esa-sceva/satcom-chunk-collection huggingface dataset:
# Online Monitoring of Torpedo Car Shells Based on Equal Angle Scanning
## 1 Introduction  
The torpedo car is a very important large-scale molten iron transportation equipment in the steel industry. It is a torpedo-type molten iron tank used in a large blast furnace. It is also called a mixed iron car. It has good insulation conditions, small heat loss, and insulation time. Furthermore, it is energy saving and enduring. Because of the large volume of the car, it can also store molten iron to coordinate the temporary imbalance of ironmaking and steelmaking. At the same time, it can replace the steel-making mixer and ordinary molten iron car to complete desulfurization, dephosphorylation, and other operations during the transportation of molten iron [1, 2].  
The lining may crack, bulge, and spall due to chemical erosion, mechanical erosion, and rapid cooling and heating. Due to its high cost of maintenance, repair, and replacement, continuous monitoring and temperature detection are required to assess the lining of the torpedo car. When the refractory material is locally damaged severely but not found, it will cause a serious accident [3, 4].  
Infrared thermography (IRT) is a science dedicated to the acquisition and processing of thermal information from non-contact measurement devices [5]. It is an effective, fast, and noncontact measurement technology that is especially suitable for measuring the temperature of moving objects [6, 7, 8]. To our knowledge, the traditional measurement method is commonly used in the steel industry; that is, the relevant staff carry portable infrared instruments to detect the quantity of torpedo cars on the transportation railway [9]. Not only is this method labor intensive, but also the measurement results are not stable, especially in hot or rainy and snowy weather. In order to improve the efficiency of monitoring and measurement stability, and effectively reduce the labor intensity of related operators, online infrared sensors are wanted for scanning and imaging [10, 11].  
In this work, based on equal angle scanning, a 3-5 \(\upmu\)m infrared scanner is designed. A temperature calibration model is proposed and the panoramic thermal image of the torpedo car is obtained. Furthermore, an online monitoring analysis system for the temperature field of the torpedo car is developed. The inner lining thickness is simulated by thermalmap, and the temperature map is drawn according to the body shell size. Finally, the safety alarm and accurate positioning are realized.
# Space Robot Sensor Noise Amelioration Using Trajectory Shaping
###### Abstract  
Robots in space are necessarily extremely light and lack structural stiffness resulting in natural frequencies of resonance so low as to reside inside the attitude controller's bandwidth. A variety of input trajectories can be used to drive a controller's attempt to ameliorate the control-structural interactions where feedback is provided by low-quality, noisy sensors. Traditionally, step functions are used as the ideal input trajectory. However, step functions are not ideal in many applications, as they are discontinuous. Alternative input trajectories are explored in this manuscript and applied to an example system that includes a flexible appendage attached to a rigid main body. The main body is controlled by a reaction wheel. The equations of motion of the flexible appendage, rigid body, and reaction wheel are derived. A benchmark feedback controller is developed to account for the rigid body modes. Additional filters are added to compensate for the system's flexible modes. Sinusoidal trajectories are autonomously generated to feed the controller. Benchmark feedforward whilphals compensation is additionally implemented for comparison. The method without random errors with the smallest error is the sinusoidal trajectory method, which showed a 97.39% improvement when compared to the baseline response when step trajectories were commanded, while the sinusoidal method was inferior to traditional step trajectories when sensor noise and random errors were present.  
structural dynamics; flexible robotics; bandpass filter; notch filter; structural filtering; trajectory generation; whiplash compensation 202424666. [[https://doi.org/10.3390/s24020666](https://doi.org/10.3390/s24020666)]([https://doi.org/10.3390/s24020666](https://doi.org/10.3390/s24020666))  
[PERSON] and [PERSON]  
1 Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853, USA; [EMAIL_ADDRESS]
## 5 Future Directions  
Publicly available datasets are necessary to compare the performance of different models effectively. Presently, models are tested on custom datasets, which makes comparison challenging. While most studies report satisfactory classification performance on quantitative metrics, there are still some limitations. Many studies test their model's capability by comparing healthy subjects to those with AD. However, in clinical settings, the population is more diverse and includes individuals with various diseases. Therefore, models should aim to discriminate AD from similar conditions with high specificity. Furthermore, ML and DL models may not perform consistently on images obtained from various imaging machines. Currently, most models are typically tested on images from a single machine. Therefore, it is important to evaluate these models on images from different machines as well. Various approaches such as the domain adaption technique can be explored to increase the adaptability of a model for different images [21].  
Another interesting direction for future research is the application of foundational models for AD detection. Foundational models are a type of AI model that are trained on massive datasets, mainly in a self-supervised manner, and can be tailored to various specific tasks [40]. These models can address the need for extensive labelled datasets and have strong generalization capability. However, in the medical domain, they have challengessuch as varying types of medical images and the scarcity of large available datasets [41; 42]. In a recent study, a self-supervised foundational model was trained based on 1.6 million OCT and color fundus images. The fine-tuned model showed good performance in the classification of eye disease but had lower performance in predicting non-ocular diseases such as heart failure, showing the need for further research [43].
# Assessment of Areal Methane Yields from Energy Crops in Ukraine, Best Practices
## 3 Results and Discussion
### Maize
As it is shown in Figure 2, the highest SMY of 0.41 \(\pm\) 0.00 m\({}^{3}\) kg\({}^{-1}\)VS was measured for the variety \"Svitanok MV\" from the harvesting time H2 (BBCH-codes 83-85), while the highest AMY value (6365.67 \(\pm\) 55.49 m\({}^{3}\) ha\({}^{-1}\)) was determined for the same variety, which was harvested at H3 (BBCH-code 87). For Western European countries (Austria, Belgium, Germany) specific methane yields between 0.295 and 0.430 m\({}^{3}\) kg\({}^{-1}\)VS are reported [10; 12; 14; 42; 43; 44], for Southern European countries (Italy, Spain) between 0.203 and 0.419 m\({}^{3}\) kg\({}^{-1}\)VS [25; 45] and for Northern European countries (Sweden) between 0.280 and 0.370 m\({}^{3}\) kg\({}^{-1}\)VS [20]. The investigations from the different countries show that areal methane yields of between 2900 and 12,390 m\({}^{3}\) ha\({}^{-1}\) can be achieved with maize silage [12; 43; 44], with large fluctuations between the individual years. The results of these investigations on the specific methane yields are in the middle range of those values for the Western European countries. The data on areal methane yields should be verified in multi-year studies.
# Reversal of the Word Sense Disambiguation Task Using a Deep Learning Model
## 4 Debugging and the Knowledge Base Management System  
The WSD model relies heavily on established lexical resources such as WordNet and the SemCor corpus. While these resources have proven valuable for NLP tasks, their lack of recent updates (over a decade) can limit their ability to handle the evolution of language and the emergence of new word senses. This study proposes a systematic approach to address this limitation. We aimed to enhance the quality and coverage of WordNet and SemCor for WSD tasks. This section will focus on debugging and expanding the knowledge base within WordNet, as well as enriching the annotated textual sentences in SemCor. By improving these resources, we aimed to increase the adaptability and applicability of WordNet to diverse linguistic contexts and domains.  
This study proposes a multifaceted methodology to address the limitations in existing lexical resources such as WordNet. The methodology focuses on two key aspects: (1) debugging the WordNet knowledge base to identify and rectify inconsistencies and inaccuracies in the synsets' descriptions, thereby enhancing their reliability for WSD tasks; (2) expanding WordNet with new synsets to accommodate the evolving nature of language and ensure its continued relevance for contemporary NLP applications. By improving the quality and coverage of WordNet and similar resources such as SemCor, this approach aimed to increase the adaptability and applicability of these tools to diverse linguistic contexts and domains.  
Figure 5 illustrates a schematic of the proposed WSD debugging methodology, which is a five-stage process designed to address inconsistencies and inaccuracies in WordNet. The initial stages (1 and 2) focus on preparation of the data and semantic representation. Stage 1 involves rigorous selection of the data and preprocessing of the training and testing data (including cleaning and augmentation) to ensure the data's quality and representativeness. Stage 2 leverages the pretrained \"KerasNLP BERT\" module to acquire embedded word vectors from the prepared data. This captures rich semantic representations for each word within its context.  
Stages 3-5 address exploration and refinement of the knowledge base. Stage 3 performs cluster analysis on the embedded vectors. Principal component analysis (PCA) reduces the dimensionality and groups words with similar semantic properties, facilitating consolidation of the synsets and revealing potential inconsistencies. Stage 4 utilizes projection analysis to provide an interactive interface for exploring the semantic relationships between synsets. This visual exploration (2D/3D) helps identify and rectify inconsistencies within the hierarchy of synsets. Finally, Stage 5 involves the evaluation and integration of various candidate models of WSD. This rigorous assessment ensures the selection of the most effective model for integration, ultimately improving the performance of disambiguation. Next, we describe these steps in the WSD debugging task in more detail.
## 3 System Model and Problem Statement  
### Sensor and Network Model  
Sensor nodes usually have limited communication ability in WSNs. When they are deployed into a large-scale sensor field, multiple BSs are necessary for the purpose of information gathering, as shown in Figure 1. In addition, for the sensor nodes distributed within a relatively smaller circular area covered by a BS, as shown in Figure 2, they can self-organize into a subnet to collect and transmit information independently. In this paper, we solve the clustering problem for such a subnet consisting of the EH-Sensors. In addition, these EH-Sensors are uniformly deployed in a circular sensor field with dense \(\rho\). The BS is located at the center of this field. Let \(S\) denote the area of this field. We divide the circular sensor field into \(m\) concentric ring-based units with equal area \(S/m\). CHs in different units cooperatively transmit data to BS by a multi-hop communication method during the data transmission process. Each CH in unit \(i\) (\(i>1\)) selects a routing head from the CHs in unit \(i\)-1, and it only needs to transmit data to this routing head. Every CH in the first unit can transmit data to BS directly.  
For the development of our protocol, we make several assumptions about the sensors as follows:  
Figure 1: Network deployment scenario.  
Figure 2: Network model.  
* All EH-Sensors are homogeneous and have the same ability to harvest energy from the ambient environment;
* Each EH-Sensor is stationary or nearly stationary after deployed in the sensor field;
* Each EH-Sensor can estimate the distance to the transmitter according to the received signal strength indicator (RSSI), if the transmitting power is known in advance;
* Each EH-Sensor can adjust its communication power level according to the distance to the expected destination;
* If an EH-Sensor serves as a CH, it compresses each member node's data with a fixed compression ratio \(a\).
# Optimization on Linkage System for Vehicle Wipers by the Method of Differential Evolution
## 2 Materials and Methods
### Establishing the Wiper Linkage Motion Model
#### 2.1.2 Motion of the Passenger Side Linkage of CDLS  
Similar mathematical formulas are valid for the motion of the passenger side linkage of CDLS. In this case, \(\theta_{2}\) is the input variable (of the input link AB) and \(\theta_{6}\) is the unknown output (of the output link FE) to be determined. Using the vector loop method and the half-angle formula for tan, we may show that:  
\[\theta_{1}-\theta_{6}=2\cdot\arctan\left[\frac{-E\pm\sqrt{E^{2}-(F^{2}-D^{2}) }}{F-D}\right] \tag{15}\]  
and  
\[\theta_{1}-\theta_{5}=\arctan\left[\frac{-r_{2}\cdot\sin(\theta_{1}-\theta_{ 2})+r_{6}\cdot\sin(\theta_{1}-\theta_{6})}{-r_{2}\cdot\cos(\theta_{1}-\theta_ {2})+r_{6}\cdot\cos(\theta_{1}-\theta_{6})+r_{11}}\right] \tag{16}\]  
with \(D=-2\cdot r_{2}\cdot r_{6}\cdot\cos(\theta_{1}-\theta_{2})+2\cdot r_{11} \cdot r_{6}\), \(E=-2\cdot r_{2}\cdot r_{6}\cdot\sin(\theta_{1}-\theta_{2})\), and  
\[F={r_{2}}^{2}+{r_{6}}^{2}+{r_{11}}^{2}-{r_{5}}^{2}-2\cdot r_{11}\cdot r_{2} \cdot\cos(\theta_{1}-\theta_{2})\]  
The solution we adopt for \(\theta_{6}\) is the following  
\[\theta_{1}-\theta_{6}=2\cdot\arctan\left[\frac{-E+\sqrt{E^{2}-(F^{2}-D^{2})}}{ F-D}\right] \tag{17}\]  
Using the method introduced in Section 2.1.1, it can be shown that the angular velocities \(\omega_{5}\) (the derivative of \(\theta_{5}\) with respect to time) and \(\omega_{6}\) (the derivative of \(\theta_{6}\) with respect to time) respectively of Link BE and of Link FE can be expressed as:  
\[\omega_{5}=\frac{r_{2}\cdot\omega_{2}\cdot\sin(\theta_{2}-\theta_{6})}{r_{5} \cdot\sin(\theta_{6}-\theta_{5})} \tag{18}\]  
and  
\[\omega_{6}=\frac{r_{2}\cdot\omega_{2}\cdot\sin(\theta_{2}-\theta_{5})}{r_{6} \cdot\sin(\theta_{6}-\theta_{5})} \tag{19}\]Besides, the angular acceleration \(\alpha_{5}\) of Link BE and the angular acceleration \(\alpha_{6}\) of Link FE can be expressed as follows:  
\[\alpha_{5}=\frac{r_{2}\cdot[{\omega_{2}}^{2}\cdot\cos(\theta_{2}-\theta_{6})+ \alpha_{2}\cdot\sin(\theta_{2}-\theta_{6})]}{r_{5}\cdot\sin(\theta_{6}-\theta_ {5})}+\frac{r_{5}\cdot{\omega_{5}}^{2}\cdot\cos(\theta_{6}-\theta_{5})-r_{6} \cdot{\omega_{6}}^{2}}{r_{5}\cdot\sin(\theta_{6}-\theta_{5})} \tag{20}\]  
and  
\[\alpha_{6}=\frac{r_{2}\cdot[{\omega_{2}}^{2}\cdot\cos(\theta_{5}-\theta_{2})+ \alpha_{2}\cdot\sin(\theta_{5}-\theta_{2})]}{r_{6}\cdot\sin(\theta_{6}-\theta _{5})}+\frac{r_{5}\cdot{\omega_{5}}^{2}-r_{6}\cdot{\omega_{6}}^{2}\cdot\cos( \theta_{6}-\theta_{5})}{r_{6}\cdot\sin(\theta_{6}-\theta_{5})} \tag{21}\]  
Using the Law of Cosines as in Section 2.1.1, it can be shown that the _transmission angle_\(\mu_{6}\) on the passenger side of CDLS can be expressed as follows:  
\[\mu_{6}=\arccos\left[\frac{r_{5}^{2}+{r_{6}}^{2}-{r_{2}}^{2}-{r_{11}}^{2}+2{r_ {2}}\cdot{r_{11}}\cdot\cos(\theta_{1}-\theta_{2})}{2{r_{5}}\cdot{r_{6}}}\right] \tag{22}\]
# Clutter-Masked Waveform Design for LPI/LPD Radarcom Signal Encoding
## 5 Clutter-Masked Radarcom Waveform Performance Evaluation
### Intercepted Signal Classification by Unauthorized Receivers  
Our local clutter model in the frequency domain was modeled with the Arcsine distribution with the b-parameter equal to 100, which was also used to create our transmitted signals, as per (1) and (11). We assume that an unauthorized interceptor will attempt to analyze our transmissions after gaining access to them. We also assume that the interceptor is aware of the frequency band used by our radar, as well as of the method of signal design using OFDM. The interceptor is expected to classify the received signals in order to determine whether they may also contain other useful data.  
We model this classification process as goodness-of-fit (GoF) tests, which compare the intercepts to distributions of the local clutter with a slightly varying b-parameter, as well as to uniform and normal distributions. Any significant deviations of intercepts from the local clutter distribution would be regarded as those containing useful data. The GoF was evaluated by using the MSE, NRMSE, and NMSE for 200 intercepted signals. The results of these GoF tests are shown in the Table 2 below, where MSE = 0 indicates the closet fit and \(\infty\) the worst; for NMSE and NRMSE, 1 indicates the closest fit and \(-\infty\) is the worst. All three methods demonstrate the agreement that the Arcsine distribution with b = 100 is the best fit, which means that the intercepted radarcom signal would be classified as clutter by an unauthorized platform.
###### Abstract  
Aim: This study aimed to assess the action of a chlorhexidine-based brush-on gel application in the treatment of plaque-related gingivitis. Methods: The enrollment involved consecutive patients diagnosed with plaque-induced gingivitis. Each participant's full mouth plaque score and gingival index were recorded at the first appointment (t0) and at follow ups after 1 week (t1), 2 weeks (t2) and 3 weeks (t4). All patients were randomly sorted into two groups: A study group, who was given instruction to brush their gums daily with a chlorhexidine gel, and a control group who received a placebo gel. The two groups' data at baseline were compared using a chi-square test, while the t-Student and Mann-Whitney tests were employed to analyze the index's trends, both separately and compared. Results: In total, 30 patients were enrolled, 15 referring to the study group and 15 to the control group. The study group had an 87% decrease in their plaque score and an 84% decrease in their gingival index (\(p<0.05\)). The control group displayed a reduction in plaque score and gingival index of 74% and 84%, respectively (\(p<0.05\)). The plaque score decreased statistically more significantly in the study group than in the control group. Conclusion: Topical employment of a chlorhexidine-containing brush-on gel appears to be a useful home tool in the treatment of plaque-induced gingivitis, associated with professional debridement.  
g gingivitis; chlorhexidine; oral hygiene; topical gel +
Footnote †: journal: Applied Physics Letters
## 3 Results and Discussion
### Deformation Analysis of Coal Samples with Different Gas Pressures
#### 3.3.2 Effect of Different Gas Pressures on Coal Sample Strain and Adsorption Equilibrium Time  
Although the adsorption equilibrium times of different gases with different adsorption characteristics are different, different gases exhibit a similar variation law under the different gas atmospheres. Under the same pressure, the overall trend of the strain in the adsorption process is consistent. In other words, the strain rate is high in the initial stage. As the adsorption progressed, the strain rate tended to be gradual. The equilibrium strains of the coal sample in axial and radial directions increased with the increase of the pressure. The effect of different gas pressures on coal sample strain and adsorption equilibrium time is shown in Figure 13. When the adsorption pressure was 0.4 MPa, the corresponding adsorption equilibrium times under N\({}_{2}\), CH\({}_{4}\), and CO\({}_{2}\) gas atmospheres were 9.5 h, 10 h, and 13 h, respectively. When the gas pressure increased to 0.8 MPa, their corresponding adsorption equilibrium times were 10.6 h, 11 h, and 15 h, which were increased by 1.1 h, 1 h, and 2 h, respectively. When the gas pressure was 1.2 MPa, the adsorption equilibrium times were 12.3 h, 13.5 h, and 17 h, which were increased by 1.7 h, 2.5 h, and 2 h, respectively. It can be seen from Figure 13 that under the same pressure conditions, the equilibrium times with different gas environments were in the order of CO\({}_{2}\)\(>\) CH\({}_{4}\)\(>\) N\({}_{2}\). The equilibrium time difference between N\({}_{2}\) and CH\({}_{4}\) gas environments was insignificant, whereas the equilibrium time of CO\({}_{2}\) was considerably different. In other words, the stronger the gas adsorption characteristic of gas, the longer it took to reach the adsorption equilibrium state. The adsorption equilibrium time increased gradually with increasing gas adsorption pressure.  
Figure 13: _Cont._
# Survey on Q-Learning-Based Position-Aware Routing Protocols in Flying Ad Hoc Networks
## 6 Lessons Learned, Open Issues, and Research Challenges
### Open Issues and Research Challenges
#### 6.2.1 Adopting the Appropriate Channel Model  
To simulate the routing protocols in FANETs, it is necessary to adopt an appropriate channel model for all links such as U2U, U2 BS, and U2 GU links. With the suitable channel model considering the mission environment and type of wireless links, it is possible to estimate accurate SINR, data rate, delay, and packet error rate in FANETs [1; 23]. These parameters also help to make better adjustments to mobility and transmission power and improve the performance of MAC and routing protocols. Usually, owing to the advantages of 3D positioning adjustment in high altitude, the U2U links are treated as free-space paths and U2 BS links are dominated by the LoS. However, U2U links face dynamism owing to the high mobility of UAVs, and path loss mostly depends on the inter-UAV distance, weather condition, beam pointing error, and the density of nodes. Depending on the mission environment and distribution of GUs, the U2 GU links create the probability of having both NLoS and LoS cases, which entirely depends on downlink antenna elevation, azimuth angle, and transmission power toward the GU [4; 60]. Here, all the routing protocols only consider the U2U links to the channel model. In [61], the authors discuss the millimeter-wave-based A2A and A2G channel models for UAV swarms, and consider different antenna types that are specially designed by taking into account the UAV platform size, power, and payload constraint. A comprehensive study on the A2G channel model for UAV-based communication is given in [62].
## 5 Experimental Setup
### Evaluation Measures  
For the experiments evaluated, we used several metrics to evaluate the models' performance. The formulae are defined as follows:  
\[Accuracy=\frac{TP+TN}{TP+FP+FN+TN} \tag{1}\]  
\[TPR=\frac{TP}{TP+FN} \tag{2}\]  
\[FPR=\frac{FP}{FP+TN} \tag{3}\]  
\[Recall=\frac{TP}{TP+FN} \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **\# Before Aug.** & **\# of the Aug. Images** & **\# After Aug.** & **\# Test Set (20\%)** & **\# Train Set (80\%)** \\ \hline D0 & 38 & 962 & 1000 & 200 & 800 \\ \hline D1 & 33 & 967 & 1000 & 200 & 800 \\ \hline D2 & 33 & 967 & 1000 & 200 & 800 \\ \hline D3 & 65 & 935 & 1000 & 200 & 800 \\ \hline D4 & 40 & 960 & 1000 & 200 & 800 \\ \hline D5 & 33 & 967 & 1000 & 200 & 800 \\ \hline Total & 242 & 5758 & 6000 & 1200 & 4800 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Our dataset before and after augmentation techniques.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyper-Parameters** & **Value** \\ \hline Batch size & 32 \\ Epochs & 20 \\ Activation function & ReLu \\ Learning rate & 0.0001 \\ Momentum & 0.9 \\ Optimizer & Stochastic Gradient Descent (SGD) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Hyper-parameters setting.  
\[Precision=\frac{TP}{TP+FP} \tag{5}\]  
\[F_{1}Score=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{6}\]  
where \(TP\), \(FN\), \(TN\), and \(FP\) represent the true positive prediction, false negative prediction, true negative prediction, and false positive prediction, respectively, as shown in Equations (1)-(6).