esa-sceva/satcom-chunk-collection huggingface dataset:
# Wood Defect Detection Based on Depth Extreme Learning Machine
###### Abstract  
The deep learning feature extraction method and extreme learning machine (ELM) classification method are combined to establish a depth extreme learning machine model for wood image defect detection. The convolution neural network (CNN) algorithm alone tends to provide inaccurate defect locations, incomplete defect contour and boundary information, and inaccurate recognition of defect types. The nonsubsampled shearlet transform (NSST) is used here to preprocess the wood images, which reduces the complexity and computation of the image processing. CNN is then applied to manage the deep algorithm design of the wood images. The simple linear iterative clustering algorithm is used to improve the initial model; the obtained image features are used as ELM classification inputs. ELM has faster training speed and stronger generalization ability than other similar neural networks, but the random selection of input weights and thresholds degrades the classification accuracy. A genetic algorithm is used here to optimize the initial parameters of the ELM to stabilize the network classification performance. The depth extreme learning machine can extract high-level abstract information from the data, does not require iterative adjustment of the network weights, has high calculation efficiency, and allows CNN to effectively extract the wood defect contour. The distributed input data feature is automatically expressed in layer form by deep learning pre-training. The wood defect recognition accuracy reached 96.72% in a test time of only 187 ms.  
wood defect; CNN; ELM; genetic algorithm; detection Article Wood Defect Detection Based on Depth Extreme Learning Machine
## 5 Biosensor Application of Electrodeposited Composite Nanostructures  
There are many reasons for preparing films on electrode surfaces for biosensor application that contain multiple structural and functional components. For example, the preparation of composite nanostructured films containing both metallic nanoparticles and carbon nanomaterials such as  
Figure 9: The scanning electron microscopy (SEM) images of three Pd NMEs (nanostructured microelectrodes) fabricated with different levels of nanostructuring: (**a**) smooth surfaces, (**b**) moderately nanostructured surface with a feature size of 100 to 150 nanometers, and (**c**) finely nanostructured surface with a feature size of 20 to 30 nanometers. Reproduced with permission from ref. [104], Copyright 2010, American Chemical Society.  
graphene or carbon nanotubes allows for enhanced conductivity within the structure. Graphene, with its superior conductivity and electron transfer capacity, is a recent and popular choice, as are carbon nanotubes. Prussian blue, when deposited along with metallic nanoparticles, improves electrocatalytic properties. Conducting polymers can also aid the conductivity and electronic properties of electrode coatings. Other polymers including Nafion or chitosan may improve the film-forming ability and stability of protected components.
## 3 Data Fusion in the Integration of SINS/DVL/MCP/PS
### Data Fusion with the Subsystems
#### 3.1.2 System and Measurement Equations of SINS/DVL  
Assuming the constant error in DVL is completely compensated, misalignment angles, velocity and position errors, and gyroscope and accelerometer biases in SINS are chosen as components of the state vector \(\mathbf{X}_{\text{SINS/DVL}}\):  
\[\mathbf{X}=\left[\begin{array}{cccccccccccccccc}\delta V_{E}^{n}&\delta V_{N}^ {n}&\delta V_{U}^{n}&\phi_{E}&\phi_{N}&\phi_{U}&\delta L&\delta\lambda&\delta h &\varepsilon_{x}^{b}&\varepsilon_{y}^{b}&\varepsilon_{z}^{b}&\
abla_{x}^{b}& \
abla_{y}^{b}&\
abla_{z}^{b}\end{array}\right]^{\text{T}} \tag{14}\]When the elements of the state vector are fixed, the state transition matrix defined in Equation (12) can be acquired by the discretization of Equations (3)-(6), and the variance matrix \(\mathbf{Q}\) can be set according to the sensor precision.  
Take the velocity from DVL as the aided velocity, and the measurement vector can be constructed as:  
\[\mathbf{Z}_{\text{SINS/DVL}}=\left[\begin{array}{cc}V_{E}^{n}-V_{\text{DVLE}}^{n} &V_{N}^{n}-V_{\text{DVLN}}^{n}&V_{U}^{n}-V_{\text{DVLU}}^{n}\end{array}\right]^{ \text{T}} \tag{15}\]  
where \(V_{\text{DVL}\sim U}^{n}\) denotes the projected velocity in frame \(n\), which can be calculated as \(V_{\text{DVL}}^{n}=C_{b}^{n}V_{\text{DVL}}^{b}\), and \(V_{\text{DVL}}^{b}\) denotes the measured DVL velocity in frame \(b\).  
According to the relationship between the measurement and state vector, \(\mathbf{H}_{\text{SINS/DVL}}\left(t\right)\) can be expressed as:  
\[\mathbf{H}_{\text{SINS/DVL}}=\left[\begin{array}{cccc}1&0&0&\\ 0&1&0&0_{3\times 12}\\ 0&0&1&\end{array}\right] \tag{16}\]  
The variance matrix \(\mathbf{R}\) corresponding to measurement noise \(\mathbf{V}\) in Equation (12) is set according to the random noise in \(\mathbf{V}_{\text{DVL}}^{n}\). From the projection process \(\mathbf{V}_{\text{DVL}}^{n}=C_{b}^{n}V_{\text{DVL}}^{b}\), it can be observed that there is cross-noise between \(\mathbf{C}_{b}^{n}\) and \(\mathbf{V}_{\text{DVL}}^{b}\). In this paper, the enlarged variance of \(\mathbf{V}\) is used to include the crossed noise, and more details to address this noise can be found in [12]. In the following sections, this noise is not considered, and SINS and DVL are assumed to be independent.
# Graphene Passively Q-Switched Nd:YAG Laser by 885 nm Laser Diode Resonant Pumping
## 3 Experimental Results  
Figure 2 shows the average output power depending on the absorbed pump power of the Nd:YAG laser in the CW and Q-switching operation. The CW operation was studied first without the graphene. At an absorbed pump power of 11.7 W, the maximum average output power was 1.8 W with a slope efficiency of 51.2%. After the graphene was inserted into the cavity, under the same conditions as the above absorption pump power, the average maximum time output power was 639 mW with a slope efficiency of 25.3% vs. the threshold of 9.21 W. The lines of the output power vs. the absorbed pump power are shown in Figure 2. The equation of the lines is y = a + bx, and the R\({}^{2}\) of the lines are 0.99777 and 0.99602, respectively. The instability of the average output power of the Q-switching operation was \(\pm\)3.1%. Compared with the CW operation, during the Q-switching operation, the average output power and the slope efficiency were lower, one possible reason for this was that the graphene increased the intracavity loss.  
Figure 3 shows the pulse width (full width-half maximum) and PRF depending on the absorbed pump power in the Q-switching operation. When the absorbed power was close to the threshold of 9.21 W, the pulse width was 4.06 \(\upmu\)s, and when the absorbed power was 11.7 W, the pulse width reduced to 2.06 \(\upmu\)s. The PRF increased from 24.7 kHz to 102.7 kHz, corresponding to the absorbed power of 9.21 W to 11.7 W. The pulse width and PRF depending on the absorbed pump power were approximately linear.  
Figure 2: Average output power vs. the absorbed pump power with and without the graphene.  
Figure 4 shows the relationship between the single pulse energy and peak power vs. the absorption pump power. When the absorbed pump power was at its maximum, 11.7 W, the single pulse energy was also at its maximum of 6.2 \(\upmu\)J, corresponding to the peak power of 3 W.  
Figure 5 shows the pulse profile when the absorbed pump power reached the threshold. The pulse width was 4.06 \(\upmu\)s and the illustration of Figure 5 shows the corresponding pulse sequence. When the absorbed pump power reached 11.7 W, the pulse width decreased to 2.06 \(\upmu\)s.  
Figure 4: Single pulse energy and peak power vs. the absorbed pump power.  
Figure 3: Pulse width and PRF vs. the absorbed pump power.  
The beam quality factor, \(M^{2}\), in the Q-switching operation was studied at the average output power of 639 mW, taking advantage of the knife-edge method. Figure 6 shows the laser beam radii depending on their location relative to the focal lens, which was used for leading out the waist of the oscillating beam in the cavity. Through Gaussian fitting, the \(M^{2}\) factors was calculated to be 1.25.
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 8 Research Challenges and Future Directions  
The application of wireless communication in telemedicine is driven by technological advancements, which are usually associated with several issues and challenges. As highlighted in [16], issues and challenges related to deployment of wireless communication technologies in telemedicine include interoperability and usability, co-existence and interference, and end-to-end QoS. The need for remote diagnosis and health care delivery is expected to eventually limit human involvement in telemedicine. Consequently, artificial intelligence and machine learning are quickly gaining traction in the field of telemedicine, posing a substantial range of challenges involving accuracy, interpretability, overfitting, and computational complexity.  
Lack of a standard medical data communications protocol, interference from other wireless devices, and QoS in terms of high throughput and low delay are among the major issues affecting the full implementation of telemedicine systems; a number of these issues have to do with integration of heterogeneous wireless networks. Due to the significance of stable connectivity in WSNs for telemedicine, handover schemes are employed to maintain constant connectivity between the sensor nodes, gateway, and other wireless devices within the system. The process of connecting to the best access network is done through a Horizontal Handoff (HHO) mechanism, while Vertical Handoff (VHO) provides network access to the mobile nodes moving through heterogeneous access points [38].
# Predicting C/N0 as a Key Parameter for Network RTK Integrity Prediction in Urban Environments
## 4 Predicting Signal Strength
### Carrier-to-Noise Density Ratio  
The carrier-to-noise power density ratio is the ratio of the power of the carrier signal (C) to the noise power (\(N_{0}\)) in a 1 Hz bandwidth expressed in dB-Hz.  
\[C/N_{0}\ [\text{dB-Hz}]=10 log_{10}(P_{5})-10 log_{10}(N_{0}), \tag{13}\]  
where  
\[N_{0}=k\cdot T_{E}. \tag{14}\]  
\(k=1.38\times 10^{-23}\) is the Boltzmann constant and \(T_{E}\) is the environment temperature in Kelvin. The average temperature for the date and time of the experiment is 20 \({}^{\circ}\)C or 293.15 K. Therefore, the noise power density becomes \(-\)203.9303 dB-Hz.  
Furthermore, if there is any attenuation due to diffraction or passing through foliage, the corresponding value will be reduced from Equation (12). For multipath situation, the compound power is considered, but for NLOS situations, only the multipath component which is the reflected part is taken.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Block** & & & & & & & & & & & & & & \\ \hline
**IF/III** & 13.75 & 13.8 & 14 & 14.3 & 14.51 & 14.75 & 15 & 15.45 & 15.6 & 15.7 & 15.8 & 15.7 & 15.55 & 15.25 & 14.8 \\ \hline
**IIR** & 13.0 & 13.2 & 13.45 & 13.6 & 13.9 & 14.25 & 14.5 & 15.0 & 15.1 & 15.3 & 15.3 & 15.15 & 14.85 & 14.4 & 13.5 \\ \hline
**IRM** & 11.9 & 11.9 & 11.925 & 11.95 & 12.1 & 12.5 & 13 & 13.7 & 14.45 & 14.75 & 15.125 & 15.25 & 15.125 & 14.8 & 14.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: RHCP Gain patterns used for the GPS satellite antenna \(G_{t}\). Values are in dB.
# A Mobile Anchor Node Assisted RSSI Localization Scheme in Underwater Wireless Sensor Networks
## 4 Simulation
### Simulation Results and Discussion  
In this section, we provide the system architecture used in the simulation, and analyze simulation results from three aspects. First, we use three schemes to determine the projection of the node, and compare the mean error of node projections. Then, we compare the measured RSSI curve with the selected RSSI curve, and analyze the error of the perpendicular distance. Finally, we compare the mean location error of the three schemes.
# A Novel Fractional Fourier Transform-Based ASK-OFDM System for Underwater Acoustic Communications
## 3 Fractional Fourier Transform (FrFT)
### Noise Model  
Common sources of ambient noise in underwater acoustic channels include shipping activities, rain fall, seismic events and sounds generated by aquatic animals. The Wenz model classifies the noise sources into four main categories, according to the frequency spectrum. In the frequency range below 10 Hz, the main source of noise is turbulence due to high waves. In the frequency range of 10 Hz-100 Hz, noise is caused by shipping activities. Surface movement due to wind and waves is the primary reason of noise in the frequency range between 100 Hz and 100 kHz. Furthermore, at frequencies higher than 100 kHz, electronic thermal noise is the dominating factor. The total ambient noise level is given by adding the level sum of the participating noise factors.
# Accelerometers in Monitoring Systems for Rail Vehicle Applications: A Literature Review
## 1 Introduction
### Particular Types of Accelerometers
#### 1.4.2 Shear Mode Accelerometer  
Shear mode accelerometers are widely used in various fields, including machine and vibration monitoring as well as automotive safety. They offer several advantages, such as excellent linearity across a broad dynamic range, the ability to electronically convert acceleration signals into speed and displacement data, and the capability to record vibrations with high accuracy without the need for an external power supply. Additionally, these accelerometers are known for their durability, compact size, and high sensitivity relative to their mass [15].
# Comparative Analysis of BERT and GPT for Classifying Crisis News with Sudan Conflict as an Example
## 2 Related Work
### Text Classification by GPT  
GPT represents another significant advancement in NLP, extending the capabilities of the Transformer architecture by adopting an autoregressive learning approach. Unlike BERT, which relies on bidirectional training to understand both preceding and succeeding tokens in a sequence, GPT processes text in a unidirectional manner, predicting the next word based solely on previous tokens [3]. This autoregressive approach makes GPT particularly well-suited for text generation tasks, enabling it to excel in text completion, summarization, dialogue systems, and creative writing applications [38; 39].  
One of GPT's major strengths in text classification lies in its ability to perform tasks with minimal task-specific training. Unlike BERT, which typically requires fine-tuning on large number of labeled datasets for domain-specific classification, GPT leverages few-shot and zero-shot learning to classify text based on structured prompts [40; 41]. This characteristic enables GPT to generalize across multiple domains without extensive retraining, making it particularly useful in low-resource environments where annotated training data is limited. Additionally, GPT's prompt engineering capabilities allow users to dynamically guide the model's classification behavior, reducing the dependency on traditional fine-tuning methods [12].  
However, while GPT offers greater flexibility and adaptability, its autoregressive nature introduces inherent limitations compared to BERT. Since GPT processes text sequentially, it lacks BERT's full bidirectional context, which can be crucial for understanding nuanced relationships between words in complex classification tasks. As a result, BERT often outperforms GPT in domain-specific classification, particularly in scenarios where fine-grained contextual understanding is essential [42]. Moreover, GPT's output variability, which makes it effective in generative tasks, can introduce inconsistencies in classification performance, requiring additional prompt tuning to maintain accuracy and reliability across different datasets [28].
###### Abstract
The EBG structures belong to a specialized class of metamaterials that are composed of artificial periodic cells, which are being designed to either stop or allow the propagation of electromagnetic waves within a specific frequency range, thus effectively improving the antenna performance. A variety of EBG research works for MPA gain enhancement are recorded, for instance, using elliptical surrounding holes lattice EBG ([PERSON] et al., 2016), combining EBG together with defected ground ([PERSON] et al., 2016) or fractalizing the EBG elements ([PERSON] et al., 2016), implementing an EBG superstrate with the aperture feed MPA ([PERSON] et al., 2016), or inserting an EBG ring to act as a secondary ground plane between the MPA radiator and the normal ground ([PERSON] et al., 2016). In our study, the mushroom-like EBG type is chosen. The EBG elements are positioned to form only one ring outside a single-element MPA radiator. The optimization of the EBG ring shape and the distance from the EBG elements to the central MPA radiator, as well as the simplified design of both the EBG structure and the antenna single-probe feed to apply to the CubeSat platform are the novelties of our research compared to the aforementioned studies. Furthermore, the central MPA radiator can be simply modified by adding square-shaped slots symmetrically along the diagonal directions ([PERSON] et al., 2016) to produce circular polarization without being affected by the outer EBG ring. This paper consists of five parts. The first part is the above introduction, the second part presents the principle of the EBG and explains its effect on the antenna gain enhancement. The third part demonstrates the antenna design and simulation process, from the original antenna to the antenna after adding the EBG and modifying the radiator to create circular polarization. The simulations are carried out while taking the effect of the CubeSat platform into account. The antenna parameter study is also presented in this section. The fourth part describes the antenna measurement and analyses of the measured results. The final part includes the discussion and the details of our future work.
## 5 Discussion
### Phase Two: Spatial Audio Localization and Background Noise Testing  
Alongside comparing localization times between auditory test conditions, Phase Two of this study was carried out to determine if reported impairments in decoding speech when background noise is present would have quantifiable effects on how they would respond to similar stimuli rendered via virtual spatial audio within a VR environment. Considering the heterogeneity of disorders in the autism spectrum present within the sample group, it is encouraging to see that all participants were capable of performing this task without any particular difficulties. However, the differences in performance between participants within the experimental groups may also provide explanation for the low effect size yielded by the two-way mixed ANOVA.  
Firstly, results in Phase Two showed those in the 3D audio group performing significant better in the localization task. These results are consistent with similar research conducted with neurologicallyhealthy participants, reporting that three-dimensional auditory displays can reduce localization times and improve audio spatial attention in VR [70]. Furthermore, these results provide more evidence that those with ASD are capable of successfully interpreting binaural information to take advantage of the spatial acuity provided by spatial audio rendering techniques.  
In regards to the effect of background levels on selective hearing, results showed that localization times were significantly longer when the level of background noise increased and/or the SNR between the auditory target and ambient noise decreased. Furthermore, the type of stimuli also had a noticeable impact on localization times, with participants taking longer to correctly locate speech-like stimuli. This data is comparable to evidence of speech processing deficits in children with ASD in non-spatialized audio test conditions [44; 52; 67]. Although the results from the mono control group would be sufficient in evaluating the effects of background noise on the response to target audio, mono audio is rarely used with virtual reality environments. Therefore, for this study it was important that competing background audio had a significant effect on both experiment conditions.  
Individuals with ASD tend to demonstrate elevated speech perception thresholds, poor temporal resolution and poor frequency selectivity when compared to their typically developed peers [71]. The poor frequency selectivity alone could account for poor localization along the vertical plane [52], as this is primarily attributed to the analysis of the spectral composition of the sound arriving at the ear [49]. However, a combination of all three alongside a diminished ability to correctly translate ILD and ITD sound cues would account for poor selective hearing leading to difficulties with behavioral recognition of auditory targets in background noise [42; 71].
# Path Loss Determination Using Linear and Cubic Regression Inside a Classic Tomato Greenhouse
## 2 Materials and Methods
### Test Environment  
The tests were carried out in February 2018 inside four Tinkwino tomato greenhouse, each one an area of 10,000 m\({}^{2}\) whose production is marketed in the European market. It is located in the Canada de San Urbano, province of Almeria, autonomous community of Andalusia in Spain. The distribution of the plantation is the classic one for a greenhouse of tomato, with corridors of 1.2 m (Figure 3B), 50 cm of distance between plants (Figure 3C), separation between paired lines and ends of the foliages 60 and 100 cm respectively (Figure 3D), and with a length of the main hall being 100 m (Figure 3E).
## 5 Simulation Results
### Case Two: Moderate Partial Shading Pattern  
In this case, four connected panels receive uniform radiation conditions (1000 W/m\({}^{2}\) radiation), while two receive 800 W/m\({}^{2}\) and two receive 500 W/m\({}^{2}\). The arrangement is illustrated in Table 2. Multiple maxima occur in the P-V characteristics. The location of the GMPP is shown in the characteristics in Figure 9. Figure 10 illustrates the simulation results for this case with a performance comparison between the proposed technique and the conventional P&O and IC techniques.