esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### SICS Controller in Server-Based Mixed Mode (SMM)  
In the case of a SMM solution, the SICS runtime is executed in the server (cloud) as an instance and the SICS router in the client (browser) as an instance.  
Here, as well, a direct process data communication takes place only between the client and devices. Between SICS router and SICS runtime, there is a special bidirectional block channel for the transmission of I/O images. This channel transmits the process data as strings over a secure WebSocket. The SICS runtime is operated via an HMI proxy on the client. Figure 9 illustrates the SMM solution.  
Figure 8: SICS router.  
In terms of technical implementation, the SICS-SMM controller is a distributed elaborate solution. However, process data connection to the devices can also be performed locally and the SICS runtime can use the full performance of the server anyway. A dynamic re-configuration is easy and possible.  
Figure 10 shows the simplified implementation structure of a SICS-SMM controller at runtime.  
After opening the corresponding web page in the client, a SICS-RTs instance is generated dynamically as a runtime engine in the server, and simultaneously, the corresponding control program is compiled into an executable JavaScript (JS) program by the structured text (ST) compiler in the server. This JS program is then loaded into the CICS-RTs instance.  
The following instances are created dynamically in the client:  
* VD instance: This instance operates as a virtual device according to [12] and connects to the CPS components (devices).
* SICS-R instance: Routes the absolute I/O addresses to the physical device addresses.
* SICS-RTc instance: Serves as an HMI proxy for the visualization/operation of the CICS-RT instance in the server.  
Generally, any number of SICS-RTs instances can run in the server, each of which can work with other clients. The ST compiler works together with all SICS-RTs instances and can also be moved to another server (cloud).  
Some of the advantages of the SMM solution are:  
* The configuration of a SICS SMM controller can be carried out completely in an IoT or IIoT platform because all required instances (in the server and in the client) can be created and deleted via the client.
* Service-like C3x controllers can be built, because all SICS instances are available and manageable as separate service objects. The cloud model SaaS (software as a service) can be implemented.  
Figure 10: Implementation structure of a SICS SMM controller at runtime (s—server-based part; c—client-based part).  
Figure 9: SICS controller in the server-based mixed mode (SMM).  
* Different SICS routers, which can also be generated dynamically, enable a dynamic reconfiguration of the control system online without time delay. This forms the basis for adaptive and self-adaptive systems.
* SICS runtime can use the full performance and stability of the server. A detailed description of the implementation for the SICS-SMM is documented in [13].
# A Joint Detection and Decoding Scheme for PC-SCMA System Based on Pruning Iteration
## 5 Numerical Results
### BER Performance Analysis of PIC-JDD  
Simulation results of the joint optimization algorithm PIC-JDD which combines PI-JDD with C-JIDD are shown in Figures 13 and 14. BER performance comparison with code length \(N=256\) under five and seven iterations is shown in Figure 13. It can be seen that the BER performance of proposed PIC-JIDD algorithm is superior to that of the original algorithm under the two iterations. When Eb/N0 = 4.5 and seven iterations, the BER of S-JIDD algorithm is \(1.1\times 10^{-3}\), while that of this paper is 7.74 \(\times 10^{-5}\), the error performance has a significant improvement. Specifically, when \(N=256\), the BER performance in five iterations of the PIC-JDD is better than that of the S-JIDD in seven iterations.  
Figure 14 shows the BER performance curves of the several algorithms with \(N=1024\). It can also be seen that the BER performance of the PIC-JDD proposed in this paper is better than that of the traditional S-JIDD algorithm under the two iteration times. When Eb/N0 = 2.6 and five iterations, the BER of the S-JIDD algorithm is \(6.9\times 10^{-2}\), while that of the PIC-JDD is \(1.9\times 10^{-2}\). It is also worth noting that the BER performance of the five iterations in this paper is better than that of the S-JIDD in seven iterations. Specially, the BER performance of the PIC-JDD in five iterations is much better than that of the joint algorithm combining MPA and the BP with 500 iterations under the two code lengths [16].  
Figure 12: BER performance comparison of C-JIDD with \(N=1024\).  
Figure 13: BER performance comparison of PIC-JDD with \(N=256\).
# Quantum Leap from Gold and Silver to Aluminum Nanoplasmonics for Enhanced Biomedical Applications
## 4 Enhancing Al Plasmonic Effects
### Chemical Enhancement  
The chemical mechanism (CM) of surface enhancement is commonly used to explain additional contributions to the enhancement factor beyond the electromagnetic near-field enhancement (EM). It is attributed to the chemical interactions between the adsorbed biomolecules and the plasmonic substrate leading to the changes in electronic structure and charge transfer processes [226; 227; 228; 229; 230]. The CM enhancement depends on the nature of the molecule and NP and can play a bigger role in the Al plasmonic systems due to the smaller EM contribution compared to that of Au and Ag. This opens the possibilities to explore the interplay of both CM and EM effects in Al plasmonic substrates.  
For example, the optical response of pyridine adsorbed on Ag and Al NPs was studied using time-dependent density functional theory (TDDFT), revealing significant differences between molecule-substrate interactions [231]. Figure 8 shows the simulated optical absorption spectra of a pyridine molecule adsorbed perpendicular (A,B) and parallel (C,D) to the NP surface of Ag (A,C) and Al (B,D) NPs. These results showed that the spectra are not significantly influenced by the pyridine orientation in the case of Ag NPs, but showed drastic changes for the Al NPs. The corresponding transition density plots showed larger contributions of the plasmonic excitation in Ag NPs via the dipolar shape of the transition densities (Figure 8A,C). They also showed larger contributions of the charge transfer excitation in Al NPs via the mixed densities on NPs and large densities on pyridine  
Figure 7: Extinction spectra of Ag\({}_{\text{1-x}}\)Al\({}_{\text{x}}\) and Al\({}_{\text{1-x}}\)Ag\({}_{\text{x}}\) alloys for varying molar concentrations of Al: (**a**) With increasing concentration of Al, the SPR peak blueshifts from the elemental Ag NP. (**b**) SPR peak redshifts from the elemental Al NP with increasing concentration of Ag. (Adapted from [PERSON] [224]). (**c,d**) Fluorescence enhancement of NATA-tyr and NATA on quartz and bimetallic substrates. (Adapted from [PERSON] et al. [225]).  
(Figure 8B,D). The parallel orientation showed a larger charge transfer contribution compared to the perpendicular orientation. These results indicate potential promising applications of the Al plasmonics to investigate single-molecule interactions relevant to biosensing, light harvesting, and photocatalysis.
## 3 Signal Processing Method Based on VMD and ES  
Variational modal decomposition (VMD) is an adaptive decomposition method based on Hilbert-Huang transformation, which decomposes the raw signal into a number of intrinsic mode functions (IMFs) by searching the optimal solution of a constrained variational model. The envelope spectrum (ES) is sensitive to the impact, and it can eliminate the interference components and highlight the fault characteristic frequency. Therefore, the VMD-ES method has good adaptability in dealing with the periodic impact generated by the wheel flat.  
Figure 5 shows the flow chart of the VMD-ES method. Firstly, the raw signal is decomposed by VMD into a number of IMFs. VMD needs to set number of modal components \(K\) and the penalty factor \(\alpha\) in advance. In this paper, the decomposition level obtained by EMD is taken as the value of \(K\), and \(\alpha\) takes the default value of 2000.  
Figure 4: Illustration of the wheel flat: (**a**) wheel flat model; (**b**) the relationship between the depth and length of the wheel flat.  
Next, use kurtosis principle [33] to sift the decomposed IMFs and reconstruct the signal. The kurtosis principle is as follows:  
\[k=\frac{E(x-\mu)^{4}}{\sigma^{4}} \tag{7}\]  
Figure 5: The flow chart of VMD-Envelope spectrum method.  
where \(\mu\) is the mean of \(x\), \(\sigma\) is the standard deviation of \(x\), and \(E(t)\) represents the expected value of the quantity \(t\).  
Third, the reconstructed signal is analyzed by ES as follows:  
1. Hilbert-Huang transformation is performed on the signal: \[H[x(t)]=\frac{1}{\pi}\int_{-\infty}^{+\infty}\frac{x(\tau)}{t-\tau}d\tau\] (8)
2. Obtain envelope spectrum of the signal: \[B(t)=\sqrt{x(t)+H^{2}[x(t)]}\] (9)
## 4 Reusability and Regeneration Studies of AC-MOP  
The regeneration or reusability of adsorbents is crucial in determining the sorption method's efficacy [43]. AC-MOP regeneration was investigated using 0.4 g of the used (IBF and MB), loaded AC-MOP. The results in Figure 9 show the regeneration process, which was repeated four times. A recovery of 70.13% for MB was obtained at the beginning of the  
Figure 8: Time effect for AC-MOP at 25\({}^{\circ}\)C under the initial concentration of 100 mg/L, at 0.4 g dosage and agitation at 200 rpm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **MB** & **IBF** \\ \hline \hline \(\text{PFO}\text{q}_{\text{e}}\) (mg/g) & 52.7645 & 68.8334 \\ \hline \(\text{k}_{1}\) (min\({}^{-1}\)) & 2.2898 & 2.5522 \\ \hline \(\text{R}^{2}\) & 0.8995 & 0.8507 \\ \hline \hline \(\text{PSO}\text{q}_{\text{e}}\) (mg/g) & 62.7934 & 75.3956 \\ \hline \(\text{k}_{2}\) (g.mg.min\({}^{-1}\)) & 1.0079 & 1.0044 \\ \hline \(\text{R}^{2}\) & 0.9977 & 0.9922 \\ \hline \hline \(\text{Exp}\text{q}_{\text{e}}\) (mg/g) & 65.8978 & 72.4432 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Kinetics for uptake of MB and IBF.  
regeneration, followed by 64.12% in the first cycle, 50.37% in the second cycle, 41.12% in the third cycle and 38.35% in the last. The removal efficiency of AC-MOP for MB decreased as the number of recycles increased. The reduction in the removal efficiency may be related to the loss of the binding or active sites during the reuse [44]. Although the adsorption effectiveness decreased with increasing recycling numbers, AC-MOP was able to remove methylene blue. Optimizing the pH solution is important in increasing the adsorption capacity, is useful for the desorption process and should be considered to improve the regeneration cycle [45]. A similar procedure was used to determine the reusability for IBF, and the recovery was 87.54, 85.17, 74.87 and 65.41%, respectively, with a maximum recovery of 50.58%. These results showed that the adsorptive power of AC-MOP decreased after each cycle. This is due to the inability of the adsorbent to desorb MB and IBF from the openings and functional groups during regeneration.
# Automatic Recognition of Mexican Sign Language Using a Depth Camera and Recurrent Neural Networks
## 4 Results  
After training the models listed in Table 3, we obtained the results shown in Table 4. From these results, we found that all architectures overfit with larger model sizes. Additionally, the RNN tends to overfit with less number of units than LSTM and GRU, and GRU delivered the best accuracy at 97.11% for the model comprising 512 units on the first layer and 256 units on the second layer.  
Figure 6 shows the precision and recall curves for the best model variation of each architecture shown in Table 4. GRU performed slightly better than LSTM with less model complexity as shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network** & \begin{tabular}{c} **Layer 1** \\ **Units** \\ \end{tabular} & \
\begin{tabular}{c} **Layer 2** \\ **Units** \\ \end{tabular} &
\begin{tabular}{c} **Accuracy** \\ **(Percentage)** \\ \end{tabular} \\ \hline \multirow{6}{*}{RNN} & 32 & 16 & 93.11 \\  & 64 & 32 & **94.22** \\  & 128 & 64 & 94.0 \\  & 256 & 128 & 92.44 \\  & 512 & 256 & 61.55 \\  & 1024 & 512 & 57.55 \\ \hline \multirow{6}{*}{LSTM} & 32 & 16 & 92.44 \\  & 64 & 32 & 96.44 \\ \cline{1-1}  & 128 & 64 & 96.22 \\ \cline{1-1}  & 256 & 128 & 96.44 \\ \cline{1-1}  & 512 & 256 & **96.66** \\ \cline{1-1}  & 1024 & 512 & 95.77 \\ \hline \multirow{6}{*}{GRU} & 32 & 16 & 96.22 \\ \cline{1-1}  & 64 & 32 & 96.44 \\ \cline{1-1}  & 128 & 64 & 96.44 \\ \cline{1-1}  & 256 & 128 & 96.66 \\ \cline{1-1}  & 512 & 256 & **97.11** \\ \cline{1-1}  & 1024 & 512 & 95.77 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Testing accuracy for the different model architectures. The bold numbers represent the variation (row) with the best accuracy. Model variations with large number of units overfit the training data performing poorly on the test data.  
Figure 6: Precision recall curves for the best model of each architecture.
# A Novel Multipath Transmission Scheme for Information-Centric Networking
## 4 Path Management
### Path Selection
Our goal is to pick \(k\) optimal relay nodes from \(P\) according to the _PST_ and add them to \(S\) to form initial multiple paths. If all the relay nodes' _TSRs_ in the _PST_ are the same, the edge routers will randomly select \(k\). In this paper, we set the value of k to 3. The reason is that it has been shown in [44] that most of the benefits can be obtained by using three to four relay paths. By selecting more than one relay routers based on the _PST_, the edge router can ensure that a single unlucky selection is not fatal. The path selection strategy based on the _PST_ cannot ensure that all selected paths are in a good state, but it can improve the availability of the initial path to a certain extent. The path selection strategy needs to cooperate with the path switching strategy, which will be introduced in the next subsection. The detailed path selection algorithm is shown in Algorithm 1. ```
1:Input: _MPSID_, _PST_, \(k\)
2:Output: \(S\)
3:Initialization: _S = NULL_, _P = NULL_
4:At the beginning of transmission:
5:\(P\) = querybyNRS(MPSID)
6: update the NA entries of _PST_ according to \(P\)
7:\(S\) = SelectTop_K(P, PST, _k_)
8:During transmission:
9: execute the path switching algorithm // describe in Algorithm 2
10:At the end of transmission:
11:for every \(NA_{i}\) in _S_do
12:\(TSR_{i}\) = \(TSR_{i}\) + 1
13: release \(P\), \(S\)
14:endfor
```  
**Algorithm 1** Path Selection Algorithm
## 3 Results  
### Performances Assessment of Models  
Overall, the models that incorporated developmental factors outperformed the original phenology model (Figure 3a,b). The CDD\({}_{\text{SG}}\) model achieved the highest average \(r\) values (0.10), significantly surpassing the other models, followed by the CDDs (0.09), CDD\({}_{\text{G}}\) (0.03), and CDD models (0.01). In terms of the prediction accuracy, the EOS simulated by the CDD\({}_{\text{G}}\) model exhibited a significantly lower RMSE (14.27 days) compared to other models, followed by CDD\({}_{\text{SG}}\) (14.95 days), CDD\({}_{\text{S}}\) (15.32 days), and CDD (18.28 days). Based on these results, we introduced the EOS predicted by the CDD\({}_{\text{SG}}\) model into the CASA model.  
The NPP produced by the phenology-modified CASA model was more consistent with the MODIS NPP (Figure 3c,d). Specifically, the NPP simulated by the phenology-modified CASA model displayed a relatively higher R\({}^{2}\) (0.58 vs. 0.53) and lower RMSE (3615.5 vs. 3826.7 g C/m\({}^{2}\)/yr) compared to the counterparts of the original CASA model. Similarly, the following comparison of NEP based on the original CASA model and the phenology-modified CASA model against flux tower NEP observations revealed that the RMSE of the phenology-modified model decreased by 15.34% compared to the original model, while the R\({}^{2}\) increased from 0.27 to 0.47, representing an improvement of approximately 74% (Figure 3e,f).  
Figure 3: Model performance comparison. (**a,b**) Pearson’s correlation coefficient and RMSE between predicted EOS from different phenology models and observed EOS, respectively. Different letters indicate significant differences among models (\(p<0.05\)). (**c,d**) Comparison between the NPP modeled by the original CASA and the phenology-modified CASA with MODIS NPP, respectively. (**e,f**) Accuracy of NEP produced by the original CASA and the phenology-modified CASA compared to the NEP flux observations, respectively.
# Incorporating Epistemic Uncertainties in Ship Operability Study
###### Abstract  
Ship operability diagrams are commonly defined based on the seakeeping analysis, showing which course and speed can safely be taken at the sea state to satisfy predefined seakeeping limiting values. Although ship operability diagrams are inherently probabilistic, because of the random nature of the environmental loads, their outcome is deterministic, showing if the seakeeping criteria are satisfied or not for a certain combination of environmental and operational parameters. In the present study, uncertainties in seakeeping predictions and limiting values, which are usually neglected, are integrated into the ship operability analysis. This results in probabilistic operability diagrams, where the seakeeping criteria are exceeded with certain probabilities. The approach is demonstrated in the example of the passenger ship on a route in the Adriatic Sea. Semi-analytical closed-form expressions are used for seakeeping analysis, while limiting values for vertical bow acceleration, pitch, jamming, roll, and propeller emergence are analyzed. The second-order reliability method is used to calculate probabilities of the exceedance of the seakeeping criteria, and the results are presented as probabilistic operability diagrams. The method enables the determination of a new probabilistic operability index applicable to the ship design and represents a prerequisite for risk-based decision making in ship operation. It is also presented how the method can be validated for the existing shipping route using numerical wave databases.  
seakeeping analysis; operability criteria; uncertainties +
Footnote †: journal: Applied sciences
## 3 Proposed Vehicle Detection and Tracking Scheme
### Enhancing the Detection and Tracking of Small Objects Using YOLOv5 and Deep SORT  
In the process of enhancing the detection and tracking of small objects, we employed a comprehensive approach by leveraging the capabilities of YOLOv5 and Deep SORT. The YOLOv5 architecture inherently incorporates advanced features such as feature pyramid networks (FPN) for multi-scale feature extraction, intersection over union (IoU) filtering, and confidence thresholding to refine detections. Furthermore, data augmentation techniques have been applied during training to augment the dataset, enabling the model to more effectively handle variations in object sizes. Attention mechanisms are integrated to focus on critical details in the input image, while Kalman filtering and smoothing techniques contribute to improved object tracking. Additionally, the use of higher resolution input images and post-processing techniques, including non-maximum suppression (NMS), further enhances the algorithm's capacity to detect and track small objects with accuracy and robustness.
# A Fire Detection Algorithm Based on Tchebichef Moment Invariants and PSO-SVM
## 6 Conclusions  
In this paper, a novel algorithm for fire image detection based on TMIs and the PSO-SVM model is proposed. The TRS invariants of Tchebichef moments are obtained by the correlation between geometric moments and Tchebichef moments. Then, the TMIs of candidate images are applied to form  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(\widetilde{\mathbf{T}_{\mathbf{21}}}\) & \(\widetilde{\mathbf{T}_{\mathbf{22}}}\) & \(\widetilde{\mathbf{T}_{\mathbf{30}}}\) & \(\widetilde{\mathbf{T}_{\mathbf{40}}}\) & \(\widetilde{\mathbf{T}_{\mathbf{41}}}\) & \(\widetilde{\mathbf{T}_{\mathbf{50}}}\) \\ \hline (a) & \(-5.9312\) & \(3.4247\) & \(-4.1861\) & \(9.1684\) & \(-9.2667\) & \(-4.4579\) \\ (b) & \(-5.9645\) & \(3.4536\) & \(-4.1563\) & \(9.0566\) & \(-9.1736\) & \(-4.3712\) \\ (c) & \(-5.8993\) & \(3.4438\) & \(-4.2905\) & \(9.0983\) & \(-9.0935\) & \(-4.5017\) \\ (d) & \(-5.9399\) & \(3.3933\) & \(-4.1257\) & \(8.9619\) & \(-9.0458\) & \(-4.4266\) \\ (e) & \(-6.0124\) & \(3.4201\) & \(-4.1036\) & \(9.1818\) & \(-9.1457\) & \(-4.4338\) \\ (f) & \(-5.9461\) & \(3.4323\) & \(-4.1398\) & \(9.2082\) & \(-9.2214\) & \(-4.3910\) \\ (g) & \(-6.0049\) & \(3.4671\) & \(-4.2001\) & \(9.1286\) & \(-9.3101\) & \(-4.4073\) \\ Standard Deviation & \(0.0405\) & \(0.0242\) & \(0.0621\) & \(0.0850\) & \(0.0939\) & \(0.0435\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Tchebichef moment invariants of the images in Figure 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Hu** & **Zernike** & **Tchebichef** \\ \hline Detection amount & 29 & 31 & 35 \\ Detection rate & 80.56\% & 86.11\% & 97.22\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Detection rate of the testing sample set in the third experiment.  
the feature vectors, which effectively avoid the discretization error of continuous orthogonal moment and redundant information. To improve the performance and detection rate, a PSO-SVM model is also proposed, in which the PSO is applied to optimize the SVM parameters. The experimental results show that the detection rate of TMIs is higher than those of HMIs and ZMIs, up to 98.18%. Even if the size of the training sample set is small and the images are transformed by TRS, the algorithm based on TMIs maintains the best detection performance. Therefore, the proposed algorithm is expected to have wide application in fire image detection.  
[PERSON] and [PERSON] proposed the algorithm in the paper; [PERSON] conceived and performed the experiments, analyzed the data, and wrote the paper; [PERSON] contributed program code; [PERSON] retrieved related literature.  
The authors would like to acknowledge the support of the National Key R&D Program of China (Grant No. 2016 YFC0802900).  
The authors declare no conflict of interest.