esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Analysis
### Network-Side Comparison of AUDP-Based and UDP/TCP-Based Transmission  
After integrating reliability mechanisms into the application layer of Modbus UDP (Modbus AUDP) with the retransmission count \(N_{max}\) set to 2, a \(P_{loss}\) rate of 30%, and a out-of-order rate of 10%, the Request-Response Latency slightly increased to approximately 17 ms as shown in Figure 22a. Despite this increase, Modbus AUDP's latency remains significantly lower than that of TCP-based transmission (Figure 22b).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Configuration** & \(\mathbf{e_{j}}\) & \(\mathbf{d_{j}}\) & \(\mathbf{w_{j}}\) \\ \hline NMMS\(\_p_{24}\) & 0.8532 & 0.1468 & 20.55\% \\ NMMS\(\_p_{45}\) & 0.9030 & 0.0970 & 13.57\% \\ NMMS\(\_\pi_{3}\) & 0.9032 & 0.0968 & 13.55\% \\ NMMS\(\_\pi_{4}\) & 0.8602 & 0.1380 & 19.30\% \\ NMMS\(\_\pi_{5}\) & 0.9033 & 0.0967 & 13.53\% \\ NMMS\(\_\pi_{0}+\pi_{1}+\pi_{2}+\pi_{3}\) & 0.8607 & 0.1393 & 19.50\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Calculation results of key parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(N_{\max}\) & **Comprehensive Score (\(S_{i}\))** & **Ranking** \\ \hline
1 & 0.1802 & 6 \\
2 & 0.9462 & 1 \\
3 & 0.9319 & 2 \\
4 & 0.6559 & 3 \\
5 & 0.6633 & 4 \\
6 & 0.4374 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comprehensive scores and rankings based on the entropy weight method.  
Under a reduced \(P_{loss}\) of 20% (\(N_{max}\) = 2, no out-of-order rate), 1000 Modbus UDP request were transmitted. As shown in Figure 22c, the communication failure rate of Modbus UDP gateway is 22.6% (95% CI: 20.1-25.3%), whereas that of Modbus AUD gateway decreases to 0.3% (\(P_{45}\)) at \(N_{max}=3\), and the steady-state probability of \(S_{5}\) also decreases to 0.4% (\(P_{loss}\)).  
Figure 22: (**a**) Comparison of Request–Response Latency between UDP-based and AUDP-based transmissions under weak network conditions. (**b**) Comparison of Request–Response Latency between TCP-based and AUDP-based transmissions under weak network conditions. (**c**) Failure rate comparison of Modbus UDP and AUDP gateways with 20% \(P_{loss}\).  
remains at a very low level. The failure rate of Modbus AUDP gateway is consistently below the lower bound of Modbus UDP gateway's confidence interval (CI), indicating a significant improvement in reliability.
## 5 Policy Control  
### Policy Enforcement  
RS information is increasingly used in policy control, with the purpose of enforcing policy and promoting compliance to regulations, as well as to detecting and avoiding fraud. Examples showing the potential of remote sensing in verifying compliance to regulations on an _ad hoc_ basis include control on illegal drugs production[41], land use impacts on protected areas [42], illegal logging [43], and urban sprawl in China [44]. More recently, dedicated operational applications have been developed to control illegal marine fisheries [45] and marine oil spills [46, 47].
## 2 Materials and Methods
### Class Definitions
#### 2.4.1 Inundated Forest  
The definition of inundated forest is based on our previous work reported in [PERSON] et al. [28]. As described earlier, this study revealed an association between field observation of inundation within Amazonian floodplain forests and a double-bounce backscatter signal from co-polarised L-band imagery. Specifically, the incident co-polarised radar pulse first reflects from the water's surface, and then reflects off the vertical tree structure back to the sensor or vice versa, resulting in a strongly enhanced high backscatter in the HH co-polarised channel [28]. This phenomenon represented the basis for several subsequent efforts to map inundation within forested wetlands (notably including the Amazon Basin) [9; 10; 11; 12] and is illustrated in Figure 2.  
This double-bounce backscatter mechanism preserves the transmitted polarisation direction and is therefore observed in the co-polarised channel only, while the cross-polarised channel (HV) on the other hand is almost exclusively sensitive to volumetric scattering [26]. Following these principles, the normalised difference polarisation ratio between the HH and the HV channels is the main parameter behind the detection of inundated vegetation within the ALOS-2 PALSAR-2 imagery as it is better at discriminating inundated forest from dry forest compared with using either one of the channels in isolation.  
Figure 2: (**a**) ALOS-2 PALSAR-2 ScanSAR false colour composite (HH, HV, NPDI: Normalised Difference Polarisation Index) acquired between 12 and 25 August 2019; (**b**) reference optical Landsat 8 and Sentinel-2 false colour composite (NIR, Red, Green). Example area defined as (**c**) inundated forest, and (**d**) open water through visual interpretation of the ALOS-2 PALSAR-2 ScanSAR data. Images centre on the Coari region of the Amazon Basin.
## 7 Open Research Areas
### Distributed or Centralized Radio Transmission Power Control  
The transmission power used by a node can have a direct impact on the radio link quality, the level of interference and energy consumption. Ideally, all nodes should always use the least transmission power that will allow them to carry out their assigned tasks effectively. While the ISA100.11a specification allows the transmission power of individual nodes to be controlled, it does not describe any specific algorithms to perform adaptive power control. Several autonomous power control strategies, developed specifically for WSNs, can be found in the literature [66, 67]. However, they all have certain drawbacks which would prevent them from being used in a harsh industrial environment. For example, while the technique presented in [66] can adapt, it is not designed to handle rapid link quality fluctuations that could be caused by moving metal objects or electromagnetic interference from motors or pumps that may be common in an industrial environment. While the authors in [67] rightly point out that interference is an issue that needs to be addressed when developing adaptive power control algorithms, the presented solution does not perform optimally as it is unable to correctly distinguish between weak signals and interference. This is an area that still requires further investigation.
# Rapid Restoration Techniques for Software-Defined Networks
## 3 Proposed Path Recovery Methods
### Community Detection-Based Approach
#### 3.2.2 CD Example  
A schematic example is provided to clarify the strategy of our community detection-based approach. The European Reference Network (ERnet) [30] topology, which has 37 nodes and 57 edges is used as a case study. We use different coloured regions in Figure 2 to illustrate the non-overlapped communities, which were learned by the Girvan and Newman algorithm [31]. Consider the path between Dublin and Sofia. The least cost path between these two nodes is through the route:  
\[\texttt{Dublin-London-Paris-Lyon-Marseilles-Rome-Zagreb-Belgrade-Sofia}\]  
Now, let us assume that the link Paris-Lyon fails. After discovering the change in the network topology due to the failure, the network controller is required to calculate a new path and to update the network. As the selected path passes through 4 communities, _yellow, orange, red and blue_, this means that only one community needs to be considered in order to find an alternative path. In this case, only the affected segment of the path, which passes through the _orange_ community, will be involved in the process of rule replacement and updating. To update the rules, the proposed solution finds the shortest path between the two routers on both sides of the failed link, that is, between Paris and Lyon. Therefore, the controller only needs to replace and update four routers of the sub-path (Paris-Strasbourg-Zurich-Lyon). The rest of the nodes, which are distributed over the other communities, remain in the same configuration and order.
## 2 Material and Methods
### Application of Cell Biology to TEVG  
Endothelial cells and smooth muscle cells are the main components of the intima and media of a blood vessel, respectively. They were incorporated in some of the first tissue engineered graft designs given their importance in vascular function. ECs, SMCs, and fibroblasts are all essential for creating a stable intima. In addition, SMCs account for the majority of an ECM and ultimately define the mechanical properties of scaffolds. Therefore, early TEVG studies enthusiastically studied EC and SMC populations. Early TEVG studies have shown that seeding mononuclear cells on biodegradable grafts promotes rapid intima formation [35] and demonstrated physiological properties comparable to human blood vessels [36]. However, in order to avoid neointimal hyperplasia, hyperproliferation of SMCs must be controlled.  
ECs are responsible for many physiological functions and the synthesis of many important regulators and growth factors [37]. It is extremely important to establish a confluent EC monolayer on the luminal surface of a TEVG to confer resistance to hyperplasia and thrombosis. There is a report that transplantation of EC-seeded ePTFE grafts resulted in a significantly higher patency rate compared to an uninoculated ePTFE control [38]. Interestingly, in another study looking at the use of Dacron seeded grafts, it has been reported that ECs in the neointima of the conduit functions at less than 10% of the physiological level found in natural vasculature [39]. Furthermore, it has been reported that 95% ECs seeded onto a graft is lost within 24 hours [39]. Although the limited number of ECs in the lumen of a TEVG may provide beneficial resistance to neointimal hyperplasia and seems to prevent acute thrombosis, the role that EC seeding has on TEVGs should be further investigated in the future.  
[PERSON] et al. pioneered the tissue engineering by self assembly approach (TESA), where they cultured sheets of autologous fibroblasts, and fused these sheets via dehydration around a stainless steel mandrel [40]. Subsequently, cultured autologous ECs would be seeded onto the lumen of the scaffold followed by more culturing in a bioreactor. The TESA approach showed promising functional results in early clinical trials as the first ten patients that underwent arteriovenous shunt graft transplantation displayed a primary patency rate of 78% at 1 month (7/9) and 60% at 6 months (5/8) [41].  
However, in addition to a manufacturing cost of greater than $15,000 per graft, another criticism of this approach is that it will not help patients who need rapid intervention because it can take up to 9 months to manufacture [42].
# Generation of Flat Optical Frequency Comb Using Cascaded Electro-Optic Modulators
## 1 Introduction
Based on combining a cascaded MZM and two _P_Ms, we derived the relevant formulas, extracted the system's key parameters, and utilized an algorithm to identify the parameters that achieved the lowest flatness. We enhanced the program by integrating a Residual Network with a Bayesian optimization algorithm, which resulted in a flatness of 0.5508 dB with 33 comb lines. Additionally, we verified the optical communication transmission of the generated electro-optic comb signal. From this, we extracted eight carrier channels spaced at 50 GHz and applied 16-QAM modulated signals at a rate of 40 Gbaud/s. The excellent transmission results clearly demonstrate the feasibility of this flat electro-optic comb generation scheme.
# Improved Remote Photoplethysmography Using Machine Learning-Based Filter Bank
## 2 Materials and Methods
### Extraction of RGB Signal on Facial Regions  
To extract RGB signals from facial video, detecting the desired area from video data is necessary. [PERSON] et al. (2021) reported that the accuracy in rPPG signal extraction depended on the facial region and provided a rank list for each region [23]. By optimizing the number of regions, nine regions (central forehead, left forehead, right forehead, upper-left cheek, upper-right cheek, lower-left cheek, lower-right cheek, nose, and glabella) of interest (ROIs) were selected in this study (see the white-lined area of video in Figure 1). Each ROI was manually categorized from the 468 landmarks marked in green-dots in Figure 1, which were computed by the media-pipe face mesh library [24]. Each R, G, and B channel data was averaged across each segmented ROI as follows:  
\[C_{i}=\sum_{n\in ROI}\frac{P_{n}}{N} \tag{1}\]  
where \(C_{i}\) represents the averaged value for each color channel \(i\) (i.e., \(C_{R}\), \(C_{G}\), and \(C_{B}\) for red, green, and blue, respectively), \(P_{n}\) is the color intensity, \(N\) is the total number of pixels in the segmented ROI, and \(n\) is the pixel index.
## 4 Two-Stage Algorithm Combining Genetic Algorithm and Quasi-Physical Approach
### Layout Task  
#### 4.3.1 Formulation of the Layout Subproblem  
The lower stage solves the layout task. This stage takes as input the assignment list provided by the upper stage. The outputs are the lists of positions and orientations of each component in its assigned container. In the multi-container SMLP, the layout of each container can be independently solved. In other words, the constraints are individually defined for each container and there is no constraint function that requires a simultaneous resolution of the containers' layouts. Consequently, the design variables to optimize are \(\mathbf{p}_{l}^{c}=\{p_{x,i^{\prime}}^{c},p_{y,i}^{c}\}\), \(i\in\{1,...,N\}\) and \(\mathbf{p}_{a}^{d}=\{p_{a,i}^{d}\}\), \(i\in\{1,...,N_{cub}\}\), where \(\mathbf{p}_{a,i}^{d}\in\{0,90\}\). Those variables are optimized with respect to the assignment scheme \(\mathbf{p}_{s}^{d}\) from the upper level.  
The corresponding optimization subproblem solved for each container is formulated as follows:  
\[\begin{split}\min_{\mathbf{p}_{l}^{c},\mathbf{p}_{a}^{d}}& I_{tot,SC}(\mathbf{p}_{l}^{c},\mathbf{p}_{a}^{d},\mathbf{p}_{s}^{d})\\ \text{w.r.t.}&\mathbf{p}_{l}^{c}\in F_{p_{l}^{c}} \subseteq\mathbb{R}^{n_{l}},\mathbf{p}_{a}^{d}\in F_{p_{l}^{d}}\subseteq\{0,90 \}^{n_{l}}\\ \text{s.t.}&\mathbf{h}_{overlap,SC}^{C}(\mathbf{p}_{ l}^{c},\mathbf{p}_{a}^{d},\mathbf{p}_{s}^{d})=0\\ &\mathbf{h}_{overlap,SC}^{E}(\mathbf{p}_{l}^{c},\mathbf{p}_{a}^{ d},\mathbf{p}_{s}^{d})=0\\ &\mathbf{h}_{functional,SC}^{C}(\mathbf{p}_{l}^{c},\mathbf{p}_{a}^ {d},\mathbf{p}_{s}^{d})=0\\ & g_{CG,SC}(\mathbf{p}_{l}^{c},\mathbf{p}_{a}^{d},\mathbf{p}_{s} ^{d})\leq 0\end{split} \tag{26}\]  
where \(I_{tot,SC}\) stands for the inertia of one container and \(\mathbf{h}_{overlap,SC}^{C}\), \(\mathbf{h}_{overlap,SC}^{E}\), \(\mathbf{h}_{functional,SC}\) and \(g_{CG,SC}\) are the overlapping, functional and balancing constraints calculated for one container.
## 2 Related Work  
### Time-Series Forecasting with Statistic Theory  
Statistical methods serve as the origin and foundation of time-series prediction, utilizing mathematical modeling techniques to model and analyze the trends, periodicity, and randomness within data. Early statistical methods include Moving Average (MA) and simple exponential smoothing (ES), which predict future trends by smoothing data in a weighted average or exponentially weighted manner [1,2].  
Subsequently, the Autoregressive (AR) and Moving Average (MA) models were integrated to develop the Autoregressive Moving Average (ARMA) model [3], which can effectively capture the auto-correlation and average characteristics of time series by combining the characteristics of Autoregressive and Moving Averages. To deal with non-stationary time series, the Autoregressive Integrated Moving Average (ARIMA) [4] model is proposed to transform non-stationary series into stationary series by difference operation, to improve the prediction accuracy.  
In addition, [PERSON] [5] introduced the exponential smoothing method, which demonstrates excellent performance in modeling time series with trend and seasonality. [PERSON] [6] extended this work by developing the state space model and the local linear trend model,enhancing the adaptability of models to dynamic changes in real-world data. [PERSON] [7] made groundbreaking contributions to nonlinear state transition modeling, enabling the capture of mutational events in time series. [PERSON] [8] further advanced the field by introducing the ETS framework, which facilitates automatic model structure selection and significantly improves the generalization capability of forecasting models.  
These methods perform well under small-scale data and strong assumptions, which effectively enhances seasonality, trend, and periodicity. However, with the expansion of data scale and the complexity of the data generation mechanism, traditional statistical methods face bottlenecks in high-dimensional multivariate, nonlinear, and long-term dependence modeling, lacking sufficient expressive power and making it difficult to capture complex dynamic patterns. Aiming at this problem, this paper proposes an ensemble strategy that exploits short-term dependence to mitigate long-term prediction bias.
## 4 Feedback Control System and Field-Null Implementation
### Feedback Control System
#### 4.1.2 Closed-Loop Control System in Simulink  
The closed-loop control system for the CS and PF coils is built using Simulink(tm) (version: 24.1), with the state-space equation set (2), which were tuned in Section 2.2, serving as the model for the coils. The _Varying State Space_ module in Simulink is used to process the time-dependent parameters of the state-space equations into a structure. The _From Workspace_ module is used to import data from the MATLAB (version: 24.1) workspace. The closed-loop control system structure, taking the CS coil as an example, is shown in Figure 8.
## 4 Performance Results and Discussion
### Evaluation of Setup 3: 5G URLLC Test System and 5G Mid-Band System
Especially in industrial environments, a lot of metal components are moved and used; therefore, the use case is highly relevant for communication systems in industrial environments. The results of Setup 4 with introduced disturbances, using one 5G mid-band and one 5G mmW connection, are shown in Figure 14. The end-to-end latency of the individual UEs one and two are given in Figure 14a,b as a distribution over time. It can be seen how the performance of the mmW system drops when a metal plate is put in front of the UE, blocking the line of sight to the 5G base station radio unit. In Figure 14, the performance results can be seen: UE1 with a mean latency of 6.76 ms and UE2 with 4.13 ms. The mean latency of UE2 is much higher than in the previous trial due to the spikes in latency caused by the metal plate. The combined transmission after elimination on Switch 2 is shown in Figure 14c. The primary communication path changes when the mmW system is slowed down by the introduced interference, and the mid-band system takes over. As a result, despite the mmW system's high average latency of 4.13 ms, the overall system has the lowest average latency of all three measured values with 3.47 ms and a latency of 9.24 ms for 99.99% of the messages meeting the use case requirements despite the introduced disturbances. Further, due to the introduced disturbances, six packets were not transmitted via the mmW system during the measurements, whereas in Switch 2, no packet loss could be identified. Again, the mid-band system has been used as the primary transmission path, equalizing the overall performance. As a result of Setup 4 with introduced disturbances, FRER has a large impact if being used in harsh environments with a lot of metal or large components being moved around. The overall pipeline achieved the lowest mean latency and could avoid packet loss using  
Figure 14: Latency of the wireless transmission. (**a**) Latency for packets send by 5G UE1 (mid-band), (**b**) latency for packets send by 5G UE2 (mmW) with introduced disturbances, and (**c**) latency of the first packet arriving at Switch 2.  
the mid-band system as a temporal primary communication path. Therefore, it can be observed that FRER can enhance communication reliability on industrial shop floors with radio propagation suffering from strong multipath, blockage, etc.
## 3 Results
### Color Measurement (L, a, and b Values)
#### 3.1.4 Color Change (\(\Delta\)E, \(\Delta\)L, \(\Delta\)a, and \(\Delta\)b)
The comparison of the same crown material at different measurement time points is indicated by lower-case letters. Yellowness-Blueness Difference (\(\Delta\)b)  
Considering the analysis of \(\Delta\)b, Figure 10A shows that Lisi and Celtra crowns after immersion in saliva and at baseline presented positive differences, indicating yellowish, while E-max showed negative differences, indicating blueness. After immersion in saliva followed by brushing, all the crown materials showed slight \(\Delta\)b differences with comparable results (\(p>0.05\)). E-max and Celtra presented positive differences, indicating yellowish, while Lisi showed negative differences, indicating blueness. Regarding the difference in yellowness-blueness between immersion in coffee and at baseline, Figure 10B shows that different materials showed different behaviors. Lisi and Celtra showed positive differences, with a highly significant difference between the two materials (\(p<0.001\)), while E-max presented negative differences. After immersion in coffee followed by brushing, all materials showed a negative difference that was significant only for Lisi and Celtra (\(p<0.05\)). Figure 10: Mean \(\pm\) SD of \(\Delta\)b values (**A**) between immersion in saliva and at the starting point, and between saliva and brushing; (**B**) between immersion in coffee and the starting point, and between coffee and brushing. Different letters denote statistically significant differences. The comparison of different crown materials at the same measurement time point is indicated by upper-case letters. The comparison of the same crown material at different measurement time points is indicated by lower-case letters. Figure 9: Mean \(\pm\) SD of \(\Delta\)a values (**A**) between immersion in saliva and the starting point, and between saliva and brushing; (**B**) between immersion in coffee and the starting point, and between coffee and brushing. Different letters denote statistically significant differences. The comparison of different crown materials at the same measurement time point is indicated by upper-case letters. The comparison of the same crown material at different measurement time points is indicated by lower-case letters.
###### Abstract
Firstly, the amount of information for all wavelengths is calculated by LBI, and the data are sorted and screened according to the amount of information. Further, GA-BPSO (Particle Swarm Optimization with a Genetic Algorithm) is used to optimize the above hyperspectral data subset, and the optimal wavelength combination that meets the requirements is the final result. Finally, LBI-BPSO, IOIF and GA-BPSO are used for wavelength selection, respectively, combined with SVM to classify the simulation data set established in this paper and show their performance evaluation and visual classification results. The specific objectives of this paper are as follows: Section 2 briefly introduces the IOIF algorithm for DR of HSI based on the amount of information and the GA-BPSO algorithm with the inter-class distance as the objective function; Section 3 proposes a novel wavelength selection algorithm that combines information amount and inter-class separability: LBI-BPSO; Section 4 introduces the sample measurement experiments and the process of establishing hyperspectral simulation images; Section 5 uses three methods for wavelength selection of simulated HSI, respectively. The DR performance and discussion of LBI are reported in this section, and a comprehensive comparison is made with the effect of IOIF and BPSO. Finally, conclusions and future works are presented in Section 6.