esa-sceva/satcom-chunk-collection huggingface dataset:
# The Role of Ultrasonography in the Diagnostic Evaluation of Patients with Polyneuropathy
## 2 Materials and Methods
### Primary Outcome Measures  
2.4.1. Electrodiagnostic Evaluation (EDx)  
EDx examination was performed according to a standard polyneuropathy protocol that includes motor nerve conduction studies (NCS) of the median, ulnar, fibular, and tibial nerves, as well as sensory NCSs of the median, ulnar, and sural nerves, at least unilaterally. Measured NCS parameters included motor and sensory latency, F-wave latency, response duration, response amplitude and area, temporal dispersion, conduction block (at least 30% amplitude reduction), and motor and sensory conduction velocities. These parameters were compared to our laboratory reference values (Table 1). The evaluation protocol also included a needle electromyography (EMG) examination of at least two leg muscles to assess spontaneous denervation activity or reinnervation changes. Based on clinical and EDx criteria for the type of polyneuropathy (Table 2), patients' diagnoses were divided into: (1) demyelinating polyneuropathy, (2) axonal polyneuropathy, (3) mixed polyneuropathy, and (4) multifocal neuropathy. Several differential diagnostic questions were formulated: (1) demyelinating vs. axonal polyneuropathy, (2) hereditary vs. acquired polyneuropathy, (3) demyelinating vs. diabetic polyneuropathy, (4) MMN vs. motor neuron disease, (5) focal neuropathy superimposed on polyneuropathy, (6) multiple mononeuropathy vs. polyneuropathy, and (7) diagnostic question not clear.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Nerve**} & \multirow{2}{*}{**Latency (ms)**} & \multirow{2}{*}{**Amplitude** **(mV/uV)**} & \multirow{2}{*}{**Velocity (m/s)**} & \multirow{2}{*}{**F-Wave** **Latency (ms)**} \\ \cline{1-1}  & & & & \\ \hline Motor & & & & \\  Median & \textless{}4.4 & \textgreater{}4.0 & \textgreater{}49 & \textless{}31 \\  Ulnar & \textless{}3.3 & \textgreater{}6.0 & \textgreater{}49 & \textless{}32 \\  Radial & \textless{}2.9 & \textgreater{}2.0 & \textgreater{}49 & \\  Fibular & \textless{}6.5 & \textgreater{}2.0 & \textgreater{}44 & \textless{}56 \\  Tibial & \textless{}5.8 & \textgreater{}4.0 & \textgreater{}41 & \textless{}56 \\ \hline Sensory & & & & \\  Median & & \textgreater{}20 & \textgreater{}50 & \\  Ulnar & & \textgreater{}17 & \textgreater{}50 & \\  Radial & & \textgreater{}15 & \textgreater{}50 & \\  Sural & & \textgreater{}6 & \textgreater{}40 & \\  Medial & & \textgreater{}2 & \textgreater{}35 & \\  plantar & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: EDx reference values for the main motor and sensory nerves used in the present study.
# Monitoring Forest Diversity under Moso Bamboo Invasion: A Random Forest Approach
## 3 Results
### Model Performance  
According to the performance metrics for the four RF prediction models, the mean R\({}^{2}\), RMSE, and MAE values for the Margalef, Shannon, Simpson, and Pielou indices were 0.67, 0.73, 0.69, and 0.55; 0.14, 0.12, 0.08, and 0.11; and 0.10, 0.08, 0.06, and 0.08, respectively. These results indicate that the RF model had strong predictive ability for the Margalef, Shannon, and Simpson indices and average predictive ability for the Pielou index.  
Scatterplots provide a visual indication of the predictive performance of the model. Therefore, we produced scatterplots of predicted and observed values at typical levels of the models (Figure 3). The results showed that the model predicts the Margalef, Shannon, and Simpson diversity indices well and the Pielou index moderately well. In the case of the Margalef index, the predicted values were significantly higher than the observed values in areas with low diversity indices. For the Shannon and Simpson indices, the predicted values were significantly higher than the observed values in areas with low diversity indices and lower than the observed values in areas with high diversity indices. For the Pielou index, the predicted values were inaccurate for areas with low diversity indices and significantly lower than the observed values for areas with high diversity indices.  
Figure 2: Ranking of feature importance of random forest for four diversity indices.
# Recent Developments in Modulation Spectroscopy for Methane Detection Based on Tunable Diode Laser
## 4 Application of Modulation Spectroscopy for Methane Detection
### Two-Tone Frequency Modulation Spectroscopy Applied for Methane Detection  
#### 4.3.1 Principle of Two-Tone Frequency Modulation Spectroscopy  
The frequency modulation spectroscopy is called single-tone FMS (i.e., Standard FM) or two-tone FMS, depending on the number of modulation tones [107]. The modulation of lasers in TTFMS is completed by a pair of closely spaced frequencies simultaneously, which are \(\omega_{1}=\omega_{c}+\Omega/2\) and \(\omega_{1}=\omega_{c}-\Omega/2\), respectively. Wherein \(\omega_{c}\) is the center frequency and \(\Omega\) is the difference frequency. TTFMS has higher modulation frequencies in comparison with FMS. In the TTFM technique, the  
Figure 4: Schematic of the experimental set-up based on the FM technique. Reproduced with permission from [65], [PERSON], 2006.  
advantages of standard FM are used in conjunction with the benefits of a considerable reduction in detection bandwidth, with additional improvement in SNR [107].
## 3 Spatio-Temporal Distribution Characteristics of PMGIM
### PMGIM Distribution Law of 182602 Working Face
#### 3.3.2 Frequency-Energy Characteristics of PMGIM  
A total of 900 PMGIM events were recorded on the floor of the coal seam between 10 June and 20 August, prior to mining at the 182602 working face. Figure 8 illustrates how these events are categorized into four distinct stages based on their frequency and energy characteristics, as follows:  
Phase I: characterized by low frequency and low energy, with a daily frequency \(<15\), total energy \(<10^{5}\) J, and single event energy \(<10^{3}\) J;  
Stage II: characterized by high frequency and low energy, with a frequency \(\geq 15\), total energy \(\leq 2\)\(\times\)\(10^{5}\) J, and 95.8% of events with energy \(<10^{3}\) J;  
Phase III: characterized by high frequency and high energy, with a frequency \(\geq 20\), total energy \(>5\times 10^{5}\) J, and a short duration;  
Stage IV: transition stage with large frequency fluctuations, small total energy, maximum \(<2\)\(\times\)\(10^{5}\) J, and 87.4% of events with energy \(<10^{3}\) J;  
Since 10 July, the microseismic events have repeated the aforementioned phase cycle.
# The Detection of Fake News in Arabic Tweets Using Deep Learning
###### Abstract
To find a suitable model for detecting fake news in Arabic Tweets, we conducted experiments using two deep learning techniques: convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM), and five-word embeddings were explored to extract the textual representation. Broadly speaking, word embeddings are mainly classified into classic and context-based word embeddings. Classic word embeddings learn the embeddings and disregard the contextual information and meaning within the sentence. Hence, each word is represented by a distinct and unique vector. Whereas context-based embeddings learn diverse text representations for the same word depending on the context within the text [4]. In this study, we empirically evaluated both classic word embeddings (Keras embedding layer, word2 vec [7], and fastText [8]) and context-based embeddings (ARBERT and MARBERT [9]) with deep learning models. As far as we know, no previous study has investigated ARBERT and MARBERT as textual feature extractors in the area of news credibility detection. This study proposed a model for detecting fake news that differs from other detection systems that used a deep learning approach and were trained on the Arabic dataset. The proposed model benefited from both the text of the news Tweet and the news publisher's characteristics, whereas previous works utilized only news text. Furthermore, the model was trained to detect fake news related to diverse domains instead of a specific topic, such as COVID-19. The main contributions of this study are as follows:  
* Construction of a comprehensive, manually labelled dataset that contains Arabic fake and real news Tweets. * Proposed a deep learning model for eliminating and detecting fake news on different topics. * Exploited the news content and social context for more accurate detection of news credibility. * Investigated the influence of contextual embeddings and compare them to classic word embeddings. * Ascertained the generalization capability of the proposed model using a publicly available dataset. * Verified the efficiency of the proposed model architecture by comparing it with current models. The remainder of this work is arranged as follows: Section 2 reviews recent studies on credibility detection in Arabic news. Section 3 illustrates the proposed methodology in detail. Section 4 shows a series of conducted experiments, obtained results, and a discussion. Finally, the conclusion of this work and directions for further research in this field are summarized in Section 5.
## 3 Dual-Polarized 3D absorber
### Simulation and Analysis  
To illustrate the effects of the short pins, Figure 7a presents the simulated reflection coefficients of the dual-polarized 3D absorber with/without the shorting pins. It can be seen that without the shorting pins, there are reflection poles at \(f_{1}\) = 4.70 GHz for TM polarization and \(f_{2}\) = 10.04 GHz for TE polarization, respectively, which are caused by the slots between two unit cells with a length of \(L/2\). It can be seen that \(f_{2}\approx 2f_{1}\) because the slot for TM polarization is open at one end, while the slot for TE polarization is short at both ends. Figure 7b shows the current distributions around the slots. Typical quarter-wavelength and half-wavelength current distributions can be observed at \(f_{1}\) for TM polarization and at \(f_{2}\) for TE polarization, respectively. These resonances cause reflections at \(f_{1}\) and \(f_{2}\), which are within the absorption band. They can be simply eliminated by adding some shorting pins. The shorting pins should be loaded in the open end for TM polarization, as well as in the middle of the slots for both TM and TE polarizations. In this way, the lengths of the slots are reduced by half; therefore, the resonant frequencies are doubled and out of the absorption band. It can be seen in Figure 7a that with the shorting pins, the reflection poles are eliminated, and the absorption band from 2.64 GHz to 10.96 GHz for both TE and TM polarizations is obtained, which is similar to the one with single- polarization proposed in Section 2.  
Finally, the design guidelines of the proposed 3D absorber are presented as follows:  
1. The design of the open-ended notch-slot resonator. The length of the notch slot (\(L_{s}\)) is a quarter wavelength at the fundamental resonant frequency \(f_{41}\), which is close to the  
Figure 7: (**a**) Simulated \(\mid S_{11}\mid\) of the proposed dual-polarized 3D absorber with/without the shorting pins; and (**b**) current distribution at the reflection frequencies.  
starting frequency of the target absorption band (\(f_{low}\)). Therefore, \(L_{s}\) can be initially calculated by \(L_{s}=c/(4\times f_{low})\). On the other hand, a larger width of the notch slot (\(W_{s}\)) results in a wider absorption band. However, \(W_{s}\) should not be close to the period of the unit cell (\(L_{p}\)) because the metal of the notch-slot resonator serves as the ground of the U-shaped microstrip line. Hence, the initial choice of \(W_{s}\) is \(L_{p}/2\).
2. The design of the coupled U-shaped microstrip line. The length of the \(y\)-oriented part (\(L_{m2}\)) is between \(W_{s}\) and \(L_{p}\); therefore, the initial value can be chosen as 0.75\(L_{p}\). The length of the \(x\)-oriented part (\(L_{m1}\)) can be determined by the center frequency of the absorption band, which is roughly 2\(f_{low}\), so that \(L_{m1}=c/(2\times f_{low})-L_{m2}/2\).
3. A substrate with a low permittivity and high rigidity is preferred. RO4350B was therefore chosen in our design.
4. Optimize the parameters (especially the above-mentioned key parameters) to obtain a wide absorption band.
# Micro-Satellite Systems Design, Integration, and Flight
###### Abstract  
Within the past decade, the aerospace engineering industry has evolved beyond the constraints of using single, large, custom satellites. Due to the increased reliability and robustness of commercial, off-the-shelf printed circuit board components, missions have instead transitioned towards deploying swarms of smaller satellites. Such an approach significantly decreases the mission cost by reducing custom engineering and deployment expenses. Nanostatellites can be quickly developed with a more modular design at lower risk. The Alpha mission at the Cornell University Space Systems Studio is fabricated in this manner. However, for the purpose of development, the initial proof of concept included a two-satellite system. The manuscript will discuss system engineering approaches used to model and mature the design of the pilot satellite. The two systems that will be primarily focused on are the attitude control system of the carrier nanosatellite and the radio frequency communications on the excreted femto-satellites. Milestones achieved include ChipSat to ChipSat communication, ChipSat to ground station communication, packet creation, error correction, appending a preamble, and filtering the signal. Other achievements include controller traceability/verification and validation, software rigidity tests, hardware endurance testing, Kane damper, and inertial measurement unit tuning. These developments matured the technological readiness level (TRL) of systems in preparation for satellite deployment.  
systems engineering; MBSE; RF communication; GPSK; CDMA; forward error correction; matched filtering; TI-RTOS; RTL-SDR; TinyGS; controller optimization controller modeling; controller verification and validation; Kane damper; PD controller; IMU tuning
## 6 Simulation Results
### Transmission Using an ABCS-Enabled Relay  
When using a system that operates entirely through the ABCS, the SNR of the signals received at \(D\) for the transmission within the warehouse is given in Figure 9.  
Figure 8: Variation of Energy harvested at \(R\) when using SWIPT for different S locations within the warehouse.  
Figure 7: Variation of SNR at \(D\) node when when transmitting with SWIPT from \(R\) to \(D\).  
Compared to the performance of SWIPT, the SNR performance of ABCS is lower, and the maximum SNR achieved for the R-D link is approximately 14 dB. High-SNR points are visible at critical transmission points near the relay when SWIPT is implemented as the selected methodology in the relay, as can be seen from Figure 7. This is one of the inherent features of ABCS, since the transmission process relies on the reflection of the received signal, and the reflection coefficient plays an important role in the received signal SNR.  
The lower ranges of SNR for both SWIPT and ABCS take almost equal values, and the performance of the two systems at these regions can be evaluated using the energy required for transmission while maintaining an appropriate data rate. ABCS, however, provides a better opportunity when implementing low-energy transmission, in scenarios where energy harvesting is not possible.
# Application of Machine Learning Ensemble Methods to ASTRI Mini-Array Cherenkov Event Reconstruction
## 6 Tests on New Methods for Energy Reconstruction
### Energy Bias  
Performing the tests on ensemble methods, we noticed that the energy bias was always positive when using Random Forest or Extra Trees methods, while it turned negative when the XGB method was used. As a result, an ensemble composed of methods based on trees combined with XGB can be tuned to obtain a value of the bias very close to zero in the approximate range 2-100 TeV. While the specific combination of methods used in this study may have been optimized for the particular dataset and range of energies analyzed, the general approach of using a combination of tree-based methods with XGB is a widely-used technique in Ensemble Learning. Therefore, it is likely that similar combinations of methods could be effective for other datasets and ranges of energies. Among the Stacking Ensemble considered, the best performing method is ENSEMBLE 4, which is made of 0.45 XGB, 0.275 Random Forest and 0.275 Extra Trees. In Figure 13, we show our results for different Machine Learning methods and two different sets of parameters against the benchmark (A-SciSoft).
# An Improved Biomimetic Olfactory Model and Its Application in Traffic Sign Recognition
## 3 Problems and Improvement of KIII Model
### Feature Extraction  
The KIII model is inspired by the biological olfactory system and employs a biologically plausible feature extraction method. This method processes sensory data through the olfactory neural pathway, effectively capturing features such as texture and edges in one-dimensional sequences and simple images. One-dimensional sequences and simple images typically exhibit relatively simple structures, making them more amenable to feature extraction and pattern recognition. The KIII model performs well when dealing with one-dimensional sequences and simple images. However, its performance significantly deteriorates when applied to more complex and diverse images. Therefore, handling images and extracting features that represent original data effectively [16; 17; 18] is a challenging and critical task when using the KIII model for pattern recognition. In this study, in order to extract high-quality feature vectors, we incorporated previous research on image enhancement. In the feature extraction stage, we applied adaptive histogram equalization and Gaussian filtering to improve the overall image quality. Subsequently, we used gridded Discrete Cosine Transform (DCT) and a combination of global and local DCT to extract image features.
## 2 Materials and Methods
### Data Collection  
A brute force approach was used to vary TA and CT muscle activations. TA and CT muscle activations were varied between 0% and 100% of their maximum muscle activation levels in steps of 5%. Lung pressure, LCA, PCA, and IA activations were initially also varied randomly for the given CT and TA activations, but ultimately needed to be constrained to focus only on cases of self-sustained oscillation (a small percentage of the initial exploratory set). The constraint equations are given in the Results section. A total of 441 signals were generated using this approach. Each signal was 0.4 s in duration. The waveforms, oral pressure (\(p_{o}\)), vocal fold length (\(L\)), and fiber stress (\(\sigma_{y}\), see Equation (6)) in the three vocal fold layers (SLLP, vocal ligament, and TA muscle) were recorded for every signal. Using these waveforms, acoustic and postural features were computed. Fundamental frequency was computed using the SWIPE' algorithm [6]. Sound pressure level radiated at 30 cm from mouth was computed using the following equation [7]:  
\[SPL\ =\ 10\log_{10}\frac{I}{I_{0}}\ =\ 10\log_{10}\frac{\left(\overline{p}_{o} \right)^{2}}{4\pi R^{2}R_{m}I_{0}} \tag{7}\]  
where the bar denotes a time-average of the oral pressure, \(p_{o}\), \(R\) is the radius from mouth, \(R_{m}\) is radiation resistance [52; 55], and \(I_{0}\) is the standard reference intensity (\(10^{-12}\) watt/m\({}^{2}\)).  
Normalized spectral centroid was computed to quantify brightness [9]. Spectral centroid (SC) measures the center of mass of the spectrum in Hz. It is computed using the following equation:  
\[SC\ =\ \frac{\sum_{k\ =\ 0}^{N}f_{k}x_{k}}{\sum_{k\ =\ 0}^{N}x_{k}} \tag{8}\]  
Here, \(N\) is total number of frequency bins, \(f_{k}\) is the center frequency of the bin in Hz, and \(x_{k}\) is the spectral magnitude in bin \(k\). The normalized spectral centroid was then computed by dividing the spectral centroid by the fundamental frequency of the signal.  
Approximate entropy (ApEn) quantifies the amount of irregularity and unpredictability present in a signal [56]. For a given time series signal \(x(t)\), an m-dimensional delay-coordinate phase space \(X\ =\ \{x(t),\ x(t-\tau),\ \ldots,x(t-(m-1)\tau)\}\) was first constructed [18]. Here, \(m\) is the embedding dimension and \(\tau\) is the time delay. Then, ApEn can be computed using the following equation:  
\[ApEn(m,r)\ =\ \lim_{N\rightarrow\infty}\left[\Phi^{m}(r)-\Phi^{m+1}(r)\right] \tag{9}\]  
where  
\[\Phi^{m}(r)\ =\ (N-m+1)^{-1}\sum_{i\ =\ 1}^{N-m+1}\log C_{i}^{m}(r) \tag{10}\]  
Here, \(C_{i}^{m}(r)\) is the correlation integral computed as suggested in [56], \(r\) is the radius of similarity (chosen as 0.2 \"variance(\(x\))), and \(N\) is the number of samples of the signal \(x(t)\). In the current study, \(m\) was set to 2, and \(\tau\) was set to 1 for all the signals.
## 3 Noise Analysis
### Determining the Effect of Noise on Population  
#### 3.2.1 The Formula for Determining the Percentage of Population Affected by Noise  
The operation of eVTOL when UAM is commercialized will result in noise around the operating path, thus affecting the population living in those areas. Therefore, for generating less noise in the UAM operating paths, it is important to measure the percentage of the population in each residential district affected, as shown in Equation (7).  
\[Highly\ annoyed,\%=\frac{100}{1+e^{(11.13-0.141L_{dn})}} \tag{7}\]  
Equation (7) is a curve fitting function of the Shultz curve used to calculate the number of residents negatively affected by noise [22]. [PERSON] et al. stated that the difference in noise levels between \(L_{dn}\) and \(L_{den}\) in Equations (5) and (6) is small, making it possible to replace \(L_{dn}\) in Equation (7) with \(L_{den}\)[23].  
According to the original Shultz curve, high-level noise impact at 65 dB is about 12%. However, the noise impact of aircraft tends to be higher than other noise sources, and the Miedema curve shows that the noise impact of aircraft is about 27% [22].