esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### StaMPS
In addition, a one-arc-second shuttle radar topography mission (SRTM) (approximately 30 m \(\times\) 30 m) was used to extract the topographic phase from the interferograms. StaMPS parameters used in the processing of SAR images are given in Table 2 [33]. Finally, we test two different approaches (linear phase-based tropospheric correction and GACOS) within the TRAIN to predict and correct errors caused by atmospheric interactions. A flowchart for the methodology that was used in this study is presented in Figure 3. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Selected Value** \\ \hline max\_topo\_err & 10 \\ filter\_grid\_size & 40 \\ clap\_win & 64 \\ percent\_rand & 1 \\ unwrap\_grid\_size & 100 \\ unwrap\_time\_win & 180 \\ scn\_time\_win & 180 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters used for StaMPS processing. Figure 3: Processing workflow of the study. Figure 2: The temporal and perpendicular baselines for the descending (**a**) and ascending (**b**) S1 datasets. The black plus symbols represent the master images of all the pairs.
# Leaking Gas Source Tracking for Multiple Chemical Parks within An Urban City
## 4 Experiments and Discussion
### Experiment of Sensor Failure and Model Robustness  
In the real world, sensors can lose their ability to detect gas concentration for a variety of reasons. The model may produce bad results when some of the input concentrations are lost if such a condition is not considered. Thus, to simulate sensor failure, three kinds of dropout are tested to find the most suitable one:  
1. Only set a dropout layer before the first fully connected layer and keep the layer active while testing.
2. Set a dropout layer before every fully connected layer but only keep the first one active while testing.
3. Model without any dropout is set as a control group.  
For a more intuitive observation, the probability of sensor failure is uniformly set to 10%. The accuracy of the scenarios above are shown in Table 1:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Train Acc.** & **Test Acc.** \\ \hline Only dropout the input & 97.34\% & 96.62\% \\ Dropout before every FC layer & 90.28\% & 93.03\% \\ Without dropout layers & 99.98\% & 98.86\% \\ \hline \hline \end{tabular} All the models above are trained for 3000 epochs.  
\end{table}
Table 1: The training accuracy and test accuracy of models when sensors have a probability of 10% for failure.  
Under the circumstances that 10% of the sensors fail, due to the high effectiveness and strong fitting ability of our proposed model, the classification accuracy on the test set is still above 96%, which is enough for ordinary leaking source tracking. Setting a dropout layer before every fully connected layer can enhance the generalization performance of the model, which makes the test accuracy even higher than the train accuracy. However, the final result is still worse than that of the model only dropping out the input data.  
Actually, most of the time the probability of sensor failure will not be as high as 10%. It is rare to have three sensors fail at the same time, which means the probability of sensor failure in this issue is less than 5%, for there are 79 potential leaking sources in total. A further test was carried out and it was found that with 5% of sensors failing, the proposed model achieves an accuracy of 97.41%.  
For further verifying the robustness, a random data noise from \(-1\) to 1% is added to each sensor's concentration separately. This data perturbation only leads to an accuracy decline of 1.75% compared with the original model. The model's prediction accuracy is still greater than 97%.
## 3 Results
### Match the Detection Result with the Industrial Complex Information  
Figure 10 shows the detection results of the SIGIS-2 for vinyl chloride in the monitored section G2, and Figure 11 shows the delta T and the calculated result of the spectrum of vinyl chloride detected by the SIGIS-2 analysis. Figure 11 shows that the detection result was almost identical to the reference spectrum of vinyl chloride in the SIGIS-2 library.  
Figure 11: Delta T and the calculated result of the spectrum of vinyl chloride detected by the SIGIS-2 analysis.  
Figure 9: Result of each chemical substance detected in the monitored section at the (**a**) Jeongnyang site, and (**b**) Hwachi site.  
The data used to detect vinyl chloride in section G2 are consistent with the actual handling information of vinyl chloride at the workplace, which is 2.2 km away from the steel tower at the Jeongnyang site in Figure 12.  
The overall results confirm that the industrial complex chemical accident monitoring system, based on a SIGIS-2 installed on a 70m-high steel tower, can detect chemical substances from a distance. In Korea, when chemical accidents occur, any person who handles relevant chemicals can immediately (within 15 minutes) report the occurrence of such chemical accidents to the competent local government, local environmental agency, national police agency, fire agency, or local employment and labor agency [29]. As such, chemical accident reports can be delayed depending on the speed of the people reporting, but this system can automatically alarm chemical accidents within 5 minutes after detecting chemical leakages, fires or smoke. Therefore, it is effective for early detection. The accumulated detection data shows that the detection frequency of a specific chemical substance is high in each section. This can be used as a basis for modifying the threshold of the anomaly detection model. Additionally, it can identify chemical substances that are routinely emitted from workplaces, and based on this, the government can manage and supervise workplaces in industrial complexes.
# An Investigation of Applying Large Language Models to Spoken Language Learning
## 1 Introduction  
Spoken language learning is the process of acquiring the ability to communicate verbally in a new language. It involves developing skills such as listening, speaking, pronunciation, vocabulary, grammar, and discourse [1]. Spoken language learning can be accomplished through diverse methods, including formal instruction, self-study, immersion, interaction, and technology. One of the approaches to integrating technologies into spoken language learning is computer-assisted pronunciation training (CAPT), a subfield of computer-assisted language training (CALL) [2; 3]. It mainly involves the assessment of pronunciation errors and the detection and correction of prosody errors. Most current systems focus on the pronunciation of phonemes. Evaluating prosody has always been a challenging task since precise prosody can only be comprehended by grasping the context, which was beyond the capability of previous models [4]. To help language learners develop their spoken language skills, it is necessary to have an open and unconstrained language learning system that allows them to freely express themselves. For example, the contentfor pronunciation training does not need to be pre-set, allowing users the freedom to choose and switch between topics at their leisure.  
The highly parallelable Transformer architecture [5], with its massively parallel computation hardware, and the self-supervised learning technique promise to leverage a vast amount of raw data (e.g., text, images, audio) to learn general-purpose deep contextualized representations [5; 6; 7]. These pre-trained context-aware representations are now ubiquitous in natural language processing and very effective as general-purpose semantic features, which have largely improved the performance of natural language processing (NLP) tasks [8; 9]. Large language models (LLMs) are poised to make this dream a reality due to their ability to evaluate the emotions, prosody, and even rhythm of a sentence in any given context.
# Effect of Soil Reinforcement on Tunnel Deformation as a Result of Stress Relief
## 3 Results and Analysis
### Effect of Reinforcement Depth on Tunnel Diameter Change  
Figure 9 shows the measured and computed changes in the tunnel diameter with different soil reinforcement depths. The positive and negative values denote the elongation and compression of the tunnel, respectively. It is also noted that the described tunnel deformation was located in the tunnel section in a circumferential direction. Tunnel diameter was elongated in the vertical direction and compressed in the horizontal direction after stress relief. The elongated and compressed values increased with the unloading ratio. As the reinforcement depth increased, the elongation and compression of the tunnel lining decreased. Obvious changes were not observed when the reinforcement depth increased further. Thus, it was concluded that the reinforcement depth exerted only a slight influence on the change of the tunnel diameter. When the unloading ratio was 0.5 and the soil area S3 was reinforced, the tunnel diameter change was 0.061%. Compared with the 0.049%  
Figure 8: Measured and computed (**a**) tunnel heave and (**b**) maximum tunnel heave with different reinforced soil areas.  
change for no reinforcement, a maximum tunnel diameter change ratio of 19% occurred. A summary of the comparison between the computed and measured values is presented in Table 2.
# Multihop Latency Model for Industrial Wireless Sensor Networks Based on Interfering Nodes
###### Abstract  
Emerging Industry 4.0 applications require ever-increasing amounts of data and new sources of information to more accurately characterize the different processes of a production line. Industrial Internet of Things (IIoT) technologies, and in particular Wireless Sensor Networks (WSNs), allow a large amount of data to be digitized at a low energy cost, thanks to their easy scalability and the creation of meshed networks to cover larger areas. In industry, data acquisition systems must meet certain reliability and robustness requirements, since other systems such as predictive maintenance or the digital twin, which represents a virtual mapping of the system with which to interact without the need to alter the actual installation, may depend on it. Thanks to the IEEE 802.15.4e standard and the use of Time-Slotted Channel Hopping (TSCH) as the medium access mechanism and IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) as the routing protocol, it is possible to deploy WSNs with high reliability, autonomy, and minimal need for re-configuration. One of the drawbacks of this communication architecture is the low efficiency of its deployment process, during which it may take a long time to synchronize and connect all the devices in a network. This paper proposes an analytical model to characterize the process for the creation of downstream routes in RPL, whose transmission of multi-hop messages can present complications in scenarios with a multitude of interfering nodes and resource allocation based on minimal IPv6 over the TSCH mode of IEEE 802.15.4e (6 TSCH). This type of multi-hop message exchange has a different behaviour than the multicast control messages exchanged during the synchronization phase and the formation of upstream routes, since the number of interfering nodes changes in each retransmission.  
TSCH; RPL; synchronization; analytical model +
Footnote †: journal: Applied Sciences  
ultihop Latency Model for Industrial Wireless Sensor Networks Based on Interfering Nodes
## 4 Experimental Analysis
### Reconstruction Ability to Malicious Network Traffic  
We implement our novel model TCReC and compare it with existing models: mean padding algorithm, k-Nearest Neighbors (KNN), Recurrent Neural Network (RNN), and Transformer, adopting CNN as a classifier for analyzing attack traffic. The raw data of CIC-IDS-2017 dataset are used to simulate the complete network flows and the flows with lost packets. In order to show the reconstruction effect of the models, we adopt a relatively serious packet loss ratio of 70%. The reconstruction effect of these models at other packet loss rates will be shown in Section 4.6. Figures 8 and 9 show the results at the packet loss ratio of 70%. The groups of columns represent the classification accuracy (F1-score) of each type of traffic.  
Figure 8: Experiment of different models based on CIC-IDS-2017 dataset (1).  
Figure 7: Experiment of different models based on ISCX-VPN-2016 dataset.  
According to the results, TCReC shows better reconstruction ability than the other models for all traffic types. This advantage is reflected in two aspects. On the one hand, the reconstruction ability of TCReC shows strong stability, and it can obtain good results in reconstructing characteristics of all types of data flows. On the other hand, in terms of the identification of some specific attacks, TCReC is able to help the detection model to obtain results very close to the original traffic in the case of 70% packet loss, such as FTP_Patator, Infiltration, PortScan, and HeartbleedPort in Figure 9.  
We also conducted an ablation study comparing Transformer and TCReC. From the experimental results, it is evident that although TCReC and Transformer share similarities in terms of model structure and parameters, TCReC exhibits significantly stronger overall reconstruction ability. This observation suggests that the approach of TCReC in handling masked and unmasked data contributes to the enhanced data reconstruction capability of TCReC.  
From the data distribution perspective, we compare the reconstructed data distribution of five methods with the original one in Figure 10, in which different colors represent different traffic types and each point stands for a single traffic flow. The results depicted in Figure 10 demonstrate that TCReC exhibits a high degree of consistency with the original data flows, both in terms of the overall distribution on the two-dimensional plane and the clustering degree within each flow type. Furthermore, the reconstructed data using the KNN method shows a close resemblance to the original distribution as a whole; however, the overlap between different data flows is high, resulting in poor discrimination among them. Conversely, the three models shown in the second row of Figure 10 exhibit inferior performance in terms of both the overall distribution and the discrimination between different flow types.  
Figure 9: Experiment of different models based on CIC-IDS-2017 dataset (2).
# Experimental Demonstration of MmWave Vehicle-to-Vehicle Communications Using IEEE 802.11 ad
## 3 GiV2V Communication Architecture
### GiV2V Communication Antenna  
The directional antenna gain is  
\[g(\theta,\phi)=\eta\frac{u(\phi,\theta)}{u_{0}},\]  
where \(\eta\) is the antenna efficiency with loss (\(0<\eta\leq 1\)) and \(u_{0}\) is the average power density transmitted in all directions.  
If we assume that the omnidirectional transmission power is nearly constant, then \(u_{0}=1/4\pi P_{t}\) and the directivity \(D=4\pi u(\theta,\phi)/P_{t}\) if the loss \(\eta\) is negligible.  
\[g(\phi,\theta)=\frac{|u(\phi,\theta)|}{1/4\pi\int_{0}^{2\pi}\int_{0}^{\pi}|u( \phi,\theta)|sin(\theta)d\theta d\phi}. \tag{1}\]  
The beamforming gain \(g(\theta,\phi)\) of the a uniform linear array antenna (ULA) or uniform circular array antenna (UCA) is derived from Equation (1). The ULA antenna gain for beam directions is shown in Figure 2. The beam-width is the narrowest in the orthogonal direction (i.e., \(\pm 90^{\circ}\)) of bore sight and widest at \(\pm 0^{\circ}\) when the antenna array is aligned at zero. Conversely, the UCA antenna can consistently form the same beam width at each sector.  
The beam patterns shown in Figure 2 can be dynamically applied to the GiV2V topologies illustrated in Figure 1. For the convoy model of vehicles, the beam pattern of Figure 1(a) is appropriate while that of Figure 1(b) is suitable for the diagonal topology. Typically, the IEEE 802.11 ad module explores all the possible sectors for beam directions provided by the RF front-end and antenna arrays. This type of beam sweeping procedure follows the IEEE 802.11 ad standard. Furthermore, the vehicles can utilize the vehicular topology information to decide a beam direction. Assuming that all vehicles exchange hello messages (e.g., cooperative awareness message (CAM)) with neighbor nodes and recognize their location by GPS information, vehicles can choose a beam direction without sweeping or by at least reducing the number of sweeping sectors.  
Figure 1: GiV2V topology.
## 3 Results and Discussion
### Stability Study
#### 3.2.6 In Vitro Dissolution Study  
To simulate lung conditions, an in vitro dissolution test was conducted using a simulated lung medium (pH 7.4). As shown in Figure 11, sample F1 demonstrated fast KTP release, attributed to the nanosized form of KTP after the milling process, which increases the surface area [33]. The freshly prepared sample (F1) achieved complete drug release (100%) within the first 10 min, facilitated by the presence of MNT, which promotes disintegration and accelerates drug release [34]. Fast dissolution is particularly advantageous in pulmonary delivery as it minimizes the likelihood of particle elimination by mucociliary clearance [35]. Additionally, the rapid release profile persisted after storage, with samples F1_1m and F1_3m releasing approximately 75% of the drug within 5 min. However, after 10 min, the release from F1_3m declined, potentially due to solid-state change or increased interparticle interactions, reducing the effective surface area and compromising the consistent drug release [36]. This observation aligns with previously published results [37].  
Figure 11: In vitro release study of raw KTP, and spray-dried sample (F1) was analyzed as a freshly prepared sample (F1—0m) and after 1 (F1—1m) and 3 (F1—3m) months of storage, in simulated lung media (SLM). Results are expressed as mean \(\pm\) SD (\(n\) = 3 independent measurements).  
Figure 10: In vitro aerodynamic distribution of the F1 sample was analyzed as a freshly prepared sample (F1—0m), and after 1 (F1—1m) and 3 (F1—3m) months of storage, at a flow rate of 60 L/min. Data are presented as means \(\pm\) SD (\(n\) = 3 independent measurements). Statistical significance is indicated as follows: \({}^{*}p<\) 0.05, \({}^{**}p<\) 0.01.
# Effect of Asparaginase Treatment on Biscuit Volatile Compounds
## 2 Materials and Methods
### Measurement of Quality Attributes of Biscuits
#### 2.3.2 Volatile Organic Compounds (VOCs)  
Biscuit samples were frozen with liquid nitrogen and ground by a laboratory grinding device (Ika, Staufen, Germany) in order to obtain a powder that was stored for each sample at \(-70\)\({}^{\circ}\)C until analysis. Analysis was carried out within three weeks of grinding. For VOC analysis, the approach developed in our laboratory and previously described [19] was adopted with some changes. Volatile compounds were determined on each sample by a semi-quantitative HS-SPME/GC-MS method. The same amount (0.5 g) of biscuit powder was placed in a 15 mL vial for SPME extraction. The vial containing the sample was immersed in a water bath kept at 60 \({}^{\circ}\)C. Then HS-SPME extraction was carried out by exposing a 50/30 \(\upmu\)m DVB/CAR/PDMS fibre (Supelco, Sigma-Aldrich, Milano, Italy) to the headspace of the biscuit powder for 45 min.  
At the end of the extraction, the fibre was immediately inserted into the GC split-spitless injection port, for the desorption step, and the GC run was started. GC/MS analyses were performed on an Agilent 6890 GC 5973N MS system equipped with a quadrupole mass filter for mass spectrometric detection (Agilent Technologies, Palo Alto, CA, USA). The desorption was carried out by the split mode (split-less) at 240 \({}^{\circ}\)C for 8 min. Then GC separation was achieved on a DB-WAX column (0.25 mm i.e., 60, 0.5 \(\upmu\)m film thickness); J&W, Agilent Technologies, Palo Alto, CA, USA) by setting the following conditions: inlet temperature 240 \({}^{\circ}\)C; oven temperature programmed from 40 \({}^{\circ}\)C (10 min) to 210 \({}^{\circ}\)C at 4 \({}^{\circ}\)C min\({}^{-1}\), and then to 220 \({}^{\circ}\)C (5 min) at 30 \({}^{\circ}\)C min\({}^{-1}\) (the total run time of 57.83 min); constant flow of the carrier gas was 2 mL min\({}^{-1}\) corresponding to a linear velocity of 36 cm s\({}^{-1}\). The MS detector operated by the electron ionisation mode at 70 eV; transfer line, source, and quadrupole temperatures were set, respectively, at 220, 230, and 150 \({}^{\circ}\)C. Detection was performed by full scan and single ion monitoring (SIM) mode for, respectively, identification and quantification purposes. Identification of the sample volatiles was done by comparison of linear retention indices (LRI) and mass spectra of chromatographic peaks with those reported in the literature and in the NIST/EPA/NIH Mass Spectra Library 2005. The list of the 36 individual VOCs determined, along with their retention index and _m_/z ion signal selected for SIM detection was reported in Table S1. For the semi-quantitative determination of each volatile compound, the peak area of the target analyte obtained by SIM detection was used. A triplicate analysis was performed on the biscuit powder obtained from each of the biscuit samples. The average coefficient of variations of triplicate determination of individual VOCs, as determined on all samples, was below 10% (9.8%).
## 4 Result and Validation
### Comparison of Final Weightings for Different Observables/Constraints  
In this section, the change of final weighting for different observables / constraints has been analyzed with the interval of one hour over the period of Doy 303 to 317, 2015 (See Figure 10). It can be seen from Figure 10 that the weighting of different observables/constraints are changed with time, where the weighting of GPS observation is regarded as one in the experiment. For BDS and GLONASS observations, their fluctuatation of final weightings is very small, but the value is different from that with GPS observation, while the values of final weightings for constriaints are very large. The above phenomenon manifested the significance of the proposed method in this paper, which has the ability to tune the weightings of different input information for different epochs.
## 5 Simulation Results
### Numerical Simulation  
To check the results obtained from the developed testing platform, the proposed ASC is simulated numerically with a tracking controller that is similar to that one implemented in PX4 firmware, Figure 24. The time histories of the load swing angles with and without the ASC, due to an initial disturbance in hover flight, are shown in Figures 25 and 26, respectively. We can observe the same trend--the ASC can improve the damping of the suspended load oscillations. The differences in the initial swing angles are due to the method of generating these initials values. The same trend can be observed also in the forward flight.  
Figure 21: Time history of the load swing angle during travel maneuver with the ASC.  
Figure 22: Time history of the quadrotor position during travel maneuver without the ASC.  
Figure 23: Time history of the quadrotor position during travel maneuver with the ASC.  
Figure 24: Simulink model of the anti-swing control system.