esa-sceva/satcom-chunk-collection huggingface dataset:
# Code-Switching in Automatic Speech Recognition: The Issues and Future Directions
## 6 Conclusions  
CS is an integral part of developing bilingual and multilingual speech recognition systems. Much of the speech recognition system development now focused on the bilingual and multilingual as monolingual speech recognition system has been a commercial success. As humans are commonly fluent in two or more languages, so should the ASR system be. However, unlike humans, who can naturally change from one language to another quickly, most of the existing ASR systems do not have similar abilities, particularly in handling unexpected changes in pronunciation when languages are mixed. CS may involve two languages that differ in terms of linguistic, pronunciation, and speech features or have a high degree of similarity between them. In both situations, the ASR systems need to differentiate the two languages from the uttered speech.  
The aim of this research is to perform a review on CS in bilingual and multilingual ASR systems. This is because there is very little research that summarises the progress in the development of bilingual and multilingual ASR systems that can handle CS with acceptable performance.  
To meet the objective of this study, two research questions were formulated. The first research question identifies the issues affecting the recognition performance of CS ASR systems in bilingual and multilingual settings. For this, we reviewed 66 selected papers (both review and experimental papers), where the issues in CS recognition can be grossly categorized into two, which are database sparsity and recognising CS. The database sparsity is mainly caused by the variability in the occurrence of CS, which is not fully covered by the existing CS databases. Recognising CS is difficult as CS speech is highly unpredictable and difficult to model.  
The second research question looks at the direction in the development of CS ASR systems in terms of focus, databases, acoustics and language modelling, and evaluation metrics. For this, we reviewed 42 selected experimental papers (reflected in Table 1). These papers cover many well-resourced and under-resourced languages and techniques to recognise CS in ASR systems, such as mapping, combining, and merging the phone sets of the languages experimented with. Our review also examined the performance of those techniques. This review found a significant variation in the performance of CS experimental papers in terms of WER, indicating the inconsistency in the ability of the existing ASR systems to handle CS.
# Development of a Structural Monitoring System for Cable Bridges by Using Seismic Accelerometers
## 2 Structure of the Monitoring System
The latter process, in general, includes decision of limit states and generation of formatted report to be submitted to the facility management organization. Table 1 is an example of limit states used for the condition evaluation of a cable stayed bridge. While the threshold of the limit states for those indices that can be obtained from the time domain responses can be determined directly from references and structural analyses, decision of limit states for changes in natural frequency of a major structural component such as pylon or cable needs chronical data for a reliable condition assessment. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Response Indices** & **Values** & **Limits** & **Result** & **Limit States Decision** \\ \hline Peak ground acceleration & 80 gal & 110 gal & Safe & Design ground acceleration \\ Frequency change of the pylon & 5\% & 16\% & Safe & Empirical-chronical data \\ Frequency change of the cable & 2\% & 10\% & Safe & Empirical-chronical data \\ Max. vertical displacement of deck & 230 mm & 450 mm & Safe & Structural analysis \\ Max. torsion of deck & 0.005 rad & 0.01 rad & Safe & Structural analysis \\ Overall Condition Evaluation & & Safe & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Response indices and limit states. Figure 4: Overview of the data processing procedures and response indices.
# Mapping Surface Flow Velocity of Glaciers at Regional Scale Using a Multiple Sensors Approach
## Data and Methods
### Description of the Workflow
#### 2.2.2 Image Preparation  
The image preparation module automatically downloads all available image scenes in our regions of interest. Landsat 7/8 and Sentinel-2A/B images are downloaded from either Google Cloud, Amazon AWS, USGS EarthExplorer or Copernicus Scihub Cupernicus Scihub using their respective API services and depending on the availability of the repository, while Venus and Pleiades are downloaded manually from the CNES Kalideos platforms and THEIA. Landsat, Sentinel, Venus and Planet Labs data are provided in the form of ortho-rectified images. No additional ortho-recticification correction is applied. For Pleiades data, an additional step is required to obtain accurate ortho-rectified images useful to calculate surface displacement. Hence, we rely on the Ames Stereo Pipeline [20; 21] to generate a DEM and an ortho-image at a resolution of 0.5 m for each stereo or tri-stereo acquisition over the Mont-Blanc area. Rational polynomial coefficient (RPC) information is used to convert image coordinates to ground coordinates, which are then referenced to the WGS84 UTM 32 N projection. Finally, we use the methodology developed by [22] to calculate the shift between the generated DEMs and co-register the orthoimages.  
In order to document seasonal and sub-seasonal variations in glacier flow and to obtain more accurate time-averaged maps of ice velocity suitable for comprehensive regional-scale mapping, we form all possible pairs of images with repeat cycles ranging from the sensor nominal cycle (2-16 days) to 100 days and from 330 to 400 days. We choose not to include repeat cycles between 100 and 330 days, as we expect surface characteristics and solar illumination to change significantly for inter-seasonal cycles. In addition, only acquisitions made in the same orbits are matched in order to minimize residual stereoscopic effects that may occur between adjacent orbit paths [23] that have different observation geometries. Finally, we apply a Sobel filter in the -x and -y directions to enhanced surface features, which seems to increased spatial coherence. The result of this filter is stored as a complex binary file [24]. As shown in Table 1, we used band 8 of Sentinel-2 and Landsat 7-8, band 6 for Venus, band 4 for Planet Labs and the panchromatic band of the Pleiades satellites. These bands have the best spatial resolution and low radiometric noise, and seem therefore better suited for image offset tracking [25; 26].
## 2 Materials and Methods
### Analytical Thermal Methods Applied to Composites
#### 2.5.2 Melt Flow Index  
The melt flow index (MFI) is defined as the mass flow through a standard die under a given pressure. The units are reported as grams throughput in 10 min, in response to a constant piston pressure at a temperature at/above the melting temperature of the polymer or compound. For a pure polymer, the MFI is an assessment of average molecular mass and is an inverse measure of the melt flow viscosity at the conditions of the test [18]. It must be kept in mind that the resulted viscosity value for a measured material is only valid on the applied force and temperature. The MFI is particularly used here while it is commonly applied in the polymer industry to provide a process-related index of particular relevance to extrusion, where throughput flowability is a criterion for efficiency and processability under a given pressure. It is, therefore, more easily related to practice than a rheometrically determined dynamic viscosity, which refers specifically to an equilibrium shear condition rather than an industrially relevant response to a given pressure driving the response rate of passage through processing die.  
The MFI was determined with the use of a CEAST Melt Flow Index Tester Model 702,700 (Schenck Technologie- und Industriepark, Landwehrstrasse 65, 64,293 Darmstadt, Germany), according to the DIN EN ISO 1133 guidelines.  
The compound was preheated in the cylindrical instrument barrel for 5 min without pressure load at 190 \({}^{\circ}\)C. Afterward, a constant piston pressure, arising from the loading of 2.16 kg, was applied to the material and pressed through a die with a diameter of 2.095 mm. The measuring length, which the material travels in the barrel, was kept constant at 20.00 mm.
## 3 Results and Discussion
### Interfacial Tension Measurements (IFT)  
IFT is an important index of enhanced oil recovery in tertiary oil recovery technology. An excellent surfactant solution is required to form ultra-low interfacial tension (10\({}^{-3}\) mN/m) with crude oil. At this time, the capillary size of the rock is significantly increased, the starting pressure of residual oil drops is decreased, and the dispersed oil drops gradually gather into oil bands in the pore throat and are displaced. The surfactant solution's interfacial tension with crude oil is usually influenced by its concentration, reservoir temperature, salinity (Na\({}^{+}\), Mg\({}^{2+}\), Ca\({}^{2+}\) plasma concentration), and other factors, and there is a suitable value for the oil-water system to reach the lowest interfacial tension (IFT\({}_{\rm min}\)).
## 3 Results and Discussion
### LC-MS Method Development
#### 3.1.2 Optimising Chromatographic Separation  
For simultaneous separation of AA from other interfering substances in the blood serum, two different C18 chromatographic columns were tested: BEH C18 (1.7 \(\upmu\)m, \(2.1\times 100\) mm) and AccQ-TAG Ultra (1.7 \(\upmu\)m, 2.1 \(\times\) 100 mm), both working at 50 \({}^{\circ}\)C. Better resolution and peak shape were achieved using the AccQ-TAG Ultra column. Mobile phases, containing LC-MS water and organic solvents (acetonitrile and isopropanol) in different ratios, were tested. For achieving better ionization of AA and detection as an unfragmented deprotonated molecular ion (M-H)\({}^{-}\), as well as to improve peak shape and resolution, mobile phase modifiers 0.1% formic acid and 5 mM ammonium formate were added [35].  
Initially, we used isocratic elution starting with a low water ratio in A mobile phase (10 water, 90 acetonitrile, 0.1% formic acid, 5 mM ammonium formate), whereby the target analyte eluted too fast, before the first minute. Therefore, we improved to gradient program and the mobile phase composition, increasing the water ratio in A to 60% and adding isopropanol (IPA) to mobile phase B (89:10:1 IPA:CH3 CN: H2O, 0.1% formic acid, 5 mM ammonium formate), which results in improved retention and better separation of AA from the other endogenous compounds in less than 5 min. A flow rate of 0.3 mL/min was used throughout the method development. By running AA standard and d8-AA standard, the retention times (RT) were determined: RT AA 4.72 min, RT d8-AA 4.69 min. By optimizingthe chromatographic parameters and conditions, we achieved better retention, resolution, peak shape, and separation, leading to a more reliable chromatographic method for the quantification of AA in serum. This optimized approach serves as a foundation for accurate and reproducible analysis in future studies.
# Array Design and Transmitter Coils Selection for Underwater Wireless Power Transfer System
## 3 Coupling Mechanism Design and Array Switching Control Strategy
### Transmitting Array Switching Control Strategy Based on Receiving Coil Position Estimation
#### 3.2.1 Receiver Coil Position Estimation
By comparing the power supply coil TX(\(x\), \(y\)) with the four unpowered coils TX(\(x\)+1, \(y\)), TX(\(x\), \(y\)+1), TX(\(x\), \(y\)-1), and TX(\(x\)-1, \(y\)), the receiving end reflection impedance \(Z_{(x,\,y)}\), \(Z_{(x+1,\,y)}\), \(Z_{(x,\,y+1)}\), \(Z_{(x,\,y-1)}\), and \(Z_{(x-1,\,y)}\) of the four unpowered coils TX(\(x\), \(y\)), TX(\(x\), \(y\)+1), TX(\(x\), \(y\)-1), and TX(\(x\)-1, \(y\)), the position of the receiving coil is estimated, and the coil power supply corresponding to the maximum value of the receiving end reflection impedance \(Z_{\max}\) is selected to realize the optimal power supply coil switching control. The expression of \(Z_{\max}\) is as follows:  
\[Z_{\max}=\max\Bigl{\{}Z_{(x,y)},Z_{(x+1,y)},Z_{(x-1,y)},Z_{(x,y+1)},Z_{(x,y-1) }\Bigr{\}} \tag{10}\]  
A hysteresis mechanism is incorporated to prevent chattering: the system switches coils only if a neighboring coil's \(Z_{(x,\,y)}\) exceeds the active coil's by 5%, ensuring stability near boundaries.
# Enhancements and Challenges in CoAP--A Survey
## 4 Enhancements in CoAP
### Enhanced CoAP for Streaming Services  
Owing to its lightweight capability, CoAP can be used for streaming services in constrained environments where devices have limited processing and power capabilities. For content delivery in CoAP, packets are sent using stop-and-wait method where a message carries some content from one node to another and then waits for the acknowledgement from receiver to confirm the content reception before sending more content in next message. The stop-and-wait method is not an efficient choice for streaming services as it involves large amount of data to be transmitted which exceeds the payload size of CoAP message. The upper bound of payload size in CoAP message is 1024 bytes only for limiting the communication overhead at IP layer due to packet fragmentation. Therefore, in order to use CoAP for streaming services, [47] proposes an extended version of CoAP. To overcome the inefficiency of stop-and-wait method of CoAP in streaming, the proposed scheme uses single request to retrieve multiple data blocks. This is beneficial for request messages in reducing the number of channel access. Therefore, the throughput performance can be increased. The number of data blocks that can be retrieved in a single request vary according to the network and channel conditions. In the case of stable network and no congestion, more packets can be sent, and higher throughput is achieved. The maximum number of data blocks that can be requested at a time is set to 8 in order to help in case of loss recovery. The paper also proposes a loss recovery method in case when data blocks are not received at the receiver due to lossy network environment.
# Prediction of Air Pollution Concentration Based on mRMR and Echo State Network
## 3 PM2.5 Time Series Prediction Model
### Algorithm Flow  
In order to accurately predict the PM\({}_{2.5}\) time series, this paper proposes the prediction structure based on feature extraction and improved echo state network model. The process is summarized as follows.  
1. Feature selection: select the optimal subset from original dataset based on mRMR feature selection method.
2. Phase space reconstruction: reconstruct phase space of the selected optimal subset based on Takens' theorem and form a new set of input features.
3. Data division: divide training set and testing set according to a certain proportion.
4. Model training: train SLI-ESN model using ridge regression algorithm on training set.
5. Prediction: predict PM\({}_{2.5}\) time series using SLI-ESN model on testing set.  
In order to briefly explain prediction process of the proposed model, the schematic diagram of the method is shown in Figure 2.
# Maximum Transmit Power for UE in an LTE Small Cell Uplink
## 3 Network and Propagation Model
### HetNet Deployment with Small Cells near Macro Cell eNodeB
#### 3.3.1 Transmit and Receive Power  
The expressions for transmit and receive power of macro and small cells are taken from Equations (2) and (3).
## 3 Results  
We utilized an open-source dataset and employed Equation (1) to transform sampled insect sounds into Mel spectrograms for data processing. With the parameter settings described above, the model achieved an accuracy of 80.26%, showcasing its proficiency in distinguishing between sounds produced by different insect species.  
Figure 8: Visualization of the DFSM: Tower 1 exhibits multiple dark features, discerning various sound frequencies, and Tower 2 has only one or two dark areas, capturing subtle differences in the sound spectrum.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Batch\_Size** & **Lr** & **Channel** & **Noise** & **Pitch\_Shift** & **Time\_Shift** & **Sr** & **Mel** \\ \hline
10 & 0.001 & 2 & 0.004 & 0.15 & 0.4 & 44,100 & 64 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Specific Model Configuration Parameters.  
When assessing the model's performance, we partitioned the dataset into training, and test sets, aligning them with the official CSV files where each class corresponds to a unique _class_id_ (as detailed in Table 4). The confusion matrix showing the performance of the dual-tower network reflects the overall performance as it shows a clearer diagonal for accurate classification. During our analysis, we identified specific trends and patterns of misclassification. Notably, a large portion of misclassifications occurred within the genera _Myopsis_ and _Platypleura_ from the InsectSet32 dataset, encompassing 5 and 14 distinct species, respectively (illustrated in Figure 9). It is worth mentioning that species within these genera were frequently mislabeled as other members of the same genera. Within its genus, we observed that one insect species called _M. melanobasis(9)_ caused a significant number of misclassifications, and the model has a lot of confusion in this category. Similarly, 14 species within the _Platypleura_ genus, including _P. capensis(14)_ and _P. divisa(18)_, were often incorrectly categorized as other members within the same genus. _Brevisiana brevis(1)_ and _Pholidoptera griseoaptera(13)_ were never correctly classified. Compared with other network models, the model performance of the dual-tower network is significantly better for insect sound recognition.  
Figure 9: Classification results of 32 insect species in the test set using the best run of the dual-tower network, achieving a classification accuracy of 80.26%. The horizontal axis represents the predicted labels, while the vertical axis represents the true labels, with 0-31 corresponding to the insect species listed in Table 4 above. The classification highlights two genera: Myopsis (6–10) and Platypleura (14–27).
# New Framework for Human Activity Recognition for Wearable Gait Rehabilitation Systems
## 3 Signal Processing Techniques  
The EMG signal must be decomposed to understand how muscle and nerve control works. Several approaches have been developed for the decomposition of EMG. To decompose the EMG signal, wavelet spectrum matching is used to calculate the use of principal component analysis of wavelet coefficients. [PERSON] et al. investigated the effectiveness of using multi-level wavelet decomposition for extracting key features from electromyography (EMG) signals [32]. Various mother wavelets and decomposition levels were tested to isolate optimal resolution components for signal reconstruction, effectively eliminating noise and irrelevant signal parts. Key features such as mean absolute value and root mean square were extracted from the reconstructed signals to enhance class separability.  
The process for the multi-unit EMG signal decomposition algorithm comprises four sub-processes: signal de-noising sub-process, spike detection sub-process, spike classification sub-process, and spike separation sub-process. [PERSON] et al. showed that the wavelet coefficients of lower bands were more relevant in the ability to distinguish action potential (AP) characteristics than wavelet coefficients of higher bands, as shown here for various AP features [33]. Nevertheless, the authors of [34] showed experimentally that high-frequency information must be considered in the classification of MUAP. To surpass the subjective criterion for selecting the appropriate features, they suggested another approach by applying PCA on wavelet coefficients. Their approach consists of four processing stages: segmentation, wavelet transform, PCA, and a decomposition algorithm based on clustering.  
The advantage of this approach is that it takes into consideration all frequency information. In [35], the authors proposed the use of nonlinear least mean square (LMS) optimization to decompose higher-order cumulants of EMG signals. Their terms have a decomposition in the third-order cumulants, multiplicative factors that show up as coefficients in nonlinear equations of motion. It is also solved in a nonlinear LMS sense, as shown next. This method adopted a multiple-input multiple-output model type due to its ability to model several MUAPs simultaneously overlapped on the EMG signal. Raw EMG signals include information grouped in random patterns. To extract this information, raw EMG signals must undergo diverse signal processing techniques. Hereinafter, a brief overview of EMG signal processing techniques is provided.
# Simplified Vibration PSD Synthesis Method for MIL-STD-810
## 4 Conclusions  
In this paper, the DP formula used to synthesize vibration test conditions in MIL-STD-810 was further simplified and GRS formula which is the most concise form for vibration synthesis is proposed. GRS is used under the assumption that vibration conditions are Gaussian random, constant PSD, and the response system has low damping ratio (less than 0.1).  
DP is presented as a descriptor for comparing random test conditions in their fatigue damage potential perspective, which MIL-STD-810 uses to synthesize a Gaussian random vibration environment. The results of combining PSDs with DP are no different from the results by DP's original FDS formula. But in the same context, the same synthesis results can be obtained with the simplified formula, GRS. The numerical verification was done through simple example.  
However, both DP and GRS methods are subject to the same constraints as of the original FDS as mentioned. The assumption that the damping coefficient is less than 0.1 when synthesizing vibration using DP or GRS in MIL-STD-810 is valid if the material of the component considering fatigue damage is metal. However, the effectiveness of this method should be reconsidered for synthetic resins with high damping coefficients for example. In addition, it is not valid when the input vibration includes a lot of shocks and is not Gaussian distribution. Otherwise, if the DP can be used, it is efficient to use the simplified GRS formula instead for synthesis of random vibration condition. Lastly, it is emphasized that it would be helpful to have an intuitive understanding of the relationship between PSDs through Equation (15).  
**Author Contributions:** Formal analysis and writing, J.J.; Project administration, review & editing, J.-W.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by \"Virtual Engineering Platform Construction for Future Transportation Vehicle Components Manufacturing Project\" supported by Ministry of Trade, Industry and Energy.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Nomenclature**  
\begin{tabular}{l l l l} \(n\) (s) & peak distribution & \(a\) & acceleration \\ \(f_{n}\) & natural frequency & \(N(s)\) & Number of stress cycles to failure \\ T & duration & \(b\) & fatigue parameter \\ \(s\) & stress & \(C\) & constant of proportionality \\ \(\sigma\) & standard deviation & \(\Gamma\) & gamma function \\ \(p\)(s) & Rayleigh distribution function & \(D\) & cumulative damage index \\ \(G_{s}\) & PSD of stress & \(DP\) & damage potential \\ \(G\) & PSD of input acceleration & \(T_{i}\), \(G_{i}\) & exposure time and PSD of event \(i\) \\ \(k\) & proportional constant & _Trest_ & exposure time for test \\ \(H\) & frequency response function & _Gtest_ & PSD for test \\ \(\zeta\) & damping ratio & \(Q\) & amplification factor \\ \(pv\) & pseudo-velocity & _GRS_ & Gaussian random synthesis \\ \end{tabular}