esa-sceva/satcom-chunk-collection huggingface dataset:
# Long-Term Structural State Trend Forecasting Based on an FFT-Informer Model
## 3 Experiments and Discussion
### Feature Extraction by FFT  
In our experiment, the feature vectors were extracted from the data in sliding by FFT. These features can be interpreted as the best estimation of the amplitude and phase of the  
\begin{table}
\begin{tabular}{c c c c} \hline  & & **SMC** & **ASCE** \\ \hline \multirow{3}{*}{Feature extraction} & Feature window length & 100 & 50 \\  & Window sliding distance & 100 & 50 \\  & Feature vector length & 3 & 3 \\ \hline \multirow{4}{*}{Informer model} & Attention window & 96 & 96 \\  & Predicted length & 5 (5 \(\times\) 100 = 500) & 5 (5 \(\times\) 50 = 250) \\ \cline{1-1}  & Multi-head number & 8 & 8 \\ \cline{1-1}  & Dimension of the vectors \(q_{i},k_{i},v_{i}\) & 2 & 2 \\ \cline{1-1}  & Encoder–Decoder layers & 2 & 2 \\ \hline \end{tabular}
\end{table}
Table 1: Parameter setting of FFT–Informer model.  
structural state data. The fitted curve is directly interpreted as the characteristic estimator of the window data, and its parameter vectors can be regarded as the feature vectors of the windows. The SMC data set is taken as an example to illustrate this step in Figure 7.  
As shown in Figure 7, under a window length of 100, the fitting curve of feature extraction basically simplifies and fits the data features of each window, and the amplitude, vibration frequency, and other features of structural state data are preserved by the simplification.  
To verify the performance of FFT for trend feature extraction, it is compared with several time-frequency domain feature extraction methods that can also capture the characteristics of amplitude and frequency and have the ability to assist the forecasting algorithm, as shown in Table 2. Among them, direct signal extraction does not simplify the state characteristics between segments, stabilize the data, and reduce the randomness of the data. Each feature (extreme value, disturbance, etc.) still exists independently. The time-frequency domain feature extraction methods (Fourier polynomial, wavelet transform, and least-squares approximation) need more items to achieve the efficiency function fitting, which greatly increases the complexity of prediction.
# Management Platforms and Protocols for Internet of Things: A Survey
## 4 IoT Network Management Protocols
### Open vSwitch Database (OVSDB)  
Open vSwitch Database (OVSDB) is a management protocol in a SDN environment [39]. Most network devices allow remote configuration using legacy protocols, such as SNMP. The goal of Open vSwitch (OVS) consists in creating a modern programmatic management protocol interface--OVSDB.  
According to Figure 8, the OVSDB management protocol handles OVS that consists of a database server (OVSDB Server), a virtualized switch (OVS Switch) and, optionally, a module for fast-path forwarding. Each OVS is managed by, at least, one manager. An OVS module supports several data paths referred to as \"bridges\", where this controller uses OpenFlow.  
The OVSDB protocol interface to execute the configuration and management operations on the OVS instance. OVSDB is used to create/delete/modify bridges, ports, and interfaces. The OVS represents an evolution of network management protocols, allowing programming and configuring bridges, ports, and interfaces for SDN equipment platforms and Network Functions Virtualization (NFV) [40].  
_Pros: Interoperability of the networks and SDN management network. Cons: No standard model to other networks and security features._  
Figure 8: Illustration OVSDB Protocol for IoT Architecture.  
Use Case  
The open-source OpenDayLight platform for SDN uses open network management protocols, i.e., SNMP, OVSDB, and NETCONF to provide modular functions, extensible control, and network device monitoring [41].
# Key Issues on Integrating 5G into Industrial Systems
## 5 Integration of 5G with Industrial Systems
### Key Technologies for Replacing Field Buses with 5G
#### 5.3.2 Control in the Field  
In industrial systems, control modules (i.e., functional blocks) can run on field devices, so the calculations of control algorithms (including sensing, control, output, and execution steps) are also executed in the field. Similarly, if a 5G base station is designed as an edge server, it also needs to be able to run control modules. At this point, the local network composed of a 5G base station server and its connected terminals can be regarded as a wireless field bus network, where both the 5G base station server and the 5G terminals are important components of the entire network. Specifically, the 5G base station server plays the role of the master in the field bus system, while the 5G terminals are equivalent to other field devices. Given that the control modules can run on both the base station server and terminals, all control algorithms and communications can be implemented at the 5G base station and below, without the need for access to 5G backbone networks. In addition, communications between the base station and the outside world can be regarded as occurring at another layer of the network, so the base station server also needs to integrate gateway functions, so as to support the ability of 5G terminals to communicate with the outside.
# Aminated Covalent Organic Polymers for Anionic Dye Adsorption in Aqueous Systems
## 3 Results and Discussion
### pH Effect  
To evaluate the dye adsorption efficiency as a function of initial pH, the initial pH of the dye solution was adjusted to 4, 6, 8, and 10 by adding NaOH and HCl. In the case of MO (Figure 7), the adsorption efficiency of the prepared ACOP decreased gradually (around 10%) from 77.2% (pH 4) to 69.9% (pH 10). In the case of OG, the adsorption efficiency of the prepared ACOP decreased from 58.9% to 46.4% (approximately 22%). The changes in the surface charge of ACOP could be attributed to this decrease in adsorption efficiency at high pH (pH 10). According to the results of zeta potential analysis (Figure 4), the prepared ACOP exhibited a negative surface charge above pH\({}_{\text{pzc}}\) (pH 8.26). Notably, the adsorption efficiency of ACOP for the anionic dyes, MO and OG, was affected by the surface charge of ACOP because of the charge-charge interaction between ACOP and dye molecules. Nonetheless, the ACOP exhibited fairly high adsorption efficiencies of 46-69% at pH 10, even though the adsorbent and the dyes had opposing charges. This result indicates that the adsorption proceeded through charge-charge interaction. In addition, on the basis of the structure of ACOP, \(\pi\)-\(\pi\) interaction between the benzene and triazine rings of the dye molecules and the ACOP, respectively, may have accounted for some of the adsorption. \(\pi\)-\(\pi\) interactions between aromatics can occur over a wide pH range because the \(\pi\)-electrons on the ACOP and the dye molecules are unaffected by pH [70]. Moreover, \(\pi\)-\(\pi\) interactions occur between the electron-deficient surface of the triazine ring in the ACOP and the electron-rich surface of benzene in the anionic dye molecules, which facilitates the adsorption of dye molecules on ACOP even when the surface of ACOP is negatively charged.
# Modified Index Policies for Multi-Armed Bandits with Network-like Markovian Dependencies
## 3 Preliminaries
### Exploration vs. Exploitation in Markovian Bandits  
A key challenge in MAB problems is the trade-off between exploration and exploitation. This dilemma is more pronounced in Markovian bandits due to the changing state of each arm. Exploration involves pulling less-understood arms to gain more information about their reward distributions and state transitions. Exploitation means choosing arms that are currently known to offer higher rewards based on accumulated knowledge.  
Balancing these strategies is crucial for achieving optimal performance, especially when the bandit arms exhibit state-dependent rewards that evolve according to Markov dynamics. The player must not only consider immediate rewards but also the potential future benefits of being in favorable states.  
The concepts introduced in this section provide the necessary background to appreciate the complexities involved in our study of MABs with Markovian rewards. Understanding these principles is essential for developing effective strategies and algorithms to tackle the dynamic and probabilistic nature of the problem.
# Ground Based Hyperspectral Imaging to Characterize Canopy-Level Photosynthetic Activities
## Results  
### Representative Meta-SIF Images  
Meta-SIF images showed obviously different trends between the control and Diuron-treated groups (Figure 3). In the control groups, SIF values had the same trend as PAR values: SIF values increased with the increase of PAR values and decreased with the reduction of PAR values. In contrast, SIF values for Diuron-treated plots exhibited no relation with PAR values: SIF values were low and relatively constant, irrespective of PAR changes throughout the day. This observation agreed with the experimental design. The control groups were healthy, showing fluorescence intensity changes along with varied PAR levels, whereas an inhibitor of electron transport beyond PSII would be expected to cause damage to the photosynthetic apparatus and potentially increase the NPQ of the fluorescence signal.  
Figure 3: Representative meta-SIF images for control and Diuron-treated plots. In meta-SIF images, each pixel had both an SIF value and a corresponding photosynthetically active radiation (PAR) value.  
In addition, variations were observed within a plot (see the control group at 1400 h in Figure 3). Leafy regions in the plot showed higher SIF signals than less leafy regions, because the leafy regions usually had a faster vegetative growth (and thus more mature leaves), resulting in a higher capacity for photosynthesis. This suggests that calculated meta-SIF images could be used for analyzing the spatial variation of photosynthesis. As the present study aimed to explore the possibility of using SIF measured by passive HSI method to characterize photosynthetic efficiencies at the canopy level, experiments were not designed to analyze spatial variations of SIF in a plot. Thus, successive studies are needed to take the full advantage of HSI data to analyze spatial variations of SIF (and potentially other photosynthetic parameters).
## 4 Discussion  
The present study provides a basis for future developments, as the applied methodology can be expanded and adapted to other complex mathematical disciplines. Such an expansion would allow for a broader validation of the systems approach in the design of interactive learning environments and confirmation of its effectiveness in different educational contexts.  
For a more reliable statistical confirmation of the results, the collection of additional data from student tasks is currently ongoing. The expansion of the sample will contribute to a more accurate analysis of the effectiveness of interactive software and will allow to draw conclusions applicable to different groups of learners.  
The following improvements can be applied in future studies:  
* Expansion of the data set--a collection of more data from different cohorts and educational institutions. This will allow us to assess the effectiveness of interactive software in different educational contexts and to identify the factors that most strongly influence the results.
* Application of advanced statistical methods--using more complex statistical tests for a deeper analysis of the impact of interactive elements on the learning process. More precise measurement of the effect will help to better adapt the methodology to different groups of students.
* Research on additional factors--analysis of the role of cognitive learning styles and the effects of gamification on student motivation and results. A better understanding of these aspects will allow adapting the learning process to the individual needs of students.  
In addition to improving the methodology, a new mode for solving tasks is being developed, which will integrate more game elements to increase student engagement. In this mode:  
* Students will be able to earn badges and medals based on the results achieved.
* Theoretical test questions will be included, which, if answered correctly, will facilitate obtaining higher honors.
* Rankings will be created, reflecting individual achievements and stimulating the competitive element in learning.  
This approach is expected to encourage more active participation and to support a better understanding of theoretical concepts through game elements.  
We believe that future improvements will contribute to the development of interactive teaching methods in coding theory, as well as in other disciplines requiring a high degree of mathematical abstraction. The successful implementation of this approach can lead to more effective learning processes that combine theory, practice, and student engagement.
## 3 Results and Discussion
### Smart Nose  
The Smart Nose analysis was used to detect differences in the volatile profile of the samples at the 0.0-kGy (\(b\)) and 5.0-kGy (\(a1\) and \(a2\)) dosages. The score plot (Figure 1) showed a wider separation (PC1: 68.79%; PC2: 25.27%) both between the \(b\) and \(a\) samples, as well as between the \(a1\) and \(a2\) groups. Furthermore, aroma profiles were determined on two samples (identified as samples 278 and 369), including the respective subsamples, based on the results from the Smart Nose analysis (Table 4). The Gas Chromatography Olfactory results showed a higher frequency of ketone compound presence than the other chemical classes in both samples 278 and 369 (Table 4). The Ketone class is usually formed in heat-processed milk, and they are the major contributors to their cooked odour perception [39; 40]. Diacetyl could be formed by \(\alpha\)-dicarbonyl, an intermediate compound formed during nonenzymatic browning reactions. Terpenes are derived from secondary plant metabolites, and they were not affected by the treatment. A study on the plants present in the mountain area demonstrated that the pastures are rich in terpenes, especially monoterpenes [41]. The terpenes have an important chemical property represented by chirality, and therefore, different enantiomers of the same molecule may differ in taste, aroma or bioactivity. Observing 278 and 369 samples at the 0.0-kGy gamma irradiation dosage, 278 samples showed the highest number in AOCs. The differences in AOCs were not only in the number but also in the qualitative volatile compounds. Considering the groups (_b_ and _a_) and subgroups (_a_1 and \(a\)2) of the same sample, 278 samples did not show differences in number of AOCs among the \(b\), \(a\)1 and \(a\)2 samples, whereas 369 samples showed a fewer number of AOCs in the \(b\) and \(a\) samples. In detail, 278 samples had methylbutyric acid, 2-nonenal, 2,6-nonadienal and methional-like unique compounds in the \(b\) vs. \(a\) groups. On the other hand, safrole, heptanone, 3,5-ocatadien-2-one and thiophene were absent in the \(b\) group but present in both the \(a\)1 and \(a\)2 subgroups. Safrole and thiophene are responsible for the unpleasant garlic odour perception. Another possible problem with the irradiation treatment is that animal [42,43] in vitro and in vivo studies [44,45] have confirmed that
# Review of the Research Progress in Soft Robots
## 2 Driven Methods and Materials for Soft Robots
### Smart Materials and Their Driven Methods
#### 2.2.7. Liquid Crystal Elastomer (LCE)
\begin{table}
\begin{tabular}{c c c} \hline \multicolumn{2}{c}{**Advantages**} & **Disadvantages** \\ \hline SMA & Simple fabrication, low cost, light weight, high & Easy aging of material, difficult to accurately \\  & power density, and high driving force & control the accuracy, and low driven frequency \\  & Large deformation, light weight, and & Driving force of SMP driven is smaller than SMA, \\  & simple structure & less stable, less precise control \\  & & \\  & Fast response, lighter material mass, and higher & high driven voltage for electronic EAP, ionic EAP \\  & driven efficiency & requires a wet environment, and poorer \\  & & load-bearing capacity and stability \\ Piezoelectric material & High response speed, high operating stress and & The high driven voltage required and the small \\  & frequency, and large operable bandwidth & amount of driven displacement produced. \\  & Biocompatibility, permeability, and responsiveness & The preparation and control process is complex \\  & to external stimuli & and the driven cannot be precisely controlled \\  & & Good fluidity, strong electrical conductivity, \\  & LM & excellent thermal characteristics, and easy to \\  & realize solid-liquid conversion & working process. \\  & & Unique bi-directional shape memory, large \\  & & \\  & deformation, fast response time, and scalability & \\ \hline \end{tabular}
\end{table} 
Table 1: Advantages and disadvantages of different smart materials. From the Table 1, it can be observed that most smart materials can meet the requirements of the fast response of drive elements, which can make soft robots respond quickly after being \"stimulated\" and reduce the reflection lag phenomenon, such as EAP, Piezo-electric material, LCE and other smart materials. At present, \"lightweight\" has gradually entered the vision of human beings. Soft robots should also pursue lightweight when choosing _smart_ materials that are _driven_, such as SMA, SMP, and EAP. In addition, most of the smart materials have the characteristics of a _large_ driving force and large deformation, which are beneficial for soft robots to achieve complex motion patterns. LM is suitable for flexible sensors because of its good fluidity and strong conductivity. However, most of the smart materials have problems such as accuracy that is difficult to control and poor stability, which is one of the defects of smart materials as driven materials. Therefore, improving the control accuracy and stability of smart materials will also become the focus of future research on smart materials that are driven.
## 2 Materials and Methods
### Preliminaries
#### 2.2.2 Node Labeling  
Node labeling has emerged as a crucial enhancement strategy, significantly contributing to the refinement of graph topological structures in various experiments An effective node labeling methodology strives for two critical objectives: ensuring that similar nodes garner comparable rankings across diverse subgraphs and maintaining a structured labeling process. Specifically, the latter implies that if node \(i\) receives a lower label than node \(j\) during the node labeling procedure, this hierarchical relationship should persist in the final node labeling output.  
While the classic WL algorithm adeptly addresses the former objective, ensuring similarity among nodes, it falls short in ensuring the orderly arrangement of labels. It fails to pinpoint the target node within the labeled local subgraph. In response, our devised node labeling algorithm introduces a meticulous approach. It assigns a label of 0 to the source node \(x\) in the link prediction target node pair (\(x,y\)) and designates the target node \(y\) with a label of 1. Subsequently, an initial string is generated by examining the connections of neighboring nodes, with the length of the string corresponding to the number of connected nodes. Then, nodes are relabeled in ascending order based on this initial string. This process is iterated multiple times until each node obtains a unique label, as illustrated in Figure 2.  
This systematic labeling approach imparts distinct characteristics to the labeled local subgraph, notably assigning the label pair (0, 1) to the link prediction target node pair, thereby explicitly distinguishing them from ordinary nodes within the graph.  
Figure 2: Illustration of the node labeling algorithm for a graph. In the depicted graph, the source node is denoted as 0, and the target node as 1. The algorithm, showcased in each iteration, involves Step 1, which calculates a distinctive string for each node by recording the indices of the nodes and their respective neighbors. Following this, Step 2 orchestrates the re-labeling of nodes in adherence to the devised node labeling algorithm.
## 2 System Model
### Estimation of FSO PE with BIGRU-Attention Model
#### 2.4.2 BiGRU-Attention Model Training and Structure  
BiGRU-Attention uses historical operating data from a free-space optical communication system as a training set to train the model. The training set includes received optical signal intensity data \(y_{FSO}\) measured by photodetectors, real-time angle error data PE recorded by the pointing sensor, and the annualized link outage probability obtained by statistics. \(y_{FSO}\) and PE are obtained at a 50 Hz sampling rate, and the network input layer sequence length is set to 200, which is 4 s of data. The data are collected using a custom-built free-space optical communication system, which consists of a transmitter and a receiver. The transmitter includes a laser diode, a beam expander, and a pointing mechanism. The receiver consists of a photo detector, a pointing sensor, and a signal processing unit.  
BiGRU-Attention consists of an input layer of length 200, two bidirectional GRU hidden layers, and an output layer. The input layer corresponds to the input sequence. Each hidden layer contains 128 nodes. The output layer outputs a pointer to the error correction control signal. The structure of the BiGRU-Attention model is shown in Figure 3.  
Training process  
Train the network using the AdaGrad optimization algorithm. The learning rate is set to 0.01. The number of training iterations is 1000. The first step is to initialize the BiGRU attention parameters. Then, repeat the training iteration, that is, enter the training sample and calculate the loss through forward propagation. The second step is to calculate the parameter gradient using AdaGrad and update the network parameters with backpropagation until the training loss converges or the number of iteration rounds is reached.