esa-sceva/satcom-chunk-collection huggingface dataset:
# Identifying Earthquakes in Low-Cost Sensor Signals Contaminated with Vehicular Noise
## 3 Proposed Methodology
### Ground Truth Earthquake Dataset  
After collecting the vehicular dataset, we also had to collect a dataset about earthquake events that would act as the ground truth. To complete this task, a data pipeline was created using the Obspy framework [28], which extracts waveform events from the European Integrated Data Archive in the National Observatory of Athens (NOA) [29] and saves the value of data and the timestamp of each signal wave in raw format. The seismic events were recorded and downloaded from the station \(VLS\) in Valsamata, Kefalonia, Greece, which is part of the NOA network. After collecting data for 773 seismic events from NOA for the VLS station, data cleaning was applied. All earthquakes were visually inspected for anomalies during their recording process. Earthquake signals that displayed irregular patterns in their recording before or after the main event were discarded. Figure 5 shows examples of regular and irregular earthquake signals. After the above phase, a total of 503 seismic events remained in our training dataset. To label the data and find the timestamps of the  
Figure 4: Three-axis geophone with cutoff frequency \(fc=4.5\) Hz.  
start time and end time of each event, we used the STA/LTA Z-Detect [30] algorithm. This task was conducted using Obspy, which also features libraries for this task. Finally, and in preparation for feeding these data to the neural network, we created data frames for all the seismic events with their original values, and their values normalized in the range of \((-1,1)\). In order to further support the reproducibility of our work, the data of this dataset are available online ([[https://drive.google.com/drive/folders/1](https://drive.google.com/drive/folders/1) AgB4c3 yI7 axPM9 Jp4 Rh]([https://drive.google.com/drive/folders/1](https://drive.google.com/drive/folders/1) AgB4c3 yI7 axPM9 Jp4 Rh) vkwORBjOK5 ST?usp=drive_link, accessed on 13 September 2023).  
Figure 5: (**a**) Regular earthquake signal; (**b**) irregular earthquake signal; (**c**) irregular earthquake signal.
# A Review of Current Trends, Techniques, and Challenges in Large Language Models (LLMs)
## 4 Pretraining--Strategies and Objectives
### Learning Strategies
#### 4.2.2 Multilingual Pretraining  
Researchers have investigated incorporating a multilingual corpus during pretraining to make models perform in multiple languages. For example, XLM-R [64] is a multilingual model pretrained on CommonCrawl100 corpus with text from around 100 languages. It obtained SOTA performance on cross-lingual tasks such as question answering, classification, and sequence labeling. XLM-R also demonstrated how pretraining the multilingual model at scale helps to improve performance across various cross-lingual transfer tasks. For low-resource languages, XLM models trained on CommonCrawl-100 performed better than those trained using Wikipedia.  
Another example is mT5 [32], which uses multilingual corpus mC4 to pretrain the model. When dealing with multilingual models (especially in zero-shot settings), there is a chance of 'accidental translation', where the model might translate the prediction in the wrong language. For example, suppose the model proceeds through English-only finetuning. In that case, the probability of generating non-English tokens decreases, reaching a point where English becomes the most likely language to answer any question. Here, the model outputs English when provided a non-English test input because the model never observed a non-English target during finetuning. To address 'accidental translation', mT5 [32] mixed the unlabeled pretraining data during finetuning, dramatically alleviating this issue.  
AlexaTM 20B [51] is the first seq2 seq model trained using multilingual that can perform in-context learning and provide SOTA performance on multilingual tasks. When tested on the Flores-101 machine translation benchmark dataset, it outperformed existing supervised models almost across all language pairs using only the one-shot method. It also achieved a significant performance boost on machine translation tasks involving to-and-from low-resource languages, such as Telugu, Marathi, and Tamil. AlexaTM20B achieved SOTA performance on Paws-X, XWinograd, NNLI, and XCOPA multilingual tasks in a zero-shot setting. It also performed better on SuperGLUE and SqUADv3 datasets than GPT-3 under zero-shot setting. In the one-shot summarizing task, AlexaTM20B performed better than models that were much larger in scale than its size, such as 540B PaLM decoder model.
## 4 Methods  
The soil moisture estimation workflow is completely implemented within the cloud-processing platform Google Earth Engine (GEE), allowing to process CPU-intense time series data sets on a catchment scale in considerably shorter time. The workflow can be divided into two major parts: (1) preprocessing of Sentinel-1 SAR data and (2) soil moisture estimation using the alpha approximation method (Figure 4). As the algorithm is constructed in a customizable way, both the observation period as well as the study region can be changed at a later stage. The output can be both visualized directly within the browser-based GEE platform and downloadable for local post-processing. In this study, the output was resampled to a resolution of 200 m, thus aggregating all introduced input grids (Sentinel-1, CORINE, OpenLandMap, GLDAS) into the same 200 m \(\times\) 200 m pixel grid by spatial averaging. The Sentinel-1 data was down-sampled, while the coarser datasets (CORINE, GLDAS, OpenLandMap) were up-sampled. In this regard, the down-sampling of Sentinel-1 data to a resolution of 200 m further reduced the effect of speckle noise in the retrieval and averaged out the spatial heterogeneity, as a coarser scale reduces the effect of heterogeneous surface parameters (e.g., soil roughness, plant canopy structure and vegetation water content). On the other hand, the relationship with soil moisture is strengthened as it adjusts more uniformly across space [64]. Nevertheless, the different original resolutions of the used data sets can have varying influence on the resulting soil moisture. Regarding the CORINE data set, both an unnecessary loss of \"valid\" as well as not correctly masked Sentinel-1 pixels in the spatial domain are possible. In terms of the coarser GLDAS and OpenLandMap data set, multiple Sentinel-1 pixels are assigned to the same soil moisture starting and soil moisture range value, depreciating the correlation to the spatial soil moisture distribution that can possibly be achieved by using high-resolution Sentinel-1 time series.
## 4 Experimental Evaluation
Table 4 provides the overall classification outcome of the CL-DLBIDC model as compared with recent models, such as Lightweight on-device Intent Detection (LIDSNet), Stack-Propagation-Bidirectional Encoder Representations from Transformers (Stack-PBERT), stack-propagation, Capsule-NLU, slot-gated bidirectional long short-term memory with attention (Attention-SG-BiLSTM), and slot filling and intent detection using bidirectional long short-term memory (SFID-BLSTM) models [26]. Figure 8 showcases the comparative \(accu_{y}\) examination of the CL-DLBIDC model with existing techniques. The figure indicates that the Attention-SG-BiLSTM model attained a lower \(accu_{y}\) of 96.87%, whereas the LIDSNet, Capsule-NLU, and SFID-BLSTM models obtained a clearly raised \(accu_{y}\) of 97.79%, 97.43%, and 97.36%, respectively. Next, the stack-propagation model resulted in a moderately improved \(accu_{y}\) of 98.29%. Although the stack-PBERT model obtained a \(accu_{y}\) of 99.25%, the CL-DLBIDC model obtained a higher \(accu_{y}\) of 99.51%. Figure 9 showcases the comparative \(F1_{score}\) analysis of the CL-DLBIDC technique with existing techniques. The figure shows that the Attention-SG-BiLSTM approach obtained a lower \(F1_{score}\) of 96.99%, whereas the LIDSNet, Capsule-NLU, and SFID-BLSTM methodologies obtained a clearly raised \(F1_{score}\) of 95.61%, 96.40%, and 97.21%, respectively. The stack-propagation method resulted in a moderately improved \(F1_{score}\) of 96.23%. Although the stack-PBERT approach has obtained a reasonable \(F1_{score}\) of 97.98%, the CL-DLBIDC methodology obtained a higher \(F1_{score}\) of 98.29%. These results demonstrate the enhanced outcomes of the CL-DLBIDC model in comparison with recent models. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Accuracy (\%)** & **F1-Score (\%)** \\ \hline CL-DLBIDC & 99.51 & 98.29 \\ Stack-PBERT & 99.25 & 97.98 \\ LIDSNet & 97.79 & 95.61 \\ Stack-Propagation & 98.29 & 96.23 \\ Capsule-NLU & 97.43 & 96.40 \\ SFID-BLSTM & 97.36 & 97.21 \\ Attention-SG-BiLSTM & 96.87 & 96.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative analysis of the CL-DLBIDC approach with recent algorithms. Figure 8: \(Accu_{y}\) analysis of the CL-DLBIDC approach with recent algorithms.
# Design Model of Flow Lines to Include Switch-Off Policies Reducing Energy Consumption
## 4 Reference Context and Simulation Scenarios
The factors considered are \(N^{U}_{\mathit{on}}\), \(N^{D}_{\mathit{off}}\), \(N^{D}_{\mathit{on}}\), and three levels for each factor are evaluated as follows:  
* \(N^{U}_{\mathit{on}}=[1,2,3]\);
* \(N^{D}_{\mathit{on}}=[4,5,6]\);
* \(N^{D}_{\mathit{off}}=[7,8,9]\);  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**MinTT\({}_{d}\)**} & \multicolumn{2}{c}{**MaxD\_25\%**} & \multicolumn{2}{c}{**MaxD\_5\%**} & \multicolumn{2}{c}{**MaxD\_10\%**} \\ \hline \multirow{2}{*}{**Station**} & **Station** & **Station** & **Station** & **Ratio** & **Station** & **Station** & **Ratio** & **Station** & **Station** & **Ratio** \\  & **Processing** & **Time [s]** & **Time [s]** & **Time [s]** & **Time [s]** & **Time [s]** & **Time [s]** & **Time [s]** & **Time [s]** \\ \hline
1 & 298 & 6 & 24  
According to Reference [23], the levels of buffer to switch off or switch on machines respect the following constraints (Equations (14)-(16)):  
\[N_{off,j}^{D}\geq N_{on,j}^{D} \tag{14}\]  
\[N_{off,j}^{D}\geq N_{on,j+1}^{U} \tag{15}\]  
\[N_{on,j}^{D}\geq N_{on,j+1}^{U} \tag{16}\]  
Figure 5 reports on the experiment results for model 1. The design with the lowest inactive time has a value of 0, the design with the highest inactive time has a value of 1. These results are used to set these parameters for the simulation tests. The results of the other experiments for models MaxD_2.5%, MaxD_5%, and MaxD_10% are reported in Appendix A. As shown in Figure 2, and in the figures (Figures A1-A3) in Appendix A, for all the four design models, the set \(N^{U}_{on}=3\), \(N^{D}_{on}=4\), \(N^{D}_{off}=9\), gives the maximum time spent in the idle and inactive state. Therefore, the simulation scenarios and the evaluation of the performances have been obtained by considering the best control parameters. According to Reference [23], the discrete event simulation length is \(10^{7}\) s, and the initial transient is \(10^{5}\) s.  
Figure 6 shows the setting of simulation scenarios and performance evaluations. Using mathematical optimization, the flow line design and the operations assignment to the stations have been achieved. Using discrete event simulation, the best set of switch off-parameters have been obtained. Finally, by employing this set, the performances of the four lines have been evaluated and compared. Figure 5: Experiments results for line design MinTT\({}_{\text{d}}\).
# ROAD Statistics-Based Noise Detection for DME Mitigation in LDACS
## 2 Related Work
In this paper, we have used GAE and OBE enhanced pulse peak attenuators and limiters to compare the performance of the proposed ROAD pulse blanking. ROAD statistics-based impulse detector was proposed in 2005 to detect the impulse pixels in an image. The idea can be extended to remove any mix of Gaussian and impulse noise [31]. In [32], the ROAD value of the received signal is used as one of the inputs to train the deep neural network for the detection of signal instances corrupted with impulse noise. The most affected or least acceptable data are present on those subcarriers whose powers are much different from neighboring subcarriers at each time epoch. Hence, it is possible to use ROAD statistics to identify the subcarriers affected with impulse noise. To the best of our knowledge, no work has been reported that employs ROAD statistics for the detection of DME interference to date. In this paper, ROAD pulse blanking is proposed which uses ROAD statistics for the detection of DME interference and pulse blanking for noise mitigation. The performance of the proposed method is compared with absolute value-based DME interference detection methods such as pulse blanking, GAE enhanced pulse peak attenuators/limiters and OBE enhanced pulse peak attenuators/limiters. All these methods include two basic operations:  
1. Detection of DME interference. 2. Mitigation of DME interference. The advantage of the proposed method compared to other methods is that it could identify affected subcarriers more accurately and hence could eliminate noise more effectively. The performance of pulse blanking is observed to be improved when ROAD statistics-based noise detection has been employed. The improvement in performance is such that it outperformed GAE enhanced pulse peak attenuators/limiters and could stand with OBE enhanced pulse peak attenuator/limiter.
# Joint Energy Supply and Routing Path Selection for
## 4 Simulation Results and Analysis
### The Impact of Network Size on Network Performances  
In this section, we show the results of several simulations in the case of varying the network size \(n\) from 100 to 300, meanwhile we set the fusion index \(\partial\) to 1 and the harmonic coefficient \(\alpha\) to 0.4. Finally, the impact of node number on network lifetime is shown in Figure 11. It can be seen that under the same charging duration, the network lifetime gradually decreases under a decrescent slope when the network size increases. Meanwhile, when the charging durations are different, the impact of network size on the network lifetime is also discrepant.  
In the WSNs, the \"inner ring\" nodes closer to the sink often consume energy faster than the peripheral nodes because of the more demand for forwarding data. When the network size gradually increases, more and more nodes need to send data to the sink node, which means that the heavier data forwarding task is undertaken by the \"inner ring\" nodes, and greater total transmission energy consumption exists in these nodes. Finally, it accelerates the emergence of the phenomenon of energy hole [48], that is, the \"inner ring\" node dies due to energy depletion, so the network lifetime becomes smaller and smaller as the network size expands. In addition, according to the charging algorithm  
Figure 10: Average energy consumption with varying charging duration.  
in Section 3, the heavier the node's task is, the more energy it can get during charging. Therefore, when the charging duration is continuously increased, the energy that the \"inner ring\" nodes can obtain gradually increases until they are fully charged, which can effectively prolong the network lifetime. Of course, as the network size continues to increase, the energy consumption rate of the \"inner ring\" nodes will eventually exceed the limited energy supply capacity of WCD, as a result, the network lifetime may increase with the charge duration, but the growth rate is getting smaller and smaller.
## 2 Materials and Methods
### Determination of Sound Absorption Capacity of Cement Composites  
The determination of sound absorption capacity of cement composites was performed according to CSN EN 1793-1 [48]. The test specimens were prepared on the basis of the designed mixtures in the shape of a cylinder with the diameter of 98 mm and the thickness of 10 mm, 30 mm and 50 mm. These dimensions of the test specimens were manufactured using a mould printed on a 3D printer. The dimensions of the test specimens had been designed to correspond to the dimensions of the Kundt tube in which the sound absorption capacity was measured. The specimens were stored in a water bath for 28 days, and then they were dried and tested. The measurements were performed for audio frequencies of 125, 250, 500, 1000 and 2000 Hz. The range of frequencies used was chosen based on a tube diameter of 100 mm, which also corresponds to the work of [PERSON] et al. [18].  
The tested material was placed into a holder at one end of the Kundt tube. The acoustic signal source was mounted on the other end of the Kundt tube. A sound wave of a defined frequency was transmitted from the source into the tube. The sound wave was reflected from the tested material, while, together with the incident wave, it created a standing wave in the Kundt tube, which was caused by the interference of both waves. The sound reflected from the tested material was read by a microphone and analyzed using an octave filter so that the frequency dependence could be determined as well. Depending on the distance of the microphone from the measured material in the tube, the values of the maximum and minimum pressure of the acoustic signal can be determined.  
The microphone with an amplifier was connected to a voltmeter. The maximum acoustic pressure \(P_{max}\) and the minimum acoustic pressure \(P_{min}\) correspond to the respective voltages measured on a voltmeter, i.e., \(U_{max}\) and \(U_{min}\).The ratio of acoustic pressures is then proportional to the voltage ratio:  
\[n=\frac{P_{max}}{P_{min}}=\frac{U_{max}}{U_{min}} \tag{1}\]  
Their ratio \(n\) makes it possible to calculate the reflection coefficient or absorption coefficient \(\alpha\). Since we do not take into consideration the energy penetration, \(\tau=0\) and the reflection coefficient expressed by acoustic pressures:  
\[r=\frac{P_{R}^{2}}{P^{2}} \tag{2}\]  
where \(p\) is the sound pressure of the incident wave and \(p_{R}\) is the sound pressure of the reflected wave  
This means:  
\[n=\frac{P_{max}}{P_{min}}=\frac{P+P_{R}}{P-P_{R}} \tag{3}\]  
After adjustment:  
\[\frac{P_{R}}{P}=\frac{P_{max}-P_{min}}{P_{max}+P_{min}} \tag{4}\]\[r=\left(\frac{P_{R}}{P}\right)^{2}=\left(\frac{P_{max}-P_{min}}{P_{max}+P_{min}} \right)^{2} \tag{5}\]  
\[\alpha=1-r=1-\frac{p_{R}^{2}}{P^{2}}=\frac{4p_{max}\cdot p_{min}}{\left(p_{max}+ p_{min}\right)^{2}} \tag{6}\]
## 4 Case Studies
### Case Study 1: Predicting Vessel Arrival Times  
* Problem: The accurate prediction of vessel arrival times (ETAs) is essential for efficient port operations. However, due to the complex factors influencing a ship's journey, these predictions can often be inaccurate, leading to disruptions in port operations [44].
* Solution: A study employed a gradient boosting machine learning algorithm to predict ETAs. The algorithm was trained on historical Automatic Identification System (AIS) data, which included information on ship speed, course, location, and more.
* Implementation: The model was continuously updated with live AIS data, and the output was visualized on a dashboard that was accessible to port operators [44].
* Results: The machine learning model significantly improved the accuracy of ETA predictions, leading to the more efficient scheduling of port resources and reducing idle time and associated costs.
# Side-Illuminating Optical Fiber for High-Power-Density-Mediated Intraluminal Photoacoustic Imaging
## 4 Discussion and Conclusions
### Optimization and Future Directions  
While it is possible to fabricate the elongated side-fire fibers with an excimer machining laser, and process optimization can improve some of the limitations discussed above, the method leads to fragile fibers that degrade when transmitting ns PA imaging laser pulses. Micromachining processes like femto-second (fs) laser would be better suited for altering the optical fiber core because of the short pulse duration and their ability to perform subsurface ablations without affecting the fiber surface [49; 50]. The pulse duration of fs lasers is shorter than the thermalization time scale, resulting in smaller temperature diffusion length and a more precise ablation [51]. This is expected to reduce fractures in the fiber and improve strength and robustness post fabrication. Another advantage of using fs laser ablation is that patterns of ablated points can be created across the full diameter of the fiber so that light in all the modes in the fiber can be redistributed. This has the potential to significantly improve the side-fire efficiency of the fibers if an appropriate pattern and density of ablated spots can be created.  
We demonstrated PA imaging of India ink-filled tubes embedded in chicken breast tissue and vasculature in a rat ear in vivo using the side-fire fiber prototypes. Targets were visible in the PA-mode over an angle of 102\({}^{\circ}\) and for the full width of the US transducer field of view. The initial device design considered optical fibers positioned within and parallel to the ultrasound probe imaging plane so as to benefit from the illumination from the full longitudinal length of the side-fire window. Yet, the 3D imaging of the tissue and ink phantom (Figure 5f) demonstrates the feasibility of PA imaging of a volume using these extended illumination side-fire optical fibers aligned perpendicular to the image plane. In this setup, the optical fiber can be stationary, illuminating a constant segment of tissue, and the ultrasound probe is translated to cover its entire length. In the end, PA imaging of the phantom with India ink tubes and rat ear in vivo was performed at 5 mJ and 3 mJ, respectively, which is quite low when compared to the 35 mJ delivered by the commercial PA system used in our lab. Yet, we were able to visualize tubes in the chicken at 10 mm depth and blood vessels in the rat ear, demonstrating a potential for deeper PA imaging with this elongated side-fire fiber design.
## 2 Classification of HEVs
### HEV Classification by Hybridization Rate
\begin{table}
\begin{tabular}{c c c c c} \hline
**Function or** & \multicolumn{4}{c}{**Types of HEV**} \\ \cline{2-5}
**Component Parameters** & **Micro** & **Mild** & **Full** & **Plug-In** \\ \hline Idle Stop/Start & \(\bullet\) & \(\bullet\) & \(\bullet\) & \(\bullet\) \\ Electric Torque Assistance & & \(\bullet\) & \(\bullet\) & \(\bullet\) \\ Energy Recuperation & & \(\bullet\) & \(\bullet\) & \(\bullet\) \\ Electric Drive & & & \(\bullet\) & \(\bullet\) \\ Battery Charging & & & \(\bullet\) & \(\bullet\) \\ Battery Charging (from Grid) & & & & \(\bullet\) \\ Battery Voltage (V) & 12 & 48–160 & 200–300 & 300–400 \\ Electric Machine Power (kW) & 2–3 & 10–15 & 30–50 & 60–100 \\ EV Mode Range (km) & 0 & 0 & 5–10 & \(>\)10 \\ CO\({}_{2}\) Estimated Benefit & 5–6\% & 7–12\% & 15–20\% & \(>\)20\% \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of micro, mild, full and plug-in HEVs. Braking Regenerative: During coasting or braking, regenerative braking restores energy normally lost. Drive/Assist Electric Motor: In order to help the engine accelerate, move, or hill climb, the electric motor provides power. This enables the use of a smaller, more efficient engine. Stop/Start Automatic: When the car comes to a halt, the engine automatically shuts off and restarts when the accelerator is pressed. It minimizes wasted energy from idling. The key to achieving these benefits is the ability to coordinate real-time control strategies for onboard power supplies to maximize fuel economy and reduce emissions.