esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The BER, eye-pattern, and OP have been assessed for an unamplified intra-datacenter link with 2 km with PAM4 signal transmission in the interfered core and OOK signalling in the interfering cores [25]. The ICXT impact on the system performance of an optically amplified inter-datacenter link with a single interfering core, optical amplification, and chromatic dispersion compensation has been assessed in terms of the BER, eye-patterns, and OP [26]. Inter-DCI links can reach up to 100 km and are optically amplified using erbium-doped fiber amplifiers (EDFAs) due to the higher distances in comparison with shorter intra-datacenter links. A possible solution for optical amplification used in links supported by single-core fibers (SCFs) is to use single-core EDFA (SC-EDFA) [27]. However, links supported by MCFs with such a solution would lead to high power consumption and become costly since they would require a SC-EDFA for each core. Hence, to support amplification in MCFs, multi-core EDFAs (MC-EDFAs) have been proposed to reduce power consumption, number of required components, and overall cost in comparison to multiple SC-EDFAs, and have demonstrated promising features such as supporting high data capacity and allowing standard cladding diameter of 125 \(\upmu\)m [28]. MC-EDFAs induce ICXT on the transmitted signals, an effect that does not occur in SC-EDFAs [27], and the total crosstalk level from six outer cores of a MC-EDFA was estimated to be \(-46.5\) dB at the center core [29]. To enable high data rate signal transmission in such long IM-DD inter-DCI links, chromatic dispersion (CD) should be mitigated using dispersion compensation techniques [6; 30]. As simple electric dispersion compensation techniques, such as adaptive equalization performed with digital signal processing (DSP), are not effective due to the high values of CD found in inter-DCI links [6], the CD compensation (CDC) must be performed in the optical domain. In inter-DCI links, optical CDC is most commonly performed using dispersion compensating fibers (DCFs) [31; 32; 33; 34]. In this work, the OP due to ICXT induced by multiple interfering cores in IM-DD inter-DCI links up to 80 km-long with PAM4 signal transmission, full loss and perfect optical dispersion compensation, is assessed and analyzed using numerical simulation. This work extends the preliminary work presented in [26] by assessing the ICXT impact on the OP, for a single interfering core and MCF lengths ranging from 10 km to 80 km, for several interfering cores with the same skew and for two interfering cores with different skews.
## 3 Results and Discussion  
In Figure 1a, an IR-thermogram and the measurement areas of the surface and the side views of five cuvettes and the shelf temperature are shown as R1 to R16 (Irbis 3.1 professional software denotes these geometries as rectangles even though they are squares). In Figure 1b, a photo is shown of the same setup with the final dried product, the five cuvettes, the position of the probes and the IR-mirror. The spatial average temperature of R1 to R5 and R16 was used to prepare Figure 2, displaying the surface temperature of cuvettes 1 through 5 throughout the freeze-drying cycle and the shelf temperature. Machine readings of shelf-temperature, pressure, product-temperatures, resistivity and ice condenser temperature are shown in Figure 3 for comparison, which is a graphical representation of the freeze-drying program. The emissivity correction for all thermograms of 0.93 was deduced by matching the readout of the average shelf temperature from the freeze-dryer and from the rectangle shown in Figure 1 (R16) using the IR-camera. In Figure 4a,b the nucleation and sublimation behaviour of the mannitol/sucrose solution is shown. In Figure 5a-d, thermograms are shown for different stages of the freeze-drying programme displaying the measurement profiles (line 1 to line 5) of the surface temperature. A measurement line in the thermogram offers the possibility of displaying a temperature profile across the cuvette's surface. Each temperature readout is given per pixel, which here translates to every 0.3 mm. The temperature readout of these profiles are shown as a function of position at the cuvette's surface in Figure 6a-d, respectively. Finally, Figure 7 shows a thermogram of cuvettes 1, 2 and 3 next to each other, clearly showing the superior spatial resolution in comparison to the previous work during the primary drying (sublimation) [6]. Each of the different figures summarised here are discussed in more detail below. Additional figures are also available in the electronic Supplementary Materials Figures S1-S6 and are described there separately.
## 6 Experimental Results and Analysis
### Ablation Experiment
#### 6.1.2 Results of Ultrasound Image Denoising with Different Loss Functions  
In the experimental part of this study, we can be centered on evaluating the influence of different loss functions on model performance. Specifically, we employ different loss functions for model training: no structural similarity loss function or structural similarity loss function \(L_{ssim}\). To fairly evaluate the effect of these loss functions, we tested them on an ultrasound test image dataset under simulated speckle noise with standard deviation \(\delta\) = 0.5. The test consequences are displayed in Table 4, providing a comparison of the mean \(PSNR\) and \(MSSIM\) values of the models with different loss functions. Specifically, the model incorporating the \(SSIM\) loss function, \(L_{ssim}\), demonstrated an improvement of  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Denoising Algorithms**} & \multicolumn{6}{c}{**The Standard Deviation \(\sigma\) of the Speckle Noise**} \\ \cline{2-7}  & **0.1** & **0.2** & **0.3** & **0.4** & **0.5** & **0.6** \\ \hline No \(L_{ssim}\) & 30.3681 & 30.7160 & 31.4335 & 30.3293 & 31.7896 & 30.9377 \\ \(L_{ssim}\) \(\eta\) = 0.5 & 31.1547 & 31.8176 & 32.0143 & 32.4097 & 33.4576 & 31.0501 \\ \(L_{ssim}\) \(\eta\) = 1.5 & 35.6007 & 35.2461 & 33.5341 & 32.9331 & 36.2193 & 31.2005 \\ \(L_{ssim}\) \(\eta\) = 1.5 & 30.8513 & 31.8456 & 32.4519 & 31.0646 & 33.0053 & 30.7907 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation experiment of hyperparameter \(\eta\) (\(PSNR/\text{dB}\)).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Denoising Algorithms**} & \multicolumn{6}{c}{**The Standard Deviation \(\sigma\) of the Speckle Noise**} \\ \cline{2-7}  & **0.1** & **0.2** & **0.3** & **0.4** & **0.5** & **0.6** \\ \hline No \(L_{ssim}\) & 0.6112 & 0.6530 & 0.6685 & 0.6701 & 0.6775 & 0.6736 \\ \(L_{ssim}\) \(\eta\) = 0.5 & 0.6201 & 0.6419 & 0.6789 & 0.6797 & 0.6810 & 0.5801 \\ \(L_{ssim}\) \(\  
0.96 dB in \(PSNR\) and 0.03 in \(MSSIM\) when compared to the model without the structural similarity loss function.  
In addition, Figure 5 displays the denoising outcomes of different loss functions on the ultrasound test images dataset, from which it can be clearly observed that compared with the traditional structural similarity loss function, the improved \(L_{ssim}\) loss function proposed in this study shows better \(PSNR\) and \(MSSIM\) performance in the fusing trained model. This result highlights the effectiveness of the modified loss function in this research in building up the denoising metrics of the model.
## 3 Results
### Optimisation of the Biosensor Conditions  
#### 3.3.1 Hydrodynamic Voltammetry  
Hydrodynamic voltammetric studies were performed with NADH under alkaline buffer conditions (pH 8-10) using the MB-SPCEs only. The resulting voltammograms exhibited a maximum electrocatalytic current at 0.0 V vs. Ag/AgCl (Figure 5). Therefore, this potential was used for all further studies. The electrocatalytic oxidation reaction is illustrated in Figure 2. This shows that 2 electrons per NADH molecule are transferred to the oxidised form of Meldolas Blue (MB\({}_{\text{ox}}\)) to produce the reduced form (MB\({}_{\text{red}}\)), which is subsequently transferred to the SPCE, generating the response current.  
Figure 4: FTIR analysis of the glycerol MB-SPCE biosensor surface (green line) and glutaraldehyde alone (pink line).  
Figure 5: Hydrodynamic voltammograms for NADH were obtained using an MB-SPCE in buffer solutions of pH 8, 9, and 10.  
Figure 3: High-resolution (digital) microscopy images at 80\(\times\) magnification; (**A**) the surface of the MB-SPCE; (**B**) the MB-SPCE with a deposited layer of \(\text{GLDH/NAD}^{+}\); (**C**) the \(\text{GLDH/NAD}^{+}\) layer cross-linked to the MB-SPCE surface with glutaraldehyde.  
3.2 Effect of pH on Amperometric Responses of Glycerol Solutions Containing Free GLDH and NAD\({}^{+}\)  
Next, we examined the effect of pH (8-10) on the amperometric responses of different glycerol concentrations over a range of 0.04 to 0.20 mM (Figure 6). All the solutions were stirred at a fixed speed and contained 10 units of GLDH and 5 mM NAD\({}^{+}\), and the solution temperature was controlled at 30 \({}^{\circ}\)C. Figure 5 shows the current response-concentration profiles obtained with these solutions, which indicates that pH 10 produces a slightly higher current than pH 9, with pH 8 producing the lowest response. It was shown that the response-concentration profiles obtained with these solutions follow [PERSON]'s behaviour. We also analysed the data using Hanes plots, which confirmed [PERSON]'s behaviour (Figure S1). Enzymes generally have bell-shaped pH profiles with an optimal pH in the middle, showing increased activity as pH rises initially, followed by decreased activity with further pH increase. The results obtained by [PERSON] [21] indicated an optimum pH for NAD\({}^{+}\) at pH 8-9. From these results, we concluded that pH 9 would be appropriate as the supporting electrolyte for further studies with our amperometric biosensor.
## 3 The Proposed Protocol
### Password Update Phase  
\(U_{i}\) can update the password by following steps U1-U2 below without interacting with \(GWN\):  
U1: At first, \(U_{i}\) enters \(\big{(}ID_{i}^{*},PW_{i}^{*}\big{)}\). PDA then uses \(PUF_{sum}(\cdot)\) to verify the identity of \(U_{i}\). Specifically, PDA computes \(V_{ii}^{*}=PUF_{sum}\big{(}PW_{i}^{*}\big{)},V_{i}^{*}\parallel S_{M}^{U_{i} *}\parallel k_{ii}{}^{*}=h\big{(}ID_{i}^{*}\parallel V_{ii}^{*}\big{)}\oplus A _{1},k_{i}{}^{*}=FE.REP\big{(}V_{ii}^{*},k_{ii}{}^{*}\big{)}\), and \(A_{2}^{*}=h\big{(}V_{i}^{*}\parallel k_{i}^{*}\parallel S_{M}^{U_{i}*}\big{)}\) mod \(n_{0}\). If \(A_{2}^{*}\) is equal to \(A_{2}\), PDA continues to run step U2; otherwise, this session is terminated.  
U2: Via the new password \(PW_{i}^{new}\), which is chosen by \(U_{i}\), the PDA computes the new parameters: \(V_{ii}^{new}=PUF(PW_{i}^{new})\), \(\big{(}k_{ii}^{new},k_{ii}^{new}\big{)}=FE.GEN\big{(}V_{ii}^{new}\big{)}\), \(A_{1}^{new}=V_{i}\parallel S_{M}^{U_{i}}\parallel k_{ii}^{new}\oplus h\big{(} ID_{i}\parallel V_{ii}^{new}\big{)}\), and \(A_{2}^{new}=h\big{(}V_{i}\parallel k_{i}^{new}\parallel S_{M}^{U_{i}}\big{)}\) mod \(n_{0}\). Finally, the PDA updates the value of \(\big{\{}A_{1},A_{2}\big{\}}\) to \(\big{\{}A_{1}^{new},A_{2}^{new}\big{\}}\).
## 3 Results
### Monitoring the Sound Travel Speed during the Curing Process Using Ultrasonics  
The longitudinal sound speed was first evaluated in three adhesive baseline samples during their curing process. The samples were fabricated and tested simultaneously to determine the repeatability of the analysis technique. It can be observed in Figure 5 that the sound speed of the three samples behaved very similarly, where a sharp increase in sound speed occurs during the first four hours of curing, followed by a convergence until the sample is fully cured. After four days of curing, the final sound speeds for the first, second, and third baseline samples were 2319 (\(\pm\)47) m/s, 2317 (\(\pm\)46) m/s, and 2320 (\(\pm\)47) m/s, respectively. The difference between the samples with the highest and lowest sound speeds was ~0.13%.  
After adding 2, 4, and 6 wt.% of IPA to the adhesive samples to simulate a trapped solvent, their sound speed was also evaluated during the curing process. Figure 6 shows how, unlike as previously seen with the baseline samples, the curing process differs for each sample. The sound speed of the sample with no IPA content started converging at a lower cure time, while adding IPA delayed the convergence process. To give some perspective, the curve onset of the sample with 0 wt.% IPA occurred at 1.89 curing hours, while the onset for the samples with 2, 4, and 6 wt.% IPA occurred at 2.77, 3.34, and 3.99 curing hours, respectively. This shows a difference of approximately 2.1 h between the baseline sample without IPA and the sample containing 6 wt.% IPA. In addition, the sound speeds after four curing days for the samples with 0, 2, 4, and 6 wt.% of IPA were 2322 (\(\pm\)47), 2369 (\(\pm\)49), 2354 (\(\pm\)48), and 2351 (\(\pm\)48) m/s, respectively. This shows how IPA content not only affected the curing mechanics of the adhesive, but also its final sound speed.  
Figure 5: Sound speed with respect to cure time of three adhesive baseline samples during (**a**) the first 30 curing hours; (**b**) the first 10 curing hours located in the dashed rectangle in Figure 5a.  
Figure 6: Sound speed with respect to cure time of adhesive samples containing 0, 2, 4, and 6 wt.% of IPA during (**a**) the first 30 curing hours; (**b**) the first 10 curing hours located in the dashed rectangle in Figure 6a.
## 4 Simulation of Use Cases
### Design of the Generic Use Case
#### 4.1.2 Key Performance Indicators  
The results are calculated from the simulation for all models to evaluate the response according to the following key performance indicators:  
* Cumulated demand (# thous. products);
* Cumulated production (# thous. products): the cumulative sum of all car units produced over the 500 simulated production weeks;
* \(\O\) Availability of the production plant (%);
* \(\O\) Performance at the final production step (# thous. products/week);
* \(\O\) Quality at the final production step with one-way and no loops (%);
* \(\O\) Capacity level (# thous. products);
* Implementation time (time period);
* Labor productivity (products/employee \(\times\) week);
* Cumulated stocks (# mill. products);
* \(\O\) Production lead time (# weeks): the number of weeks between the placement of the order and the delivery of the product for its distribution;
* Cumulated service level (%): the quantity of units delivered on time divided by the total number of delivered units;
* Profits (million euros): the result of the multiplication of the number of produced units by the margin that was provided for the type of produced car;
* Cumulated operational costs (million euros): consists of all costs related to the production system operations. It is the sum of procurement, production and distribution costs considering raw materials costs, transportation activities, working capital, labor costs, working shifts, and maintenance costs. The running costs of the project initiative are also included;
* Cumulated investment (million euros): the amount of the investment made to improve the production system. It can be due to the implementation of Industry 4.0 technologies;
* Return on investment (ROI) (million euros): the margin of the products that can be produced thanks to the Industry 4.0 related investment minus the investment value.
## 4 Discussion
### Moisture Content  
Figure 11a depicts the MC of the silt sample, which displays a decreasing trend and a steep slope as the storage period increases. The coefficient of determination (R\({}^{2}\)) is 0.8765. This value suggests that the relationship between the decrease in MC and storage period is well represented by the data.  
Figure 10: Modulus of elasticity. (**a**) Silt; (**b**) organic Soil.  
Figure 9: Unconfined compressive strength. (**a**) Silt; (**b**) organic Soil.  
In addition to Figure 11a, Equation (1) was obtained based on laboratory results according to the established storage period.  
\[y=0.000357x^{2}-0.07531x+29.49 \tag{1}\]  
where \(x\) is time (days).  
Similarly, the organic soil sample showed a decreasing trend in MC as the storage period increased, with a steep slope up to day 28, and a gentler slope as the material dried up to day 56. The coefficient of determination (R\({}^{2}\)) is equal to 0.6879, which means that only a portion of the variability observed in the data can be explained, and it cannot be assumed that all moisture loss is only due to the passage of time but can also be caused by other factors such as the dielectric permittivity existing in the ground [29].  
Equation (2) was also obtained based on the laboratory results and the storage period shown in Figure 11b.  
\[y=0.002882x^{2}-0.4459x+158 \tag{2}\]  
where \(x\) is time (days).
# A Comprehensive Study on EDFA Characteristics: Temperature Impact
###### Abstract  
In this paper, a comprehensive study on erbium-doped fiber amplifier (EDFA) characteristics under temperature variation has been performed. The rate and propagation equations that characterize EDFA performance pumped at 980 nm and 1480 nm in the forward direction are solved numerically. The Boltzmann distribution between the pump and the gain wavelength is taken into account, and is found to be effective when pumping only at 1480 nm. In addition, a full comparison between the effect of temperature on some of the EDFA characteristics such as the maximum peak gain, optimum fiber length, saturation input power, and saturation output power has been carried out. The temperature variation in the range from \(-40\)\({}^{\circ}\)C to \(+80\)\({}^{\circ}\)C is taken into account.  
Keywords:optical amplifiers; fiber optic amplifiers and oscillators; erbium; pumping; emission; temperature +
Footnote †: journal: Applied Physics Letters
# Fast Terahertz Coded-Aperture Imaging Based on Convolutional Neural Network
## 5 Conclusions  
In this paper, a fast TCAI method is presented. We first introduced the TCAI system and learning-based approach. Subsequently, an end-to-end network was developed and tens of thousands of training pairs were generated to learn the mapping relationship between the echo signal and original target. The developed network includes the nonlinear part of the encoded information and the linear part of the decoded information. The experimental results of the simulation data set demonstrated that the proposed method can outperform the state-of-the-art iterative algorithms in both accuracy and efficiency. Also, it can quickly reconstruct targets of different sizes and the pixel interval that can be resolved is no larger than 0.005 m. With the advantages of the neural network based approach, it has a potential application in security screening, battlefield reconnaissance, nondestructive detection and so on. In further work, we intend to improve the network performance and experimentally verify our method with training data sets and echo signals.  
Figure 8: Compare the imaging performance of different networks.  
Figure 7: (**a**) Target. (**b**) The corresponding reconstruction result.  
Author Contributions:Conceptualization, F.G. and C.L.; methodology, F.G., C.L. and H.W.; software, F.G.; validation, F.G.; formal analysis, F.G. and C.L.; investigation, F.G., X.L. and L.P.; resources, C.L.; data curation, F.G.; writing--original draft preparation, F.G.; writing--review and editing, X.L., H.W. and L.P.; visualization, F.G.; supervision, C.L.; project administration, C.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (NSFC) (61701513, 61971427, 61871386).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} TCAI & Terahertz coded-aperture imagin \\ SNR & Signal-to-noise ratio \\ THz & terahertz \\ CNN & Convolution neural4 network \\ DL & Deep learning \\ CS & compressed sensing \\ MSE & Mean Square Error \\ SBL & Sparse Bayesian Learning \\ \end{tabular}
## 4 Discussion  
This section evaluates the performance of the proposed MENROM based on the MDP algorithm. The simulation results are presented, and the key parameters are analyzed. In the network environment, the UAV as a MEC server or relay node is arranged between the terminal equipment and the G-MEC at a distance of \(H\) from the ground. In the terminal equipment, the calculation tasks of different sizes are selected for simulation analysis to verify the offloading mode and performance of the system. The simulation settings are based on the work of [16], [21], and [22]. Table 1 shows the detailed simulation parameters unless otherwise specified.  
With regard to the network environment in the model, heterogeneous networks include Wi-Fi networks and cellular networks (4G/5G), where the service coverage of the cellular networks is larger than that of the Wi-Fi counterparts [28]. That is, \(C_{\chi}=[c_{1}c_{2}]\) and and \(P_{ij}=1\) in the model, where \(c_{1}\) and \(c_{2}\) represent Wi-Fi and cellular networks, respectively. The average residence time of the Wi-Fi and cellular networks can be obtained according to the network performance evaluation [29], and \(1/\eta_{1}\) and \(1/\eta_{2}\) are set to 10/6 and 10/4, respectively. The time slot length \(\tau\) is set to 1. The probabilities of the random occurrence of energy levels 1, 2, 3, 4 (that is \(E_{\text{level}}=1\), \(E_{\text{level}}=2\), \(E_{\text{level}}=\)3, and \(E_{\text{level}}=4\)) are 0.15, 0.35, 0.3, and 0.2, and the corresponding energy level weights \(\omega_{1}=\omega_{2}\) are 0.8, 0.7, 0.6, and 0.5, respectively. The path decay index \(q\) is 4.  
For the comprehensive consideration of multiple situations, the effects of the key parameters were analyzed, including the system utility function, reliability index (\(\mu\)), UAV initial energy level state (\(E_{\text{level}}\)), delay and energy weight factor (\(\varphi_{2}\)), and the UAV relay transmit power (\(P_{uav}\)).