esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results
### Effect of Particle/Powder Material  
Particle/powder material is an important factor affecting particle damping. The difference includes material density, particle surface hardness, and friction coefficient.  
This section compares four kinds of particles or powder, which are 2 mm stainless steel particles, lead particles, stainless steel powder, and tungsten carbide powder.  
The A1 cavity was adopted to test the selected particles and powder. The filling factor \(V_{\mathrm{r}}\) is 0.9, the results are shown in Table 9, and the corresponding amplitude-frequency responses are shown in Figures 15 and 16. It can be seen that the lead-filled particles are superior to the stainless steel particles in damping, especially under random excitation conditions, the former (\(-45.12\%\)) is 50% lower than the latter (\(-30.39\%\)). For the lead particles, the response peak \(A_{\mathrm{sin}}\) and \(A_{\mathrm{spec}}\) reduction amplitudes are 47.42% and 45.12%, respectively. Therefore, 2 mm lead particles can be used as an alternative to the best granular material. Compared with stainless steel powder and tungsten carbide powder, tungsten carbide powder is obviously dominant under sinusoidal excitation, \(A_{\mathrm{sin}}\) is reduced by 86.30%, slightly dominated under random conditions, and \(A_{\mathrm{spec}}\) is reduced by 52.14%.  
Therefore, tungsten carbide powder can be used as an alternative to the best powder filling material. In summary, the tungsten carbide powder has the best vibration damping effect, followed by stainless steel powder, 2 mm lead particles, and stainless steel particles had the worst vibration damping effect.  
The amplification factor ratio \(A^{\prime}\) is defined as the ratio of the amplification factor to the control scheme in the different experiment scheme. Figure 18 shows the effect of the filling coefficient on the damping effect of the above tungsten carbide filling scheme with the filling coefficient \(V_{\mathrm{r}}\) as the abscissa and the magnification factor ratio \(A^{\prime}\) as the ordinate. As can be seen from Figure 17, when the filling coefficient increased to \(1.05\), \(A^{\prime}_{\mathrm{sin}}\) stops decreasing under sinusoidal excitation, and the random excitation decreases slowly, thereby determining that \(1.05\) is the optimum filling coefficient. The damping effect of the 2 mm particle filling scheme was tested in accordance with the fill factor. Table 12 shows the test results for increasing the filling coefficient of lead and stainless steel particles. It can be seen that increasing \(V_{\mathrm{r}}\) helps to improve the damping effect.  
Figure 17: The effect of the filling coefficient on the experiment results: (a) Sinusoidal excitation; (b) Random excitation.
## 4 Experiments
### Global Settings
#### 4.2.3 Sentiment Analyzers  
In experiments, sentiment scores for English and Chinese statements were obtained using two sentiment analysis tools, referred to as Stanford CoreNLP and Baidu Sentiment Analyzer, as mentioned in Section 2.3. The reason for choosing these two sentiment analyzers is that they are among the best-performing sentiment analyzers in English and Chinese, respectively. To verify the performance and compatibility of these two analyzers, we constructed 100 Chinese-English test statement pairs (listed in Appendix A.2), where each pair consists of semantically identical English and Chinese statements containing both concept/exemplar words and attribute words. We analyzed the sentiment polarity of English and Chinese statements in each pair using CoreNLP ([[https://stanfordnlp.github.io/CoreNLP/](https://stanfordnlp.github.io/CoreNLP/)]([https://stanfordnlp.github.io/CoreNLP/](https://stanfordnlp.github.io/CoreNLP/)), accessed on 12 March 2024) and Baidu Analyzer ([[https://intl.cloud.baidu.com/product/body.html](https://intl.cloud.baidu.com/product/body.html)]([https://intl.cloud.baidu.com/product/body.html](https://intl.cloud.baidu.com/product/body.html)), accessed on 12 March 2024), respectively. The results showed that CoreNLP achieved a 94% accuracy rate on 100 English statements, while Baidu Analyzer achieved a 97% accuracy rate on 100 Chinese statements, with a 96% matching degree between the two analyzers. This indicates good consistency between their sentiment analysis results.  
To examine the effects of different sentiment analyzers, in the testing of effects of procedural variables, we chose two comparative analyzers: NLTK sentiment package for English and Tencent Sentiment Analyzer [[https://ai.qq.com/doc/nlpemo.shtml](https://ai.qq.com/doc/nlpemo.shtml)]([https://ai.qq.com/doc/nlpemo.shtml](https://ai.qq.com/doc/nlpemo.shtml)) (accessed on 12 March 2024) for Chinese.
## Objective  
Review recent developments of acoustic analysis and its role in assessing laryngeal function.  
**Introduction**  
Acoustic analysis is a non-invasive and objective method for distinguishing normal from disordered patients. Traditional analysis includes perturbation parameters and nonlinear dynamic methods such as correlation dimension and Lyapunov exponents. While these nonlinear dynamic methods are successful in distinguishing between chaos and periodicity, error frequently occurs in the analysis of highly disordered voice signals or what is now considered type 4 voice. The voice typing paradigm was first introduced by [PERSON] to describe type 1 voice which is nearly periodic, type 2 voice which is primarily periodic with subharmonics and bifurcations, and type 3 voice which is aperiodic and chaotic. Type 4 voice was later introduced to describe chaotic signals that contain a significant portion of stochastic noise, stemming from turbulent air in the vocal tract. More recent analyses have successfully differentiated between type 3 and 4 voices; however, they only produce singular values and simply reflect the degree of aperiodicity or disorder in a voice signal. In reality, periodic elements and complex nonlinear phenomena, such as subharmonic frequencies, bifurcations, deterministic chaos and stochastic noise, are simultaneously present in voice.  
**Methods**  
Two parameters, the intrinsic dimension and diffusive chaos methods, were recently developed to analyze the distribution of voice type components (VTCs) that are present in the voice signal. The intrinsic dimension represents the lowest dimension at which the data remains fully intelligible, and this method functions by making multiple estimates of local dimension throughout the signal. Diffusive chaos analysis is a signal processing technique that functions by repeatedly assessing the bounded or unbounded trajectories of two variable parameters within the time series. Both methods output the percentage of voice type components 1, 2, 3, and 4 that are present. To assess the effectiveness of these parameters, 135 disordered voice samples of sustained /a/ vowels were selected from the Disordered Voice Database 4337, classified according to the voice type paradigm using spectrogram analysis, and analyzed with diffusive chaos and intrinsic dimension analyses.  
**Results**  
Both methods demonstrate that the distribution of VTCs varies distinctly across traditional voice type groups. Highly disordered type 4 voices contain high proportions of voice type component 4 (VTC4), indicating the presence of noise; however, there are also smaller proportions of VTC1, indicating an underlying periodicity. Notably, the VTCs of type 3 voices are all significantly different from the VTCs of type 4 voices (\(p<0.001\)). These results were compared to calculations of correlation dimension and spectrum convergence ratio, which both demonstrated limited effectiveness in differentiating between all four voice types.
## 5 Experimental Evaluation and Results
### Load Factor  
By limiting the number of buckets within a single segment to four, the total number of buckets in a segment is minimized. This limitation leads to a better load factor, which represents the ratio of the number of elements stored in each bucket to the total number of positions available in the segment. A higher load factor indicates improved memory efficiency in the hash table. Since operations like insertion and search are performed at the segment level, having a larger number of buckets within a segment does not offer an advantage. In fact, limiting the number of buckets in a segment results in better linear probing performance. ESH achieves this by employing a balanced insertion strategy, where records are moved to neighboring buckets to improve the load factor.  
However, increasing the segment size or bucket size reduces the directory size at the expense of a decreased load factor. In order to ensure efficient storage of records within the hash table, ESH prioritizes achieving a higher load factor by utilizing a limited number of buckets within each segment.  
Figure 10 depicts the variations in load factor as the number of insertions increases, measuring the load factor at different record counts in the hash tables. CCEH demonstrates a relatively stable result, as it splits a segment after four cache line probes. This finding aligns with observations made in [18], where it is noted that longer probing lengths increase  
Figure 9: Multiple thread performance comparison.  
the load factor at the expense of performance, while shorter probing lengths result in premature splits.  
In contrast, both Dash and ESH exhibit higher load factors, indicating that the design changes have proven effective. The modifications in Dash and ESH have led to improved load factors compared to CCEH. The improved load factor in ESH compared to Dash can be attributed to the delayed segment split and directory doubling operation. By storing records in all available spaces of neighboring buckets before initiating the split or doubling operation, ESH effectively utilizes the existing space. The experimental results exhibit \"valley\" shapes, which correspond to the points where segment splitting or directory doubling occurs, leading to rehashing.  
As a result of these design choices, ESH achieves a load factor of up to 91%, surpassing the load factors achieved by state-of-the-art schemes.
## 5 Conclusions  
In this paper, a detection algorithm based on feature point matching is proposed. First, the feature vectors are obtained based on the improved sparse Haar-like feature. Then, the feature similarity is calculated by Euclidean distance and activation number to obtain the most similar matching point in the region. However, Haar features are easily affected by illumination conditions. In order to improve the robustness of detection, from the point of view of algorithm theory, a method based on image similarity is adopted in this paper, which maps the images with different illuminations to the same space to reduce its interference. In engineering practice, the model index database is established, and model images with different illuminations are added. Experiments show that the improved sparse Haar-like feature has smaller detection errors and the space mapping method based on image similarity has strong illumination robustness.  
After accumulating enough data in engineering practice, by using artificial intelligence and deep learning methods, a network model can be trained in the future. It can automatically select valid features without human intervention. In addition, the faults of other parts of the engine will also be taken into account.  
Conceptualization, W.Z. and Y.C.; Methodology, W.Z.; Software, W.Z.; Validation, W.Z., and Y.C.; Formal Analysis, W.Z., and S.L.; Investigation, W.Z.; Resources, W.Z.; Data Curation, W.Z.; Writing--Original Draft Preparation, W.Z., and S.L.; Writing--Review & Editing, Y.C., and S.L.; Visualization, S.L.; Supervision, Y.C.; Project Administration, Y.C.; Funding Acquisition, Y.C.  
This research was funded by the National key R & D Plan Program of China (2018 YFB0106100), the National Natural Science Foundation of China (51607027), the Sichuan Science and Technology support Program (2016 GZ0395, 2017 GZ0395, and 2017 GZ0394), and the Central University basic Research Business funds (ZYGX2016J140 and ZYGX2016J146).  
The authors declare no conflict of interest.  
Figure 12: Average pixel errors of the radii of detected circles on the surface of engine cylinder head.
# LoRaWAN Base Station Improvement for Better Coverage and Capacity
## 4 Results  
### Measurement Setup  
The aim of the measurements was to evaluate the proposed dual setup in a real-world LoRaWAN communication scenario. Two LoRaWAN base stations were implemented: one using existing code [33] based on Raspberry Pi (case A in Figure 5) with one concentrator  
Figure 4: Schematic of novel base station for example application utilizing metal reflectors.  
Figure 3: Raspberry Pi and concentrators connected via SPI0 and SPI1.  
and antenna, and the other using customized code running on second Raspberry Pi with two sets of concentrators and antennas connected via two predefined SPI connections according to Figure 4.  
The base stations are set on the top of the 60 m high building of the Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia. There were two rounds of measurements. The first was conducted without metal surface behind antennas of the novel base station (case C), and for the second one the metal surface was added (case D). Since the base stations are registered to TTN, all messages sent from devices in the vicinity of the building are received. In both cases base stations worked for five hours.  
In order to test the impact of metal surface on received signal strength indicator (RSSI) for each spreading factor, we developed a dedicated LoRaWAN end device. Pycom board [37] was programmed to send LoRaWAN messages with different spreading factors using LoRa radio module on LoPy board [38]. The antenna connected to the board was the same as those used on the base stations [35]. The end device sent messages from six locations in the dense urban area of Zagreb, Croatia approximately 1-2 km away from the building with the base stations.
# Concept Drift Detection Based on Deep Neural Networks and Autoencoders
## 2 Related Work
### Concept Drift Detection Methods Based on Deep Learning Models
Compared to other business process detection methods, this approach leverages deep models to autonomously define monitoring features and focuses specifically on detecting abrupt concept drift within the business process. The Graph Convolutional Network for Drift Detection (GDD) [33] transforms event sequences into directed and sequential graphs. It utilizes GCNs to extract features and introduces virtual nodes to represent potential features. The distances between these virtual nodes are calculated, and the K-means algorithm is applied to identify outliers among candidate change points. A filtering mechanism is then employed to confirm the actual change points, allowing GDDs to autonomously capture evolving features while maintaining reasonable performance levels. Currently, there is limited research on applying deep learning to address concept drift, likely due to the fact that deep learning models typically require more computational resources and time. Under the same computational resources, deep learning models tend to have slower processing times compared to traditional shallow models with simpler architectures, and excessive delays can hinder the effectiveness of drift detection [34]. There are several challenges in using deep learning techniques to handle concept drift: the online learning scenario of streaming data demands models with flexible structures, capable of adjusting their capacity at different stages of the data stream. However, neural network models with different layers exhibit varying convergence speeds, resulting in data of different sizes and complexities being suitable for processing. If the model is shallow, it may suffer from insufficient representational power, limiting its ability to address complex nonlinear problems. Conversely, deeper models often struggle with slow convergence speeds. Additionally, typical issues in deep neural networks, such as vanishing gradients and reduced feature reuse, can also impair the classifier's ability to adapt to new concepts [35]. As a result, while previous studies have explored deep learning for concept drift detection, this area remains in its early stages, with significant untapped potential. Therefore, this paper proposes a concept drift detection method based on deep neural networks and autoencoders, aiming to contribute to this emerging field of research.
# Design of MIMO Antenna with Double L-Shaped Structure for 5G NR
## 2 Design of Proposed MIMO Antenna
### Design and Analysis  
A rectangular slot is a popular antenna construction, however, unlike most other elongated rectangular slots, the width of the proposed rectangular slot is approximately 55% of its length. To verify the effect of adding an open branch on antenna capacitance, the comparison of the proposed L-slot to the rectangular slot is shown in Figure 2. When these two antenna elements are placed in the upper left corner (at the position of Ant1), the performance of the antenna elements is significantly different. As shown in Figure 2b, the impedance matching performance of the reference antenna element is extremely low. While in contrast, the proposed antenna element generates resonant modes at 4.4 GHz through the utility of an I-shaped open branch on one side of a rectangular slot, which exhibits superior impedance matching properties. It can be concluded that the addition of open circuit branches can increase the capacitance of the antenna, so the impedance matching performance of the antenna can achieve broadband coverage.
## 4 Connectivity Architecture
### Spectrum and Interference Management  
Spectrum resources are important in order to support the capacity requirements of an autonomous vessel and be able to send required data to the remote operators over the depicted satellite-terrestrial systems. The available bandwidth is currently limited and there are two basic options to increase this--first, allocating a dedicated spectrum resource for the maritime connectivity system, which would most probably mean the use of higher-frequency bands, since bands below 6 GHz are allocated and heavily used; second, using spectrum sharing and dynamic spectrum access (DSA) technologies to obtain more resources. We believe that connections should be mainly implemented with a dedicated spectrum and DSA is an optional method that could be used in the overall architecture as depicted. The shared band would be especially useful for non-latency critical data.  
It has recently been demonstrated how to use licensed shared access (LSA) for critical communication services [44] such as public safety connectivity. The spectrum database should include information on where, when, and using which transmission power the transmission would be possible. With DSA technology, the autonomous ship could, e.g., re-use cellular frequencies in national or international waters and the spectrum regulators will play a key role in enabling this possibility.  
Spectrum management and re-selection is also needed to avoid intentional interference and jamming. When interference is detected, the connection can be changed to another frequency band.
# An Improved Monte Carlo Reliability Analysis Method Based on BP Neural Network
## 3 Calculation Theory of Improved BP-MCS Method
### Definition of Weight \(\omega_{i}\)  
In the design space, a sample point \(X_{i}\) contains multiple random variables, denoted as \(X_{i}^{j}(j=1,\ 2,\ \cdots,\ m)\), and \(m\) is the number of random variables. Each random variable in a sample point obeys a different probability distribution, and the probability density function value of the sample point in the design space is used as its weight. Because the distribution of each random variable has its own probability density function, when they are independent of one another, the weight of sample point \(X_{i}\) can be defined as the product of probability density functions, that is:  
\[\omega_{i}=f_{1}\left(x_{i}^{1}\right)f_{2}\left(x_{i}^{2}\right)\cdots f_{m} \left(x_{i}^{m}\right) \tag{3}\]  
When each random variable is not independent of one another, it is replaced by the joint probability density function of each variable for calculation, that is:  
\[\omega_{i}=f_{1}\left(x_{i}^{1}\right)f_{2}\left(x_{i}^{2}\right)\cdots f_{m- 1,\ m}\left(x_{i}^{m-1},x_{i}^{m}\right) \tag{4}\]where: \(f_{m}\) is the probability density function of the \(m\)-th random variable; \(f_{m-1,\;m}\) is the joint probability density function of the \(m-1\) and \(m\) random variables.  
For an \(m\)-dimensional independent normal distribution, the weight of each sample point is:  
\[\omega_{i}=\frac{1}{\left(\sqrt{2\pi}\right)^{m}\sigma_{1}\sigma_{2}\cdots \sigma_{m}}\exp(-\left[\frac{\left(x_{1}-\mu_{1}\right)^{2}}{2\sigma_{1}^{2}}+ \frac{\left(x_{2}-\mu_{2}\right)^{2}}{2\sigma_{2}^{2}}+\cdots+\frac{\left(x_{m }-\mu_{m}\right)^{2}}{2\sigma_{m}^{2}}\right]) \tag{5}\]  
where: \(\mu_{m}\) is the mean of the \(m\)-th random variable; \(\sigma_{m}\) is the standard deviation of the \(m\)-th random variable.  
When it is an \(m\)-dimensional standard normal distribution, the weight of each sample point is:  
\[\omega_{i}=\frac{1}{\left(\sqrt{2\pi}\right)^{m}}\exp(-\frac{1}{2}(x_{1}^{2}+ x_{2}^{2}+\cdots+x_{m}^{2})) \tag{6}\]  
It can be seen from Equations (5) and (6) that the weight of the sample point is the largest at the center point \((\mu_{1},\mu_{2},\cdots,\mu_{m})\). By observing the exponential term, \(\omega_{i}\) is essentially equivalent to the distance between the sample point and the center point. The closer the sample point is to the center point, the greater the weight. The farther away from the center point, the smaller the weight. According to the normal distribution law, a large number of sample points randomly selected by MCS will have an aggregation effect to the central point \((\mu_{1},\mu_{2},\cdots,\mu_{m})\), and the distribution of sample points is not uniform enough at this time. In order to solve this problem, this paper uses random integer method to design the sample points evenly.
# An Overview of Lidar Imaging Systems for Autonomous Vehicles
## 3 Sources and Detectors for Lidar Imaging Systems in Autonomous Vehicles
### Sources in Lidar Imaging Systems
Simple construction methods are enough to obtain short pulses in the nanosecond-scale (typical \(<\)5 ns). Q-switched microchip lasers may also allow the generation of unusually short pulses with a duration below 1 ns, although in lidar this poses problems in the bandwidth of the amplification electronics at the detector. Particularly with passive Q-switching, it is possible to achieve high PRR in the MHz region combined with short pulses of few ns, very well suited to present lidar needs. For lower repetition rates (around 1 KHz), pulse energies of some microjoules and pulse duration of a few nanoseconds allow for large peak powers (\(>\)1 kW). Beam quality can be very good, even diffraction-limited [100; 101; 102; 103]. Microchip lasers provide an acceptable balance between peak power and pulse duration in a compact and cost-effective design in scale production. They have found their own distinctive role, which makes them very suitable for a large number of applications. Many of these applications benefit from a compact and robust structure and small electric power consumption. In other cases, their excellence at generating sub-nanosecond pulses and/or the possible high pulse repetition rates are of interest. For example, in laser range-sensing, it is possible to achieve a very high spatial resolution (down to 10 cm or less) due to the short pulse duration [104; 105]. The cost stays midway between DL and fiber lasers, although closer to the level of fiber lasers. Figure 9: Schematic diagram of the fiber laser.