esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Evaluation
### Decision Model Results Analysis  
#### 5.3.1 Necessity of Estimation Model  
To demonstrate the importance of the estimation model, we selected the data rate without using the estimation model and used the agent to read the data directly from the environment. The results for when historical information is fully considered, for example, when \(\gamma\) is 0.9 or 0.95, are shown in Figure 6. The network can sometimes learn the correct strategy for choosing the data rate, but the variance is enormous and does not ensure the system's stability. Moreover, the performance is lower than the baseline even after the network converges. Experiments also show that when \(\gamma\) is smaller than 0.9, such as when \(\gamma\) is 0.5 or 0.7, the training results are a straight line, indicating that the network cannot learn to select a data rate effectively. When \(\gamma\) is more significant than 0.95, for example, when \(\gamma\) is 0.99, the network fails to learn the correct strategy because it overlooks historical information.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(n\) & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline MAE & 0.06 & 0.013 & 0.014 & 0.016 & 0.014 & 0.019 & 0.019 \\ \hline \hline \end{tabular}
\end{table}
Table 6: MAE of Different Number of Past Moments \(n\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(n_{W}\) & **1** & **2** & **3** & **4** & **5** \\ \hline
1 & 0.93 & 0.54 & 0.21 & 0.029 & 0.028 \\ \hline
2 & 0.93 & 0.60 & 0.20 & 0.024 & 0.018 \\ \hline
3 & 0.91 & 0.61 & 0.18 & 0.027 & 0.014 \\ \hline
4 & 0.90 & 0.65 & 0.15 & 0.06 & 0.06 \\ \hline
5 & 0.88 & 0.67 & 0.12 & 0.08 & 0.06 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance According to Differences in the Number of Neurons in Embedding Layers.  
This set of experiments demonstrates that the simple DRL framework is not sufficient to extract useful information from complex states and make choices at the same time. The need for the estimation model is thus confirmed.
## 2 Materials and Methods  
### Data Retrieval and Filtering  
Twitter (rebranded as X in July 2023, having had previously restricted free API access in February) provided a data stream for academic use through its API. This API returned a randomly sampled 1% of all data produced on the Twitter platform [37]. Using this methodology, we continuously harvested Twitter data for several years and inserted it into a PostgreSQL database. In order to retrieve the dataset for this study, we first queried earthquakes with a magnitude greater than 5 that occurred in Turkishye between the years 2017 and 2021. According to the Richter Scale, a magnitude >5 refers to moderate, strong, major, or great earthquakes [38]. This means that everyone felt the earthquake when it was of moderate scale or higher. Based on the Bogazici University Kandilli Observatory and Earthquake Research Institute-Regional Earthquake-Tsunami Monitoring Center (KOERI-RETMC) [39] database, there were 28 earthquakes (Table 1). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**ID** & **Place Name** & **Date/Time** & **Latitude** & **Longitude** & **Magnitude** & **Tweet Count** & **Account Count** \\ \hline
1 & Ayvacik Aciklari-EGE & 6 February & 39.5014 & 26.0621 & 5.1 & 38 & 33 \\  & DENIZI & 2017 10:58:00 & & & & & \\ \hline
2 & Ayvacik-Canakkale & 7 February & 2017 02:24:03 & 39.5164 & 26.0997 & 5.3 & 25 & 22 \\ \hline
3 & Ayvacik-Canakkale & 12 February & 2017 13:48:16 & 39.5048 & 26.1076 & 5 & 22 & 18 \\ \hline
4 & Samsat-Adiyama & 2 March 2017 & 37.5961 & 38.4724 & 5.2 & 27 & 26 \\ \hline
5 & Ula-Mugla & 13 April 2017 & 37.1397 & 28.684 & 5 & 15 & 15 \\ \hline
6 & Saruhanli-Manisa & 28 May 2017 & 38.7182 & 27.7715 & 5 & 23 & 23 \\ \hline
7 & Milas-Mugla & 14 August 2017 & 37.1881 & 27.6585 & 5 & 13 & 13 \\ \hline
8 & Sehitkamil-Gaziantep & 12 November 2017 18:19:58 & 37.1918 & 37.52 & 7.1 & 77 & 59 \\ \hline
9 & Koycegiz-Mugla & 24 November 2017 21:49:14 & 37.0935 & 28.5999 & 6 & 29 & 29 \\ \hline
10 & Sivrice-Elazig & 4 April 2019 & 17:31:11 & 38.3728 & 39.1712 & 5 & 60 & 58 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Earthquakes with a magnitude of 5 or greater between 2017 and 2021 in Türkiye. After an earthquake, Twitter data are generally queried using keywords similar to \"earthquake\". This method was replicated to determine our initial raw dataset for the 28 earthquakes listed in Table 1. While filtering the raw Twitter dataset for each earthquake in this study, three conditions were considered: the time interval, presence of the \"earthquake\" (e.g., \"deprem\", \"sarsntn\", and \"zelzele\" in Turkish) keyword, and spatial boundary. The time interval was the first condition, starting with the time the earthquake occurred and ending 72 h afterward. The second condition was the presence of the word \"earthquake\" in the tweet content. The last condition was that the tweets had to be located within Turkive's spatial boundary. When these three conditions were met for a tweet, the tweet was inserted into the earthquake raw dataset (Figure 1).
# Design and Performance Analysis of BDS-3 Integrity Concept
## 3 Integrity Risk Probability Distribution
### ModelBottom Event Probability  
#### 3.2.1 Bottom Event Probability for Space Segment  
The failure probabilities of the two bottom events of the space segment are estimated as following:  
1. Satellite signal and data anomaly probability  
For the B1I signal, the integrity failure mode and effect analysis (IFMEA) has been conducted on the operating status of the BDS-3 basic constellation (consisting of 18 BDS-3 satellites) since its completion on 27 December 2018, and no satellite signal and data anomaly events have been found during the assessment period. According to the design requirements of BDS-3, the probability of this anomaly is \(5\times 10^{-6}/\text{h}\), that is:  
\[P_{\text{event}_{6,7}}^{\text{B1I}}=5\times 10^{-6}/\text{h} \tag{10}\]  
where indices 6, 7 refer to the bottom events 6, 7 in the integrity risk tree.  
For the B1C and B2a signals, there has also been no satellite signal and data anomaly events occurred since 27 December 2018. According to the design requirements of BDS-3, the probability of such anomaly is \(4\times 10^{-6}/\text{h}\), that is:  
Figure 1: BDS SIS integrity risk tree.  
\[\mathrm{P}_{\mathrm{event}_{6,7}}^{\mathrm{B1C/B2a}}=4\times 10^{-6}/\mathrm{h} \tag{11}\]  
where indices 6, 7 refer to the bottom events 6, 7 in the integrity risk tree.  
2. Satellite clock anomaly probability  
The IFMEA has been conducted and no satellite clock anomaly events have been found. According to the internal system design requirements of BDS-3, the probability of such an anomaly is 4 \(\times\)\(10^{-6}/\mathrm{h}\).
# Distributed Information Compression for Target Tracking in Cluster-Based Wireless Sensor Networks
## 4 Performance Analysis
### Analysis of the Data Size  
In order to assess the energy consumption during the data processing procedures, the data size is estimated, assuming that the sensor nodes transmit data in a binary system. Instead of using the floating point representation like IEEE standards, which reserve bits for exponents to represent a wider range of numbers, we use fixed point representation, which may cost less bits than floating point representation. This is because we only transmit the compressed data, _i.e._, \(\Delta_{x_{i}}\) and \(\Delta_{y_{i}}\), which are the differences of \(x_{i_{j}}\) and \(\bar{X}_{i}\) and \(y_{i_{j}}\) and \(\bar{Y}_{i}\), respectively. Since the values of the differences are usually small, our algorithm can dynamically decide the number of bits for the integer part and reserve more bits for the fractional part, depending on the value and the required precision. Specifically, before transmitting the data stream, active sensor nodes scan the data stream first to find the maximum of it, which decides the largest bit number of the integer part and the position of the decimal point.  
For instance, in Table 1, the first bit represents the sign of the value; the second bit to fourth bit specify the position of the decimal point; the fifth to seventh bit represent the integer part of the value; the eight to 16 th bit represent the fractional part of the value; the code line is the bit stream to represent \(-5.678\). Thus, if the required precision is to \(2^{-5}\), we only use 12 bits to represent a value in that case. Observe that, in the example of Table 1, using IEEE standard with nine-bit mantissa to represent the difference leads to an error between the original value and the value after processing lower than \(2^{-7}\). In contrast, our approach can achieve better precision with an error lower than \(2^{-9}\).
## 4 Results  
### Human-Exoskeleton Interaction Measurement Experiment  
Figure 9 shows the results of subject H1 (male, 24 years old, weight/60 kg) in the human-exoskeleton interaction measurement experiment. The motion trajectories of the hip joint and knee joint are shown in Figure 9a,b, demonstrating the interaction torque of the knee joint, the positive direction of which is counterclockwise. The 5-25 s data is selected for further processing. The measured angular displacement of the knee joint is used as an independent variable, and the interaction torque of the knee joint is used as a dependent variable to make a graph in the same coordinate system, as shown in Figure 10. It can be seen that there is a linear relationship between angular displacement of the knee joint and standard interaction torque of the knee joint, which can be fitted by using the least square method. The fitted
# Nanomaterials for Healthcare Biosensing Applications
## 3 Applications of Inorganic Nanomaterials in Healthcare Biosensing
### Nanocages, Nanoshells and Nanowires
A silicon nanowire (SiNW)-based microfluidic electrical sensor was recently reported by Nuzaihan and co-workers for the sensitive recognition of the dengue virus (DENV) DNA oligomer [156]. The SiNW was synthesised by a top-down approach on a silicon-on-insulator (SOI) wafer. The SiNW was then functionalised by surface modification, DNA immobilization, and DNA hybridisation. This neoteric molecular gate control mechanism allowed the sensor to achieve an LOD of 2.0 fM. Kim et al. developed a silicon nanowire FET biosensor using a honeycomb nanowire (HCSiNW) architecture for the ultrasensitive detection of cTnI [225]. The device showed exceptional sensitivity and selectivity due to the Debye effect. Antibodies of cTnI were immobilised on the HCSiNW surface. The LOD of this sensor was 5 pg mL-1 and the detection range was 5 pg mL-1-5 ng mL-1. In another study, a nanohybrid of three-dimensional platinum nanowire array (PtNWA) and AuNPs was used to develop a sensitive enzyme-based amperometric glucose sensor [226]. GOx was then immobilised on the nanohybrid. The vertically aligned platinum nanowires had a greater density of AuNPs in comparison to a 2D planar modification. The nanowires were responsible for improving the signal-to-noise ratio by enhancing the electron density on the electrode surface. CV and amperometric measurements proved that the sensor achieved a detection limit of 15 uM with a detection range of 15 uM-2.5 mM.
# Flexural Strength and Stiffness of Donut-Type Voided Slab
## 3 Experimental Program  
### Configuration of Donut-Type Voided Slab Specimens  
The objective of the flexural test was to evaluate the flexural capacity of donut-type voided slab by comparing the failure behavior, ductility, flexural strength and stiffness with those of a solid slab and non-donut-type voided slab. The specific objective of the test was to verify the effect of the center hole and fixing methods of the void shapers.  
To generate voids in the voided slab specimens, two types of void shapers were used: the donut-type void shaper and the non-donut-type void shaper, as shown in Figure 7. The donut-type void shaper was a hexahedron with rounded edges and a hole penetrating the center. Reflecting the results of the FE model, the void height and width were 140 mm and 270 mm, respectively. The hole diameter was 50 mm, and the distance between the voids was set to 30 mm in both the longitudinal and transverse directions. The non-donut-type void shaper has the same specifications as the donut-type void shaper, but there is no hole penetrating the center.  
Figure 6: Determination of the optimal void shape. (**a**) by the load-bearing capacity (1 st try); (**b**) by the load-bearing capacity (2 nd try).  
To hold the donut-type void shapers in place, keeping them in the center of the slab's depth, two types of fixing methods were used: the spacer and the merged type, as shown in Figure 8. The spacer type consisted of void shaper with protrusions, which functioned as the spacers between the top and bottom rebars without requiring additional steel cages to hold the void shapers. The merged type fixing method holds void shapers using a steel cage, which was fabricated by welding the top and bottom rebars with D6 diagonal rebars.  
Four slab specimens were designed to investigate the effect of void shape and the fixing method: a conventional solid reinforced concrete slab (Solid), a donut-type voided slabs with the spacer-type fixing method (OF-V-S-D), a donut-type voided slabs with the merged-type fixing method (OF-V-M-D) and a non-donut-type voided slabs with the  
Figure 8: Fixing methods of the donut-type voided slab: (**a**) spacer-type fixing method; (**b**) merged-type fixing method.  
Figure 7: Details of donut and non-donut-type void shaper: (**a**) donut-type void shaper; (**b**) non-donut-type void shaper.  
spacer-type fixing method (OF-V-S-R). The widths and lengths of the slab specimens were 1250 mm and 3300 mm, respectively. The thicknesses of slab were 250 mm. In general, voided slabs are vulnerable to shear strength deterioration; hence, the slab specimens were designed to have low tensile reinforcement ratios (\(\rho\)) of 0.384% to induce flexural failure prior to shear failure. The merged-type fixing device was placed in the longitudinal direction of the specimen. Detailed specifications of the specimens are presented in Table 5 and Figure 9.
# Automatic Crack Classification by Exploiting Statistical Event Descriptors for Deep Learning
## 3 Framework for the Real-Time Classification of Acoustic Emission Data
### Analysis of Acoustic Emission Events Using Feature Extraction
#### 3.2.3 Spectral Kurtosis  
The \(SK\)[31; 76] is a statistical tool that can identify the non-Gaussian behavior in the frequency domain. SK has been effectively used for detecting and extracting signals associated with faults in rotating mechanical systems [81]. The \(SK\), or \(x(f)\), of a time-varying input signal \(s(t)\) can be computed based on the short-time Fourier transform (STFI) of the signal, \(S(tf)\):  
\[SK\equiv\kappa(f)=\frac{\left\langle\left|S(t,f)\right|^{4}\right\rangle}{ \left\langle\left|S(t,f)\right|^{2}\right\rangle}-2=\frac{m_{4}\{\left|z(t) \right|\}}{\left(m_{2}\{\left|z(t)\right|\}\right)^{2}}-2 \tag{6}\]  
where \(f\
eq 0\) and \(\left\langle\ldots\right\rangle\) is the time-average operator, whereas \(m_{4}\) and \(m_{2}\) are the raw 4 th and 2 nd order moments, respectively [31]. Figure 5c shows the \(SK\) as calculated from the signals of Figure 4a. The \(SK\) for tensile events (red line) is positive and higher than the corresponding function for shear (blue line) and mixed-mode (green line) events. Particularly, any \(SK\) point falling within 5-22 kHz (mostly above \(SK=1.0\)) is likely to be non-stationary and non-Gaussian [82]. A possible explanation is that tensile modes are characterized by a large generation of (short-lived) micro-displacements associated with the opposite movements of the crack surfaces [83] whose spectral components do not exhibit a normaldistribution. The occurrence of several transient events causes multiple discontinuities in the analyzed signal, which are well-captured by \(SK\) whereas other operators (for instance, power spectral density) are not able to preserve such information [31].  
Indeed, it is reasonable to assume that high frequency components are generated by such small discontinuities, that mainly distinguish the tensile [84; 85] from shear fracture, whereas relevant frequency contributions are the results of few but larger deformations that usually develop during the final collapse [16]. It is well-known how variations in the shape of the waveform during the loading process identify a change in the dominant damage mechanism of the specimen. Shear events and mixed-mode events are characterized by smaller values of \(SK\) if compared to tensile deformations. This is because a shear event generates a relatively smaller number of transients, which corresponds to a lower value of \(SK\), (here is slightly above 0). Interestingly, we observe that mixed-modes exhibit predominantly the lowest \(SK\) (between 5 to 20 kHz) and in particular around 11 kHz the \(SK=-0.5\) which represents a locally stationary behavior. This is because the signal maintains a relatively constant amplitude at the corresponding frequency (as shown in Figure 5c).
## 4 Metabolomics
sinensis_ from various countries for metabolomics analysis by \({}^{1}\)H NMR, in order to establish whether teas could be discriminated according to the country of origin or with respect to quality. With PCA application, it was found that Longjing teas (highest quality Chinese tea), compared to other teas, had higher levels of theanine, gallic acid, caffeine, epigallocatechin-3-gallate (EGCG), and epicatechin (EC) gallate, and lower levels of epigallocatechin (EGC). The different cultivation methods affect green tea quality by altering the metabolomics profile [37; 39]. Metabolome changes were investigated in green tea and shade cultured _C. sinensis_ by LC-MS and gas chromatography-mass spectrometry (GC-MS) coupled with a multivariate dataset [40]. PCA and orthogonal projections to latent structures DA (OPLS-DA) of green tea clearly showed higher levels than tencha of galloylquinic acid, EGC, EC, succinic acid, and fructose, along with lower levels of gallocatechin, strictinin, apigenin glucosyl arabinoside, quercetin \(p\)-coumaroylglucosylrhamnosylgalactoside, kaempfer \(p\)-coumaroylglucosylrhamnosylgalactoside, malic acid, and pyroglutamic acid. The effects of climatic conditions on _C. sinensis_ metabolites in three different growing areas of Jeju Island, South Korea, were investigated through \({}^{1}\)H NMR spectroscopy [41]. Pattern recognition methods, such as OPLS-DA and PCA, revealed clear discriminations of green teas from the different growing areas. Variations of theanine, quinic acid, glucose, EC, EGC, EGCG, caffeine, and amino acid profile were responsible for the discriminations. The dependence of global _C. sinensis_ metabolome on plucking positions was investigated through \({}^{1}\)H NMR analysis coupled with multivariate statistical dataset [42]. OPLS-DA and PCA were employed to find a metabolic discrimination among fresh _C. sinensis_ leaves plucked at different positions, from old to young leaves. The results showed a clear metabolic discrimination among _C. sinensis_ leaves (increased levels of theanine, caffeine, and gallic acid levels, and decreased levels of catechins, glucose, and sucrose) as the green tea plant grows up. Moreover, a different metabolism of the tea plant was observed between the tea leaf and stem. Seasonal variations of phenolic compounds were studied, using a HPLC method, in fresh tea shoots grown in Australia [43]. The content of EGCG, ECG, and CG were higher in fresh tea shoots in the warm months and lower in the cool months. Mechanisms that induce seasonal variations in tea shoots may be day length, sunlight, and temperature, which vary markedly across seasons. In another study, GC coupled with time-of-flight MS and multivariate data analysis was employed to evaluate green tea quality, showing changes dependent on green tea varieties and manufacturing processes [44]. In conclusion, the synthesis of several phenolic compounds, such as EGCG, ECG, and CG, related with tea quality, is temperature sensitive or dependent [43; 45; 46]. Moreover, it has also been suggested that EGCG synthesis depends on daytime length or stronger sunlight during the summer months [36; 39].
# ALPINE: Active Link Prediction Using Network Embedding
## 5 Experiments and Discussion
### Quantitative Evaluation of ALPINE  
In the quantitative evaluation, we mainly wanted to compare the performance of different query strategies from Section 4 with passive learning, as well as with the state-of-the-art baseline method HALLP [9]. Passive learning is represented by the random strategy that uniformly selects node pairs from the pool. As for HALLP, we implemented its query strategy shown in Equation (2) (since the source code is not publicly available), and set \(c_{1}\) and \(c_{2}\) both to one. Note that, as we wanted to compare query strategies in the fairest possible way, the link prediction was performed using CNE_K also for HALLP.  
**Setup:** As before, we constructed a PON by randomly initializing the observed node pair set \(K_{0}\) with a given ratio \(r_{0}\), while making sure \(K_{0}\) was connected. Then, we applied ALPINE with different query strategies for a budget \(k\) and a step size \(s\). More specifically, we investigated three representative different cases depending on the pool \(P\) and the target set \(T\):  
1. \(P=U\) and \(T=U\): all the unobserved information was accessible, and we were interested in knowing all link statuses in \(U\);
2. \(P\subset U\) and \(T=U\): only part of \(U\) was accessible, and we still wanted to predict the entire \(U\) as accurately as possible;
3. \(P\subset U\), \(T\subset U\), and \(T\cap U=\emptyset\): only part of \(U\) was accessible, and we were interested in predicting a different set of unobserved link status that was inaccessible.  
For all datasets, we investigated four values of \(r_{0}\): \([3\%,10\%,30\%,80\%]\), to see how the percentage of the observed information affected the strategy performance. All quantitative experiments used a step size depending on the network size: \(1\%\) of the network information. For a network with \(n\) nodes, it means that \(s=1\%*\frac{n(n-1)}{2}\) unobserved candidate link statuses will be selected for querying in each iteration. Then, the budget \(k\), pool size \(|P|\), and target set size \(|T|\) were multiples of \(s\) for different cases. The random strategy was a baseline for all three cases, while the HALLP strategy was only used in the first case because it was designed only for this setup.  
Below, we first discuss our findings for each of the three cases on the five smallest datasets. After that, we discuss some results on the two larger networks for the most scalable query strategies only.