esa-sceva/satcom-chunk-collection huggingface dataset:
# Label-Guided Data Augmentation for Chinese Named Entity Recognition
## 6 Results and Discussion
### Label Annotation Experiment  
To address **RQ4**, we conduct exploratory experiments in label annotation on the datasets OntoNotes 4.0 (Chinese) and MSRA. As shown in Figure 3, incorrect or meaningless label annotation can lead to varying degrees of performance decline in the model. Figure 3 presents the results on the OntoNotes 4.0 (Chinese) dataset, where incorrect label annotations cause the metrics F1, P, and R to decrease by 0.74%, 0.50%, and 1.41%,  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**Settings**} & \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**OntoNotes 4.0 (Chinese)**} & \multicolumn{3}{c}{**MSRA**} \\ \cline{3-8}  & & **P (\%)** & **R (\%)** & **F1 (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** \\ \hline \multirow{3}{*}{**1-shot**} & LEAR & - & - & 7.28 & - & - & 0.39 \\  & LGDA w/o Data Augmentation & 15.09 & 11.13 & 12.81 & 3.74 & 13.35 & 5.85 \\  & LGDA & **23.13** & **22.41** & **22.76** & **12.70** & **23.63** & **16.53** \\ \hline \multirow{3}{*}{**5-shot**} & LEAR & - & - & 41.32 & - & - & 26.22 \\  & LGDA w/o Data Augmentation & 42.41 & 46.16 & 44.20 & 39.44 & 48.13 & 43.35 \\ \cline{1-1}  & LGDA & **44.88** & **63.67** & **52.65** & **50.98** & **64.08** & **56.78** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Few-shot experiments on the OntoNotes 4.0 (Chinese) and MSRA datasets.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**OntoNotes 4.0 (Chinese)**} & \multicolumn{3}{c}{**MSRA**} \\ \cline{2-7}  & **P (\%)** & **R (\%)** & **F1 (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** \\ \hline LGDA & 81.72 & 82.67 & 82.19 & 96.52 & 96.21 & 96.36 \\ - Data augmentation module & 80.98 & 81.92 & 81.45 & 95.63 & 96.16 & 95.89 \\ - Label semantic fusion module & 80.08 & 82.12 & 81.09 & 95.75 & 96.25 & 96.00 \\ - Loss function optimization & 80.13 & 82.35 & 81.22 & 96.01 & 95.80 & 95.91 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Ablation study results on the OntoNotes 4.0 (Chinese) and MSRA datasets.  
respectively, while meaningless label annotations result in decreases of 1.44%, 2.18%, and 0.71% in the metrics F1, P, and R, respectively. Figure 2(b) shows similar results on the MSRA dataset. This indicates that label annotations indeed play a positive role in improving the model's recognition accuracy, and the label semantic fusion module proposed in this paper can use labels to guide model training. At the same time, these experimental results also reflect the importance of selecting label annotations, as their quality can directly impact the model.  
* **Conclusion 4:** Correct label annotation contributes to enhancing effectiveness in named entity recognition.
## 4 Conclusions  
Homogeneous TPS based materials were prepared starting with corn starch and using water as well as glycerol as plasticisers. The obtained TPS was then blended with pine resin derivatives by melt extrusion and further by injection moulding process. All TPS-resin blend formulations showed higher thermal stability than TPS counterpart, improving the processing window of TPS. FTIR spectra showed that gum rosin and its derivatives established hydrogen bond interactions with the hydroxyl groups of starch and glycerol in TPS matrix. Interactions were stronger in the case of blends with higher amounts of carbonyl groups in their chemical structure such as CM, UG and particularly LF. In fact, although all resins produce a stiffening effect, CM, UG and LF generated a significant stiffening effect, shifting the T\({}_{\text{g}}\) of TPS from 45 \({}^{\circ}\)C to higher temperatures greater than 70 \({}^{\circ}\)C. Consequently, in TPS-CM, TPS-UG and TPS-LF the Young's modulus slightly increase while were among the TPS-resin based formulations with the highest hardness values following the same trend as the T\({}_{\text{g}}\) increment.  
Therefore, it can be concluded that gum rosin and particularly modified gum rosin derivatives showed good compatibility with the developed TPS from corn starch and allowed to develop  
Figure 8: Scanning electron microscopy (SEM) images of the crio-fractured surface of: (**a**) TPS, (**b**) TPS-GR, (**c**) TPS-CM, (**d**) TPS-LF, (**e**) TPS-RD and (**f**) TPS-UG.  
homogeneous fully bio-based materials, ensuring thermal stability for melt-extrusion and injection moulding processing as well as a significant stiffening effect. Consequently, these bio-based materials show their potential for industrial applications such as disposable cups for hot beverage and/or lids containers for hot food applications.  
Conceptualization, M.A. and M.P.A.; methodology, M.A.; validation, M.A. and C.P.; formal analysis, M.A. and C.P.; investigation, M.A., C.P., M.P.A. and J.L.-M.; resources, J.L.-M.; data curation, M.A., C.P. and M.P.A.; writing--original draft preparation, M.A., C.P. and M.P.A.; writing--review and editing, M.A. and M.P.A.; visualization, M.A., C.P., M.P.A. and J.L.-M.; supervision, J.L.-M.; project administration, M.P.A. and J.L.-M. funding acquisition, M.P.A. and J.L.-M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Spanish Ministry of Economy and Competitiveness (MINECO), project: PROMADEPCOL (MAT2017-84909-C2-2-R) as well as by Santander-UCM (PR87/19-22628) project. M.A. thanks Secretaria Nacional de Educacion Superior, Ciencia, Tecnologia e Innovacion (SENESCYT-Ecuador) and Escuela Politecnica Nacional. C.P. thanks [PERSON] fellowship (GRISOLIAP/2019/113) from Generalitat Valenciana and [PERSON] thanks MINECO for her postdoctoral contract Juan de la Cierva-Incorporacion (FJCI-2017-33536).  
The authors declare no conflict of interest.
## 4 Results
### miRNA Pathway and Gene Ontology (GO) Analysis
Another AD cell model, however, found that the inhibition of hsa-miR-143-3p fostered neuronal survival and indirectly slowed down AD progression, which was an upregulated expression in the serum of AD patients [43]. That finding was contradictory to that of the present study, which is probably due to the different sample types used. Several genes that are related to AD, those being _GRIN2B_, _EIF2 AK3_, _NDUIFB9_, _NDUIFA9_, _PSEN1_, _MAPK1_, _NDUIFS1_, _RYR3_, _BACE1_, and _COX4I1_, have been identified as the target genes for hsa-miR-143-3p. The roles and functions of the target genes in AD pathogenesis are summarized in Table 5. \begin{table}
\begin{tabular}{l l l} \hline \hline \multicolumn{1}{c}{**Gene**} & \multicolumn{1}{c}{**Roles and Functions as Related to AD**} \\ \hline \multirow{4}{*}{**ATP synthase F1 subunit epsilon (_ATP5E_)**} & \(\bullet\) & A mitochondrial-encoded oxidative phosphorylation (OXPHOS) gene [46]. \\  & \(\bullet\) & OXPHOS dysfunction increases the level of reactive oxygen species (ROS) and oxidative stress, which subsequently leads to neuronal damage in the AD brain [47]. \\ \hline \multirow{4}{*}{**Protein phosphatase 3 catalytic subunit**} & \(\bullet\) & A catalytic subunit of calcineurin, which is involved in the calcium \\  & & signalling and inflammatory pathways related to AD [48]. \\ \cline{1-1}  & \(\bullet\) & Dysregulation of _PPP3 CA_ was observed in the AD brain through its \\ \cline{1-1}  & & involvement with oxidative stress and pathological cellular dysfunction \\ \cline{1-1}  & & losses [48; 49]. \\ \hline \multirow{2}{*}{**Glutamate Ionotropic Receptor NMDA Type**} & \(\bullet\) & Takes part in glutamate-mediated neurotoxicity, which stimulates the \\ \cline{1-1}  & \(\bullet\) & progressive decline of cognitive function in AD patients [50]. \\ \hline \multirow{2}{*}{**Calmodulin 3 (_CALM3_)**} & \(\bullet\) & An indicator for calcium signalling dysfunction, where lower expressions \\ \cline{1-1}  & & were detected in AD patients as compared to normal controls [51; 52]. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: List of target genes and their roles and functions as related to AD. The roles and functions of the DEMi signatures and their respective target genes further corroborate the results of the KEGG pathway and GO analysis (see Tables 3 and 4). Pathways related to oxidative phosphorylation, mitochondrial dysfunction, and calcium-mediated signalling are particularly highlighted in the present study. The interaction of the genes is demonstrated in Figure 7.
# A Comparison of an Adaptive Self-Guarded Honeypot with Conventional Honeypots
## 4 Description of Honeypot Systems
### Asgard: Adaptive Self-Guarded Honeypot
#### 4.2.3 Reward Function  
Asgard is designed to achieve two objectives: (1) to capture attacker's tools via download commands, and (2) to limit the risk of being compromised by preventing the execution of custom commands because the attackers generally resort to their downloaded custom tools to further compromise the honeypot and use it for their advantage. These objectives are rather opposing to the way a honeypot is normally intended. The reason is that we believe that once the honeypot is fully compromised and participates actively in the attack, its value is no longer significant. Asgard differentiates itself from the existing honeypots [22; 23; 26] because its reward function depends on (1) the environment state and (2) the action, while other systems only use the environment state in their reward functions. Asgard's reward given at a time-step \(t\) is defined as the piecewise function below:  
\[r_{a}(s_{t},a_{t})=\begin{cases}1&\text{if }s_{t}\in D\quad\text{and}\quad a_{t} \in\{allow\}\\ -1&\text{if }s_{t}\in C\quad\text{and}\quad a_{t}\in\{allow\}\\ 0&\text{otherwise}\end{cases} \tag{5}\]  
The first objective can be achieved by rewarding the agent when it allows the attackers to download their programs. That is, when an attacker transitions to a download command \(D\), and the action taken is _allow_, the agent is rewarded with 1. For the second objective, however, the agent gets punished instead when it chooses to allow the execution of the malicious programs. Consequently, when the attacker transitions to a custom command \(C\) and the agent selects _allow_ as the action, it receives \(-1\).
## 2 Method
### Bearing Failure Cases
However, these measurements have consistently been taken during high rotational speeds compared to earlier in the dataset and cannot be attributedto the present defect with any high degree of certainty. For failure case 3, enveloped FFT CI-values from 1.28 s long measurements and with a frequency range of 0-5 kHz were compared to CI-values extracted directly from the database from in-system enveloped measurements where the frequency range was extended to 0-10 kHz and the measurement time was taken over 32 revolutions of the gearbox output shaft, between 1.5 and 2.75 s. This type of in-system enveloped measurement is generally not stored in the databases and cannot, therefore, be used in a post-failure investigation. This comparison can be seen in failure case 3 in Figure 3 with the 0-5 kHz CI-values in blue in the top graph and 0-10 kHz in red in the bottom graph. Additionally, since the number of CI-values for each frequency range differs, each CI-value is trended with respect to the number of days from the start of the dataset. The resonance frequencies demodulated by the enveloping technique primarily exist above the 5 kHz max frequency range of the extracted measurements until the last days of the dataset time period. Therefore, the 0-10 kHz CI-values can indicate the defect at a much earlier date. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Failure Case** & **1** & **2** & **3** \\ \hline Bearing & Output & Pl.2 Planet & Output \\ position & shaft & & shaft \\ Bearing & SKF & SKF & SKF \\ designation & NU2230E & RN2238 & 32236]2 \\ Damage & Inner ring & Inner ring & Inner ring \\  & spalling & spalling and flaking & spalling \\ Meas. type & Time sync & Time sync & Time sync and enveloped \\ Meas. time and & 1.28 s, 0–5 kHz & 6.4 s, 0–1 kHz & 1.28 s, 0–5 kHz, \\ freq. range & & & 32 rev, 0–10 kHz \\ Envelope filters & 0.5–6.4 kHz, & 0.2–1 kHz, & 0.5–6.4 kHz, 0.2 kHz \\  & 0.2 kHz & 0.2 kHz & 0.5–10 kHz, 0.5 kHz \\ Dataset size & 562 & 1377 & 2102 and 754 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Bearing, damage and measurement information for each failure case.
# Acoustic Scattering Models from Rough Surfaces: A Brief Review and Recent Advances
## 2 Rough Surfaces Parametrization
### Statistical Properties  
Surface roughness is described here as the variation of height \(z=h(x,y)\) above or below a mean surface. \(<\)\(l\)\(>\), the average of the height variation over the surface, is by definition 0. \(h\) is assumed to be random and described statistically using parameters such as the RMS height and spatial correlation lengths. In the theory presented hereafter and developed by [PERSON] [6; 27], it is assumed that the height distribution is well represented by a Gaussian.  
The probability of any measured height being between h and h + dh above or below the mean plane is then given by p(\(h\)) dh, where:  
\[p(h)=\frac{1}{\sigma(2\pi)^{\frac{1}{2}}}e^{-h^{2}/h\sigma^{2}} \tag{1}\]  
where \(\sigma\) is the RMS height:  
\[\sigma=\left\{\left\langle h^{2}\right\rangle\right\}^{\frac{1}{2}} \tag{2}\]  
Random rough surfaces show some spatial correlation, as the height at a location is not totally independent of surrounding heights. This may be described by the correlation function of a surface, \(C(\mathbf{R})\)  where:  
\[C(\mathbf{R})\ =\frac{1}{\sigma^{2}}\langle h(\mathbf{r})h(\mathbf{r}\ + \mathbf{R})\rangle \tag{3}\]  
which represents the extent to which the surface height at any one point \(\mathbf{r}\) is, on average, related to the surface height at point \(\mathbf{r}+\mathbf{R}\) away. Gaussian functions are often used as an approximation of the correlation of real surfaces. Assuming the same Gaussian correlation in every direction, \(C\) can be written:  
\[C(\mathbf{R})\ =e^{-h/z} \tag{4}\]  
where \(L\) is called the correlation length of the surface. Anisotropic correlation functions can also be used to describe surface with a direction-dependent correlation. Such surfaces often occur in practice in industrial structures, as roughness can be generated in anisotropic manner during the manufacturing process.
# On Analyzing Beamforming Implementation in O-RAN 5G
## 2 Related Work
### O-RAN Interfaces
These specifications are expected to be the first standard to enable interoperability between different vendors.
# A Survey on Monitoring Quality Assessment for Wireless Visual Sensor Networks
## 6 Discussion and Evaluations
### Comparing the Surveyed Categories and Metrics
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Works** & **Advantages** & **Disadvantages** \\ \hline \multirow{2}{*}{\begin{tabular}{c} Distance range \\ \end{tabular} } & \multirow{2}{*}{[6; 17; 63; 67; 73; 85; 87; 88; 89]} & Simple to compute and \\  & & apply for discrete space \\  & & (target coverage) & space \\ \hline \multirow{2}{*}{\begin{tabular}{c} Mean square error \\ \end{tabular} } & \multirow{2}{*}{[37]} & Simple, easy to calculate. \\  & & Encompass identity, \\  & & symmetry and \\  & & nonnegativity \\ \hline \multirow{2}{*}{\begin{tabular}{c} Quadratic error \\ \end{tabular} } & \multirow{2}{*}{[39; 40; 42; 43; 46; 47]} & Accurate metric, \\  & & commonly used in \\  & & optimization methods \\ \hline \multirow{2}{*}{\begin{tabular}{c} Probabilistic \\ distance \\ \end{tabular} } & \multirow{2}{*}{[44; 45; 68]} & Suitable for imprecise \\  & & and non-homogeneous \\  & & models \\ \hline \multirow{2}{*}{\begin{tabular}{c} Bounding box \\ \end{tabular} } & \multirow{2}{*}{[69; 70; 71; 72]} & Simple to compute and \\  & & apply \\ \hline \multirow{2}{*}{\begin{tabular}{c} Target speed; \\ Velocity; Moving \\ direction \\ \end{tabular} } & \multirow{2}{*}{[73]} & Suitable for networks \\  & & with mobility \\ \hline \multirow{2}{*}{\begin{tabular}{c} Perspective \\ distortion; \\ Entropy \\ \end{tabular} } & \multirow{2}{*}{[48; 49]} & Model external aspects: \\  & & light sources, light \\  & & attenuation, reflection \\ \hline \multirow{2}{*}{\begin{tabular}{c} Angle of view \\ \end{tabular} } & \multirow{2}{*}{[31; 63; 74; 75; 87; 88; 89]} & Realistic and accurate \\  & & metric \\ \hline \multirow{2}{*}{\begin{tabular}{c} Importance index \\ \end{tabular} } & \multirow{2}{*}{[86]} & Manage dynamic \\  & prioritization of the \\  & & application \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Peak \\ signal-to-noise \\ ratio \\ \end{tabular} } & \multirow{2}{*}{[77; 78]} & Can deal with dynamic \\  & & changes of bright in a \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} May vary its performance \\ depending on the \\ scenario \\ \end{tabular} } \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative analysis of content quality metrics.
## 4 Cases Study
### Problem Statement and Opportunity Identification  
Airbag systems provide a passive protection for drivers and passengers. When the car collides, the sensor senses the intensity of the collision and transmits the signal to the controller. The controller receives the signal from the sensor and judges the severity of the impact. If the airbag needs to be opened, the controller immediately sends an ignition signal to the gas generator so that the gas generator can inflate the airbag in a very short time. The composition of the airbag system and the operation process between the subsystems are shown in Figure 7.  
Figure 6: Knowledge transfer and concept generation.  
The power (DS) supply provides the driving force for the airbag system, the sensor (TS) is responsible for compiling and transmitting signal, the safety controller and airbag controller (CS) recognize the signal from the sensor and determine whether to activate the air bag, the gas generator and air bag (ES) form a protection system.  
ESL's investigation and analysis of many traffic accidents show that drivers can be re-injury because of the airbag system in some accidents. When collisions are enough, the gas generator relies on the chemical reaction of gunpowder to produce gas, accompanied by high temperature (Peak value of about 2000 \({}^{\circ}\)C) and high pressure (Close to 400 Kpa). The result is that the air pocket pops up at a speed of more than 200 km per hour to protect passengers. However, the speed is so fast that when the driver or passenger is very close to the airbag, it will cause re-injury to them--especially for drivers with short legs, because drivers with short legs need to operate brakes and other equipment closely. At the same time, a large amount of hot gas produced by the chemical reaction is discharged from the airbag vent hole, which may cause skin burns. Therefore, ESL hopes to develop a new gas generator (execution subsystem, ES) to eliminate the above problems.
# Performance Evaluation of Classification Algorithms to Detect Bee Swarming Events Using Sound
## 3 Materials and Methods
### SVM and k-NN and U-Net Processing and Training Methods  
Firstly, the SVM classifier was applied to the dataset with a polynomial kernel and penalty hyperparameter C = 1 (see Appendix A, SVM parameters). The classifier was applied to the dataset, using 20% of it for testing (845 vectors) and the rest for training (3380 vectors). The kernel and parameters used arise from experimentation to find the optimal and most accurate result and are analytically explained in Appendix A.  
Next, using the same dataset, the k-NN classifier was trained. k-NN's hyperparameters include the kernel and the number of k-neighbors. After fine-tuning (Figure 10), the Euclidian kernel with k = 9 neighbors was selected. Twenty percent of the dataset was used for testing the classifier and the rest was used for training it. For the classifier evaluation experiments described in Section 4, a different dataset was used, including a different proportion of annotated vectors of 33% swarming and 66% non-swarming data.  
A novel Convolutional Neural Network (CNN) U-Net [27] was also used (Figure 2). The U-Net parameters used are the default parameters presented in Appendix A.  
Figure 1: Conversion of bee raw sound data to 128-bin Mel spectrogram Image.  
The U-Net CNN training dataset used was the same as that used by the SVM and k-NN classifiers. The half U-Net model was used in order to simplify the segmentation process using five convolution layers, as illustrated in Figure 2, along with the following parameters:  
* Activation function: Rectified Linear Unit (ReLU)
* Epochs: 20
* Input size: 128 \(\times\) 128 pixels
* Number of layers: 6 (5 convolutions + 1 decision)  
The U-Net CNN training data imagery input includes swarming and non-swarming 1-min images (128 px on the x-axis of Figure 3) and 128 bins on the y-axis expressed in the Mel scale, providing a better resolution at low frequencies. The logarithmic transformation of frequencies to Mels is given in Appendix A.
## 4 Discussion
### Comprehensive Service Life Based on Analytic Hierarchy Process  
#### 4.2.1 Analytic Hierarchy Process  
Taking an AHP as an example, the structure of the decision hierarchy would as shown in Figure 13. Level 1 is the goal of the system. Level 2 is multi criteria that consist of different factors and this level may include several other levels of sub criteria. The last level (Level 3) is the alternative options. In level 2 there is one comparison matrix corresponding to pair-wise comparisons between all factors with respect to the goal. Specific procedures will be shown from Section 4.2.2 to Section 4.2.5 by using it to calculate the comprehensive service life of RABs.  
Figure 12: Degree of aging for RABs. (**a**) Reclaimed SBS modified asphalt; (**b**) Reclaimed Base asphalt.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Classification Tests 1** & **Reclaimed Base Asphalt** & \multicolumn{4}{c}{**Reclaimed SBS Modified Asphalt**} \\ \hline
**135 \({}^{\circ}\)C Viscosity/Pa-s** & \(\boldsymbol{\eta}\)\({}^{2}\leq\) 1.6** & \(\boldsymbol{\eta}\leq\) 1.6 & & **1.6 \(<\)\(\boldsymbol{\eta}\leq\) 3** & & **\(\boldsymbol{\eta}\)\(>\) 3** \\ \hline Penetration/0.1 mm & \(P^{3}>30\) & \(10<P\leq 30\) & \(P>30\) & \(P>30\) & \(20<P\leq 30\) & \(10<P\leq 20\) & \(20<P\leq 30\) & \(10<P\leq 20\) \\ Sample Number & 11, 12, 13 & 14, 15, 16 & 1, 2 & 3, 4 & 6 & 8 & 5, 7 & 9, 10 \\ Aging Grades & 1 & II & 1 & II & III & IV & V & V \\ \hline \hline \end{tabular}  
* All items were tested as per test specification of asphalt and asphalt mixtures for highway in China; 2 \(\eta\) represents viscosity; 3 \(P\) represents penetration.  
\end{table}
Table 3: Physical properties of original asphalt binders.