esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Passive surface wave imaging based on noise cross-correlation has been a research hotspot in recent years. However, because randomness of noise is difficult to achieve in reality, prominent noise sources will inevitably affect the dispersion measurement. Additionally, in order to recover high-fidelity surface waves, the time series input during cross-correlation calculation is usually very long, which greatly limits the efficiency of passive surface wave imaging. With an automatic noise or signal removal algorithm based on synchrosqueezed continuous wavelet transform (SS-CWT), these problems can be alleviated. We applied this method to 1-h passive datasets acquired in Sichuan province, China; separated the prominent noise events in the raw field data, and enhanced the cross-correlation reconstructed surface waves, effectively improving the accuracy of the dispersion measurement. Then, using the conventional surface wave inversion method, the shear wave velocity profile of the underground structure in this area was obtained.  
passive seismic; noise separation; surface wave; dispersion curve Article
## 2 Theory
### Frequency Domain Representation  
In this section, an arbitrary time signal is considered, as illustrated in Figure 2. The Fourier transform \(\mathcal{F}\) of a real function shifted in time \(f(t-\Delta t)\) results in a complex, periodic function:  
\[\mathcal{F}(f(t-\Delta t))=F(\omega)\cdot e^{-i\omega\Delta t} \tag{1}\]  
where \(F(\omega)=\mathcal{F}(f(t))\), \(e\) denotes [PERSON]'s number, \(\omega\), and \(i^{2}=-1\). This relationship can be used to describe a wave measured by a sensor placed at a varying distance from the source [71] and is used in what follows to describe the scattering behavior of waves. As a comment, it should be mentioned that the Fourier transformation requires stationary signals.  
Figure 1: Dispersion curve for a 2 mm aluminum plate evaluated with the Dispersion Calculator [70]. In the plotted frequency range, the two fundamental modes, as well as the first higher-order anti-symmetric (cut-off frequency around 800 kHz) and symmetric (cut-off frequency around 1400 kHz) modes are visible.
## 2 Related Work
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref.** & **Year** & **Languages** & **Specific AM Knowledge That Is Represented in the Constructed Ontology** & **Process** \\ \hline
[31] & 2007 & DL & Design requirements, process plans, and rules that map requirements to plans & AM \\
[32] & 2008 & DL & Design requirements, process plans, and rules that map requirements to plans & AM \\
[33] & 2010 & DL & Design features and rules for selecting manufacturing variables & AM \\
[34] & 2014 & OWL & Knowledge in development for AM processes & LPBF \\
[35] & 2015 & OWL, SWRL & Most applicable concepts of AM relevant to process planning applications & AM \\
[36] & 2016 & OWL & Laser and thermal metamodels for LPBF & LPBF \\
[37] & 2016 & OWL & Information correlating material, design, and manufacturing operations & AM \\
[38] & 2016 & OWL & Model fidelity in LPBF & LPBF \\
[39] & 2017 & OWL, SWRL & Knowledge in design for AM & AM \\
[40] & 2018 & OWL & Design features, manufacturing features, and process parameters & AM \\
[41] & 2018 & OWL & Information about innovative uses of AM technologies & AM \\
[42] & 2018 & Knowledge in process planning for AM & AM \\
[43] & 2019 & OWL, SWRL & Knowledge in design for AM & AM \\
[44] & 2019 & OWL, SWRL & Data and knowledge in AM value chain & AM \\
[45] & 2019 & OWL & Knowledge in AM product lifecycle & AM \\
[46] & 2020 & — & Metamodels and planning rules in process planning for wire arc AM & WAAM \\
[47] & 2021 & OWL, SWRL & Knowledge for LPBF design rule construction & LPBF \\
[48] & 2021 & — & Causal relations between AM parameters and quality assurance requirements & LPBF \\
[49] & 2021 & OWL & Process parameters, physical models, thermal models, and build qualities & LPBF \\
[50] & 2021 & OWL & Information about the capabilities of LCM process, printers and materials & LCM \\
[51] & 2021 & OWL, SWRL & Knowledge for cost estimation in AM & AM \\
[52] & 2021 & OWL, SWRL & Knowledge for identifying and prioritising data analytics opportunities in AM & LPBF \\ \hline \hline \end{tabular}  
* **Notes:** LCM stands for lithography-based ceramic manufacturing; WAAM stands for wire arc additive manufacturing.  
\end{table} 
Table 2: Overview of existing ontologies for AM knowledge representation. ogy dedicated to knowledge representation in process planning for LPBF is presented in this paper.
## 3 Materials and Methods
### Analysis Methods
#### 3.2.3 Clustering Analysis (DBSCAN)
For the purposes of this study, a \"most affected user\" is formally defined as one who meets both of the following conditions: (i) an individual trip waiting time \(\geq\) 20 min (the 90 th percentile of the weighted distribution of wait times) and (ii) membership in a DBSCAN cluster whose mean waiting time exceeds the global average of 7.20 min with low intra-cluster variability. This combined criterion ensures that identified users experience delays that are both systematically long and statistically significant, rather than isolated outliers.
# Isocyanide Multicomponent Reactions on Solid Phase: State of the Art and Future Application
## 9 Non-Traditional Ugi Reaction
### Alternative Acid Input
#### 9.2.2 (Thio)cyanic Acids and Hydrogen Azide  
Replacing carboxylic acids with inorganic acids, such as HOCN, HSCN, or HN\({}_{3}\), leads to alternative nitrogenous heterocycles [53; 54].  
U-4 CC of Wang resin-bound isocyanide **37**[50], aldehyde, amine, and in situ generated HOCN **411** (from pyridine hydrochloride and KOCN), led to hydantoin 4-imides **412** (Scheme 69) [53]. Several amines and aldehydes were tested. While the reaction tolerated different types of amines, it was highly dependent on the nature of the aldehyde. Aliphatic aldehydes (branched and unbranched) worked for this reaction; in contrast, aromatic aldehydes did not afford the products. One year later, the chemistry was extended and also described in the solution phase [54].  
Reagents and conditions: (i) MeOH/CHC\({}_{3}\)/H\({}_{2}\)(5:5:1), rt, 24 h; (ii) 20% TFA/DCM, 20 min, then repetition for another 10 min. Eleven examples, yield 36-81%.  
**Scheme 69. Preparation of hydantoin 4-imides by U-4 CC.**This synthetic strategy was adopted for the preparation of 2-thiohydantoin 4-imides **418** (Scheme 70). Cyanic acid was replaced by thiocyanic acid **416** (prepared in situ), and aldehyde was replaced by ketone (aldehyde was reportedly unreactive). Disappointingly, the Ugi reaction with these components afforded very low yields of **418** (5% and 11%).  
[PERSON] et al. [54] investigated the reaction of polymer-supported isocyanide resin **37** (Scheme 71) [50], aldehyde, primary or secondary amine, and hydrogen azide **421** generated in situ. The Ugi reaction resulted in the formation of resin-bound tetrazoles **422**, which were cleaved with 20% TFA in DCM. Both primary and secondary amines were tolerated as well as _p_-bromoaniline. In contrast to the Ugi reaction producing hydantoin 4-imides **413** and **418** (Schemes 69 and 70), aromatic aldehydes were compatible, although the yields were mediocre.
## 2 What Is IoNT?
### Benefits of IoNT  
Having provided an overview of the IoNT, its architecture, and relevant applications, it is evident that the IoNT is currently served in almost all the domains that the IoT is served in. In each of these domains, depending on the context, the IoNT performed better (as opposed to the IoT), owing to their miniaturized nature. Thus, to summarize, the potential benefits of the IoNT are outlined as follows:  
* Improved accuracy and precision As nanoscale devices are so small, they can be used to measure and monitor at a much higher level of accuracy as opposed to larger devices. This can be especially useful in applications where accuracy is critical, especially in medical diagnosis and treatment [1; 4; 15];
* Increased sensitivity Nanodevices can be designed to be highly sensitive to a wide range of stimuli, such as changes in temperature, pressure, or chemical composition. This can enable them to detect and respond to changes in the environment that would be imperceptible to larger devices [2; 4];
* Greater energy efficiency As nanoscale devices are so small, they can operate on very low levels of power. This makes them ideal for applications wherein energy efficiency is important, such as in wearable devices and other portable electronics [7; 12];* Enhanced functionality  
Nanodevices can be designed to perform a wide range of functions, such as sensing, computing, and communication. This makes them very versatile and allows them to be used in a wide range of applications [1; 12];
* Miniaturization  
One of the key advantages of nanotechnology is that it allows for the creation of extremely miniaturized devices. This could lead to the development of wearable or implantable IoT devices that are less obtrusive and more convenient to use [13; 14];
* Improved durability  
Nanoscale components are often more resistant to damage and wear than their larger counterparts. This could lead to longer-lasting IoT devices that require less maintenance and repair in the long run [1; 15; 16].  
Overall, the IoNT has the potential to revolutionize many different industries and areas of society by enabling the creation of a vast network of interconnected devices and sensors that operate on a nanoscale. Thus, increasing awareness regarding the various benefits of the IoNT and the development of nanomachines will significantly boost the IoNT market in the long run. As of now, it has attracted significant interest from researchers and the industry. However, there are also challenges associated with the development and deployment of nanodevices, as the use of technology always comes at a cost.
## 4 Analysis on Air Distance, Road Distance and Road Time
### Air Distance to Road Distance and Road Time Multiplier  
Although imprecise, a simple but rough multiplier for transforming air distances into a road distances or road times can be useful. For instance, when solving a vehicle routing problem, the cost of traveling between nodes that are very far apart from each other does not have to be exact. Many of the exceedingly long links are rarely considered or highlighted in problem-solving processes. When employing map APIs, precisely acquiring every single road distance or road time can be costly. It can be costly not only in terms of purchasing costs, but also response times. For this reason, it is attractive to estimate road distances or road times using rough multipliers for those links that exceed a pre-set threshold.  
For the general conversion ratio, Figure 11 can provide some guidelines. It is noted that the \(y\)-axis in Figure 11a ranges from 1.0 to 3.0. In Figure 11b, it ranges from 0.0 to 0.5. This is because Figure 11a indicates meter-to-meter conversion ratios, while Figure 11b  
Figure 10: Cost difference between outbound and return trips: (**a**) Road distance; (**b**) road time.  
indicates meter-to-second conversion ratios. Road distances are always larger than, or sometimes equal to, the air distances, which causes the ratio to exceed 1.0. However, distance and time are in different scales, and a meter-to-second conversion ratio should only be larger than zero. The conversion ratio decreases with increasing distance ranges. The differences derive from the road network structure, as well as from the speed of the vehicle. In general, average vehicle speed in a long haul is faster than a short one. Arterial roads, trunk lines or expressways are more likely to be used in a long haul. Within 10 km of air distance, the average rough multipliers for road distance and road time are calculated as 1.57 and 0.14, respectively.
## 3 The Receiving Process of the Composite Signal
### The Receiving Independence of a BOC Signal and BPSK Signal  
The code offset and Doppler frequency of a BOC signal are obtained through correlation between the received signal and the locally generated BOC signal. The correlation function ignoring the noise is as shown in Equation (5).  
\[\begin{array}{l}R(t,t+\tau)=E\big{(}s_{r}(t)s^{\prime}_{\text{BOC}}(t+\tau) \big{)}=E\big{(}(s_{\text{BOC}}(t)+s_{\text{BPSK}}(t))s^{\prime}_{\text{BOC}}( t+\tau)\big{)}\\ =E\big{(}s_{\text{BOC}}(t)s^{\prime}_{\text{BOC}}(t+\tau)+s_{\text{BPSK}}(t)s^{ \prime}_{\text{BOC}}(t+\tau)\big{)}=\alpha R_{\text{BOC}}(\tau)+\beta R_{ \text{BOC},\text{BPSK}}(\tau)\end{array} \tag{5}\]  
Here, \(s_{r}(t)\) represents the receiving signal in the IF frequency, and \(s^{\prime}_{\text{BOC}}(t)\) is the locally generated BOC signal. \(R_{\text{BOC}}(\tau)\) is the autocorrelation function of the BOC signal. \(R_{\text{BPSK}}(\tau)\) is the cross-correlation function of the BOC signal and BPSK signal. \(R_{\text{BOC}}(\tau)\) is 30 dB higher than \(R_{\text{BOC},\text{BPSK}}(\tau)\) when \(\alpha=\beta\). Therefore, the latter has little effect on the reception of the BOC signal.  
The correlation function of the BOC signal and BPSK signal within a chip can be expressed as Equation (6):  
\[\int_{0}^{T_{s}}s_{\text{BPSK}}(t)s_{\text{BOC}}(t-\tau)dt=\int_{0}^{T_{s}}b( t)d(t-\tau)c(t-\tau)sign[\cos(2\pi f_{s}(t-\tau))]dt \tag{6}\]  
where \(T_{s}=\frac{1}{f_{s}}\) is the width of a chip. Assuming that \(b(t)\), \(d(t-\tau)\) and \(c(t-\tau)\) do not change signs during \(T_{s}\), the left side of Equation (6) is equal to zero, which shows that \(s_{\text{BOC}}(t)\) and \(s_{\text{BPSK}}(t)\) are orthogonal to each other. Therefore, after ignoring the influence of the Doppler frequency, we integrate the signal in a chip period, as in Equation (7).  
\[\begin{array}{l}\int_{0}^{T_{s}}s_{r}(t)dt=\int_{0}^{T_{s}}\alpha d(t-\tau) c(t-\tau)sign[\cos(2\pi f_{s}(t-\tau))]dt\\ \hskip 14.226378 pt+\int_{0}^{T_{s}}\beta b(t-\tau)dt\end{array} \tag{7}\]  
Figure 3: The reception of composite signal.  
The first term on the right side of Equation (7) is equal to zero under ideal conditions. Thus, the demodulation of the BPSK signal is not affected by the BOC signal.
# Comparative Analysis of Volatile Compounds in Flowers of Different _Actinidia_ Species
## 2 Results and Discussion
### Differences in the Composition of Volatile Compounds in Flowers of Different Actinidia Species
kolomikta_, yet in a minor amount (3.5%) (Figure 2b,c). Generally, only few volatiles of _A. polygamma_ were found in other species. Along with the already mentioned linalool and beta-citronellol, 3-hexen-1-ol, (Z) (4.5%) and 1- hexanol (8.6%) were identified. Both were also found in two male _A. arguta_ genotypes ('Rot', 'Rubi'). Additionally, 1-hexanol was present in 'Nostino' and 'Joker'. It is worth noting that no common volatile compounds for _A. chinensis_ and _A. polygamma_ flowers were identified. In _A. kolomikta_, monoterene \(\beta\)-myrcene was the compound representing the highest relative content (\(\sim\)75% in male and \(\sim\)67% in female) (Table 1, Figure 2c). Myrcen, an isomer of ocimene, is said to be unstable in the air and tends to polimerate easily [26] (p. 280). Nerol and geraniol (4.0% and 3.4%, respectively), identified in male _A. kolomikta_, turned out to be characteristic of this species alone and were not traced in other actinidia species. Another characteristic feature of _A. kolomikta_ compounds were citral isomers (geranial and neral), responsible for the lemon note in the aroma. Neral accounted for less than 1%, yet its aroma is very strong. Certain volatiles were found not only in _A. kolomikta_, but also in other actinidia species. Citronellol was one of them, mentioned above, while the others were terpen ocimene \(\alpha\) and \(\beta\), found in _A. kolomikta_ and _A. arguta_. Beta-ocimene was also identified in _A. chinensis_ var. _deliciosa_'s flowers. Other substances found in _A. kolomikta_ and in a few _A. arguta_ genotypes were alpha-Farnesene, 1,4-Hexadiene, 5-methyl-3-(1-methylethylidene) and a group of esters, with each of these accounting for less than 1%. Alpha-Farnesene was also found in _A. chinensis_ var. _deliciosa_. Summing up the above, it appears that the number of volatile compounds and their composition differ in the particular actinidia species. Moreover, every species seems to have one or few dominating compounds affecting their flower aroma.
# A Discrete Hidden Markov Model for SMS Spam Detection
## 3 The Discrete HMM for SMS Spam Detection
### The Workflow of the Discrete HMM for SMS Spam Detection  
#### 3.7.1 Data Preparation and HMM Learning  
Step 1: Training an SMS dataset by first splitting or segmenting it into word sequences to keep their original order. Then, stop words are removed from the sequence and rest meaningful words form the observation sequence.  
Step 2: Observation state probability distributions in ham and spam datasets are statistically analyzed and obtained.  
Step 3: The compromised word label sequences are generated based on the labeled training SMS messages.  
Figure 4: Baum–Welch algorithm.  
Step 4: The discrete HMM model is initialized as \(\lambda_{0}=\left(\pi_{0},\ A_{0},\ B_{0}\right)\). The initial parameters are given in Section 3.5.  
Step 5: The hidden state sequence and the observation sequence feed the discrete HMM. The discrete HMM is optimized by the Baum-Welch algorithm until convergence.  
The training process workflow is shown in Figure 5.
# A Blockchain Protocol for Authenticating Space Communications between Satellites Constellations
## 4 Proof of Space Transactions Protocol
Algorithm 2 specifies the authentication methodology of the proposed PoST protocol to verify SDTs between two satellites within two different constellations. Figure 3: Proof of Space Transactions (PoST) protocol: Authenticating SDTs between two satellites within different satellite constellations. ```
Input: Space Zones \(Z\) = (Z\({}_{1}\), Z\({}_{2}\), Z\({}_{3}\),..., Zn) Input: Space Digital Tokens: SDT = (SDT1, SDT2, SDT3, ----SDTn). Output: Blockchain + new Block Procedure: PoT for authenticating two satellites within different constellations For all transactions in SDT Create (SDT\({}_{i}\), Z\({}_{1}\),S\({}_{1}\), Z\({}_{2}\).S\({}_{1}\)). Z\({}_{2}\).S\({}_{1}\) = Ask (Z\({}_{1,2}\).Crypto evidence). Z\({}_{2}\).S\({}_{1}\) = Ask (SDT1.Crypto evidence). Z\({}_{1}\).S\({}_{1}\) = Send (Z\({}_{1,2}\).Crypto evidence). Z\({}_{1}\).S\({}_{1}\) = Send (SDT\({}_{i,Crypto evidence}\)). Verify (Z\({}_{1,2}\).Crypto evidence, Z\({}_{3}\), Z\({}_{4}\),..., Zn) IF (Z\({}_{1,2}\).Crypto evidence is valid) Verify (SDT\({}_{i}\), Z\({}_{2}\).S\({}_{3}\),...,..., Zn) IF (SDT\({}_{i}\) is valid) Z\({}_{2}\).S\({}_{1}\) = Ask (last block's Nonce code). Z\({}_{1}\).S\({}_{1}\) = Send (last block's Nonce code) Connect (Z\({}_{1}\).S\({}_{1}\), Z\({}_{2}\).S\({}_{1}\)) New block = Add (SDTi) Blockchain = Blockchain + New block Else Reject (SDT\({}_{i}\)) Else Reject (Z\({}_{1,2}\).Crypto evidence) End Procedure
```  
**Algorithm 2**PoST for authenticating two satellites within **different** constellations
## 2 Related Work
[19] proposed a system for pose detection and tracking of multiple insects and animals, which they used to monitor the traffic of honeybees and mice. The system uses a deep neural network to detect and associate detected body parts into whole insects or animals. The network predicts a set of 2D confidence maps of present body parts and a set of vectors of part affinity fields that encode associations detected among the parts. Greedy inference is subsequently used to select the most likely predictions for the parts and compiles the parts into larger insects or animals. The system uses trajectory tracking to distinguish entering and leaving bees, which works reliably under smaller bee traffic levels. The dataset used by the researchers to evaluate their system consists of 100 fully annotated frames, where each frame contains 6-14 honeybees. Since the dataset does not appear to be public, the experimental results cannot be independently replicated. [PERSON] et al. [20] proposed a system for pollen bearing honeybee detection in surveillance videos obtained at the entrance of a hive. The system's hardware includes a specially designed wooden box with a raspberry pi camera module inside. The box is mounted on the front side of a standard hive above the hive entrance. There is a glass plate placed on the bottom side of the box, 2 cm above the flight board, which forces the bees entering or leaving the hive to crawl a distance of \(\approx\)11 cm. Consequently, the bees in the field of view of the camera cannot fly. The training dataset contains 50 images of pollen bearing honeybees and 50 images of honeybees without pollen. The test dataset consists of 354 honeybee images. Since neither dataset appears to be public, the experimental results cannot be independently replicated. The DPIV-based bee motion estimation algorithm presented in this article improves our previously proposed two-tier method of bee motion counting based on motion detection and motion region classification [6] in three respects: (1) it can be used to measure not only omnidirectional but also directional honeybee traffic; (2) it does not require extensive training of motion region classifiers (e.g., deep neural networks); and (3) it provides insect-independent motion measurement in that it does not require training insect-specific recognition models. Our evaluation results are based on four 30-s videos captured by deployed BeePi monitors. Each video consists of 744 frames. Each frame is manually labeled by a human observer for the number of full bee motions. Thus, the evaluation dataset for this investigation consists of 2976 manually labeled video frames. To ensure the replicability of our findings, we have made the videos and frame-by-frame bee motion counts publicly available (see the Supplementary Materials).
## 4 Results
### Tests with Seven Classification Classes
#### 4.2.3 Mixed-Signals Approach  
A network trained using both the current and the flux signal is presented in this section. The number of images used was twice the number of images used in the single-signal analysis. The maximum number of epochs was set to 24 while the weight and bias learning factor was increased to 25. The validation accuracy reached 89% and convergence occurred in about 18 epochs, as visible in Figure 13. The time for the computation was about 13 min.  
Figure 11: Training and validation accuracy progress for the seven classes of current signal classification: training in light blue and in blue (smoothed), validation in black dashed.  
Figure 12: Confusion matrix (test set) for the seven classes of current signal classification.  
The same test was repeated with the decimated data. With these data, the validation accuracy was reduced by about 13% (it reached 76%), with a rapid convergence of about 10 epochs. The confusion matrices of both the cases of non-decimated and decimated data are presented in Figure 14.  
As can be seen from Figure 14, the only misclassified class for the non-decimated data was the \"BF Step 1\", with 50% misclassified images. The total test accuracy was 95%. For the decimated data, many classes were misclassified, for example \"Bas\", \"BF Step 1\", \"H12\", and \"H45\". The total test accuracy for the decimated data was 81.2%. This could be interpreted with the fact that the network also sees patterns at a high  
Figure 14: Confusion matrix (test set) for the mixed-signals approach of (**a**) non-decimated data and (**b**) decimated data.  
Figure 13: Training and validation accuracy progress for the mixed signal: training in light blue and in blue (smoothed), validation in black dashed.  
frequency in the scalograms, so it could reach higher validation and test accuracies for the non-decimated data.