esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Results  
In this section, the SE bounds achieved by the 5G-NR precoding matrices are evaluated for SU-MIMO and MU-MIMO scenarios. Simulation results are obtained in MATLAB by using a CDL channel model with a delay spread of \(300\cdot 10^{-9}\) s, and a maximum Doppler shift of 50 Hz. A delay profile CDL-C defined according to TR 38-901 is assumed [38]. A different offset in the angles of departure in the azimuth (AoD) and zenith (ZoD) dimensions is added to each UE to simulate a deployment of \(K\) users randomly distributed with uniform probability within a circular coverage area. It is assumed that the gNB is located at a height of 30 m in the center of the circle. Then, the UEs are spread in the azimuth dimension between \(\left[-\frac{\pi}{2},\frac{\pi}{2}\right]\) and within a radio between 50 and 500 m from the gNB. The simulation results are averaged over 50 time slots and 100 random distributions of the UEs. The typical 5G-NR numerology is used for simulation as illustrated in Table 2.  
Without loss of generality, the transmission power of the gNB is normalized to \(\|\mathbf{W}_{n}\|_{F}^{2}=1\). This work is mainly focused on obtaining the SE bounds of the 5G-NR precoding matrices assuming a downlink data transmission. Therefore, the power control mechanism for different UEs is not considered. At each experiment, the PMI is obtained at the UE side by evaluating all the precoding matrices that can be computed according to the 5G-NR standard for a given antenna array configuration and a value of \(N_{s}\). Finally, the PMI corresponding to the matrix that achieves the highest SINR is selected. For simplicity, codebook mode 1 is considered for the simulations.
## 2 The Signal of Faulty Rolling Bearing with Variable Speed: An Angle-Time Cyclostationary View
### The Angle-Time Cyclostationarity of the Faulty Bearing with Variable Speed  
The vibration signal generated by a rolling bearing with constant speed is typically cyclostationary. However, the transient impulses and the carriers related to time are no longer constant when the rotating speed is time-varying. The system kinematics of rolling bearings (periodical impulses and modulations) is reflected in the angular domain. Moreover, the system dynamics of the rolling bearings (system resonances) are still shown in the time domain. Therefore, the measurement should be evaluated by combining the time domain and angular domain, which can employ the concept of angle-time cyclostationary (AT-CS) signal.  
An effective tool for AT-CS signal analysis is called order-frequency spectral, which can transfer the analyzed signal into the (cyclic) order-frequency domain. Due to the periodic characteristics of rolling bearings in the angle domain, which are contained in the process of variable speed rotation, this tool can be used to effectively extract the cyclic orders of the fault characteristics. Let \(\mathbf{Z}\) denote the measured signal transferred from \(z(t)\) in the OFSC, which can be decomposed as  
\[\mathbf{Z}=\mathbf{X}+\mathbf{N}, \tag{2}\]  
where \(\mathbf{X}\) and \(\mathbf{N}\) in OFSC are related to \(x(t)\) and \(n(t)\) respectively. When the noise \(\mathbf{N}\) is strong, the expected fault feature may be overwhelmed in \(\mathbf{Z}\). Hereafter, the problem can be re-described as how to feasibly extract the time-varying fault feature \(\mathbf{X}\) from the measurement \(\mathbf{Z}\).
# Sentinel-1 GRD Preprocessing Workflow +
## 2 Workflow
### Thermal Noise Removal  
Sentinel-1 image intensity is disturbed by additive thermal noise, particularly in the cross-polarization channel [2]. Thermal noise removal reduces noise effects in the inter-sub-swath texture, in particular, normalizing the backscatter signal within the entire Sentinel-1 scene and resulting in reduced discontinuities between sub-swaths for scenes in multi-swath acquisition modes. The thermal noise removal operator available in SNAP for Sentinel-1 data can also re-introduce the noise signal that could have been removed during level-1 product generation, and update product annotations to allow for re-application of the correction [3]. Sentinel-1 level-1 products provide a noise look-up table (LUT), provided in linear power, for each measurement data set and used to derive calibrated noise profiles matching the calibrated GRD data [3].
## 2 Results and Discussion
### Analysis of Cinnamon Volatiles by SPME/GC-MS
#### 2.1.2 Alcohols  
Alcohols are major constituents in cinnamon bark VOCs, mainly represented by phenylethyl alcohol, isoborneol, benzyl alcohol, and \(\alpha\)-terpineol. Yet, they are reported at trace levels s in the range of 0.01-0.15% [28; 30]. The current study showed that alcohols amounted to the largest number of peaks, i.e., 24 peaks though detected at low levels ranging from 0.12-6.8%. The highest levels were found in CT (4.2%) and CVM (6.8%); Table S1 represented (\(Z\))-3-hexen-1-ol (P6) and 3-hexen-1-ol isomer (P9) as major peaks. (\(Z\))-3-Hexen-1-ol is an aroma compound that imparts a green-grassy odor of freshly cut leaves, recognized as the main fragrant in green tea [31]. However, for commercial products, including CP-1 and CP-3, alcohols accounted for 53 and 55%, respectively, represented by P6 and P9. The higher content of these alcohols might be attributed to the method of production, preparation methods, and or additional flavoring agents and odorants though not specified on the label.  
Other alcohols that have been previously reported in cinnamon bark identified in this study included \(\alpha\)-terpineol (P19) at 0.03-0.25% in authenticated drugs. Similarly, isoborneol (P18) was identified at 0.07-0.9% and 0.2-1.7% in authenticated and commercial products, respectively. In addition, cinnamyl alcohol (P27/P33) was detected at trace levels ranging from 0.01-1.3%. Other alcohols, i.e., linalool and borneol, were not detected in the current study though reported in the essential oil of _C. verum_ J. Presl in North Brazil [32], linalool in _C. verum_[28; 30], and borneol in _C. cassia_[7].  
Major alcohol detected in commercial samples included cumic alcohol (P30) detected at 2.9 and 2.3% in CP-2 and CP-4, respectively, Table S1. This compound is reported to possess a spicy caraway-like odor [33] and might be added intentionally by manufacturers as a flavoring agent.
# Domain Adaptation Speech-to-Text for Low-Resource European Portuguese Using Deep Learning
## 3 Data
### LibriSpeech and Multilingual LibriSpeech  
As the amount of content available online increases, more data can be collected for research purposes. The LibriSpeech ([[https://www.openslr.org/12](https://www.openslr.org/12)]([https://www.openslr.org/12](https://www.openslr.org/12))) (accessed on 12 December 2021) dataset is built with data from the LibriVox ([[https://librivox.org](https://librivox.org)]([https://librivox.org](https://librivox.org))) (accessed on 6 January 2022) audiobook catalogue. Composed of 1000 h of audio recordings of English speech, with a sampling rate of 16 kHz, LibriSpeech was created with the purpose of being used to build and test ASR systems. In the work presented in this paper, the training set of 100 h of \"clean\" speech of the LibriSpeech ([[https://www.openslr.org/resources/12/train-clean-100.tar.gz](https://www.openslr.org/resources/12/train-clean-100.tar.gz)]([https://www.openslr.org/resources/12/train-clean-100.tar.gz](https://www.openslr.org/resources/12/train-clean-100.tar.gz))) (accessed on 12 December 2021) dataset was used as a control dataset for the English domain [29]. This control was meant to verify the performance of the transfer learning process, i.e., to verify if the models pre-trained in English were properly learning the Portuguese data. The 100 h set contains transcripts with a total of 990,101 words, from which 33,798 are unique words.  
Multilingual LibriSpeech (MLS) is an extension of LibriSpeech which increases the amount of English speech to 44,500 h of audio recordings and adds 6000 h of audio recordings from seven other languages, including Portuguese [30].  
MLS provides the dataset split into three subsets--train, development, and test--in which there is no speaker overlap. For the last two sets, it is also guaranteed that the gender and duration of the speaker are balanced. Audio recordings are also ensured to unambiguously contain only one speaker. With respect to Portuguese, MLS contains a large number of hours (\(\simeq\)167 h of Brazilian Portuguese and \(\simeq\)1 h of European Portuguese audio recordings). These hours correspond to a total of 1,321,326 words, of which 77,292 are unique, with an average of 33.68 words per transcription.
## 3 Signal Compression Method  
Signal compression methods are useful tools for the frequency analysis of mechanical systems under physical limitations. The impulse response can show the dynamic characteristics of a system in the whole range of frequency. However, many mechanical systems cannot make an ideal impulse input. In the signal compression method, a pseudo-impulse input that is expanded with phase delay is applied to the system. The output that is obtained from the pseudo-impulse input is mathematically equivalent to the impulse response. After the equivalent output is compressed, the frequency response of the  
Figure 4: Diagram of the experimental apparatus.  
Figure 3: Free body diagram of thruster force and reaction moment.  
dynamic system can be observed within the desired frequency range. A diagram of the signal compression method is shown in Figure 5. In this study, the frequency response was analyzed for system modeling with the Bode plot before transformation into the time domain.  
The dynamic characteristics due to rotational motion are affected by the angular velocity, acceleration, and higher-order terms. In this study, the angular velocity was input to observe the system characteristics. The power spectrum of ideal impulse input is constant in the whole range of frequency. The power spectrum within the desired frequency was designed as shown in Figure 6a, considering the capability of servo motors. It can be derived by a function of the power spectral density as follows:  
\[P(n)=Q\exp\left\{-\left(\frac{n}{a}\right)^{12}\right\}, \tag{1}\]  
where \(Q\) and \(a\) are parameters, and \(n\) is the number of data values.  
The range of frequency of the power spectrum is manipulated using the parameter \(a\). In this study, \(a\) was set as 450, and the range of frequency was designed as up to 10 Hz, as  
Figure 5: Diagram of signal compression method.  
Figure 6: Input and output in the signal compression method. (**a**) Power spectrum within desired frequency; (**b**) pseudo-impulse signal input; (**c**) output for pseudo-impulse signal; (**d**) Bode plot of signal compression result.  
shown in Figure 6a, considering the capability of the servo motors. The pseudo-impulse input was derived by signal expansion as follows:  
\[X(n)+jY(n)=\left\{\begin{array}{ccc}P(n)H(jn),&0\leq n\leq\frac{N}{2}-1\\ 0,&n=\frac{N}{2}\\ X(N-n)+jY(N-n),&\frac{N}{2}+1\leq n\leq N-1\end{array}\right. \tag{2}\]  
\[H(jn)=\exp\left\{-\left(\frac{12n^{2}}{b}\right)j\right\}. \tag{3}\]  
\(H(jn)\) is a function of the filter for signal expansion with a time delay. The time delay depends on the parameter \(b\), which is 2100 in this case. Through the inverse fast Fourier transform (IFFT), the expanded pseudo-impulse signal can be obtained in the time domain as shown in Figure 6b, and it is applied to the system. The equivalent output is generated as shown in Figure 6c, and then the impulse response can be obtained via signal compression with an inverse filter. Finally, the Bode plot shows the frequency response of the system, as shown in Figure 6d.
## 2 Numerical Simulation of Ytterbium-Based Amplification
### Ytterbium-Neodymium Hybrid Amplification Chain  
The last results show that both materials can be considered promising candidates for milijoule-level regenerative amplification at \(\sim\)1049 nm. Since the gap between this wavelength and the emission peak wavelength of Nd:glass (phosphate) at 1053 nm is very small, one can envisage their use in the pre-amplifier stages in a hybrid laser chain.  
For evaluating this possibility, we used a simple routine based on the spectral response of an Nd:glass amplifier that is fed with the output data from the previous ytterbium-based amplification routine, such as energy and spectrum. The amplifier is modeled as a single-pass in an Nd:glass slab working far from saturation, described by the following unsaturated gain equation:  
\[I_{\textrm{\tiny{cut}}}(\lambda)=I_{\textrm{\tiny{in}}}(\lambda)e^{\sigma_{ \textrm{e}}(\lambda)\textrm{\tiny{V}}\textrm{\tiny{V}}} \tag{5}\]  
Here \(I_{\textrm{in}}\) and \(I_{\textrm{out}}\) are the spectral intensities of the pulse before and after the amplification, \(\sigma_{\textrm{e}}\) is emission cross-section of Nd:glass, \(N\) is the inverted population density, and \(L\) is the length of the gain medium.  
For this hybrid configuration we chose the same realistic input parameters than for the previous simulations. The pump pulse, seed pulse and crystal parameters are those used in the previous section with the exception of the pump powers that were chosen to be 66 W for Yb:CaF\({}_{2}\) and and 24 W for Yb:YAG, due to the favorable percentage of inverted population for amplification near 1053 nm and to prevent the generation of undesired high energy amplified spontaneous emission (ASE) that competed with the main pulse. We assume a 23.5 cm long Nd:glass with an inverted population density of \(7.5\times 10^{18}\) cm\({}^{-3}\).  
Figure 6: Simulation of regenerative amplification based in Yb:YAG in injection mode. **Left**: Output intensity _vs_. wavelength for a range of pump power; **Right**: output energy and required number of passes _vs_. pump power.  
Figure 7 shows the output spectra calculated for the two hybrid chain combinations. The pre-amplified and final pulse energies for Yb:CaF\({}_{2}\) were 13.4 mJ (after 219 passes) and 1.43 J while for Yb:YAG we obtained 1.4 mJ (822 passes) and 230 mJ respectively. For both cases there is strong gain narrowing during the pre-amplification stage, leading to a final bandwidth of 2 nm.  
These results confirm that both Yb-doped media studied here are viable alternative pre-amplifier stages for a hybrid amplification chain operating at 1053 nm. However, the number of passes for a millijoule-level Yb:YAG amplifier is typically much larger.
# Building Fire Evacuation: An IoT-Aided Perspective in the 5G Era
## 4 Preliminary Design of an IoT-Aided Building Fire Evacuation Control System
### Information Sources and Data Transmission
#### 4.2.1 Fire Hazard Monitoring
Former tests carried out by [PERSON] and [PERSON] [60] showed that sensor data transmitted under 2.4 GHz signals (e.g., Wi-Fi, ZigBee, Bluetooth) might not be affected much by the dense smoke and high fire temperature within their test regime. A similar conclusion was obtained by [PERSON] et al. [61], also showing that smoke and fire do not influence much the communication in the 2.4 GHz frequency band. Therefore, working in a lower frequency band with stronger penetration, these proposed narrowband networks are believed to support sensor data communication under fire conditions.
# Review of Physical Layer Security in Integrated Satellite-Terrestrial Networks
## 3 Existing Surveys
In this paper, the authors discuss the challenges of spectrum utilization in the transition from 5G to 6G and highlight integrated sensing and communication (ISAC) as a key 6G technology [48]. ISAC aims to improve energy and spectrum efficiency by combining radar and communication signals for environmental awareness and scene interconnection. The integration of ISAC with ISTNs has become a significant research direction for achieving seamless 6G coverage. The paper reviews key ISAC technologies, describesthe development of ISTNs, and explores key topics and future applications for 6G ISAC systems in ISTNs. However, to our best knowledge, a signal processing perspective for understanding the problem and its solution has not yet been presented in any review paper. Our goal is to gain a better understanding of how PLS might improve the security of future communication networks, such as 6G, by tackling vulnerabilities that are brought about by merging terrestrial and satellite systems. This is an important subject generally, but it takes on further significance when considering 6G communication systems, as new attack surfaces are introduced by the seamless integration of NTNs and TNs. This research fulfills a particular need in PLS from a signal processing standpoint, and the intricacy of ISTNs causes several weaknesses. For this reason, this subject is novel and will play an important role in the development of future forms of secure communication. This research takes a more targeted approach than previous works by examining, from a signal processing standpoint, the most recent state-of-the-art PLS methods developed for ISTNs. Benefiting from the classification of PLS methods into four main areas--physical properties, cooperative communication, resource allocation, and beamforming--this study provides value. These categories provide a holistic view of how to optimize the physical features of satellite-terrestrial communication for security.
# Spatiotemporal Traffic Prediction Using Hierarchical Bayesian Modeling
## 3 Methodology and Data
### Study Area and Data Preprocessing  
The datasets we use in this study are collected from the Chicago Transit Authority (CTA). The data include information about public transportation bus counts in Chicago and the neighboring areas. We select this dataset, due to the importance of public transport, which plays an essential role in the development of large cities and is considered an economical way of transportation. However, this mode of transportation is often involved in traffic congestion [1]. Understanding the public transit traffic data helps in improving public transport services and generally enhances road traffic management. The two study areas include 29 sensors, where the first dataset has spatial points distributed mostly in downtown Chicago. The second dataset contains sparsely distributed spatial point data as shown in Figures 1 and 2. Both datasets include the number of buses, their speeds, and the number of sensor readings every 10 min from August 2018 to December 2019.  
A quick exploration of the data presents the aggregate number of buses for each day as shown in Figures 3 and 4 The reason we are showing this visual analysis is to illustrate the variations in traffic patterns over days, which does not exhibit normal distribution. Unlike most of the other spatiotemporal analysis techniques, HBM can efficiently deal with data that do not have a normal distribution [59].  
Figure 1: The sensor locations in the dense dataset.  
Figure 2: The sensor locations in the sparse dataset.  
We estimate the distance between all spatial points in both datasets and find that the minimum distance is 2 km and the maximum distance is 20 km in the first dataset. The second dataset shows a minimum distance of 4 km and a maximum distance of 79 km. On the first dataset, we apply the GP and AR models, whereas, on the second dataset we apply all the three models GP, AR, and GPP with three days of missing observations. Due to the small distances between spatial points, the GPP model cannot be used with the first dataset. We define the knot size with a grid of 4 \(\times\) 4, which includes the random effect of 16 spatial points. We cannot define a grid of 6 \(\times\) 6 or higher since it requires 36 spatial points and our dataset only includes 28 spatial points. The approach does not perform accurately with short-time units \"hour\", due to implementation limitations in the spTimer package. That is why we opt to use the daily aggregate data. We train the model on 21 locations for the time period starting from 1 January 2019 to 29 January 2019. Then, we test the model performance on the eight locations that will be defined in the Gibbs sampler.
## 4 Results  
### Training CNN with PD Data Recorded at the RPDIV  
The CNN model was trained using the PD data recorded at the RPDIV for a duration of one minute. As mentioned in Section 2, 300 PRPD images were captured for training the CNN. This training phase allowed the CNN to learn distinctive features and patterns associated with each void position. The CNN model was tested using new PD data after the training was complete. Data were recorded for one-minute and one-hour durations at a RPDIV lower than the PDEV to evaluate accuracy in recognizing and classifying the defect types. This will be discussed in more detail in Section 4.2.  
The training of the CNN model using 300 PRPD patterns labeled Void_Top, Void_Center, and Void_Bottom for the one-minute duration at RPDIV yielded promising results. The learning curves for accuracy and loss during the training process are shown in Figure 7.  
The graph of the accuracy curve demonstrates how the accuracy of the model improved over the iterations until convergence. The accuracy steadily increased as the iterations progressed, indicating that the model effectively learned the patterns and features associated with each type of defect. A training accuracy of 98.33% demonstrated that the model correctly classified the majority of the training samples.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Defect Type** & **Number of PRPD Patterns** \\ \hline Void\_Top & 100 \\ \hline Void\_Center & 100 \\ \hline Void\_Bottom & 100 \\ \hline Total Training Data & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 1: PD data from epoxy test samples at different void positions at the RPDIV.  
The loss curve represents the loss of the model, which measures the dissimilarity between the predicted and actual labels. The decreasing trend of the loss curve indicated that the model predictions gradually aligned with the true labels as training proceeded. This indicates that the model effectively minimized the errors in its predictions and improved its overall performance.  
The high training accuracy achieved in this study is a positive outcome, indicating that the CNN model could effectively learn and recognize PRPD patterns associated with different void positions in the epoxy resin. However, it is important to note that the performance of a model with unseen data may vary. To validate the robustness of the model, it was crucial to evaluate its performance on a separate testing dataset that the model did not observe during training.  
The confusion matrices represent the metrics for evaluating the performance in predicting the three defect types and were obtained during the training process, as shown in Figure 8. These confusion matrices provide important information regarding the correct and incorrect predictions of every combination of the CNN model during the training process.  
Figure 8: Confusion matrix of the CNN model trained with RPDIV data.  
Figure 7: Learning curve of a CNN trained with PD data recorded at the RPDIV.  
No incorrect predictions were found, suggesting that the trained CNN model achieved perfect accuracy on the training dataset, as shown in Figure 8.