esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works  
### Collection of KMNs  
The collection of KMNs is a prerequisite for determining whether a flight can be launched according to the normal flight schedule. The automatic collection of KMNs can help the airport operation control center to generate an optimal push-back sequence and improve the airport ground operational efficiency [13]. It is therefore crucial to implement A-CDM to establish the next generation of smart airport systems. Previous works on the KMNs of aircraft turnaround focused on the prediction of the operational situation based on historical and real-time shared data from the airport operation control center [14; 15; 16]. Nevertheless, it is difficult to construct effective predictive models due to the uncertainties and dynamics of the ground handling process.  
Compared with research on node prediction, there have been few published academic papers on the automatic collection of KMNs. To the best of our knowledge, the Gate Activity Monitoring Tool Suite (GAMTOS) is one of the earliest tools that uses computer vision to monitor the turnaround process [17]. GAMTOS contains two modules. Specifically, the first module uses a deformable part model (DPM) [18] to detect different objects moving on the airport ground. The push-back time is predicted using a Bayesian framework in the second module. The DPM used in GAMTOS is computationally expensive, and the framework thus seems ineffective. As one of the pioneering and representative works in the field of automatic collection of KMNs, [PERSON] et al. proposed a computer vision-based method of aircraft push-back prediction and turnaround monitoring [19]. They conducted the real scenarios tests by using surveillance videos of Obihiro Airport (IATA: OBO, ICAO: RJCB) [20]. Related experimental results show that its object detection model achieved up to 87.3% average precision (AP), and the average error of push-back prediction is less than 3 min. The other representative work was presented by [PERSON] et al. (2018). They set up a turnaround system to automatically detect and track ground services at the airport. In their work, ground services at airports are collected by using the motion status (stopping and moving) of the vehicles providing services. Moreover, [PERSON] et al. offered a demo application of aircraft turnaround management using computer vision ([PERSON] et al., 2018). Undoubtedly, employing computer vision to complete KMN collection by detecting and tracking the ground support equipment (GSE) has become a consensus. Although the proposed framework follows a similar technical line, our approach differs from the two previous representative approaches in terms of implementation. The specific differences are detailed in Table 1. The above studies revealed that several tasks of GSE detection, recognition, localization, and movement state estimation are crucial parts of the computer vision-based automatic collection of KMNs. To this end, the present paper thus designs a novel preprocessing module to accomplish the above functions.
# Bioactive Glass as a Nanoporous Drug Delivery System for Teicoplanin
## 3 Results and Discussion
### In Vitro Ion Release Test of B-Doped Mesoporous Bioactive Glass  
Figure 3 shows the pH values and B ions released into SBF after BG and B-doped BG samples were soaked for 7 days. In order to determine the variation in SBF after sample soaking, the variation in the pH value is shown in Figure 3a. The initial pH values of SBF were kept at 7.49 \(\pm\) 0.01.  
\begin{table}
\begin{tabular}{c c c} \hline
**Samples** & **Average Size (nm)** & **PDI** \\ \hline BG & 436.23 \(\pm\) 23.27 & 0.51 \(\pm\) 0.01 \\ BG\_B25 & 347.60 \(\pm\) 99.93 & 0.50 \(\pm\) 0.03 \\ BG\_B50 & 556.60 \(\pm\) 58.84 & 0.53 \(\pm\) 0.02 \\ BG\_B75 & 431.57 \(\pm\) 18.60 & 0.43 \(\pm\) 0.04 \\ BG\_B100 & 361.93 \(\pm\) 17.15 & 0.42 \(\pm\) 0.02 \\ \hline \end{tabular}
\end{table}
Table 1: The average sizes and polydispersity index (PDI) index values of BG and B-doped BG.  
Figure 2: X-ray diffraction spectra of BG and B-doped BG soaking in simulated body fluid (SBF) for (a) 0 and (b) 7 days.  
After the soaking period of 7 days, the basic components of all samples were dissolved in SBF and produced increases in the pH value, especially for BG. The pH value increment trend was similar to that observed in a previous study [28], where the basic ions of BG were released in great amounts, causing the pH value to increase after soaking in SBF. On day 7, the pH values increased to \(7.73\pm 0.03\), \(7.75\pm 0.04\), \(7.75\pm 0.08\), \(7.62\pm 0.02\), and \(8.02\pm 0.09\) in samples BG_B100, BG_B75, BG_B50, BG_B25, and BG, respectively. This shows that the addition of borate can slow down the increment in the pH value. The difference in pH values between BG and BG_B100 on day 7 was very statistically significant with a \(p\) value of 0.001, which indicates that B doping was an effective way of keeping pH values stable during soaking in SBF. The pH value of BG_B25 on day 7 was lower than that of other B doped samples. The statistical differences among the four B-doped samples were determined using the one-way analysis of variance (ANOVA) technique. The \(p\) value was 0.012, with values \(<\)0.05 being statistically significant. In order to determine why B25 had a lower pH than the other substrates, BG_B25 was further measured by BET. BG_B25 has a larger hysteresis loop of N\({}_{2}\) adsorption-desorption isotherms, which means that BG_B25 has a greater area available for apatite deposition. The basic ions from BG_B25 were used to form apatite, causing the pH value to decrease. In Figure 3b, the release of B ions also increased as the soaking period increased. There was a controlled release profile of B ions with various concentrations of B. On day 7, B ions were released in quantities of around 8.94, 4.41, 3.13, 2.16, and 0 ppm from samples BG_B100, BG_B75, BG_B50, BG_B25, and BG, respectively. This shows that the concentration of B ions released was altered by the addition of borate.
# Combining InSAR and GNSS to Track Magma Transport at Bastic Volcano
## 3 Geodetic Measurement Description
### InSAR Data
(**Left column**) Data acquired on ascending orbits; (**Right column**) Data acquired on descending orbits; (**First row**) Cosmo-Skymed data (X-band) spanning the whole eruptive period; (**Second to Fourth rows**) Sentinel-1 InSAR ascending and descending data (C-band); (**Second row**) Sentinel-1 data spanning the whole eruptive period; (**Third row**) Sentinel-1 data spanning the first time period; (**Fourth row**) Sentinel-1 data spanning the second time period; For Cosmo-Skymed data, fringes represent 1.5 cm LOS displacement, while for Sentinel-1 data, fringes represent 2.8 cm LOS displacement. S1 D1 = Sentinel-1 descending InSAR data spanning the beginning of the eruptive crisis; S1 D2 = Sentinel-1 descending InSAR data spanning the end part of the eruptive crisis; S1 A1 = Sentinel-1 ascending InSAR data spanning the pre-eruptive period; S1 A2 = Sentinel-1 ascending InSAR data spanning only the eruption. In ascending LOS, the Sentinel-1 May 25 acquisition occurred just before the beginning of the crisis (Figures 2 and 3), enabling two interferograms to be computed. Interferogram S1 A1 only records the unrest period while interferogram S1 A2 spans the eruption and a few days after. A third ascending interferogram, S1 A, spans the whole period between 19 April and 6 June recording the unrest, the eruption and a few days after. In descending LOS, the Sentinel-1 26 May acquisition occurred during the crisis (Figures 2 and 3), allowing interferogram S1 D1 to be computed, which spans the pre-eruptive period and the beginning of the crisis and interferogram S1 D2, which covers the end of the crisis and the following few days. A third descending interferogram, S1 D, spans the whole eruptive period.
# Near-Optimal Active Learning for Multilingual Grapheme-to-Phoneme Conversion
## 3 Multilingual G2P Model
### Encoder-Decoder Model
#### 3.1.1 Encoder  
The encoder network operates according to the LSTM cell structure. At each time step, the input to the encoder is a grapheme of a word (or, in tasks such as machine translation, a word or words), and the input ends when the terminator \(<\!\!>\) is encountered, and the encoder represents the sequence of words as a fixed-length vector \(v\), based on the state of the last hidden layer. Relying on the ability of LSTM to process information over long distances, vector \(v\) is able to contain grapheme information for the entire word sequence. At each time \(t\), the state \(h_{t}\) of the hidden layer can be expressed in Equation (1),  
\[h_{t}=f(x_{t},h_{t-1}) \tag{1}\]  
where \(f\) denotes the nonlinear activation function, which is the structure of the encoded LSTM unit; \(h_{(t-1)}\) denotes the hidden layer state at the previous moment; \(x_{t}\) is the input at the current moment. The vector \(v\) is the weighted sum of the last hidden layer or multiple hidden layers, and the operation symbol is denoted by \(\varphi\), as shown in Equation (2).  
\[v=\varphi(h_{1},h_{2},\dots,h_{t}) \tag{2}\]  
Figure 3: The model structure of an encoder\(-\)decoder.  
In Equation (2), the operation symbols denoted by \(\varphi\) represent a mathematical function to compute the weighted sum of the hidden layer values \"\(h_{1},h_{2},\ldots,h_{t}\)\" as a weighted sum. This function \(\varphi\) can be any mathematical operation or transformation that combines these hidden layer values based on some weights or coefficients. The specific form of \(\varphi\) depends on the situation and the problem to be solved. It can be a simple weighted sum, a more complex aggregation function, or even a nonlinear transformation. The choice of \(\varphi\) will determine how the hidden layer values are combined to produce the final vector \(v\).
# Research on Co-Channel Interference Cancellation for Underwater Acoustic MIMO Communications
## 2 MIMO System Design with CoI Cancellation
### Performance Analysis
\[MSE=\mathbb{E}\|\hat{\mathbf{h}}_{1,n}-\mathbf{h}_{1,n}\|_{2}^{2}\, \tag{23}\]4  
We combine (19), (20) and (30); the output of CE-DFE for branch 1 is  
\[MSE=\mathbb{E}\|\hat{\mathbf{h}}_{1,n}-\mathbf{h}_{1,n}\|_{2}^{2}\, \tag{23}\]5  
Substituting (21) into (31), the following can be obtained  
\[MSE=\mathbb{E}\|\hat{\mathbf{h}}_{1,n}-\mathbf{h}_{1,n}\|_{2}^{2}\, \tag{23}\]6where \(\mathbf{v}_{1}+\mathbf{v}_{2}=\mathbf{w}_{n}\). The output error of CE-DFE for brach 1 is  
\[MSE=\mathbb{E}\|\hat{\mathbf{h}}_{1,n}-\mathbf{h}_{1,n}\|_{2}^{2}\, \tag{23}\]7 \[MSE=\mathbb{E}\|\hat{\mathbf{h}}_{1,n}-\mathbf{h}_{1,n}\|_{2}^{2}\, \tag{23}\]8  
The terms in the brace in (33) are called the minimum achievable error of the equalizer. This error can be obtained based on the perfect knowledge of the channel impulse response and noise statistics. Focusing on the last term in (33), which is called the excess error, one may observe that the excess error related to the channel error has a significant impact on the output error of CE-DFE. The lower the channel estimate error, the lower the CE-DFE error. In sum, the proposed structure for underwater acoustic MIMO communication cancels the CoI, not only in channel estimation, but also in channel equalization; hence, the communication performance significantly improved.
# Clustering-Based Modified Ant Colony Optimizer for Internet of Vehicles (CACOIOV)
## 6 Conclusions  
Effective communication and management of the network in IoVs are very difficult tasks due to vehicles in fast movement on roads and the effect of speed on data delivery in the formed network. The important need for a method that is systematic and efficient to adjust the parameters of routing  
Figure 9: Average Number of Cluster.  
protocols and clustering is expressed throughout this research. Also, the IoV and its architecture of communication has been studied. The implementation of the ACO in several ways, and how it is used to optimize solutions, is also addressed. An original methodology, which engages a heuristic multi-objective clustering algorithm based on modified ant colony optimizer (CACOIOV) in two distinct phases is proposed to reach scalable and stable topology in networks of the IoV that are heterogeneous. In the first phase, we proposed a new method for choosing a first node intelligently in the search space and, in the second phase, we introduced a novel way to balance the convergence speed of the ACO by proposing the dynamic evaporation rate method. The presented algorithm makes use of a new proposed DA-TRLD algorithm on the local traffic density basis to maintain connectivity of the IoV network. This method is compared alongside well-known protocols such as ACO and AODV algorithms. The outcome shows satisfactory results regarding packet delivery ratio, delay, cluster number, throughput, and packet drop ratio. Based on developing our novel ideas, we succeeded in accomplishing our study objectives in terms of avoiding local optimum problems and avoiding network dissemination problems. Also, our algorithm clearly demonstrates optimal performance to create efficient and reliable V2I communications that are qualitative, and confirm reliable information delivery to each vehicle. Subsequently, our method provides sustainable transportation in the IoV highway environments for smart cities by developing efficient and reliable protocol for information delivery. Thus, our new framework is a suitable tool for a wide range and large-scale (congested traffic) optimization problem. We hope our proposed protocol insight will help to improve the services provided in the IoV network for the highway environment, and assist drivers in traveling safely.  
To verify the CACOIOV performance in realistic environments, it is our intention to implement this framework in traffic simulations with the aid of Open Street Map. Moreover, for future work, several other ACO aspects can be modified to enhance IoV routing. Additionally, urban scenarios can be considered for the deployment of our protocol. Also, the issue of hand-off on the IoV can be explored for the presented protocol.  
Conceptualization, methodology, software, validation, formal analysis, writing--original draft preparation, S.E.; writing--review and editing, S.E., E.E. and Z.D.; supervision, E.E. and Z.D. This research received no external funding. The authors declare no conflict of interest.
# Detection of Geomagnetic Signals as Precursors to Some Earthquakes in China
## 3 Polarization Results and Earthquakes
### Polarization Results of the KSH Station  
The KSH observatory is located in northwestern China, as shown in Figure 1a. Three geomagnetic components of \(D/H/Z\) were observed through fluxgate sensors. The geomagnetic data from 2014 to 2017 were used for polarization analysis. A series of earthquakes were recorded near the KSH observatory during this time (Table 1). Three earthquakes may have been preceded by seismic electromagnetic signals. The first earthquake (\(M_{\rm s}\) 7.4) occurred on 7 December 2015 in Tajikistan. The focal depth of this earthquake was 30 km  
Figure 4: Polarization ratios of \(Y_{\rm ZH}\) at **(a)** CDP, **(b)** GLM, and **(c)** CNH stations at approximately 0.1 Hz during 2013.  
with an epicenter of 291 km from the KSH station. The second earthquake (\(M_{\rm s}\) 6.7) occurred on 26 June 2016 in Kyrgyzstan with the focal depth of 10 km, nearly 207 km from the KSH station. The final earthquake (\(M_{\rm s}\) 6.7) occurred on 2 November 2016 in the Aketao region of China with a focal depth of 10 km, 155 km from the KSH station. The KSH observatory and distribution of earthquakes are shown in Figure 1b.  
According to the skin effect (Equation (3)), the possible seismic-electromagnetic emission frequencies originating from the source of these three earthquakes are 0.003, 0.02, and 0.02 Hz, respectively. We analyzed the polarization ratios of \(Y_{\rm ZH}\) and \(Y_{\rm ZG}\) of the geomagnetic data within 0.003-0.02 Hz in the KSH station. Figure 5a illustrates the temporal evolution of \(Y_{\rm ZH}\) during 2014-2017 through five-day moving average.  
Some significant enhancements (exceeded the threshold value of \(m+2\sigma\) by 62%, 69%, and 119%, respectively) of \(Y_{\rm ZH}\) clearly occurred about 1 month prior to each earthquake and lasted for about half a month. However, similar changes were not found in \(Y_{\rm ZG}\), in contrast with the Jinta and Haixi earthquake results. Similarly, Figure 5c,d show the \(Y_{\rm ZH}\) and \(Y_{\rm ZG}\) in the CNH observatory, which was nearly 4000 km from the earthquake epicenters. The calculated correlation coefficient between the \(Y_{\rm ZH}\) in the KSH station and  
Figure 5: Polarization ratios of \(Y_{\rm ZH}\) at (a) KSH, (c) CNH stations and \(Y_{\rm ZG}\) at (b) KSH, (d) CNH observatories within 0.003–0.02 from 2014 to 2017 and (e) Dst index.  
the Dst index is 0.01, which implies that the \(Y_{\rm ZH}\) variation is generally not in accordance with the Dst index. Figure 5c shows the \(Y_{\rm ZH}\) in the CNH observatory as a reference station, which does not show significant enhancements as in the KSH station, meaning that the increased \(Y_{\rm ZH}\) of the KSH station are regional and may be closely related with the series of earthquakes.
## 4 Proposed Binary Field Multiplication
### Proposed Karatsuba Technique  
With the proposed Block-Comb multiplication method using a block size of 8, 128-bit \(BF\) multiplication can be computed with 4 partial products, where each partial product is computed with Algorithm 3. To decrease the count of partial products, we combine our Block-Comb method with the enhanced Karatsuba technique [20]. Actually, even though the enhanced Karatsuba technique was originally proposed for prime field multiplication on 8-bit AVR MCUs, we modify it for our proposed Block-Comb method. By applying the enhanced Karatsuba technique rather than classic Karatsuba technique, \(s\) XOR instructions can be saved where \(s\) is the number of words for operands and 8 in Algorithm 4. Algorithm 4 depicts the proposed 1-level Karatsuba secure Block-Comb method, which computes 3 partial products with Algorithm 3. In the algorithm, \(H_{H}\), \(H_{L}\), \(M_{H}\), \(M_{L}\), \(L_{H}\), \(L_{L}\), and \(T\) are all \(s\) bytes. Therefore, Algorithm 4 saves one partial product at the expense of additional 56 XOR instructions compared with a classical 128-bit \(BF\) multiplication using Algorithm 3. In Algorithm 4, \(L\), \(H\), and \(M\) mean low term, high term, and middle term in the Karatsuba multiplication, respectively (thus, each of them is 128-bit). Lower case \({}_{L}\) and \({}_{H}\) mean the lower part and higher part of each term (thus, each of them is 64-bit).  
```
0: 128-bit wise operands \(A\) and \(B\).
0: Result \(C(128-bit)\)=\(A\cdot B\).
1: Encode \(B[7\ldots 0]\) into \(EB[7\ldots 0]\) with Algorithm 2
2: Encode \(B[15\ldots 8]\) into \(EB[15\ldots 8]\) with Algorithm 2
3:\(L_{H}||L_{L}\gets A[7\ldots 0]\times_{64-bit}EB[7\ldots 0]\) with Algorithm 3
4:\(H_{H}||H_{L}\gets A[15\ldots 8]\times_{64-bit}EB[15\ldots 8]\) with Algorithm 3
5:\(T\gets L_{H}\oplus H_{L}\)
6:\(L_{H}\gets T\oplus L_{L}\)
7:\(H_{L}\gets T\oplus H_{H}\)
8:\(M_{H}||M_{L}\leftarrow(A[15\ldots 8]\oplus A[7\ldots 0])\times_{64-bit}(EB[15 \ldots 8]\oplus EB[7\ldots 0])\) with Algorithm 3
9:\(L_{H}\gets L_{H}\oplus M_{L}\)
10:\(H_{L}\gets H_{L}\oplus M_{H}\)
11:\(C\leftarrow(H_{H}||H_{L}||L_{H}||L_{L})\)
12: (Return \(C\))
```  
**Algorithm 4**128-bit wise Karatsuba Block Comb method.
# MIMO Radio Frequency Identification: A Brief Survey
###### Abstract
Several works have explored multiple-input multiple-output (MIMO) [21] for RFID receivers. When using MIMO at the backscatter channel, significant improvements can be achieved in the bit error rate (BER) as well as extension of reverse-link interrogation range (RIR). With the extra reliability and increased throughput, such systems can be deployed in many important applications like large tag reading scenarios and accurate tracking etc. Increased throughput is directly dependent on estimation of tag quantity in a bulk reading environment and usually estimators designed for single antenna systems underperform in such settings causing average or low signal to noise ratio (SNR) when employed in MIMO systems where tag signal overlapping can happen more often. One of the major areas of recent research has been bulk reading capabilities of the RFID reader systems. To enhance bulk reading capabilities of an RFID system, collision recovery techniques can play an important role as they help in recovering the signal collisions due to interference from neighboring tags who are communicating simultaneously. Most of the recent collision recovery algorithms are based on the assumption that perfect knowledge of the channel state information (CSI) exists but it is difficult to achieve. Block diagram of a typical MIMO RFID system is depicted in Figure 1. The narrowband MIMO channel [22] can be expressed as:  
\[y(t)=\mathbf{H}^{b}\mathbf{S}(t)\mathbf{H}^{f}\mathbf{x}(t)+\mathbf{n}(t), \tag{1}\]  
where \(\mathbf{H}^{b}\) is the channel matrix from the tag to the reader while \(\mathbf{H}^{f}\) is a channel matrix from reader to the tag, \(\mathbf{x}\) is the transmitted signal and \(\mathbf{y}\) is the received signal at the reader. \(\mathbf{S}\) in Equation (1) represents the backscattering matrix and is also known as signaling matrix and \(\mathbf{n}\) is the noise at receiver. \(\mathbf{H}^{b}\) and \(\mathbf{H}^{f}\) are Gaussian distributed and mutually independent. In this work we go through latest state-of-the-art in MIMO RFID systems and briefly look at the tools and techniques used. Some of the major contributions of this work are:  
* A study of various estimation algorithms being employed in case of multi-antenna RFID interrogators. * A brief look at various anti-collision algorithms and their viability. * A brief look at the recent advancements in the domain of security. The rest of the paper is organized as follows: Section 2 details various estimation techniques and algorithms used in MIMO RFID systems. Section 3 compares various anti-collision algorithms being used in multi-antenna RFID systems. Section 4 looks at recent advances in the domain of secure MIMO RFID systems. Section 5 presents the discussion and Section 6 concludes the paper.