esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Serial Detection Using MAP Algorithm for Bit-Patterned Media Recording
## 3 Simulations
### Results and Discussions  
Figure 6 shows the comparison between the bit error ratio (BER) performance of the proposed model and the serial detection methods in [10; 11]. The proposed model outperforms serial detection method at 14 dB. The proposed model can achieve a gain of approximately 0.5 dB and 1.5 dB compared to the serial soft output [10] and serial hard output, respectively [11], at a BER of \(3\times 10^{-5}\).  
The BER performance of the proposed model in the BPMR channel with a 10% TMR effect is shown in Figure 7.  
Figure 6: BER performance of the proposed model.  
Figure 7: BER performance of the proposed model with 10% TMR.  
The BER performance of the proposed model did not change compared to that of the channel without TMR when the level of the TMR was 10%. In other words, the proposed model could resist 10% TMR.  
Next, we varied the level of the TMR effect from 10% to 30% at 15 dB to evaluate the ability of the proposed model to resist the effect of TMR. Figure 8 shows the variation in BER performance with the change in TMR effect. The proposed model shows a better performance than serial detection with VA [11] up to 30% TMR.  
We implement the simulation with the position fluctuation of 6% and compare it to the serial soft-output detection in [11]. The results are presented in Figure 9. We can see that our proposed model still can improve the BER performance. Figure 10 shows the BER performance of the proposed model when we vary the position fluctuation from 6% to 18% at 15 dB to present the BER expression as the function of the position fluctuation. The proposed model performs better than other schemes even though they converge when the position fluctuation increases.  
Figure 8: BER performance of the proposed model according to TMR at SNR = 15 dB.  
Figure 9: BER performance of the proposed model for 6% position fluctuation.
## 4 Performance Comparison
### Vibration Isolation Performance Comparison  
Figure 3 shows the vibration transmissibility \(T\) of linear design and the proposed design subjected to different fundamental excitations where amplitude \(A=[0.05,0.1,0.3]\) m/s\({}^{2}\). Table 1 lists the structure parameters. The selection of parameters considers the realization condition of quasi-zero stiffness (Equation (7)). It can be seen that the peak frequency of the proposed design increases with the increase of the base excitation amplitude, and the peak value of the vibration transmissibility corresponding to the peak frequency also gradually increases. However, the high-frequency low-orbit curves under these three excitations completely overlap and are much lower than those resulting from the linear design, exceeding 30 dB. With the linear design, once the structural parameters are determined, the vibration transmissibility is uniquely determined and unaffected by the base excitation. The peak frequency of the linear design is 2.25 Hz, with a corresponding peak amplitude of 36.43 dB. When the base excitation amplitude \(A=0.05\) m/s\({}^{2}\), the peak frequency of the proposed design is 0.96 Hz, with a peak amplitude of 32.27 dB. Compared to the linear design, the peak is reduced by 4.16 dB, and the effective vibration isolation bandwidth is increased by 57%. However, when the base excitation amplitude \(A=0.3\) m/s\({}^{2}\), the peak amplitude of the proposed design is slightly larger than that of the linear design, and the peak frequency is only slightly reduced compared to the linear design. This indicates that the vibration isolation performance of the proposed design under small amplitude excitation is significantly better than that of traditional linear vibration isolator. Therefore, in view of the common micro-vibration disturbance of spacecraft, the proposed design can effectively isolate the wide-band vibration to ensure the safety of spacecraft on orbit.  
Figure 3: The vibration transmissibility \(T\) of the linear design and the proposed design with different excitation amplitudes \(A=[0.05,0.1,0.3]\) m/s\({}^{2}\).
## 3 Results
### Effect of the Cutting Volume
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Rake Angle (Deg.)**} \\ \cline{3-11}
**UCS** & \multirow{2}{*}{\(p\) (mm)} & \multicolumn{2}{c}{**0**} & \multicolumn{2}{c}{**5**} & \multicolumn{2}{c}{**10**} & \multicolumn{2}{c}{**15**} & \multicolumn{2}{c}{**20**} \\ \cline{3-11}
**(MPa)** & & \(\mathbf{V_{c}}\) & SE & \(\mathbf{V_{c}}\) & SE & \(\mathbf{V_{c}}\) & SE & \(\mathbf{V_{c}}\) & SE \\  & & **(mm\({}^{3}\))** & **(MJ/m\({}^{3}\))** & **(mm\({}^{3}\))** & **(MJ/m\({}^{3}\))** & **(mm\({}^{3}\))** & **(MJ/m\({}^{3}\))** & **(MJ/m\({}^{3}\))** & **(MJ/m\({}^{3}\))** & **(MJ/m\({}^{3}\))** \\ \hline
40 & 3 & 2726 & 89.0 & 2102 & 101.3 & 2551 & 96.6 & 2835 & 107.0 & 2880 & 116.2 \\  & 6 & 10,317 & 74.5 & 9026 & 70.5 & 9258 & 82.0 & 9217 & 86.4 & 10,463 & 87.2 \\  & 9 & 20,744 & 83.5 & 17,675 & 77.6 & 18,504 & 84.7 & 19,323 & 78.5 & 21,082 & 72.6 \\
50 & 3 & 2574 & 181.4 & 2254 & 195.4 & 2799 & 185.8 & 3014 & 166.0 & 3185 & 170.9 \\  & 6 & 10,027 & 89.9 & 9252 & 79.6 & 9150 & 111.6 & 11,025 & 99.5 & 11,580 & 119.8 \\  & 9 & 19,448 & 94.2 & 18,693 & 87.3 & 18,457 & 102.4 & 18,866 & 102.4 & 20,065 & 107.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Cutting volume and specific energy for different values of the rake angle. Figure 16: Relationship between the cutting volume and rake angle for depths (\(p\)) of: (a) 3 mm, (b) 6 mm, and (c) 9 mm. For the clearance angle, the specific energy decreased as the angle increased, and a sharp decrease occurred when it increased from 0\({}^{\circ}\) to 5\({}^{\circ}\) (Figure 18). When cutting was performed at a depth of 3 mm, the specific energy decreased by 72% and 53% in the rocks with strengths of 40 MPa and 50 MPa, respectively (Figure 18a). When the angle increases from 10\({}^{\circ}\), the specific energy appears to converge to a constant value. When cutting was performed to a depth of 6 mm, the specific energies of both the UCS 40 MPa and 50 MPa rocks converged to approximately 80 MJ/m\({}^{3}\) (Figure 18b). For the rake angle, no remarkable characteristic was found compared to the effect of the clearance angle; however, the specific energy tended to increase with the angle (Figure 19).
## 3 Results and Discussions
### Results of the Baseline Models  
As basic tests for the detector, we conducted experiments on the default baseline (the model with the default settings, which is referred to as Baseline 1) and the model with the selected settings described in Section 2.2 and test-time augmentation (which is referred to as Baseline 2). Both models were trained with the CIOU loss function and without the auxiliary augmentation packages.  
Table 1 shows the mAP and the other scores evaluated on the test set using Baseline 1, the same baseline with test-time augmentation (TTA), and Baseline 2. The technique of TTA strengthens the inference performance with an increase of 0.7% in mAP. The advanced settings we applied improved the mAP score by another 0.6%. The F1 score grows steadily as more of the techniques are adopted. These results indicate that our settings are reasonable and constructive to the performance of the algorithm.  
However, targets missed or misclassified by the baseline algorithm remain a tricky problem in the tests, which is partly attributed to the challenging data composed of the various categories. Strategies for enhancing the object detector will be introduced, which have the potential to alleviate this situation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Tests** & **Pr** & **Re** & **F1** & **mAP (\%)** \\ \hline Baseline 1 & 0.835 & 0.766 & 0.799 & 48.3 \\ Baseline 1 + TTA & 0.849 & 0.76 & 0.802 & 49 \\ Baseline 2 & 0.844 & 0.771 & 0.806 & 49.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test results for the baseline models.
# Evaluation of Glottal Inverse Filtering Techniques on
## 2 Analysis Methodology and GIF Methods
### IAIF-Based Approaches
#### 2.2.3 Gfm-Iair  
The GFM-IAIF variant was designed by [PERSON] and [PERSON] to reduce the original IAIF algorithm complexity and improve the vocal effort estimation of real speech [25]. As in the IOP-IAIF technique, the GFM-IAIF technique builds on the original IAIF approach, in which the two iterations (a coarse iteration followed by a refinement iteration) both include low-order GS and high-order VT LPC-based modeling. It is based on limiting the  
Figure 1: Block diagram of the analysis methodology followed to compare the performance of glottal inverse filtering methods on a common reference speech dataset.  
glottal source's linear prediction filter order in both the initial and refinement estimation steps to a value of \(N_{g}=3\). Therefore, in this case, only two parameters need to be adjusted: \(N_{v}\) and \(d\).
# Risk Assessment of Soil Heavy Metals in the Jiahe River Basin of Yantai City, China
## 3 Results
### Assessment Using Pollution Indices
#### 3.3.4 Index of Geo-Accumulation (I\({}_{geo}\))  
The index of geo-accumulation (I\({}_{geo}\)) [41; 42] uses the ratio of the total concentration of a heavy metal to its background value to determine the pollution level. The calculation formula is as follows:  
\[\mathrm{I}_{geo}=\mathrm{log}_{2}[\mathrm{C}_{i}/(1.5\mathrm{C}_{ri})] \tag{5}\]  
where C\({}_{i}\) represents the concentration of element i, while C\({}_{ri}\) denotes the geochemical background value or reference value for element i. Here, the reference value is the elemental abundance in soil in China (Table 1). I\({}_{geo}\) is categorized as follows: I\({}_{geo}\)\(\leq\) 0 indicates class 0 (unpolluted), 0 \(<\) I\({}_{geo}\)\(\leq\) 1 corresponds to class 1 (from unpolluted to moderately polluted), 1 \(<\) I\({}_{geo}\)\(\leq\) 2 represents class 2 (moderately polluted), 2 \(<\) I\({}_{geo}\)\(\leq\) 3 signifies class 3 (from moderately to strongly polluted), 3 \(<\) I\({}_{geo}\)\(\leq\) 4 corresponds to class 4 (strongly polluted), 4 \(<\) I\({}_{geo}\)\(\leq\) 5 indicates class 5 (from strongly to extremely polluted), and I\({}_{geo}\)\(>\) 5 represents class 6 (extremely polluted). The I\({}_{geo}\) values of heavy metals in the 213 soil samples were calculated, and their counts in each class are listed in Table 2.  
For Pb, Cr, Ni, Co, V, and Mn, the majority of the samples were classified as class 0, unpolluted; some were classified as class 1, indicating a range from unpolluted to moderately polluted; and a few were recognized as class 2, moderately polluted. Regarding Hg, As, and Zn, most samples were also classified as class 0, some were classified as class 1, and a few were classified as class 2 or class 3. For Cu and Cd, the majority were classified as class 0, some were classified as class 1, and a few were class 2 to class 4.  
Despite the differing qualitative classifications of the four single pollution indices C\({}_{f}\), Er, EF, and I\({}_{geo}\), which are categorized into four, five, five, and seven levels, respectively, the assessment results of these indices indicate that the pollution risk of the heavy metals in this area is low to moderate, except for a few samples at a high (or significant, or strong) level.
## 2 Materials and Methods
### NDVI Time-Series Spectral Profile Curves  
Field data and data collected from the Regional Directorate of Agriculture showed the presence of five main cropping systems in the region, including cereals, other crops followed by cereals, vegetables (onionion, potatoes, tomatoes), olive trees and fruit trees. More than two hundred profiles were developed for the different crop types. These profiles were associated with the field data and the visualization of high spatial resolution Google Earth images in order to collect input data for classification (Figures 7 and 8).  
From Figure 8, it is possible to discriminate the spectral characteristics of the different crops in relation to _NDVI_ values during the year. This index, proposed for the first time by [PERSON] et al., 1973 [49], is widely used and provides information on the quantity and vigor of vegetation, taking into account the near infrared (NIR) and visible red bands of the electromagnetic spectrum [49; 50] calculated by the following equation:  
\[NDVI=\rho_{NIR}-\rho_{RED}/\rho_{NIR}+\rho_{RED}\]  
where \(\rho_{NIR}:\) the reflectance in the near \(-\) infrared reflectance, \(\rho_{RED}:\) the reflectance in the red band.  
For example, for an olive pixel, the _NDVI_ value did not change significantly throughout the year, with an increase around February. While for a pixel of cereals, the _NDVI_ values did increase with the crop growth cycle.  
Figure 6: Decision trees used for random forest classification. OCFC = Other Crops Followed by Cereals.
## 5 Discussion  
As demonstrated in the results, all wind speed retrieval methods exhibit progressively declining accuracy with increasing wind speed. This degradation is primarily attributable to limitations in the CMDD5.N model. Although CMOD5.N is a well-established C-band GMF for wind retrieval from VV-polarized SAR images, it exhibits inherent constraints that become particularly pronounced under high wind conditions (>15 m/s), leading to significantly amplified retrieval errors at elevated wind speeds. This phenomenon stems from the model's development dataset being predominantly limited to low-to-moderate wind speeds (2-15 m/s), resulting in inadequate characterization of radar backscattering behavior under high wind conditions. Furthermore, VV-polarized radar signals experience saturation effects, markedly reducing the sensitivity of radar backscattering coefficient to wind speed variations. When wind speed exceeds 20 m/s, the gradient of radar backscattering coefficient becomes notably attenuated, meaning minor measurement uncertainties in radar backscattering coefficient can propagate into substantial wind speed retrieval errors.  
To overcome the limitations of CMOD5.N in high-wind regimes, future research should focus on developing next-generation GMFs with enhanced high-wind parameterizations, as well as hybrid approaches that integrate cross-polarization (VH/VV) observations, wave state information, and ancillary meteorological data. Additionally, machine learning techniques could be explored to better capture the nonlinear backscattering-wind speed relationships under extreme conditions. These advancements would significantly improve wind field retrieval accuracy while maintaining robustness across the full wind speed spectrum.
# A Text-Generated Method to Joint Extraction of Entities and Relations
###### Abstract  
Entity-relation extraction is a basic task in natural language processing, and recently, the use of deep-learning methods, especially the Long Short-Term Memory (LSTM) network, has achieved remarkable performance. However, most of the existing entity-relation extraction methods cannot solve the overlapped multi-relation extraction problem, which means one or two entities are shared among multiple relational triples contained in a sentence. In this paper, we propose a text-generated method to solve the overlapped problem of entity-relation extraction. Based on this, (1) the entities and their corresponding relations are jointly generated as target texts without any additional feature engineering; (2) the model directly generates the relational triples using a unified decoding process, and entities can be repeatedly presented in multiple triples to solve the overlapped-relation problem. We conduct experiments on two public datasets--NYT10 and NYT11. The experimental results show that our proposed method outperforms the existing work, and achieves the best results.  
relation extraction; entity recognition; information extraction; long short-term memory network Article
# An Acoustic Way to Support Japanese Children's Effective English Learning in School Classrooms
## 2 Methods
### Sound Amplification Systems for a Classroom  
Two different sound amplification systems, \"System A\" and \"System B\", were used for a comparison. System A (Figure 1 left), was a pair of typical PC loudspeakers (S-0264C, Logitech, Lausanne, Switzerland) which had been used during regular English lessons at the school for a long time before the current study began. We kept the System A where it had been placed for their previous English lessons, on the bottom shelf of a metal rack with a projector on the top (Figure 1 left), placed in the centre front of the room (shown as \"System A\" in Figure 3 right). It enabled us to compare between \"a regular system\" found at a school and another brought in with a little more care. The System A generates point-source-like signals, thus direct signals would spread and decay by 6 dB per doubling the distance in a free filed. Sound emitted by such simple sound amplification systems are usually deteriorated by their representational features.  
System B (Figure 1 right) consisted of a signal processor, an amplifier and a loudspeaker array (DP-SP3, DA-250F, and SR-H3L, respectively; TOA, Hyogo, Japan). The System B was placed at the front corner of the room (shown as \"System B\" in Figure 3 right) so that the emitted signals could cover the whole room with little interference caused by sound reflected by the walls whilst maintaining easier installation, smaller additional costs, and less interruptions to learning activities, compared with many other alternatives. The System B is designed to realise sound fields that are more evenly spread in a room, and has a relatively flat response. The advantage of loudspeaker arrays is that emitted acoustic signals become like a line source as the distance to the listening point widens for a while; therefore, they would only decay 3 dB per doubling the distance in a free filed.  
Levels of both systems were adjusted so that the A-weighted equivalent continuous sound pressure level over 10 s, denoted by L\({}_{\text{Aeq,10s}}\)[43], was 75 dBA at position (0) (Figure 3 right), using pink noise. This level was determined based on the exposure limit for young children, suggested by the World Health Organization, as 75 dBA 8-h L\({}_{\text{EX}}\) in order to protect children from risks of noise-induced hearing loss [44].  
Figure 3: Layouts of the English room with positions of two sound amplification systems: (**left**) with dimensions and colour coded seat groups, and (**right**) with 10 measurement points, shown as numbers in brackets.
## 3 Results
### Model Performance in Simulating NEE under Extreme Climate Conditions  
Both RF and XGBoost models captured the difference of the NEE predictions under normal and extreme climate conditions, with similar R\({}^{2}\) and RMSE. However, dominant variables varied under different extreme conditions and in different biomes. In the following, we will use the prediction results of XGBoost as an example. Extreme cold conditions had little impacts on forest biomes (DBF, R\({}^{2}\) = 0.93 for normal and R\({}^{2}\) = 0.86 for extreme; ENF, R\({}^{2}\) = 0.90 for normal and R\({}^{2}\) = 0.87 for extreme; EBF, R\({}^{2}\) = 0.21 for normal and R\({}^{2}\) = 0.19 for extreme), and had a slightly greater effect on SAV (R\({}^{2}\) = 0.53 for normal and R\({}^{2}\) = 0.44 for extreme). Extreme heat conditions reduced the evaluation results of the four biomes, MF (R\({}^{2}\) = 0.81 for normal and R\({}^{2}\) = 0.76 for extreme), DBF (R\({}^{2}\) = 0.87 for normal and R\({}^{2}\) = 0.78 for extreme), ENF (R\({}^{2}\) = 0.59 for normal and R\({}^{2}\) = 0.51 for extreme), and GRA (R\({}^{2}\) = 0.78 for normal and R\({}^{2}\) = 0.66 for extreme) with different degrees. The negative effect of extreme wetness on CRO (R\({}^{2}\) = 0.84 for normal and R\({}^{2}\) = 0.64 for extreme) was significant, but showed litter effects or even positive effects on forest biomes, MF (R\({}^{2}\) = 0.83 for normal and R\({}^{2}\) = 0.82 for extreme), ENF (R\({}^{2}\) = 0.90 for normal and R\({}^{2}\) = 0.92 for extreme), and DBF  
Figure 3: Impurity-based importance of variables in RF (**a**) and XGBoost (**b**) models to NEE by biomes (NEE: Net ecosystem exchange; TA: Air temperature; SW_IN: Shortwave radiation, incoming LW_IN: Longwave radiation, incoming; VPD: Vapor pressure deficit; PA: Atmospheric pressure; P: Precipitation; WS: Wind speed; NETRAD: Net radiation; TS: Soil temperature at the shallowest; SWC: Soil water content at the shallowest). (R\({}^{2}\) = 0.86 for normal and R\({}^{2}\) = 0.83 for extreme). In the extreme dryness conditions, DBF (R\({}^{2}\) = 0.85 for normal and R\({}^{2}\) = 0.90 for extreme) was not affected, but SAV (R\({}^{2}\) = 0.73 for normal and R\({}^{2}\) = 0.43 for extreme) and GRA were evaluated with significant decreases compared to the normal condition, especially the effect for GRA was considerable (R\({}^{2}\) = 0.54 for normal and R\({}^{2}\) = 0.14 for extreme) (Table 4). ### Model Performance in Simulating NEE under Extreme Climate Conditions  
Machine learning models appear to be highly dependent on the training samples. Increasing model training samples benefits model prediction for the same quality of data [37]. Both RF and XGBoost algorithms are sensitive to the number of training samples
# Heart Attack Detection in Colour Images Using Convolutional Neural Networks
## 5 Conclusions  
This paper has introduced a method to identify a possible infarct in RGB images. Our experiments obtained 91.75% accuracy and 92.85% sensitivity in the detection of people in postures associated with infarcts. Our CNN-based algorithm could be implemented in smart and health environments or accompanying robots.  
As far as we know, there is no similar proposal to identify a possible infarct using computer-vision-based non-invasive methods. Therefore, the development of the proposal presented in this article should be considered of great importance since it could prevent the death of people.  
The paper has shown that convolutional neuronal networks (CNNs), along with adequate data sets, allow quickly and accurately finding and detecting different image patterns that are useful in the health care and medical fields. Likewise, CNNs demonstrate stability in the different forms of distribution of the available data set images for training, validation, and testing. However, it is not always possible to count on a sufficiently large set of data to perform the training of a CNN. In this sense, data augmentation has demonstrated itself to be fundamental to improving the training.  
As future work, it is intended to expand the data set and to build other neural network architectures. Moreover, it is our aim to widen the proposal to consider other types of troubles that affect the well-being and health conditions of people's lives.  
All authors contributed equally to this work.  
This work was partially supported by the Spanish Ministerio de Economia, Industria y Competitividad, Agencia Estatal de Investigacion (AEI)/European Regional Development Fund (FEDER, UE) under grant DPI2016-80894-R.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CNN & convolutional neural network \\ FN & false negative \\ FP & false positive \\ TN & true negative \\ TP & true positive \\ WHO & World Health Organisation \\ \end{tabular}