esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
This research developed and validated a smart ocean aquatic farming surveillance system that integrates multiple remote control and mobile communication technologies. This system significantly enhances the efficiency of aquaculture monitoring and the precision of management through a self-developed underwater ROV. To verify the effectiveness and stability of the system integration architecture, the research team conducted on-site deployment tests in various aquaculture environments, including controlled laboratory settings and open water scenarios. The experiments focused on assessing the collaborative operational performance of multiple remote control systems and 4G mobile communication networks under different environmental conditions. The test sites were approached sequentially, starting from controlled environments and progressing to open waters: initial system validation was conducted in laboratory tanks, followed by integration testing at the Gongliao Aquatic Center's cage aquaculture. Communication stability was assessed during offshore cage testing at Keelung Harbor, long-distance operational capabilities were verified at large-scale cage nets in Pingtung offshore waters, and, finally, the system's adaptability was evaluated in shallow water environments at traditional fish ponds in Xuejia, Tainan.
## 3 Results
### Validation of the Algorithms  
As shown in Figure 9, curved PV devices occasionally generate a significant power loss owing to the inherent mismatch loss caused by the variations in the cosine loss from direct sunlight. Unlike flat PV devices, the power output in an actual outdoor operation inherently differs from the power output or efficiency values obtained from indoor testing with temperature and irradiance corrections. The question was whether the geometrical and circuit models could precisely estimate the difference in the vector computation model in Figure 2.  
The vector computation model in Figure 2 was validated by outdoor monitoring of the power output from sunrise to sunset under various climatic conditions and orientations of the PV model (commercial VIPV product for the Toyota Prius 40 series). The results are shown in Figure 10 (clear-sky days) and Figure 11 (cloudy days).  
The purple lines in Figure 10 represent the conventional power calculations assuming that the PV device is flat. These correct the module temperature and solar irradiance (global horizontal irradiance, GHI). These were overestimated on clear-sky days owing to the mismatching losses among the solar cells on the curved surface affected by the variations in the cosine losses. However, the result of the vector computation using the algorithm in Figure 2 matched the measured results (blue region) regardless of the orientation of the PV module.  
This trend varied on cloudy days, when diffuse sunlight was dominant. The difference between the flat and curved models was insignificant, and both matched the measured trend. The exception was that the level of solar irradiance was high with a certain portion of direct sunlight (see the measurement on 15 March 2023 in Figure 11). In addition, the solar irradiance (GHI and DNI measurements) was measured on the building roof approximately 300 m north of the tested PV module. Thus, the solar irradiance may not be equal to the on-plane irradiance of the PV device (see morning and evening on 21 March 2023 and 17 March 2023 in Figure 11).  
Figure 9: The trend of FF under different climatic conditions.  
Figure 10: Comparison of the outdoor power output determined using the indoor test result on clear-sky days in four orientations of the module with (1) conventional power calculation assuming the PV device is flat (purple line), (2) vector computation using the algorithm in Figure 2 considering the curved surface (green line), and (3) measured outdoor power (blue region).
## 4 Proposed Protocol
### MST and Chain Construction Period  
In this period, all nodes in each cell are organized into one MST with a CHN as a root inspiration of the Kruskal algorithm. To finish this work, we assume that nodes in a cell of WSN are considered as an undirected graph G (V, E, D) completely, where V indicates a set of distributed sensing nodes, E denotes a set of communication links connected by the  
Figure 5: An example of network topology partitioned into logical grid cells. sensor nodes, and D represents the set of distance values on E, respectively. Furthermore, in order to decrease energy consumption in forwarding sensed data packets to the sink device, CHNs will find the shortest path from them to super-CHNs; this makes the chain approach based on the ant colony algorithm by calculating the transition probability value to select the next CHN on the path towards the sink [39; 40]. Assuming that the ant \(a\)-th is at cell \(i\)-th with CHN \(i\)-th and it tends to move to the cell destination, which contains super-CHN, it will randomly select cell \(j\)-th (containing CHN \(j\)-th) with the transition probability on the path \(p_{ij}^{a}\) that can be computed as Equation (20) below:  
\[P_{ij}^{a}= \begin{cases}\frac{\left[T_{ij(i)}\right]^{a}\times\left[\frac{1}{ a_{t0 CH}(U)}\right]^{\beta}}{\sum_{l\in S_{k}(l)}\left[T_{lj}\right]^{a}\left[ \frac{1}{a_{t0 CH}(U)}\right]^{\beta}}&,\text{if}\;j\in S_{k}(i)\\ 0&,\text{otherwise}\end{cases}  \tag{20}\]  
where \(T_{ij}\) is the amount of pheromone which expresses the current energy level of the CHN \(j\)-th at time \(t\), \(d_{toCH}(i,j)\) indicates the distance between CHN \(i\)-th and CHN \(j\)-th, \(S_{k}(i)\) is the set of direct neighbors of CHN \(i\)-th where ant \(a\)-th can move to, \(\alpha\) and \(\beta\) are parameters that can be used to control the residual energy level and the distance \(d\) to CHN \(j\)-th (\(\alpha\), \(\beta>0\)), respectively. In reality, the more ants that go along a path during the unit time, the more pheromone convenes along the path. However, at the same time, the pheromone on the path is also easily volatile; hence, to update the amount of pheromone \(T_{ij}\) at the next time on the selected path to super-CHN, CHN \(i\)-th updates the pheromone level according to Equation (21) below:  
\[T_{ij}(t+1)=(1-\phi)T_{ij}+T_{ij}(t+1)=(1-\phi)T_{ij}+\sum_{l=1}^{N_{max}} \triangle T_{ij}^{l}(t) \tag{21}\]  
where \(\phi\) denotes the volatile factor of pheromone (\(0<\phi<1\)), and \(\triangle T_{ij}^{l}(t)\) represents the addition of the pheromone on path \(j\)-th selected by ant \(a\)-th after one iteration. If the ant \(a\)-th goes over the CHN \(j\)-th, then \(\triangle T_{ij}^{l}(t)\) will be equal \(1/L(t)\), where \(L\) is the optimal path length and zero otherwise. The process of building the MST and chain is illustrated in Algorithm 3 below:  
```
0:List of the sensor nodes in cells, CH, super-CH
0:- MSTs for cells with CH as a root - A chain connected CHNs and the sink device.
## 3 State of the Art
### WuR-Based Data Collection Protocols  
WuR technology can be used to enforce an on-demand or receiver-initiated data collection paradigm by waking up long-term IoT nodes only when an update is necessary. Unmanned aerial vehicles (UAVs or drones) may be a key component of this system, efficiently gathering large amounts of dispersed data, as depicted in Figure 6. The viability of this scheme was previously demonstrated via simulation tools [42] and experimental evaluations [10]. To prevent transmission collisions during the waking-up process of a large number of nodes, hashing and partitioning schemes [43] have been proposed, including a variant tailored to mobile nodes.  
Other methods can extensively use LoRa, expanding the capabilities of that protocol. The combination of long-range LoRa transmission and short-range WuR can be leveraged for on-demand data-collection without UAVs [44]. The proposed system depicted uses  
Figure 6: A general UAV-aided data-collection scenario.  
local cluster heads to trigger the on-demand waking up of nodes, enabling them to transmit data to a singular sink. This heterogeneous architecture and its associated protocol enhance latency by 1.72 times and extend node lifetime by 1.4 times compared to the listen-before-talk media access control (MAC) scheme for LoRa.  
The radio on-demand sensor and actuator network (ROD-SAN) model [45], based on IEEE 802.15.4g, provides WuRx and WuTx to all network nodes to enable on-demand communication in contrast to the standardized duty-cycled schemes of IEEE 802.15.4g. The results of simulation studies demonstrated that ROD-SAN substantially enhances data collection. In comparison with duty-cycle approaches, ROD-SAN has a lower consumption rate and decreased latency. The results of a field test of 20 nodes showed that, with an active rate of 10%, ROD-SAN achieves a comparable performance to a standard network with a 100% active rate, while reducing energy consumption tenfold. Iterating on that approach, the creators of ROD-SAN found a method for aggregating data in the WuS [46; 47] to benefit from the possible active state of the destination, thus reducing the energy consumption and latency even more. Embedding data in the WuS also allows content-based waking up [48], using the length of the WuS to prioritize the freshest data and avoid unnecessary wake ups [49].  
WuR communication and data collection protocols can be simulated using various tools, such as Omnet ++ with the plugin Mixim [50], Castalia with GreenCastalia [51], or COOJA with the plugin WaCo [52]. They can also be modeled using Markov chain modeling [53], which has been used to test many protocols [54], allowing for the creation of a specific protocol for long-range and long-battery-life applications [25]. Developments have also covered the security aspects of such protocols, ensuring their suitability for industrial use [55].
# A Review of Research on Diagnosability of Control Systems Based on Structural Analysis
## 3 Diagnosability Evaluation Based on Structural Analysis
### Quantitative Evaluation
#### 3.2.2 Quantitative Evaluation Analysis  
The advantages of the quantitative evaluation method of diagnosability based on the system model are that the residuals are more flexible, and the physical meaning is more precise. The disadvantage is that the residuals are susceptible to model uncertainty, noise, interference, and other factors. The residual construction based on the parity space method is vulnerable to the selection of the left-zero space matrix and the length of the time window, which are hyperparameters that can easily affect the final diagnosability evaluation results.
## 2 The MPPT for Photovoltaic System
### Perturbation and Observation Method  
The P&O method is the MPPT algorithm applied most extensively at present. Through the change in the direction of perturbation, the output voltage of the photovoltaic module array is adjusted. The module array output power after perturbation and the power before perturbation are then compared with each other to determine the direction of the next perturbation. From Figures 1 and 2, it can be observed that if the output power tested is greater than the previous power measured (\(P_{(k+1)}>P_{(k)}\)), when the positive perturbation voltage increases, the positive perturbation continues to increase. Conversely, when the output power tested is less than the previous power measured (\(P_{(k+1)}<P_{(k)}\)), then change the perturbation direction and repeat the action mentioned above, where the output power can reach the MPP.  
Figure 1: Work illustration for positive perturbation voltage of the perturbation and observation method.  
Figure 2: Work illustration for negative perturbation voltage of the perturbation and observation method.  
The MPPT method of PVMA is not the main research focus of this paper, but only to effectively regulate the PVMA output maximum power, so as to carry out the adjustment performance test of the smart inverter output real power and reactive power.
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
## 3 MCU-Based Hardware Platforms for Wireless Sensor Nodes
### Environmental Monitoring and Smart Farming
#### 3.2.5 Wireless Sensor Networks for Critical Event Detection  
The sensor node proposed by [24] was developed for environmental monitoring and fire detection. It was designed around Libelium's Waspmote microcontrollers (ATmega1281) operating at 8 MHz with 8 kB SRAM, 4 kB EEPROM, 128 kB FLASH and 2 GB of SD card memory. A hybrid of communication modules was utilized. The authors opted for the limited range XBee S1 Pro operating at 2.4 GHz band and the wide range XBee 900 LP operating at the 900 MHz band. Furthermore, the humidity, carbon monoxide, temperature and pressure sensors were exploited by the authors. The node relies on a rechargeable battery for power supply. There is an option for a solar panel to harvest energy. The power consumption of the node was not specified by the authors.
## 3 Materials and Fabrication Techniques
### Conducting Polymers
#### 3.1.1 Polypyrrole  
Early examples of OECTs relied on the use of polypyrrole (PPy) (Figure 3a), a conjugated polymer exhibiting relatively low conductivity in its reduced state. Upon oxidation (\(p\)-doping), the conductivity of polypyrrole sharply increases and can reach values as high as \(10^{2}\) S-cm\({}^{-1}\)[61]. Polypyrrole is typically obtained through the oxidative polymerization of pyrrole. This polymerization process can be carried out by chemical (oxidation by iron(III) chloride, typically) or electrochemical (cyclic voltammetry or chronocoulometry) methods. The optimization of a variety of parameters, such as the choice of solvent, the pyrrole-to-oxidant ratio (chemical methods), the applied voltage (electrochemical methods), or the nature of the counter-anion (dopant), led to the production of highly conductive polymers.  
The conducting properties of PPy were used by [PERSON] et al. in 1984, to produce microelectrode arrays working as transistor-like devices [24]. In this early and very popular work, more than three electrodes were used in total: the authors describe the functionalization of a three-electrode (source, drain and gate) \"array\" with a thin film of polypyrrole, but also used a reference electrode and an auxiliary (counter) electrode. Upon application of a sufficiently positive gate voltage, corresponding to the oxidation potential of polypyrrole, the PPy film connecting the three electrodes becomes conducting and allows for a current to flow between the source and the drain, essentially turning the transistor-like device \"ON\".  
Interestingly, the conductivity of PPy is also sensitive to pH changes. [PERSON] et al. [62] noted that the conductivity of PPy varies by 2 orders of magnitude between pH 3 and pH 11, and subsequently used this property to create a penicillin biosensor based on a PPy-modified transistor-like device. In the framework of electrochemical transistors, a polypyrrole channel connecting the source and the drain can therefore be turned \"ON\" or \"OFF\" through the application of an adequate gate voltage, or through a modification of the pH in the electrolytic solution.  
One major drawback of PPy is its inherently poor electrochemical and chemical stability. When the polymer is overoxidized, either through the application of a moderately high positive potential (\(>\)+0.5 V vs. SCE) or upon exposure to oxygen or water, its resistance increases dramatically [63]. This low stability of polypyrrole therefore led to the investigation of other OSC candidates, such as polyaniline and polythiophene derivatives, for the development of OECT-based sensors.
## 4 Research Challenges and Directions
### Physics-Informed Neural Networks for Trajectory Control of UAVs  
Existing model-free data-driven algorithms applied to control the trajectory of UAVs are trained neural networks based on either supervised learning or reinforcement learning. Physics-informed neural networks (PINNs) introduce UAV motion dynamics in the form of partial differential equations [58] or motion models into the neural networks as loss functions, augmenting the accuracy, generalization, and robustness of the model by ensuring predictions adhere to known physical principles. In addition, PINNs can assist in tackling uncertainties related to wind disturbance, UAV failures, or unexpected obstacles by embedding robust physical laws. Hence, PINNs can provide better training and algorithm convergence with fewer data samples considering real-life scenarios, which need further exploration.
## 4 Results
### Comparative Analysis with Traditional Methods  
4.8.1. Statistical Performance Comparison  
We conducted extensive comparisons between our LSTM-based approach and traditional queueing theory models (M/M/c and M/G/c) across multiple metrics and scenarios. Table 13 presents a comprehensive performance comparison showing significant improvements with the LSTM model. Most notably, our approach achieved a 39.26% improvement in MAPE, reducing it from 11.87% to 7.21%. The RMSE improved by 38.03% (from 0.944 to 0.585), while MAE decreased by 38.43% (from 0.752 to 0.463). The R\({}^{2}\) value showed a 12.07% enhancement, increasing from 0.837 to 0.938. The comprehensive comparison of performance metrics between the LSTM model and traditional queueing theory approaches demonstrates consistent improvements across all measured indicators.
## 3 Simulation Model
### Comparison of System Operating Characteristics and Necessary Components by Case Types
#### 3.2.2 Case-2: Fault Management in an FB-MMC Using RCB  
FB-MMCs are a superior alternative to HB-MMCs in terms of fault handling capability. As shown in Figure 4(a), the rapid reverse of the DC voltage control of an FB-MMC can be typically provided within a few milliseconds; therefore, DCCB are not necessarily needed for protection.  
Figure 4: Case-1: DC fault handling solution for an HB-MMC based on a hybrid circuit breaker (HCB): (**a**) configuration of Case-1; (**b**) structure of the HCB; (**c**) fault handling under a DC short-circuit fault.  
When the fault is detected, all IGBTs of the SMs are blocked, and the capacitors generate a reverse voltage to block the AC side currents. Since the converter has already interrupted the fault current, an RCB is sufficient to isolate the fault.  
Also, unlike Case-1, the FB-MMC system does not require a DCCB. Figure 5b shows the fault handling mechanism in the FB-MMC system: detecting the fault, blocking the converter, and triggering the RCB to generate the natural current zero. Finally, if a reconnection is established by deblocking the converter and reclosing the RCB, system can be restored to the normal state.
## 1 Introduction
However, the PSO-GA-DWPA has high computational complexity and cannot leverage its advantages with fewer tasks. However, almost no one has studied the assignment problem in IUWSNs, so this paper studied this problem. The GA was first created by [PERSON] in the 1960s and has been widely studied, tested and applied in many fields. The GA is a random research method based on genetic mechanism and natural selection. Standard heuristic algorithm is primarily inspired by the processes of natural evolution, such as selection, crossover, and mutation [15]. The GA has attracted much attention because of its potential to obtain the optimal solution of complex problems. A detailed discussion on clustering algorithms can be obtained In ref. [16]. Although the GA has a wide space-search capability, it is normally not very effective for greater than toy-sized issues. In order to avoid the shortcomings of the traditional GA, a new CEAGA is proposed in this paper. Its characteristics can be summarized as follows: fast computing speed, easy implementation, simple concept and strong exploration ability. The main contributions are as follows:  
1. Firstly, a new CEAGA is presented to enhance the revenue of the network in IUWSNs. The CEAGA can not only balance the relationship between global optimization and convergence speed, but also balance the relationship between exploration and development. 2. Secondly, this article combines the improved sine chaotic operator with the GA. On the one hand, in the proposed problem, the chaotic operator finds a feasible solution close to the optimal solution usefully. On the other hand, compared with the traditional GA, chaos operator is more suitable for the operation of individuals with various lengths. 3. Thirdly, a new elite selection is also designed to avoid local optima and enhance algorithm convergence speed. By the mechanism of adaptive operator and elite selection, the CEAGA overall search ability is fully applied for finding the global solution. It is more effective and easy than the existing random algorithm, and can escape local optimization and obtain a good solution at the same time. * Fourthly, a new adaptive operator is designed to make the crossover probability and mutation probability more flexible and reasonable, so as to maintain the optimization performance of the algorithm. In terms of the sections, this paper's structure is arranged as follows: The second section introduces the relevant academic papers of predecessors. Section 3 introduces the system model. Section 4 proposes a CEAGA to solve the problem of maximizing network revenue in IUWSNs. Section 5 proves the validness of the CEAGA in solving the problem and analyzes this problem through simulation experiments. Finally, Section 6 gives a conclusion.
# Dynamic Online Label Distribution Feature Selection Based on Label Importance and Label Correlation
## 4 Proposed Method
### Dynamic Algorithm for Online Label Distribution Feature Selection
#### 4.3.2 Significance Analysis  
The purpose of correlation analysis is to evaluate the correlation between the new currently arriving features relative to the currently arriving features.  
Define the significance of label \(L\) as follows:  
\[\varsigma\big{(}L_{j}\big{)}=\frac{\sum_{i=1}^{n}d_{x_{i}}^{L_{j}}}{\sum_{j=i} ^{m}\sum_{i=1}^{n}d_{x_{i}}^{L_{j}}} \tag{17}\]  
For a reduced \(\text{red}_{j}\) of label \(L_{j}\), we design a mapping \(\Phi_{j}:\text{red}_{j}\to F\) that maps \(\text{red}_{j}\) to the following d-dimensional vector:  
\[\[PERSON]_{j}=\big{[}\[PERSON]_{j}(f_{1}),\[PERSON]_{j}(f_{2}),\ldots,\[PERSON]_{j}(f_{d})\big{]} \tag{18}\]  
where \(\[PERSON]_{j}(f_{i})=1\), if \(f_{i}\in\text{red}_{j}\); otherwise, \(\[PERSON]_{j}(f_{i})=0\).  
We then define the significance of feature \(f_{i}\) relative to \(D^{L}\) on A as follows:  
\[\text{SIG}^{*}\Big{(}f_{i},A,D^{L}\Big{)}=\sum_{j=1}^{m}\varsigma\big{(}L_{j} \big{)}\cdot\Phi_{j}(f_{i}) \tag{19}\]  
For a new feature \(F_{i}\), we calculate \(\text{SIG}^{*}\big{(}F_{i},S_{t_{i-1}},D^{L}\big{)}\); when \(\text{SIG}^{*}\big{(}F_{i},S_{t_{i-1}},D^{L}\big{)}>0\), we believe that the significance \(S_{t_{i-1}}\) of the new feature \(F_{i}\) for label set \(D^{L}\) is greater than or equal to the average significance of the features that have been processed in label set \(D^{L}\). Therefore, we believe that \(F_{i}\) is a significant feature and should be retained.