esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Theoretical Solution and Numerical Validation of Mechanical Parameters
### Parameter Solving and Validation
#### 4.3.1 Neutral Axis Angle
\(\varphi\) must be within the interval \(\left(-\frac{\pi}{2},\frac{\pi}{2}\right)\), and theoretically, there exist corresponding \(N,M\) at every point within the interval. Therefore, in this paper, we adopt the following treatment: Equalize the interval \(\left(-\frac{\pi}{2},\frac{\pi}{2}\right)\) and take 300,000 values of \(\varphi\) and substitute them into Equation (55) to get the corresponding 300,000 values of \(e\). Then the \(\varphi-e\) curve is plotted with \(e\) as the horizontal coordinate and \(\varphi\) as the vertical coordinate. The \(\varphi-e\) curve, derived using this method and compared with the FEM, is illustrated in Figure 9.  
Figure 9: Comparison of analytic and numerical solutions for \(\varphi-e\) curve. From the beginning of Section 4.3, \(-\frac{2}{r}\leq\frac{N}{M}\leq\frac{2}{r}\), and the transverse coordinates of the left and right endpoints in Figure 9, e, are \(-\frac{2}{r}\) and \(\frac{2}{r}\), and the corresponding vertical coordinates are \(\frac{\pi}{2}\) and \(-\frac{\pi}{2}\). When \(e=-\frac{2}{r}\), \(\varphi=\frac{\pi}{2}\), and the upper part of the segmental ring is just compacted at this time, which is the critical point (C\({}_{23}\)) of mode 2 and mode 3; When \(e=\frac{2}{r}\), \(\varphi=-\frac{\pi}{2}\), at which time the lower part of the segmental ring is just pulled apart, i.e., it is the critical point (C\({}_{13}\)) of mode 1 and mode 3; When \(e=-\frac{4u}{(2+u)\pi r}\), \(\varphi=-\frac{\pi}{2}\), the neutral axis angle is exactly at the center of the segmental ring; When \(e=0\), \(\varphi\) is equal to that of the longitudinal equivalent continuous model, that is, the particular value of the longitudinal nonlinear equivalent continuous model when the axial force is zero. In Figure 9, the numerically simulated \(\varphi-e\) curves are consistent with the theoretical result. At the special point, \(e_{0}\), the numerical simulation result is \(-57.27^{\circ}\), and the theoretical result is \(-59.66^{\circ}\); the numerical simulation result at point \(e_{\varphi 0}\) (\(-0.3036\)) is close to \(0^{\circ}\). The theoretical result agrees with the numerical result.
# Interpretability in Sentiment Analysis: A Self-Supervised Approach to Sentiment Cue Extraction
## 2 Related Works
### Interpretability of Deep Learning Models  
The interpretability of deep learning models in NLP is a vital research area, concentrating on deciphering and explaining how these models make decisions. This aspect is particularly critical in applications where trust and transparency are paramount [4; 31].  
Interpretability in deep learning models is essential to validate and improve model performance, ensure fairness, and provide information on model behavior, especially in areas such as healthcare, finance, and legal applications [31].  
Several techniques have been developed to enhance the interpretability of deep learning models. These include attention mechanisms, which underscore parts of the input data most relevant to the model decision [32], and Local Interpretable Model-Agnostic Explanations (LIME), which approximate the model locally using interpretable models [5]. In addition, researchers also use topic models such as Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) to achieve interpretability [33]. For example, [PERSON] and [PERSON] [34] combined LDA with deep learning models to not only grade student essays but also identify the characteristics of excellent essays in terms of language expression.  
Despite these advances, understanding deep learning models, particularly transformers, remains challenging. Their black-box nature often hinders the understanding of their predictive reasoning [4].  
Future research in model interpretability is likely to focus on developing more robust generalizable techniques that offer clear explanations of model decisions, including integrating interpretability directly into model architecture and training [35].  
As deep learning models continue to advance and find application in critical domains, the significance of interpretability will only escalate. Ensuring that these models are transparent and that their decisions are understandable is key to their successful and ethical application.
# Real-Time Computing Strategies for Automatic Detection of EEG Seizures in ICU
## 3 Results
### 'Generalised Seizure' Subgroup  
In the Generalised Seizure subgroup, the k-nearest neighbours (KNN) model with a k value of 15 demonstrated the best performance in terms of sensitivity and specificity. The mean F1 score for all features was 0.76. However, focusing on the most impactful variables, the averaged F1 score was 0.84 with a standard deviation of 0.03. The sensitivity achieved was 93.7%, and the specificity was 100%, as indicated in the confusion matrix (Figure 3G). False positives and negatives predominantly came from patients with brain haemorrhage, CET, and infections, with a higher occurrence of NCS and CS.
# Space Object and Background Polarization Models and Detectability Analysis
## 2 Basic Theory of the Polarization Bidirectional Reflection Distribution Function Model
### Parametric Genetic Algorithm Inversion Verification
The inversion results of the two materials after the genetic algorithm operation are shown in Table 1, and it can be seen that the results of the inversion of the complex refractive index of the two materials are closer to the real values than those of the reference literature [18]. The inverse material parameters are substituted into the multi-parameter model of this paper and the Torrance-Sparrow model [19] to verify the accuracy of the multi-parameter model, and the simulation curves of the reflected light polarization of the two materials under the two models vary with the angle of the incident zenith of the light source are obtained, and the DOP simulation curves of the materials are compared with the experimental data. Figure 2 depicts this. The results show that the actual surface of the material is not perfectly smooth, and there is also a scatter \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Materials**} & \multicolumn{2}{c}{**Truth**} & \multicolumn{2}{c}{**Reference**} & \multicolumn{2}{c}{**Estimated**} \\ \cline{2-7}  & \(n\) & \(k\) & \(n\) & \(k\) & \(n\) & \(k\) \\ \hline Aluminum & 1.24 & 6.6 & 1.37 & 3.97 & 1.231 & 6.574 \\ \hline Black Paint & 1.405 & 0.2289 & 1.46 & 1.32 & 1.429 & 0.2398 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Inversion results of complex refractive index of aluminum and black paint materials. scattering in addition to specular reflection of light on the surface of the material, and the body scattering is characterized by a deflection effect, which causes the simulation polarization value of the two materials to be low overall, so the simulation curve of the multi-parameter model in this paper is more consistent with the experiment.
## 7 Numerical Results and Discussion
### Effect of the Truncation Factors  
The truncation factors \(L_{1}\), \(L_{2}\) and \(L_{3}\) regulate the complexity and performance, so we first give evaluation results in Figure 6 for the proposed detector, which provides us with the effect \(L_{1}\) has on detection performance. Here, the CFO was randomly set to range from \(-80\) ppm to \(80\) ppm with probability density function of symmetric triangular shape [2]. The CPO was considered to be uniform distribution in interval (\(-\pi\), \(\pi\)). We observe that performance improves as sample length \(L_{1}\) increases from \(1\) to \(15\), but improvement degrades as sample length \(L_{1}\) increases. For example, Figure 6 shows that for PER of \(1\times 10^{-3}\), as sample length \(L_{1}\) increases from \(1\) to \(3\), the SNR gain is about \(4.3\) dB. As \(L_{1}\) increases from \(5\) to \(7\), the SNR gain reduces to \(1.2\) dB. As \(L_{1}\) further increases from \(13\) to \(15\), the SNR gain becomes \(0.5\) dB. Similar results were also achieved for \(L_{2}\) and \(L_{3}\), which are not given here. In pratice, suitable truncation factors can be selected according to the specific requirements on the performance, and a large degree of freedom in complexity can be achieved. For example, according to [1], the PER should be less than \(1\times 10^{-2}\) when the SNR is \(5\) to \(6\) dB. Referring to Figure 6, five samples are sufficient when the SNR of the input chip signal is \(5\) dB. In the following, the maximum values are considered for \(L_{1}\), \(L_{2}\) and \(L_{3}\) to ensure the best possible performance.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Detailed Description** \\ \hline Channel condition & Complex AWGN \\ Power of the complex noise & \(1/\)SNR \\ Detection Scheme & Bit-level noncoherent \\ Timing synchronisation & Perfect \\ Generator polynomial of PN code & \(1+x+x^{4}\) \\ Payload length of PPDU (bits) & \(160\) \\ Carrier frequency (MHz) & \(924\) \\ CPO (rads) & Uniform distribution in (\(-\pi\), \(\pi\)) or Wiener process \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters used in simulations. AWGN: additive white Gaussian noise; SNR: signal-to-noise ratio; PN: pseudorandom number; PPDU: physical layers protocol data unit; CPO: carrier phase offset.  
Figure 6: _Cont._
# Empirical Evidence Regarding Few-Shot Learning for Scene Classification in Remote Sensing Images
## 3 Materials and Methods
### FSL Approaches
#### 3.3.1 Inductive Approaches  
The first of the three selected inductive techniques is Prototypical Networks (PNs), which learn a metric space where classification is performed by calculating the distances to prototype representations of each class [39]. Compared to other FSL techniques, PNs incorporate a simpler inductive bias, which can be advantageous in scenarios with limited data. PNs are thus based on the idea that there exists an embedding in which points cluster around a single prototype representation for each class.  
In SimpleShot (SS), the authors challenge a common view to mitigate overfitting where FSL approaches extract image features using a convolutional neural network (CNN) and use a combination of meta-learning and nearest-neighbour classification to perform the recognition [40]. They thus showed that nearest-neighbour classifiers can achieve very good results without meta-learning. According to the authors, applying basic feature transformations to the features prior to nearest-neighbour classification results in a highly competitive performance.  
In [41], the authors presented a thorough comparative analysis of many FSL approaches with results showing that deeper backbones significantly decrease the performance differences among techniques on datasets with limited domain differences. They also showed a modified baseline strategy that achieves a competitive performance when compared to other approaches on both non-RS datasets mini-ImageNet [54] and CUB-200-2011 [55]. Their baseline model follows the standard transfer learning procedure of network pre-training and fine-tuning. This approach is denoted here as Fine-Tuning (FT).
## 1 Introduction
A brief sketch of the test rig is explained in detail in [15; 16] and is shown in Figure 1. The wear-related specimens (see Figure 2) were stressed tangentially, thus, the normal load was applied to shorten the runtime and tangential forces were applied as working forces. Measurements were obtained from the hydraulic system (proportional to the friction forces) and from the specimen via Acoustic Emission (AE) signals. The measured values were used to monitor the system and to evaluate the state of the system, including the state of wear-related effects based on load patterns or stress-equivalent collectives, thereby deriving representative forces. As a consequence of the system's operation (friction velocity, cycle times (ratio of stress to break time), temperature behavior of the surface, lubricant distribution, etc.) and the materials used, abrasive sliding wear appeared. During the operating period, the system has an increasing probability of losing its functionality. The following sequence of effects is observed: (1) surface changes caused by crack initiation (2) and growth, (3) located material, and (4) material losses. The surface quality and the degree of wear of the friction partners greatly affect the system's functionality and therefore finally the reliability. The stress (surface damage and degree of wear) of the friction partners can be represented as a probabilistic process, which depends on the stress history and the current operating parameters. Figure 1: Wear test rig, Chair of Dynamics and Control, U Duisburg-Essen, Germany. In [14] three approaches (Acoustic Emission analysis, diagnosis-oriented data filtering and data-based classification approach) are already introduced. Using the diagnosis-oriented data filtering with threshold-based classification approach results are obtained stating four different data sets with two different operating conditions [15]. The approach denoted as condition evaluation based on a sensitivity matrix was briefly introduced and described in [16]. A method for classifying the machine state is based on the filtering technique introduced in [15]. Two data sets based on the same machine operating conditions were analyzed. In this publication the comparison of the three approaches introduced in [14] is revised, detailed, and extended with the fourth approach introduced in [15]. With this contribution the previous publication of the author/s are detailed, finalized, and summarized to round up this research work and to establish resulting conclusions. Therefore, if necessary results are repeated. The organization of this paper is as follows. In Section 1, filtering and classification methods related to condition monitoring and wear diagnosis are reviewed, the test rig employed in this contribution is described. In Section 2, the four approaches employed for evaluations and comparison are introduced. In Section 3, the experimental results obtained using these four approaches are presented. The proposed approaches are compared in Section 4. In Section 5 the contribution is summarized, further work is suggested.
# Approximate LSTM Computing for Energy-Efficient Speech Recognition
## 2 Background  
### DNN-Based End-to-End Speech Recognition  
Figure 1 depicts how the DNN-based end-to-end speech recognition handles the input speech signal and generates the final recognition results. Note that the input speech signal in the time domain is divided  
into equal-length windows, i.e., each window contains the same number of speech samples. Then, we capture the essential features of the input window, which is normally based on the frequency-domain analysis [16]. The captured features are issued at the DNN-based recognition system, resulting in a character with the most probability for each window input. As we perform the window-level DNN processing, as illustrated in Figure 1, the dedicated decoder should be added at the end of the recognition system to construct the final output sentence.  
In the baseline system used for this work, considering the contemporary speech processing works [17], we adopt the time interval of 100 ms for making the input speech windows. After performing FFT processing, numerous Mel-frequency cepstral coefficient (MFCC) values in the frequency domain are used as features to be the input state of the LSTM-based network where the internal processing layers will be described later. Similar to the other state-of-the-art solutions [18], the connectionist temporal classification (CTC) step is finally applied for realizing the decoder architecture, removing the copies of detected characters, as shown in Figure 1.
# Impact Characteristics of a Bidirectional Pneumatic DTH Hammer for Unconsolidated Formations
## 1 Introduction
[24] designed a rotary-percussive ultrasonic drill and employed modal analysis to finely adjust the resonance frequencies of both its percussive unit and rotary unit. For increasing drilling efficiency, DTH air hammers were developed with curvilinear free surfaces piston in the literature [25] to impact rocks with the minimum energy input using no contact between the pistons with hammer bodies. In the literature [26], a large diameter reverse circulation DTH hammer was designed to improve the penetration rate during rescue well drilling, which was used to deal with underground coal mine disasters. Although the air DTH hammer has achieved good drilling results in different types of borehole drilling projects, when it is used in loose and unconsolidated layers, accidents like drilling tools sticking or burying are still inevitable to occur, which will cause damage to drilling tools and scrapping of boreholes. In order to leverage the advantages of DTH hammer drilling and deal with accidents of drilling tools sticking or burying in unconsolidated formations, a bidirectional pneumatic DTH hammer is proposed in this paper. During the drilling process, the bidirectional pneumatic DTH hammer breaks rocks and drills boreholes with forward impact, while when drilling tools become stuck in boreholes, the DTH hammer can help to release drilling tools using backward impact. To study the impact characteristics of the bidirectional DTH hammer, optimize its structure, and verify the rationality of its designed functions, a simulation method and field experiment will be applied. The paper is organized as follows: in the second part of this paper, the configuration of the bidirectional pneumatic DTH hammer is stated in detail. Then, a theoretical model and a simulation model of the bidirectional DTH hammer are constructed based on its structure and pneumatic transmission theory. Impact characteristics of the DTH hammer are studied as its piston impacts forward and backward in the fourth section. And lastly, drilling experiments are conducted to validate the operational effectiveness of the DTH hammer.
# Numerical Simulation of Flapping Airfoil Aerodynamic Characteristics
## 3 Results and Analysis
### Effects of Motion Parameters on the Aerodynamic Characteristics of Flapping Airfoils
#### 3.1.3 Impact of Motion Trajectory on Flapping Airfoil Aerodynamic Characteristics

Table 2: Periodic average values of flapping airfoil lift and drag coefficients. nism and the Trailing vortex capture mechanism exist in the flow fields of four flapping airfoil motion trajectories: elliptical, \"\(\infty\)\" shaped, horizontal straight line, and 45\({}^{\circ}\) inclined trajectories. In the cloud map of the Trailing vortex capture mechanism, the trailing vortex shed on the previous stroke is captured by the flutter airfoil, the low-pressure region near the airfoil is enhanced, and the flutter lift is increased. In the cloud image of the leading edge vortex delay velocity mechanism, the leading-edge vortex is attached to the airfoil surface, the low-pressure region is generated, and the flutter lift is increased. The lifting efficiencies of the flapping airfoils under different motion trajectories are shown in Table 3. It can be seen that the trajectory has a significant effect on the lifting efficiency: the \"\(\infty\)\" trajectory has the highest efficiency with 47.82%, followed by the elliptical trajectory (40.29%), the horizontal straight trajectory with 34.41%, and the 45\({}^{\circ}\) inclined straight trajectory with the lowest efficiency of only 31.55%. The \"\(\infty\)\" trajectory is able to maintain a more stable vortex structure, which reduces energy dissipation and achieves the highest lifting efficiency, which is 51.57% higher compared to the 45\({}^{\circ}\) inclined straight trajectory. These results are consistent with observations of flying organisms in nature, such as butterflies, where the \"\(\infty\)\" flutter pattern corresponds to a higher lifting efficiency. This further confirms the optimal selection of trajectories during the evolutionary process of organisms. Figure 17: Vorticity cloud map of wake capture mechanism. (**a**) Horizontal straight trajectory t = 0.85 T; (**b**) elliptical trajectory t = 0.86 T; (**c**) “\(\infty\)” shaped trajectory t = 0.8 T; (**d**) 45\({}^{\circ}\) tilt trajectory t = 1.3 T.
# Development of a Multi-Radio Device for Dry Container Monitoring and Tracking
## 3 Materials and Methods
### CTD's System Architecture  
As can be seen from the CTD's architecture in Figure 4, there are essentially 5 HW blocks:  
1. The microprocessor (MCU) controls all the system functions, also embedding the chip for BLE Mesh radio;
2. The modem provides NB-IoT cellular connectivity and GNSS data reception;
3. The set of sensors (temperature, humidity, pressure, 3-axis motion for movement and shock detection, container door opening);
4. The set of antennas (cellular, BLE, GNSS);
5. The power supply battery pack (not shown in Figure 4).  
The CTD provides sensing and position data to a OneM2M compliant [2] platform in the cloud through the NB-IoT cellular network. The GNSS receiver provides position and timing data, the embedded sensors provide instant value data for temperature, humidity, pressure, 3-axis motion for movement and shock detector, and container door opening. This information is transmitted to the OneM2M compliant platform periodically, with a time interval selected according to the intended use, or when an \"alarm event\" occurs, notably when some physical sensing data are out of the limits set by the user of the CTD.  
According to the architecture shown in Figure 4, the CTD also operates as a gateway between the other nodes of a BLE Mesh network (left in blue) and the NB-IoT cellular network. The information provided to the cloud platform is of the same type as the previous case, but it is referred to as a sensor node of the mesh network.  
The interfaces supported by each diagram block are described in Table 1 as follows (see Figure 4 for the names).  
Figure 3: Representation of CTDs on the ship: devices at the top of the container stack perform as the NB-IoT/BLE Mesh Gateway, and those below are nodes of BLE Meshes.
# New Results for the Error Rate Performance of LoRa Systems over Fading Channels
## 3 Main Results  
In this section, exact analytical expressions for \(P_{s}\) in terms of a single integral as well as accurate approximations will be obtained for Nakagami-\(m\), Ricean and Hoyt fading channels.
# A Distributed Autonomous Mission Planning Method for the Low-Orbit Imaging Constellation
## 6 Discussion  
From the experiments in the previous section, it can be seen that c-CBBA can effectively reduce communication volume while ensuring scheduling quality. In order to better understand the algorithm, this section further analyzes the operational mechanism of c-CBBA and the effectiveness of the two strategies in different scenarios.