esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 LeSSA--Implementation and Technical Details
### Linguistic Rules
#### 4.2.5 Self-Training-Based Sentiment Learner  
Self-training, also known as self-learning, is one of the well-known semi-supervised learning methods. In self-training, first, a classifier is trained using a small amount of initial labeled data. Then the classifier is employed to classify the unlabeled data. After that, the high confident unlabeled examples  
Figure 3: Sentiment classification based on HRPL documents.  
with their predicted labels are selected to add to the training data. The classifier is retrained, and this process is repeated many times to label unlabeled data and enrich the training data. In this paper, first, the classifier is trained using the initial set which was obtained with SBC scheme and annotated manually. Then the classifier is applied to the unlabeled text data for class prediction; thereafter, high-confidence level classified examples by the classifier, according to a specific threshold are selected and added to the labeled training text set. Furthermore, the classifier is retrained based on the enriched labeled training text set and predicts labels for the leftover unlabeled texts. This process is continued for a certain number of iterations until a termination criterion is satisfied. After a complete training process, the test data is given to the final trained classifier for sentiment classification. The general procedure used for self-training is described in Algorithm 2. The workflow for self-training is shown in Figure 4.  
```
inputs: X - [x1, x2,... xm];/\"unlabeled data of m instances\"/ Y - {y1, y2,... ym];/\"labeled data of m instances\"/ S -/classifier\"/ \(\lambda\) -/Threshold\"/ output: /* labeled data, [Y0]leq [Y1] + [X],leq: less than or equal to */ YO - {y1,y2,... yn];
```
1 Begin
2 WHILE X contain instances {
3\(\xi\leftarrow\) train classifier on Y;
4\(\chi^{1}\leftarrow\) pick a portion of the unlabeled data
5 if(confidence of S = \(\lambda\)) {
6\(\chi_{\text{p}}^{1}\leftarrow\) classify \(\chi^{1}\) by \(S\) and select high confident examples
7 Add \(\chi_{\text{p}}^{1}\) to Y
8 Remove \(\chi_{\text{p}}^{1}\), from X
9 } Return YO/*Final trained dataset\"/
10 End ```
**Algorithm 2** Self-training (ST)-based learning  
Figure 4: Self-training for learning sentiment classifier.
# A Method of Bus Network Optimization Based on Complex Network and Beidou Vehicle Location
## 5 Simulation Results  
Because the number of real bus network stations is large and the connection is complex, it is very difficult to obtain the real traffic flow, bus waiting time, number of travelers and other data of all bus stations and their sections. This paper constructs a small connected network using three bus lines to verify the feasibility of the method; it contains 30 bus stations, with bus line 1 containing 10 stations, bus line 2 containing 11 stations, and bus line 3 containing 12 stations. These three lines are intertwined and randomly weighted for each connecting edge within a reasonable range, as shown in Figure 8. Figure 9 shows part of the weighted adjacency matrix (30 * 30) of the undirected network shown in Figure 8. The matrix is symmetrical. If the elements corresponding to a row and a column in the matrix are not zero, it means that the two stations belong to the adjacent stations on the same line, that is, they are directly connected. If the elements corresponding to a row and a column in the matrix are zero, it means that there is no direct connection between the two stations. The initial network efficiency of the simulation bus network is 0.0711 by iterative calculation of the global efficiency of the network.
# Integrated Evaluation of Reliability and Power Consumption of Wireless Sensor Networks
## 7 Related Work
### Combined Power Consumption and Reliability  
The strategy proposed by [PERSON] et al. [4] to evaluate the power consumption and reliability of WSNs is very similar to one raised in this paper. They used graphs with mathematical expressions (e.g., First Order Radio Model) to represent and to evaluate the power consumption and the reliability of the WSN. They consider the battery level as reliability factor for the link communication, while we consider as the reliability of the sensor node. Furthermore, they evaluated the power consumption and reliability of the WSN at the same time when simulates the graph. While we evaluated sequentially: first, power consumption (CPN models) and, then, reliability (RBD models) using different models. Besides that, we define the power consumption of the application through its source code; while they define a constant power consumption of the application. Unlike [PERSON], the whole process is guided by a methodology and supported by a tooling.
# A Switching Observer for State-of-Charge Estimation of Reconfigurable Supercapacitors
## 2 Modeling and Motivation
### Motivation
#### 2.2.2 Limitations of Open-Loop SOC Estimation  
While Equation (8) provides a direct relationship between SOC, SOC, voltage, and current, it raises a crucial question: can we rely solely on this equation for accurate SOC estimation in a reconfigurable circuit?  
By deforming the first line of Equation (8), we can easily obtain the open-loop calculation equation for SOC:  
\[\text{SOC(t)}=\text{SOC(0)}+\Delta\text{SOC}=\text{SOC(0)}+\int_{0}^{t}\frac{s }{C_{eq}v_{rated}}i(t)dt \tag{12}\]  
In the equation, SOC(0) represents the initial SOC.  
To investigate this, let us analyze a charging scenario where a supercapacitor with a rated voltage (\(v_{rated}\)) of 2.7 V, an internal resistance (\(r\)) of 2.2 m\(\Omega\), and a capacitance of 310 F (\(C_{eq}\)) is charged with a constant current of 2 A (\(i\)). Once fully charged, switch \(s\) is toggled to open the supercapacitor branch.  
Clearly, when evaluating the SOC estimation using the open-loop method, we must consider not only the cases where the parameters are precise but also where there are errors in the parameters. Here, we simulate the real-world scenario where supercapacitor parameters may have errors by modifying the capacitance value (\(C_{eq}\)) in Equation (12). As for the true SOC, we can directly adjust Equation (8) to obtain Equation (13), using the actual parameters of the supercapacitor for calculation:  
\[\text{SOC}=\frac{v-sri}{v_{rated}} \tag{13}\]  
Figure 4 shows the SOC estimation results obtained using the open-loop method described by Equation (12) in the simulation experiment. To simulate real-world measurement uncertainties, we considered two scenarios: one where the capacitance (\(C_{eq}\)) in Equation (12) matches the true value and another where there is a 10% deviation from the true value. Although the open-loop estimation performs well when the parameters are accurate, it fails to converge to the true SOC value when there are parameter errors, as seen in Figure 4a and Table 1. This divergence highlights a significant drawback of the open-loop approach: its susceptibility to measurement errors, which are inevitable in practical applications. Therefore, a more robust approach is needed to address this limitation. Additionally, to further confirm the limitations of the open-loop estimation, we will also use this method in subsequent hardware experiments.  
Figure 3: Supercapacitors charging during circuit switching.
## 1 Introduction
The major contributions and key features of this paper are listed as follows. (1) We propose a novel cooperative positioning approach. (2) One of the main advantages of Bayesian framework is that the tasking sensor carries along a complete distribution of estimates of the target position. Thus, the distribution is inherently a measure of the accuracy of the positioning system. (3) Due to the characteristics of the cooperative information processing, the proposed estimation fusion approach ownsadaptive flexibility when dealing with uncertainty in position estimation. (4) In practice, two basic approaches can be applied to conserve power in a sensor network, either by the power management for sensor sleeping or by the design of low-power operation hardware. Our scheme is actually a distributed scheduling strategy for target tracking. The concept of \"power management for sensor sleeping\" is applied to select a subset of sensors for exploring detailed target information. The selected sub-cluster members (active nodes) are performing the tracking task and the inactive nodes can go to the idle/sleep mode. (5) We compare and contrast the existing tracking approaches with the proposed scheme. (6) We outline the technical foundations of the tracking techniques and present the tradeoffs in the algorithm design. The rest of the paper is organized as follows. Section 2 briefly reviews the literatures on target tracking. Section 3 formulates the problem, derives a distributed solution, and describes a neural networking refinement model for target positioning. Section 4 presents an estimation-theoretic analysis of the proposed mechanism to assess the achievable estimation accuracy. Then, Section 5 considers the energy consumption of the proposed tracking scheme. In Section 6, the feasibility of the proposed scheme is examined via simulation. The performance comparison between the proposed approach and the scheme with a hierarchical network topology in [5] is presented. Section 7 makes a conclusion and shows future research directions.
# RSVQ-Diffusion Model for Text-to-Remote-Sensing Image Generation
## 4 Experimental Results and Analysis
### Datasets and Evaluation Metrics
It employs a pre-trained Inception v3 network to classify generated images, assessing the clarity and diversity of these images. Specifically, the IS measures the network's confidence in the classification of generated images and evaluates the diversity of the generated samples. A higher IS indicates higher quality images and greater diversity among the images. The IS calculation involves obtaining the classification probabilities for each generated image and then measuring the Kullback-Leibler (KL) divergence between the conditional distribution for each image and the marginal distribution of all images. The calculation process is shown in Equation (13). \[IS=\exp\left((\frac{1}{N})\sum_{i=1}^{N}D_{KL}(p(y|x_{i})||p(y))\right) \tag{13}\]  
where \(N\) represents the number of generated remote sensing images; \(p(y|x_{i})\) is the conditional probability distribution of category \(y\) given image \(x_{i}\), indicating the diversity of categories in the generated set; \(D_{KL}(p(y|x_{i})||p(y)\) denotes the Kullback-Leibler divergence between the conditional distribution of image \(x_{i}\) and the marginal distribution of all images.
# Non-Invasive Intraoral Stand-Alone Tongue Control System Based on RSIC-V Edge Computing
## 3 Data Acquisition and Processing of Tongue Control Signal Based on RSIC-V Edge Computing  
### Pressure Data Processing Nodes and Transmission  
The system edge node is shown in Figure 6 below, whose design consists of four parts: data acquisition part, data processing part, master controller, and wireless transmission. Pressure sensors are distributed on the edge to collect raw pressure data, which is subsequently transmitted to the RSIC-v processor in parallel through the signal conditioning circuit. Due to the parallel transmission mode, the number of pressure sensor nodes will not affect the system data transmission rate. The signal conditioning circuit is mainly composed of a differential mode amplifier and filter. The third part is the finite state machine (FSM), which is a kind of computing model abstracted to study the computation process of finite memory and some language classes. A finite state automaton has a finite number of states, each of which can transmit to zero or more states, and the input string determines which transmission state is performed. This module converts pressure data into action instructions, and it is also the decision-maker of the whole system, which processes all data from sensors and combines them with triggers from the cloud, and then the FSM determines the state of the environment around the node [13]. For example, if the pressure signal is less than the set action threshold, then the decision-maker will judge it as invalid, and the system does not issue any action commands. The AES encryptor is the third part of this edge Internet of Things, which guarantees the security of data transmission. All data from the FSM unit are encrypted before the nodes are transmitted to the cloud. In this paper, the 128-bit version of the RSA module is employed. The last part of the edge node is the wireless transceiver module, which transmits the encrypted output from the node to the cloud [14]. The transceiver module can also send some defined information or instructions to the node from the cloud. In this paper, we use node 2.4 GHZ high-gain antenna as  
Figure 5: Layout of pressure sensor in artificial palatal film.  
the wireless transceiver module. The wireless sending nodes only need to transmit the modulated action command without transmitting a large amount of raw pressure data, which makes the system require low throughput.
## 2 Materials and Methods
### Experimental Study  
In order to verify the effectiveness of the GEO-LEO cooperation scheme proposed in this paper, we construct a scenario conforming to the actual application based on the actual orbit and payload parameters of FY-4A and GF-1/2 satellites in Tables 2 and 3.  
Figure 4: Flowchart of the onboard autonomous planning algorithm.  
The CLM/FY-4A data, which has low spatial resolution (4 km) but very high temporal resolution (15 min), provide cloud information covering the same spatial area for the whole swath of GF-1 or GF-2. For example, the swath of GF-1 is 60 km \(\times\) 60 km, which is corresponding 15 \(\times\) 15 pixels in the CLM image. Among them, GF-1 series satellites include GF-1-01/02/03/04, a total of 4 satellites, and GF-2 is 1 satellite, thus constructing a scenario of 1 GEO meteorological satellite and 5 LEO optical imaging satellites. The time period of the scenario is (1 March 2021 00:00:00, 2 March 2021 00:00:00).  
In terms of observation tasks, the point targets are selected as observation tasks, which randomly distribute on the earth surface with latitude among (50\({}^{\circ}\)E, 150\({}^{\circ}\)E) and longitude among (50\({}^{\circ}\)S, 50\({}^{\circ}\)N), the number of point targets is 600. All point targets are distributed within the coverage of FY-4A satellite. Each target is associated with a priority uniformly distributed among [1,10] and an observation duration among [5,15].  
The data processing and algorithm implementation are performed via MATLAB 2017b on a PC with Intel core i7 CPU @2.0 GHz, and 16 GB RAM under Windows 10 OS.
## 4 Applications
### Analysis and Comparison of Batching Results  
Taking a rotary hearth furnace in a steel plant in China as an example, it has 12 warehouses: the first warehouse for environmental ash; the second warehouse for CDQ; the third, fifth, sixth, seventh, and eighth warehouses for mixed ash; the fourth warehouse for ash from an iron casting plant; the ninth warehouse for secondary ash from a blast furnace; the tenth warehouse for cold-rolled mud; the eleventh warehouse for the FMQ (controlled by workers, no need to consider); and the twelfth warehouse for binder,  
Figure 10: Parameter interface of ingredient system. \(\overline{C}\) and \(\underline{C}\) represent the upper and lower limits of carbon content, \(\overline{Zn}\) and \(\underline{Zn}\) represent the upper and lower limits of zinc content, \(\overline{Cl}\) and \(\underline{Cl}\) represent the upper and lower limits of chlorine content, and \(\overline{C/O}\) and \(\underline{C/O}\) represent the upper and lower limits of the \(C/O\) ratio.  
Figure 9: System parameter interface. The index \(x_{i}\) ranges from 1 to 12. The \(x_{i}\) represents the type of material, while the restrictions indicate the various restrictions on material issuance based on the actual situation of the thread for mechanical applications. Warehouse switches indicate whether a flag for the warehouse can be used, as depending on the actual situation, some warehouses may require maintenance or experience other situations, so a warehouse switch is needed. Modifying this switch is performed with a change button that allows for the replacement of materials in the warehouse, thereby expanding its practicality.  
which is not a raw material, so it does not need to be considered. To align with actual production needs, the parameter settings are as follows: \(h=1.5\), \(\overline{C}=10\), \(\underline{C}=8\), \(\overline{Zn}=4.3\), \(\underline{Zn}=0\), \(\overline{C}\overline{l}=0.8\), \(\underline{C}\overline{l}=0\), and \(\overline{C/O}=1.5\). The \(OMQ\) ratio (\(Ratio_{OMQ}\)) is given based on expert experience from the plant as in (15):  
\[\text{Ratio}_{\text{OMQ}}=\left\{\begin{array}{ll}10&40<\text{TMQ}\leq 100\\ 8&20<\text{TMQ}\leq 30\\ 5&\text{TMQ}\leq 20\end{array}\right. \tag{15}\]  
Later, the on-site experimental data will be analyzed and discussed for two cases. In Case 1, the formula at the present moment meets the production requirements; in Case 2, the formula at the present moment does not meet the production requirements. The intelligent batching system adopts different processing strategies for these two situations. Since the performances of \(C\), \(Cl\), and \(Zn\) of the mixture are not considered in the human experience formula and the machine learning formula learns from the human experience formula, therefore the performances of \(C\), \(Cl\), and \(Zn\) of the mixture are also not considered in the machine learning formula. The following experiments illustrate the necessity of expert experience-based formula fine adjustment.
# Analysis of Fault Classifiers to Detect the Faults and Node Failures in a Wireless Sensor Network
## 6 Conclusions  
Six classifiers were used and tested with WSN. The simulated result is shown in Table 2. It depicts that the RF Classifier detects the Offset fault, Gain fault, Out-of-Bounds fault and Spike faultswith97% accuracy each and the Data loss fault with99% accuracy. The MLP detects the Data loss fault with99% accuracy. So, from the above results, the RF Classifier detects five faults out of six faults more accurately. The RF classifier outperforms all other classifiers in this category. Fault-wise analysis and classifier-wise analysis suggests that the Random Forest classifier is best suited for faultfinding in wireless sensor networks.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multicolumn{6}{c}{**Fault Detection Accuracy in \%**} \\ \cline{2-7}  & **Offset** & **Gain** & **Stuck-at** &
\begin{tabular}{c} **Out-of-** \\ **Bounds** \\ \end{tabular} & **Spike** & **Data Loss** \\ \hline PNN & 75 & 73 & 74 & 75 & 74 & 96 \\ RF & 97 & 97 & 96 & 97 & 97 & 90 \\ CNN & 79 & 76 & 81 & 76 & 78 & 76 \\ SGP & 72 & 71 & 71 & 71 & 71 & 82 \\ MLP & 95 & 91 & 92 & 92 & 92 & 99 \\ SVM & 95 & 80 & 80 & 78 & 83 & 95 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Percentage of fault detection accuracy of classifiers.  
Figure 14: Data loss fault detection accuracy.  
**Author Contributions:** Conceptualization, S.G. and M.S.; methodology, V.M.; software, G.K.; validation, R.S.A., S.A. and M.M.; formal analysis, O.I.K.; investigation, G.M.A.; resources, A.D.A.; data curation, T.H.H.A.; writing--original draft preparation, S.G.; writing--review and editing, V.M.; visualization, G.K.; supervision, G.M.A.; project administration, T.H.H.A.; funding acquisition, S.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# An Aircraft Object Detection Algorithm Based on Small Samples in Optical Remote Sensing Image
###### Abstract  
In recent years, remote sensing technology has developed rapidly, and the ground resolution of spaceborne optical remote sensing images has reached the sub-meter range, providing a new technical means for aircraft object detection. Research on aircraft object detection based on optical remote sensing images is of great significance for military object detection and recognition. However, spaceborne optical remote sensing images are difficult to obtain and costly. Therefore, this paper proposes the aircraft detection algorithm, itcan detect aircraft objects with small samples. Firstly, this paper establishes an aircraft object dataset containing weak and small aircraft objects. Secondly, the detection algorithm has been proposed to detect weak and small aircraft objects. Thirdly, the aircraft detection algorithm has been proposed to detect multiple aircraft objects of varying sizes. There are 13,324 aircraft in the test set. According to the method proposed in this paper, the f1 score can achieve 90.44%. Therefore, the aircraft objects can be detected simply and efficiently by using the method proposed. It can effectively detect aircraft objects and improve early warning capabilities.  
Keywords: aircraft object detection; support vector machines; clustering; small samples learning +
Footnote †: journal: Applied Sciences
## 4 Numerical Results
### Numerical Test 3 (Parameter Variation): Non-Homogeneous Boundary Conditions
\begin{table}
\begin{tabular}{c c c} \hline \hline
**POD Basis (\(k\))** & **Average Error** & **Average Runtime (Scaled \%)** \\ \hline
10 & \(6.9201\times 10^{-5}\) & 29.681\% \\
20 & \(1.3413\times 10^{-6}\) & 31.566\% \\
30 & \(1.2395\times 10^{-7}\) & 32.366\% \\
40 & \(4.2427\times 10^{-8}\) & 32.369\% \\
50 & \(4.7703\times 10^{-9}\) & 32.957\% \\
60 & \(3.0531\times 10^{-9}\) & 32.959\% \\
70 & \(3.0827\times 10^{-10}\) & 34.155\% \\ \hline Full & - & 100 \% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: [Test 3] Average runtime and average error of the POD reduced systems with parameter values \(\epsilon=0.0105\), \(0.01\), and \(0.0095\),  
Figure 8: [Test 3] Solution of the full discretized systems with \(\epsilon=0.0095,0.01,0.0105\) (from left to right). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**DEIM** & **Average Error** & **Avg Runtime** & **GPOD** & **Average Error** & **Avg Runtime** \\ \(m\) & & **(Scaled \%)** & \(m\) & \(q\) & **(Scaled \%)** \\ \hline
10 & \(2.8847\times 10^{-3}\) & \(0.140\%\) & 10 & 40 & \(1.8960\times 10^{-3}\) & 0.347\% \\
20 & \(1.7506\times 10^{-3}\) & \(0.140\%\) & 20 & 50 & \(5.4464\times 10^{-4}\) & 0.352\% \\
30 & \(3.5277\times 10^{-4}\) & \(0.145\%\) & 30 & 60 & \(7.5895\times 10^{-5}\) & 0.361\% \\
40 & \(4.8921\times 10^{-5}\) & \(0.151\%\) & 40 & 70 & \(1.1070\times 10^{-6}\) & 0.369\% \\
50 & \(2.8691\times 10^{-6}\) & \(0.191\%\) & 50 & 80 & \(2.4638\times 10^{-7}\) & 0.379\% \\
60 & \(1.9001\times 10^{-7}\) & \(0.203\%\) & 60 & 90 & \(1.3442\times 10^{-8}\) & 0.392\% \\
70 & \(4.7991\times 10^{-8}\) & \(0.214\%\) & 70 & 100 & \(3.8939\times 10^{-9}\) & 0.409\% \\ \hline Full & - & 100\% & Full & - & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: [Test 3] Average runtime and average error of the POD-DEIM reduced systems with with parameter values \(\epsilon=0.0105\), \(0.01\), and \(0.0095\). The POD basis has dimension \(k=50\) for both POD-DEIM and POD-GPOD approaches. of the full-order system, which is approximately 264 times faster than the full-order system. Figure 9 shows that the reduced systems become more accurate as the dimension \(k\) for POD and dimension \(m\) for DEIM increase. As in the previous numerical test, the second plot in Figure 9 also demonstrates that the POD-GPOD reduced system is more accurate than the POD-DEIM reduced system for each fixed dimension \(m\). Table 9 summarizes the accuracy and the scaled computational time of the reduced systems when compared with the full-order system. In Table 9, the ratio of the average runtime of the full discretized system is normalized to be 1. It considers a special case fixed dimensions \(k=50\), \(m=60\), and \(q=90\). These results demonstrate that GPOD can further improve the accuracy of the DEIM, while still substantially decrease the computational complexity of the nonlinear term of POD reduced system.