esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Transceivers Experimental Characterization Results  
Following both set-ups outlined in the previous section, we can collect and present the transceivers' experimental characterizations in terms of Bit Error Rate (BER) versus the received optical powers for several OSNR values. Figure 4 depicts the results that refer to the first urban-fiber test-bed, using a transceiver with offline processing, while Figure 5 depicts the results that refer to the two pre-commercial transceivers. For all graphs, we change the OSNR with a step of about 1.5 dB and the received optical power with a step of about 1 dB.  
Referring to Figure 4, the graph shows sensitivity at a BER target of \(10^{-2}\) ranging from \(-24.7\) dBm to \(-22.7\) dBm, depending on the OSNR. In addition to these OSNR curves from 20.1 to 33.3 dB, there is a case represented by a black dashed line called \"\(BitB\)\", which refers to the BER values obtained without the ROADM, meaning only the fiber is present in the \"TIM laboratories\". In this case, the OSNR is very high (more than 35 dB) and it is missing the ROADM polarization-dependent loss (PDL) contribution of about 0.8 dB, thus explaining the sensitivity gap between the \"\(BitB\)\" black curve without the ROADM and the other ones when using it (see Figure 4). Since the black curve represents the best case, it can serve as a reference when the ROADM is inserted for this test-bed.  
The results obtained for the two pre-commercial transceivers, plotted in Figure 5, have the following sensitivity performances (referring to BER = \(10^{-2}\)): TRX #1 shows sensitivity from \(-24.95\) dBm for high OSNR to \(-24.1\) dBm for low OSNR; TRX #2 sensitivity between \(-25.85\) dBm (high OSNR) and \(-25.2\) dBm (low OSNR).  
We observe that the second pre-commercial transceiver (bottom of Figure 5) has a better performance, with approximately 1 dB improvement in sensitivity compared to the first one (top of Figure 5). In conclusion, both pre-commercial transceivers' performances are very close each other.  
Figure 4: BER curves versus received optical power with OSNR varying from 20 to 33 dB for a transceiver with offline processing, in the case of the urban-fiber test-bed shown in Figure 2, with transmitted optical power of 11 dBm.  
Even considering a large OSNR range (from 20.4 to 36.7 dB), the comparison of results between both pre-commercial transceivers and the previous one with offline processing used in the urban-fiber test-bed case shows that they are very similar.
# Dynamic Analysis and Security Characteristics of Carrier-Based Aircraft Arresting in Yaw Condition
## 2 Arresting Dynamics Model of Carrier Aircraft
### Calculation Model of Arresting Device  
To calculate the blocking resistance, it is necessary to obtain the change rule of the damping force output by the energy absorber of the blocking device, which is related to the movement displacement of the oblique flow control valve, the change rule of the sectional area of the oil groove on the valve, and the pressure in the accumulator [17]. The working principle of the energy absorber is shown in Figure 4.  
The main difference between yaw arrest and centering arrest lies in the difference of impact angle between the left and right arresting cables of the arresting hook, which leads to a difference in arresting cable speed brought about by the left and right pulleys, and finally leads to a difference in piston displacement and speed on either side. For a certain arresting system, the output arresting cable tension is related to the damper stroke. When the weight of the arrested aircraft is constant, the same cable elongation corresponds to the same arresting cable tension [18].  
When in centering arrest:  
\[\left\{\begin{array}{l}S_{r}=\frac{\sqrt{S^{2}+\mathrm{D}^{2}}-\mathrm{D}}{N }\\ V_{r}=\frac{S}{N\sqrt{S^{2}+\mathrm{D}^{2}}}V_{f}\end{array}\right. \tag{3}\]  
When in right yaw:  
\[\left\{\begin{array}{l}S_{rL}=\frac{\sqrt{\left(\mathrm{D}+S\sin\varphi \right)^{2}+\left(S\cos\varphi\right)^{2}}-\mathrm{D}}{N}\\ S_{rR}=\frac{\sqrt{\left(\mathrm{D}-S\sin\varphi\right)^{2}+\left(S\cos\varphi \right)^{2}}-\mathrm{D}}{N}\\ V_{rL}=\frac{V_{f}\cos\varphi_{L}}{N}\\ V_{rR}=\frac{V_{f}\cos\varphi_{R}}{N}\end{array}\right. \tag{4}\]  
where \(N\) is the number of movable pulleys in the movable pulley block, \(S_{r}\) is the displacement of the piston, \(v_{r}\) is the speed of the piston, \(V_{f}\) is the velocity of the aircraft, \(S_{rL}\) is the displacement of the left piston, \(S_{rR}\) is the displacement of the right piston, \(V_{rL}\) is the movement speed of the left piston, and \(V_{rR}\) is the movement speed of the right piston.  
Figure 4: Principle diagram of the arresting gear energy absorber.
## 3 Solution Algorithm
### Solution Algorithm for the Stage 2  
At this stage, the minimum value of \(\varepsilon\) is obtained for a given number \(2m+1\) of covering circles. Let \(E^{\varepsilon}\) be the ellipse centered at \((0,0)\) with semi-axes \(a_{\varepsilon}=a+\varepsilon\), \(b_{\varepsilon}=b+\varepsilon\) (see Figure 4).  
Figure 3: Illustration of the solution obtained at Step 6 of the last iteration of the algorithm for Stage 1.  
Figure 2: Illustrations of the first and the second iterations of the algorithm for Stage 1: (a) the first iteration; (b) the second iteration.  
The points \(p_{k}=(p_{kx},\,p_{ky})\in R^{2}\), \(p_{k}\in C_{k}\), \(p_{k}\
otin\text{intE}\), \(p_{kx}\geq 0\), \(p_{ky}\geq 0\), \(k=0,1,2,\ldots,m\), which are referred to as the pilot points, are introduced for the \"gluing\" circles \(C_{k}(x_{k},0)\), \(k=0,1,2,\ldots,m\).  
Let \(p_{nx}=a\), \(p_{ny}=0\), \(t_{0}=\frac{\pi}{2}\), \(x_{0}=x_{0}(0,t_{0})=0\), \(x_{k}(0,t_{k})=\frac{\cos k}{a_{\epsilon}}\cdot({a_{\epsilon}}^{2}-{b_{\epsilon }}^{2})\), and \(k=1,2,\ldots,m\). Here, \(t_{k}\) is the parameter (angle) introduced in Equations (3)-(4) and obtained at Stage 1.  
The following nonlinear programing problem is used to optimize \(\epsilon\):  
\[\epsilon^{*}=\min_{v\in V\subset\mathbb{R}^{4n+1}}\epsilon.\]  
Here, \(v=(\epsilon,t_{k},k=1,\ldots,m,p_{kx},p_{ky},r_{k},k=0,1,2,\ldots,m)\) is the \((4n+1)\)-dimensional vector of unknown variables and the feasible set \(V\) is defined by the following system of inequalities:  
\[\epsilon\geq 0,\;r_{0}\leq b+\epsilon \tag{9}\]  
\[t_{k}\geq 0,\;t_{k}\leq t_{k-1},\;k=1,2,\ldots,m \tag{10}\]  
\[r_{k}\geq 0,\;k=0,1,2,\ldots,m \tag{11}\]  
\[\frac{b_{\epsilon}}{a_{\epsilon}}\cdot\sqrt{{b_{\epsilon}}^{2}\cos^{2}t_{k}+{ a_{\epsilon}}^{2}\sin^{2}t_{k}}-r_{k}\geq 0,\;k=1,2,\ldots,m \tag{12}\]  
\[p_{kx}\geq 0,\;p_{ky}\geq 0,\;k=0,1,2,\ldots,m \tag{13}\]  
\[r_{k}^{2}-(x_{k}-p_{kx})^{2}-p_{ky}^{2}\geq 0,\;k=0,1,2,\ldots,m \tag{14}\]  
\[r_{k+1}^{2}-(x_{k+1}+p_{kx})^{2}-p_{ky}^{2}\geq 0,\;k=0,1,\ldots,m-1 \tag{15}\]  
\[\frac{p_{kx}^{2}}{a^{2}}+\frac{p_{kx}^{2}}{b^{2}}-1\geq 0,k=0,1,2,\ldots,m. \tag{16}\]  
Here, inequality (9) assures \(C_{0}(0,0)\subset E^{\epsilon}\); inequality (10) reflects the monotonous decrease of the corresponding angle \(t_{k}\); inequality (11) describes the natural constraint for the radius of the circle \(C_{k}\); constraints (12) guarantee \(C_{k}\subset E^{\epsilon}\); inequalities (13) present the nonnegativity of all \(p_{k}\); constraints (14) and (15) assure \(p_{k}\in C_{k}\cap C_{k+1}\), while (16) guarantees \(p_{k}\
otin\text{int}E\).
# Outlier Reconstruction of NDVI for Vegetation-Cover Dynamic Analyses
## 3 Results
### Evaluation the Performance of SWTSA  
Figure 6 shows a quantitative evaluation of execution time and RMSE metrics of the proposed algorithm and the general Tucker decomposition method. First, our results showed that the proposed algorithm required less execution time and had a lower RMSE than that of the general Tucker decomposition method. Second, execution time increased with the increase in rank size. Meanwhile, the increase in execution time with Algorithm 1 was lower than that with the general Tucker decomposition method. Third, our proposed algorithm converged faster than the general Tucker decomposition method did in terms of RMSE. Additionally, the size of the core tensor influenced reconstructive results significantly. When the ranks of the core tensor increased to \(3\times 5\times 5\), RMSE exhibited a trend toward convergence. Generally, RMSE values obtained with Algorithm 1 in all experiments were less than 0.5, with the best RMSE was less than 0.1.  
The receiver operating characteristic (ROC) curves of MLR, RFR, and different rank sizes of core tensor are shown in Figure 7. The area under the curve (AUC) was also calculated to estimate the performance of SWTSA; for all reconstruction results, the values of AUC exceeded 0.9, which showed SWTSA to be able to achieve robust performance for reconstruction.  
Figure 5: Scatter plots between actual and reconstructed values for different rank sizes (r: Pearson correlation coefficient, \(R^{2}\): coefficient of determination).  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Datasets** & **Samples** & **Mean** & **Variance** & **Minimum** & **Maximum** \\ \hline
2019 actual value & 4,030,560 & 0.1203 & 0.0476 & \(-\)0.2433 & 0.6322 \\
2019 SWTSA & 4,030,560 & 0.1349 & 0.0326 & \(-\)0.2203 & 0.5651 \\
2019 MLR & 4,030,560 & 0.1546 & 0.0300 & \(-\)0.2083 & 0.6020 \\
2019 RFR & 4,030,560 & 0.2925 & 0.0290 & \(-\)0.2489 & 0.5651 \\
2020 actual value & 4,030,560 & 0.1052 & 0.0395 & \(-\)0.1974 & 0.5109 \\
2020 SWTSA value & 4,030,560 & 0.1203 & 0.0462 & \(-\)0.2307 & 0.5387 \\
2020 MLR & 4,030,560 & 0.1228 & 0.0480 & \(-\)0.2401 & 0.6353 \\
2020 RFR & 4,030,560 & 0.2179 & 0.0281 & \(-\)0.2830 & 0.5690 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistical characteristics of actual and reconstructed values.
# Vision, Enabling Technologies, and Scenarios for a 6G-Enabled Internet of Verticals (6G-IoV)
## 3 Future Scenarios for the Internet of Verticals  
### The Internet of Cloud Manufacturing  
Cloud manufacturing (CMfg) is an emerging manufacturing paradigm developed from existing advanced manufacturing models and enterprise information technologies with the support of cloud computing, service-oriented technologies, and advanced computing technologies. It transforms manufacturing resources and manufacturing capabilities into manufacturing services, which can be managed and operated in an intelligent and unified way to enable the full sharing and re-use of manufacturing resources and manufacturing capabilities. CMfg can provide safe and reliable, high quality, cheap and on-demand manufacturing as services. The growing use of CMfg can be realized from the fact that cloud as a digital service accounts for around 25% of the total involvements that go into complete manufactured products [20]. In the CMfg system [21], various manufacturing resources and abilities can be intelligently sensed and connected through wide area networks, and automatically managed and controlled using IoT and AI technologies. Subsequently, as is shown in Figure 2, the manufacturing resources and abilities are virtualized and encapsulated into different manufacturing cloud services that can be accessed, invoked, and deployed based on knowledge by using virtualization technologies, service-oriented technologies, and cloud computing technologies. End- users can search and invoke the qualified Manufacturing Cloud Services (MCSs) from the related manufacturing cloud according to their needs and assemble them to be a virtual manufacturing environment or solution to complete their manufacturing task involved in the whole life cycle of manufacturing processes. This means replacing high capital expenditures with pay-as-you-go manufacturing services and through-life support, which radically transforms the economics of the new product information, volume manufacturing, and lifecycle management.  
We envisage that 5G and beyond-5G wide area networking will enable the distributed network architecture for CMfg and new Over The Top (OTT) players in the manufacturing sector (Mf-OTT). This model builds in analogy with the how the internet together with cloud computing has enabled the emergence of OTTs such as Uber for the transport sector. As shown in Figure 2, it introduces three layers: manufacturing service providers, Mf-OTTs and end-users. A layer of manufacturing service providers consists of manufacturing, computational, and AI clouds. Manufacturing clouds are formed by 5G/beyond-5G interconnected manufacturing service providers. The Mf-OTTs use intelligent search mechanism analogous to Internet search engines endowed with new AI capabilities for dynamic manufacturing service composition based on end-user requirements, and the search mechanism propagates manufacturing service inquiries through the decentralized network. Software and AI tools supporting the search process are accessible to Mf-OTTs providers in computation and AI clouds.
## 4 Other genetically Encoded pH Sensitive Sensors  
By joining two or more FPs with different pH-dependency of fluorescence spectra, a new chimeric FP with novel excitation/emission spectra and altered pH-dependency is synthesized (Table 3). This approach is mainly used to convert intensity-based FPs to ratiometric pH sensors. Using a synthetic biology approach, we can generate numerous combinations of FPs and tailor the FPs to our requirements.  
Three novel ratiometric pH sensitive sensors GFPpH, YFPH [43], and pHERP [44] were obtained by fusing GFPuv, a low-pH sensitivity mutant excitable at 380 nm, with an intensity-based pH sensitive GFP, EGFP, or EYFP. The YFPpH and pHERP sensor are both fusions of GFPuv and EYFP, with changed order of named FPs. When excited at 380 nm and 480 nm, the GFPpH emits at 510 nm. The pK\({}_{\mathrm{a}}\) of GFPpH is 5.6 (380 nm excitation) and 6.2 (480 nm excitation) and the apparent pK\({}_{\mathrm{a}}\) is 6.2. The fluorescence maximum of YFPpH and pHERP is 527 nm when excited at 480 nm and the apparent pK\({}_{\mathrm{a}}\) is 6.5. Upon excitation at 380 nm, the emission of YFpH and pHERP shifts from 527 nm to 506 nm with the acidification.  
[PERSON] _et al._[45] developed the ratiometric sensor ClopHensor that is suited for real-time optical detection of chloride ions and pH in live cells. The ClopHensor exploits the spectral and chemical characteristics of the E\({}^{2}\)GFP anion-binding site [36] that is linked to the DsRed, a fluorescent protein, which is insensitive for protons and chloride ions. With tethering E\({}^{2}\)GFP to DsRed, the pH-sensitive spectral characteristics of E\({}^{2}\)GFP remain unchanged. Mutations H148 and V224L of ClopHensor shift pKa to more alkaline values and improve affinity for chloride ions [46].  
pHusion [47] is composed of a tandem concatenation of EGFP and mRFP1, and also function as a ratiometric pH sensor. To obtain a 1:1 stoichiometry, the two fluorescent proteins are fused by a short peptide linker, permitting for ratiometric measurements of pH changes, where mRFP1 functions as an intramolecular reference.  
The concatenation of pH-sensitive with pH-insensitive FPs can be used for the combination of virtually any FPs, assuming that the excitation and emission spectra of concatenated FPs do not overlap. Such fusions are sensitive for photo-bleaching; moreover, more sophisticated equipment is required due to multi-excitation/emission spectral properties.
# Accounting for Training Data Error in Machine Learning Applied to Earth Observations
## 3 Case Studies
### Agricultural Monitoring
Each model was trained on three separate randomly selected subsets of  
Figure 11: A comparison (**A**) of the accuracy (based on the true skill statistic) of cropland maps over two areas of Ghana when generated by labels of different levels of quality (red = least accurate workers’ labels; orange = most accurate workers’ labels; blue = “consensus” labels made by merging all workers’ labels). (**B**) Results from a random forest model of wheat yields trained on satellite-derived vegetation indices, showing the relationship between predicted yield and independent observed yields, in terms of the fit against the line and the regression slope of the relationship (points and regression line represent the mean of a single randomly selected model permutation). The average mean absolute error (MAE) and average regression R\({}^{2}\)s calculated across all permutations are shown for each model. 32 perturbed observations, and the predictions were made for the remaining 16 held-out (independent) yield observations, which were not perturbed. This three-fold cross validation process was repeated 50 times, with each permutation using a different random seed to construct the folds, in order to achieve stable error metrics. The model performance was assessed by calculating the averages of the mean absolute error (MAE) of the prediction, and the R\({}^{2}\) of regressions fit between prediction and observed values (Figure 11B). The results show that four models, including the baseline, compressed the range of yields, as seen in the shallow slope between observed versus predicted values, but prediction error was 18-31% higher when training yields had either the high level of random or systematic error within them. The smaller amount of random noise only added \(\sim\)6% error to the predictions, suggesting that RandomForest is tolerant to some training error. Note that the average R\({}^{2}\) of the observed-predicted regression fit was nearly the same for the systematic error case as the baseline, which shows that this metric can be an unreliable measure of performance for quantitative measures, and that it is important to assess fit against the y = x line and to use a metric such as mean absolute error.
## 6 Experimental Setup
### Measurement Environments  
The indoor environment consisted of several office rooms, meeting rooms, and hallways. It generally contained several materials, such as metal structures, wooden doors, wooden walls, and glass. The offices were filled with desks, cupboards, office chairs, and plants. The hallways consisted of glass and concrete walls, whereas the floor of the entire office was carpeted. The staircase contained stone and reinforced concrete and was accessible via wooden doors. Figure 18 depicts the indoor measurement environment. The 10 TX locations are shown as green indicators on the map while the receiving setup, i.e., the SDR and DASH7 gateways, are positioned at the red RX indicator. The receiving setup was placed entirely indoors.  
Figure 19 shows the map of the suburban measurement environment. The DASH7 gateways and SDR are positioned at the red pointer, while the TX locations are shown in green. Many of these locations had LOS, while others did not. In the suburban environment, a lot of vegetation was found, such as trees and plants. Nine points had LOS, while twelve locations had non-line-of-sight (NLOS) to the receiving point. This NLOS was created by metal fences, vegetation such as trees and bushes, small hills, and buildings. Furthermore, a private marina and shipyard also obstructed the LOS. Finally, the suburban region and receiving location were separated by a busy waterway. In Figure 19, location 21 is shown in blue. This location was the furthest measurement point while having complete NLOS. Subsequently, no packets were received, as can be seen in Table 6.  
Figure 16: Indoor and suburban measurement setup consisting of three DASH7 nodes and three DASH7 gateways which send and receive on different Lo-Rate channels. The setup was extended with an Ettus Research (Austin, TX, USA) USRP B210 SDR, which received the transmissions of the DASH7 nodes and stored these transmissions as raw I/Q data. These captured data were investigated during post-processing analysis.  
Figure 17: A cabled measurement setup consists of a DASH7 gateway, a DASH7 node having a 40 dB attenuator and a Software-Defined Radio, which are connected through coaxial cables.  
Figure 18: Map of the indoor measurement environment. The 10 TX locations are shown as green pins while the receiving setup, i.e., the SDR and DASH7 gateways, are positioned at the red RX pin. The office spans an area of 64 by 24 m.  
Figure 19: Map of the suburban measurement environment. The 21 TX locations are shown as green pins while the receiving setup, i.e., the SDR and DASH7 gateways, are positioned at the red RX pin. The blue marker indicates measurement point 21.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & **CH 0** & & & **CH 93** & & & **CH 186** & & & \\ \hline  & & & &
## 3 Methodology  
In this section, the implementation and methodology of the proposed method are described. The proposed protocol introduces advanced cooperation and relay optimization. The CR-NBEER protocol represents an improved version of the NBEER protocol. In NBEER, there are no cooperation and relay optimization approaches. Only the Nearest Neighbor Scheme (NNS) and Neighbor Head Node (NNH) are applied in its core methodology. The network is divided into three steps, each representing the NNS process. Initially, cooperation and relay optimization occur, followed by the routing, energy consumption, and optimization phases. The proposed scheme is graphically illustrated in Figure 2, which shows the implementation of the relay and cooperation optimization schemes. As shown in the figure, multiple relay nodes are installed underwater. It is clear that these relay nodes improve the network performance and decrease the distance among the nodes, resulting in less delay and less path loss. This scheme is able to preserve path loss in nature and improve the overall performance of marine communication systems. Acoustic, optical, and Radio Frequency (RF) links are significant aspects of communication.
###### Abstract  
The thermal design parameters of space telescopes are mainly optimized through traversal and iterative attempts. These optimization techniques are time consuming, rely heavily on the experience of the engineer, bear a large computational workload, and have difficulty in achieving optimal outcomes. In this paper, we propose a design method (called SMPO) based on an improved back-propagation neural network (called GALBP) that builds a surrogate model and uses a genetic algorithm to optimize the model parameters. The surrogate model of a space telescope that measures the atmospheric density is established using GALABP and then compared with surrogate models established using a traditional BP neural network and radial-basis-function neural network. The results show that the regression rate of the surrogate model based on the GALBP reaches 99.99%, a mean square error of less than \(2\times 10^{-6}\), and a maximum absolute error of less than \(4\times 10^{-3}\). The thermal design parameters of the surrogate model are optimized using a genetic algorithm, and the optimization results are verified in a finite element simulation. Compared with the design results of the manually determined thermal design parameters, the maximum temperature of the CMOS is reduced by 5.33 \({}^{\circ}\)C, the minimum temperature is increased by 0.39 \({}^{\circ}\)C, and the temperature fluctuation is reduced by a factor of 4. Additionally, SMPO displays versatility and can be used in various complex engineering applications to provide guidance for the better selection of appropriate parameters and optimization.  
spacecraft thermal design; BP neural network; optimization; surrogate-model +
Footnote †: journal: Applied Sciences  
**Critical:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A Surrogate-Model-Based Approach for the Optimization of the Thermal Design Parameters of Space Telescopes. _Appl. Sci._**2022**, 12, 1633. [[https://doi.org/10.3390/appl2031633](https://doi.org/10.3390/appl2031633)]([https://doi.org/10.3390/appl2031633](https://doi.org/10.3390/appl2031633))