocks for sesquiterpene biosynthesis. IPP and DMAPP are condensed to form geranyl diphosphate  
Figure 5: Sesquiterpene phenylpropanoids in _Ferula_ plants.  
(GPP), which contains ten carbon atoms. Then farnesyl diphosphate (FPP), which contains fifteen carbon atoms, is formed by the condensation of two molecules of IPP and GPP. FPP is a precursor to various sesquiterpenes, and it undergoes further modifications and cyclization reactions. These cyclization reactions create diverse sesquiterpene skeletons with different ring structures. After the initial cyclization step, the sesquiterpene skeleton may undergo rearrangement or further modification by various enzymes. This step introduces functional groups and structural diversity into the sesquiterpenes. After the sesquiterpenes are synthesized, they may undergo additional enzymatic modifications, such as glycosylation, acylation, or oxidation. These modifications can alter their solubility, stability, and biological activities. The biosynthesis pathways of the typical sesquiterpene skeletons in _Ferula_ are shown in Figure 6.  
The scaffold of sesquiterpene coumarins is formed through the dehydration condensation of the coumarin unit and the sesquiterpene moiety via an ether bond. The biosynthesis pathways of type Ib sesquiterpene coumarins, e.g., **219**[128], type Ic sesquiterpene coumarins, e.g., **225**, **319**, **323**, and **338**[112; 204]; type IIIa sesquiterpene coumarins, e.g., **355** and **359**[143; 179]; and type IIIb sesquiterpene coumarins, e.g., **362**, **370**, **372**, **374**, **375**, **383**, **384**, **386**, **387**, **392**, **394**, and **395**[143; 179], are shown in Figure 7.  
Figure 6: Biosynthesis pathways of the typical sesquiterpene skeletons in _Ferula_[202; 203].  
Figure 7. Biosynthesis pathways of different types of sesquiterpene coumaris. (**A**) The biosynthesis pathways of type I sesquiterpene coumaris [112,128,204]. (**B**) The biosynthesis pathways of type III sesquiterpene coumaris [145,179]. The biosynthesis pathways of sesquiterpene chromomes [115,143,195] and sesquiterpene phenylpropanoids [179,198,199,201] are shown in Figures 8 and 9, respectively.
## 4 Experiments and Discussion  
### Experimental and Algorithm Setup  
In order to verify the effectiveness of our proposed multi-scale cascaded network structure, asymmetric loss, and two-stage data augmentation, we use the network structure of Figure 1 to train and test on the DAGM data set [35], and compare it with the other three advanced defect detection algorithms.  
All experiments were trained on a single NVIDIA GeForce GTX 1060 6 GB graphics card. We use the Adam optimizer [36] with a mini-batch size of 8. While the network is training, the training set is processed by our two-stage augmentation method. Network is trained to minimize asymmetric loss. The learning rate (LR) is set to 1e-4 and is decayed by a factor 10 when the loss does not decrease within 10 epochs.
## 3 Monitoring Methods
### Sub-Pixel Algorithm for Calculating Coordinates of Center of Ring Mark  
#### 3.2.1 Marks of Monitoring Points  
In deformation monitoring, monitoring marks play crucial roles as feature points. Scholars have designed a variety of measurement marks, and several commonly used traditional measurement marks are shown in Figure 3.  
The monitoring marks must not be too complex, otherwise they will increase the processing time and algorithm complexity. To accurately determine the center of the monitoring marks, their form and size are compared and selected, and the image-processing algorithms for calculating their center pixel must be considered when selecting their form. The commonly employed algorithms include the gravity center method, edge detection method, and circular contour sub-pixel center method. In this study, the algorithm of the circular contour sub-pixel center method was used, such that a black ring mark with white background was designed, as shown in Figure 4. The inner and outer circle diameters are 80 and 150 mm, respectively. It is characterized by two circular contours and has higher accuracy when using the circular contour sub-pixel algorithm to fit the center coordinates under the same conditions.
## 1 Introduction
In particular, \(\gamma_{a}\) can be obtained by taking \(\sigma=0\) and the resulting amplifying gain can be infinitely large depending on the channel magnitude, which is often referred to an _ideal gain_ or a channel-inversion gain [8]. Thus, the performance evaluated with the approximate SNRs usually ignores the _noise propagation_ effect in an AF relay system. The noise propagation is however practically caused by the limited output power at the relay node and becomes especially significant when the first hop SNR is poor or the communication link consists of multiple hops [8; 12]. In wireless sensor networks, a battery-charged sensor usually has a very tight budget on transmit power and the first hop SNR at the relay becomes inevitably low. Thus, mixed RF/FSO AF relaying application in wireless sensor networks should be adequately evaluated by including the noise propagation effect. In this paper, we provide an exact analysis on the probability distribution of \(\gamma_{e}\) and illustrate the noise propagation effect on the performances. Unlike the existing methodology based on a moment generating function (MGF) technique (e.g., [7; 10]), the analysis in this paper mainly relies on the binomial-equation theorem [13] and the power series expansion of an exponential function [14]. Counting the noise power amplified at the relay, we could see the performance degradation due to the noise propagation in comparison to the approximate results. We assume that RF and FSO link suffer from Nakagami-_m_ and Gamma-Gamma fading with pointing errors, respectively. In [7] and [10], more generalized settings are used for modeling the fading channels but, to the best of our knowledge, no existing works have provided the exact statistical analysis of the mixed RF-FSO AF system with the power-constrained variable amplifying gain. The analysis provided in this paper is verified by simulation. Numerical investigation on the performance degradation shows that if the SNR (of each hop) is 10 dB, compared with the performances approximated by adopting \(\gamma_{a}\), the exact OP is degraded by up to 18.5%, the average BER by up to 12.17% and the ergodic capacity by up to 8.08%. Though the performance gap diminishes to around 1% if the SNR is greater than 30 dB, the exact analysis seems greatly important in the medium- or the low-SNR ranges.