esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effects of STL Addition on BT VMs
#### 3.2.4 Analysis of Differential VMs  
To further analyze the differential VMs among the BT, STL, and BoS samples, we constructed a thermography chart (Figure 4) based on 33 specific differential VMs (Table S3). The BT and BoS samples with a sweet and glutinous rice aroma were clustered into one category, whereas the STL samples formed their own category exhibiting a strong  
Figure 3: Differential volatile metabolites (VMs) in BT vs. STL, STL vs. BoS, and BT vs. BoS. S-plot generated from the PLS-DA model for BT vs. STL (**a**), STL vs. BoS (**b**), and BT vs. BoS (**c**). Green and red spots indicate all VMs involved in the model, and red spots show the most significant changes and higher contributions to the classification pattern. (**d**) Venn diagram of the differential VMs in BT vs. STL, BoS vs. STL, and BT vs. BoS. STL, _Strobilanthes tonkinensis_ Lindau; BT, black tea; BoS, black tea with added STL.  
-glutinous rice aroma (Figure 4). In addition, the 33 VMs were grouped into three major classes. The first class included eight VMs, namely, (E)-2-hexenal, 1-methyl-1H-1,2,4-triazole, benzeneacetaldehyde, linalool, phenylethyll alcohol, and nerol, which had the highest contents in BT and distinctly contributed to the sweet aroma of the BT [5; 23]. The second class included (E)-3-hexen-1-ol, _cis_-2-methyl-cyclopentanol, 1-hexanol, decane, and (Z)-hexanoic acid, 3-hexenyl ester, which were more abundant in the BoS samples than in either the BT or the STL samples; therefore, they were determined to be responsible for the sweet and glutinous rice aroma of BoS, following the combination of BT and STL. The third class included 20 VMs that were more abundant in the STL samples than in the other two samples; this category included compounds such as benzaldehyde, 1-octen-3-ol, 3-carene, 2-ethyl-1-hexanol, isophorone, linalool oxide (pyranoid), 2-methoxy-3-(2-methylpropyl)-pyrazine, 1-dodecanol, _n_-valeric acid _cis_-3-hexenyl ester, formic acid dodecyl ester, \(\alpha\)-ionone, and _trans_-\(\beta\)-ionone, which contributed greatly to the strong glutinous rice aroma of the STL samples [7; 12]. When combined with the Venn diagram, these findings clearly showed that the concentrations and proportions of the volatile substances have strong effects on the odor of each type of tea blend.
## 5 Conclusion and Future Work  
In this paper, we described our proposal for a flexible head-body matching (FHBM) algorithm for use with NIDSs and multi-core processors. Unlike the HBM algorithm, which statically constructs head-body finite automata according to pre-defined depth values, our proposed algorithm partitions head and body parts based on head size, thereby constructing more efficient HBFAs compared to the HBM algorithm. According to our results, the FHBM algorithm achieved up to 58% higher throughput for the Snort pattern set (536 MB/s vs. 339 MB/s when the match ratio is 1% and the head size is 6000 states), 46% for the Clam AV type 1 pattern set (678 MB/s vs. 465 MB/s when the match ratio is 1% and the head size is 7000 states), and 55% for the Clam AV type 3 pattern set (541 MB/s vs. 349 MB/s when the match ratio is 32% and the head size is 1500 states). Although the FHBM algorithm can partition an AC-DFA more flexibly than the HBM algorithm, there is still space to improve. Given an AC-DFA, the boundary between the H-DFA and B-NFA constructed by the FHBM algorithm lies at depth \(i\) or depth \(I+1\). In our future work, we plan to further explore the relationship between throughput and  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Depth**} & \multicolumn{6}{c}{**Match Ratio**} \\ \cline{2-7}  & **1\%** & **2\%** & **4\%** & **8\%** & **16\%** & **32\%** \\ \hline
1 & 1726 & 1710 & 1680 & 1617 & 1494 & 1244 \\
2 & 1919 & 1902 & 1868 & 1801 & 1666 & 1393 \\
3 & 612 & 607 & 599 & 581 & 546 & 475 \\
4 & 158 & 159 & 159 & 160 & 161 & 164 \\
5 & 34 & 35 & 38 & 44 & 55 & 78 \\
6 & 16 & 17 & 20 & 26 & 39 & 63 \\
7 & 5 & 7 & 10 & 16 & 29 & 53 \\
8 & 2 & 3 & 6 & 12 & 23 & 46 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Number of accesses to states at each depth for the Snort pattern set (1 unit = 1000).  
H-DFA/B-NFA boundary, and design an algorithm that can achieve higher throughput by partitioning an AC-DFA without the H-DFA/B-NFA boundary limitation with the FHBM algorithm.  
This work was supported in part by the High Speed Intelligent Communication (HSIC) Research Center of Chang Gung University, Taiwan, and by grants from the Ministry of Science and Technology of Taiwan (NSC-101-2221-E-182-074 and MOST-104-2221-E-182-005) and Chang Gung Memorial Hospital (BMRP 942).  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] contributed reagents, materials and analysis tools; [PERSON] wrote the paper. All authors have read and approved the final manuscript.  
The authors declare no conflict of interest.
# Validating Precise Orbit Determination from Satellite-Borne GPS Data of Haiyang-2D
## 3 Results and Discussion
### Accuracy Assessment of RD Orbits by Using the Three New Methods
#### 3.3.3 SLR 3D Validation  
The results of SLR range validation are relatively single, and the accuracy of the orbit cannot be more intuitively presented. The SLR 3D validation is a method that uses the position and speed of the SLR station and the results of SLR rang validation to obtain errors in the x, y, and z directions according to the law of error propagation.  
Stations with more than 50 NP data were selected for the experiment, and a total of nine stations met the experimental requirements. Each selected station provided three consecutive observation periods, and the RMS in the x, y, and z directions were obtained by solving the equations, as shown in Table 9.  
As shown in Table 9, the errors in three directions of every station were better than 0.042 m, and the 3D RMS was better than 5.8 cm. When combining all stations, the RMS values were 0.0266 m in x-direction, 0.0332 m in y-direction, 0.0269 m in z-direction, and the 3D RMS value was 0.0503 m. The 3D RMS value was close to the result of SLR range  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Time** & **Satellite** & **Max (m)** & **Min (m)** & **Mean (m)** & **RMS (m)** \\ \hline  
(-\)0.0034 & 0.0145 \\ DOY266 & Total & 0.0457 & \(-\)0.0515 & \(-\)0.0005 & 0.0106 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Inter-satellite differential validation residuals.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Station** & **X (m)** & **Y (m)** & **Z (m)** & **3D (m)** \\ \hline
7090 & 0.0257 & 0.0352 & 0.0331 & 0.0547 \\
7105 & 0.0110 & 0.0390 & 0.0364 & 0.0545 \\
7110 & 0.0240 & 0.0372 & 0.0287 & 0.0528 \\
7810 & 0.0250 & 0.0199 & 0.0260 & 0.0412 \\
7825 & 0.0300 & 0.0387 & 0.0150 & 0.0512 \\
7839 & 0.0229 & 0.0379 & 0.0203 & 0.0487 \\
7840 & 0.0265 & 0.0289 & 0.0253 & 0.0467 \\
7941 & 0.0416 & 0.0319 & 0.0338 & 0.0579 \\
8834 & 0.0229 & 0.0242 & 0.0137 & 0.0360 \\ Total & 0.0266 & 0.0332 & 0.0269 & 0.0503 \\ \hline \hline \end{tabular}
\end{table}
Table 9: RMS of SLR 3D validation residuals.  
validation, which proved that this method is reliable and can more intuitively evaluate the 3D accuracy of orbits based on the SLR range validation.
## 3 Results and Discussions
### Model Reliability Assessment of Ultrasound-Assisted Extraction (UAE)  
The optimal UAE conditions for the extraction of alkaloids from the peel, seed, columnella, and pulp are shown in Table 4. Treatments were carried out using the best conditions for each raw material, as suggested by [PERSON] et al. (2019) to modify the reliability of the model. The experimental data of TALC (Table 1) for all raw materials are consistent with the predicted data (Table 4).  
Thus, the use of UAE is useful to obtain an efficient extraction of alkaloids from _A. muricata_. RSM tools have been used to optimize the extraction of ALKs using UAE in tubers of the _Stephania camodica_ plant ([PERSON], 2017) and _Sophora alopeuroides_([PERSON], 2017).  
In all _A. muricata_ raw materials, 5 min of UAE was sufficient to enhance TALC. However, the intensity of the ultrasound (70% amplitude) was the same for peel and columnella with a 1-s pulse-cycle and seed with a 0.4-s pulse-cycle while 100% amplitude was necessary for the pulp with a 0.5-s pulse-cycle. According to [PERSON]'s ultrasound technology and the characteristics of ultrasound equipment, a 1-s pulse-cycle means a continuous acoustic irradiation with a 0.7-s pulse-cycle is 0.7 s of acoustic irradiation and 0.3 s is kept in pause and the 0.4-s pulse-cycle are 0.4 s of acoustic irradiation and 0.6 s in pause. Therefore, low cavitation was necessary to extract ALKs from the pulp or seeds likely because of the low content found.  
TALC was significantly higher (\(p<0.05\)) when ultrasound extraction was used than the alkaloid content obtained by maceration extraction (Table 5). The effectiveness of UAE was 56.3, 30.6, and 29.6 times higher for the peel, pulp, seed, and columnella, respectively, when compared to maceration. [PERSON] et al. (2019) reported an increase in the percentage (77%) of alkaloid extraction from ipecacuana root (_Cephalis ipecacuanaha_) and Jaborandi leaf (_Pilocarpus microphyllus_) when the extracts were obtained using UAE compared to the extraction using Soxhlet (22%). Additionally, [PERSON] et al. (2017) reported that alkaloid extraction from _Sophora alopeuroides_ seeds produced the highest extraction yield (4.6% and 38.3%) when compared to extraction using reflux heating and enzymatic extraction, respectively.
# Exploring Efficient Neural Architectures for Linguistic-Acoustic Mapping in Text-To-Speech +
## 5 Experimental Setup
### Linguistic and Acoustic Features  
The decoder maps linguistic and prosodic features into acoustic ones. This means that we first extracted hand-crafted features out of the input textual query. These were extracted in the label format, following our previous work in [8]. We thus had a combination of sparse identifiers in the form of one-hot vectors, binary values, and real values. These included the identity of phonemes within a window of context, part of speech tags, distance from syllables to end of sentence, etc. For more details, we refer to [8] and references therein.  
For a textual query of \(L\) words, we obtained \(M\) label vectors, \(M\geq L\), each with 362 dimensions. To inject these into the acoustic decoder, we needed an extra step. As mentioned, the MUSA testbed follows a two-stage structure with the amount of frames specified in first stage: (1) duration prediction; and (2) acoustic prediction. Here, we only worked with the acoustic mapping, thus we enforced the duration with labeled data. For this reason, and similar to what we did in previous works [27; 37], we replicated the linguistic label vector of each phoneme as many times as dictated by the ground-truth annotated duration, appending two extra dimensions to the 362 existing ones. These two extra dimensions corresponded to: (1) absolute duration normalized between 0 and 1, given the training data; and (2) relative position of current phoneme inside the absolute duration, also normalized between 0 and 1.  
We parameterized the speech with a vocoded representation using Ahocoder [39]. Ahocoder is a harmonic-plus-noise high quality vocoder, which converts each windowed waveform frame into three types of features: (1) mel-frequency cepstral coefficients (MFCCs); (2) log-F0 contour; and (3) voicing frequency (VF). Note that F0 contours have two states: either they follow a continuous envelope for voiced sections of speech, or they are 0, for which the logarithm is undefined. Because of that, Ahocoder encodes this value with \(-10^{9}\), to avoid numerically undefined values. This result would be a cumbersome output distribution to be predicted by a neural net using a quadratic regression loss. Therefore, to smooth the values out and normalize the log-F0 distribution, we linearly interpolated these contours and create an extra acoustic feature, the unvoiced-voiced flag (UV), which is the binary flag indicating the voiced or unvoiced state of the current frame. We then had an acoustic vector with 40 MFCCs, 1 log-F0, 1 VF, and 1 UV. This totaled 43 features per frame, where each frame window has a stride of 80 samples over the waveform. Real-numbered linguistic features were Z-normalized by computing statistics on the training data. In the acoustic feature outputs, all of them were normalized to fall within \([0,1]\).
## 2 Experimental Data
### Outlier Detection  
As an experimental product, raw clock offset data usually contain outliers. The outlier detection method based on median absolute deviation (MAD) has robustness and adaptability, which is applicable to satellite atomic clock data with a large amount of data. The idea of outlier detection based on this method is to compare each frequency data with the median \(m\) of the frequency data sequence plus the sum of several times of MAD [14]: if the observed measurement is \(|y_{i}|>(m+n\cdot\mathrm{MAD})\), then it will be considered as outlier data, where \(m=\mathrm{Median}(y_{i})\) and \(\mathrm{MAD}=\mathrm{Median}(|y_{i}-m|/0.6745)\). In this study, initial phase data with relatively few effective positions were first converted to average frequency value, and then processed by MAD-based method; the outliers were set to null value, and finally, the clean frequency and phase data could be obtained [23].  
Herein, C01 (BDS2-GEO), C27 (BDS3-MEO), and C37 (BDS3-IGSO) are used as examples to demonstrate the effectiveness of the above methods. The original phase data of the  
Figure 1: The Data Availability of BDS Satellites.  
three satellite atomic clocks are shown in Figure 2a, including rising and falling trends and multiple phase modulation operations. It is difficult to see the anomaly point from the phase data; thus, it is necessary to convert the phase data into frequency data, and then distinguish and eliminate the outliers. The corresponding frequency data are shown in Figure 2b, with multiple obvious outliers, as well as some gross errors that are difficult to observe. The frequency data processed by the MAD-based method is shown in Figure 2c, and the frequency trend of the three satellite atomic clocks is clearly visible. Therefore, the data processing approach taken is effective.  
Figure 3 shows the comparison of the data efficiency of each satellite with the original data efficiency after using the MAD-based method to remove the outliers. MEO satellites of BDS-3 (C19-C37) have a raw data efficiency of no less than 99%, reflecting stable data quality. As a comparison, satellites that have been in service for only a short time (C41-C60) have a lower data efficiency, approximately equal to that of the early satellites of BDS-2. In detail, there were quite a lot data gaps in the early part of the statistical time interval, probably due to the fact that they are still in the commissioning phase, ultimately resulting in a high error rate of the data. For example, the error rate of C59 was 13.12%, which can seriously affect performance evaluation and analysis. The imperf  
Figure 2: The effect of the outlier detection by MAD.  
by cycle slip, ionospheric bubbles, etc. The enumerated problems could be proved by dropouts in the C/N0 or by a similar characteristic. In order to ensure the preciseness of long-term characteristic analysis, the object of this study is specific to 35 satellite atomic clocks before PRN40.
# SCATTER PHY: An Open Source Physical Layer for the DARPA Spectrum Collaboration Challenge
## Appendix B CFO Estimation  
### Integer Estimation  
After Synch signal detection and subframe alignment, the integer (i.e., coarse) CFO estimation was carried out by correlating the received OFDM symbol carrying the Synch signal with several local versions of it with different integer frequency offset applied to each of them. The PDP between the received and a given offset local version of the Synch OFDM symbol is defined as:  
\[\begin{split}\text{PDP}(l,k_{\text{IFO}})=\left|\sum_{n=0}^{N_{ \text{FFT}}-1}y[n]x_{l}^{*}[(n+l)_{N_{\text{FFT}}}]e^{\frac{2\pi\hbar_{\text{ IFO}}n}{N_{\text{FFT}}}}\right|^{2},\\ 0\leq l\leq N_{\text{FFT}}-1\\ -36\leq k_{\text{IFO}}\leq 36,\end{split} \tag{10}\]  
where \(x_{l}(n)\) is the offset local version of the Synch symbol, \(y(n)\) is the received Synch OFDM symbol signal, \(N_{\text{FFT}}\) is the FFT length, \((.)_{N_{\text{FFT}}}\) denotes that this is a cyclic correlation, \(k_{\text{IFO}}\) is the integer frequency offset, and \((.)^{*}\) is the complex conjugate. Therefore, the integer frequency offset can be estimated by solving the following maximization problem:  
\[\hat{e}_{k_{\text{IFO}}}=\arg\max_{k_{\text{IFO}}}\left(\arg\max_{l}\text{PDP} (l,k_{\text{IFO}})\right). \tag{11}\]  
Figure 14 shows the performance of the integer CFO estimation algorithm described above for an AWGN channel over several SNR values and a PHY BW of 4.5 MHz.
# Data Lake Architecture for Smart Fish Farming Data-Driven Strategy
## 4 Big Data Technologies for Fish Farming Use Case
### Data Exposition: Hive, Hbase, Elasticsearch  
Apache Hive is a distributed, fault-tolerant data warehouse system built on top of HDFS. It is designed to efficiently store and analyze huge datasets by centralizing them in a single store. Indeed, Hive is based on structured tables. Each table has a related HDFS directory that is divided into partitions, and each partition is divided into buckets. In addition, for better data analysis, Hive provides a SQL-like language named HiveQL for querying data. Each HiveQL query is converted into MapReduce jobs processed in parallel and impacting data in HDFS directly [29].  
Apache HBase is a distributed, column-oriented, non-relational database with a key/value model built on top of HDFS. It is designed to support high table updates. In addition, HBase allows gathering multiple data elements into column families with a unique row key; however, HBase is most effectively used to store non-relational data in data-driven strategies. The HBase API enables random, strictly consistent, and real-time access to large volumes of data. In fact, HBase tables are known as HStore, and each HStore has one or more associated map files stored in HDFS [30].  
Elasticsearch is a NoSQL, document-oriented database. It stores data in an unstructured way and cannot be queried using SQL. Since it is based on indices, the entire object graph needs to be indexed to be searched. Elasticsearch is a distributed search and analytics engine. Kibana is a proprietary data visualization dashboard software for Elasticsearch. Kibana enables interactive exploration of data in Elasticsearch using Kibana Query Language to filter data using free text or field-based search. The elastic stack can be used not only for functional data but also to Store and analyze logs, metrics, and security event data [31].
# Ship Detection from X-Band SAR Images Using M2 Det Deep Learning Model
## 3 Methods
### SAR Image Preprocessing
#### 3.1.4 Image Normalization  
As the average value of the input data deviates from 0, a bias may occur in a specific direction when the weight is updated, resulting in a slower learning speed [35]. Therefore, the training data should be normalized to uniformly train the weights and expedite the training process [35]. In this study, Z-score normalization was performed to normalize the input image, and the following equation was used:  
\[Z=\frac{x-\mu}{\sigma} \tag{2}\]  
where x indicates each SAR image, and \(\mu\) and \(\sigma\) denote the mean and standard deviation of x, respectively. The learning of the neural network can be performed faster than prior to normalization by normalizing each image using Equation (2).
## 2 Materials and Methods
### Data Collaborative Detailed Requirements  
Given the broad requirements in Section 1 and information about the contest/challenge lifecycle, we generate more detailed requirements of what needs to be implemented:  
1. **A highly scalable, accessible, and elastic infrastructure**: A single competition could involve as many as thousands of teams at one time, and as each competition milestone is reached, the number could be reduced by one or two orders of magnitude.  
Figure 1: Typical lifecycle of an XPRIZE Competition.  
Teams can be from almost anywhere in the world. The ability to rapidly scale up and down is required.
2. **A stable and highly usable analytics software platform**: Teams in XPRIZE competitions will need to be able to not just access data but analyze it. It should be familiar to many users and extendable.
3. **Isolated and secure analytics software platform**: Since awards in XPRIZE competitions can be multi-millions of dollars, teams and their work need to be effectively isolated from each. In addition, since we are providing the infrastructure for teams to run arbitrary code, we need to make sure that what we provide is not abused--not used as a base for attacks or noncompetition-related work such as crypto-mining.
4. **A scalable analytics compute platform**: While the common goal is to democratize data access and the ability to analyze that data, should a team wish to purchase more capacity, the platform should enable that.
5. **Control of data**: Contest sponsors and others want to be able to protect the data they provide and know who accesses it. The data should remain within the analytics platform, with it being difficult and time consuming to copy it outside of the analytics platform. Access to data needs to be recorded and attributable to a team.
6. **Manageability**: The common infrastructure needs to be maintainable by a relatively small staff.
7. **Reasonably fast implementation**: The infrastructure needs to be scaled up in a reasonably fast amount of time in order to make a difference in the pandemic.
8. **Costs**: Costs should be minimized when possible. We will refer to these requirements as we make design decisions. With more detailed requirements, we enumerate what decisions need to be made. The following components and processes need to be implemented in order to make the Data Collaborative viable:  
1. User Analytics Software Platform
2. Team Isolation
3. Naming Design and Infrastructure
4. User Authentication
5. Protecting Data in Transit
6. Logging and Monitoring
7. Team Infrastructure Instantiation Process  
We document the design options and final choices for each item above in the next section.