esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Problem Space for DL Applications
### Application Scenarios with DL
#### 4.3.1 Precision Augmentation  
Medical imaging is one of the most prominent areas where DL, especially CNNs, has been extensively applied. CNNs have shown exceptional performance in image classification tasks, for example, identifying tumors in MRI scans or detecting fractures in X-rays [113; 190]. A notable study applied a deep CNN to classify breast cancer images, achieving higher accuracy than traditional methods. The study highlights the critical applications of DL, particularly in smart healthcare systems, providing examples like medical imaging, diagnostics, and patient monitoring systems [12]. It identifies and discusses emerging research trends, including federated learning, explainable AI, and the integration of DL with edge computing. Existing applications are discussed below.  
* Multiple sclerosis: Latent patterns of multiple sclerosis (MS) lesions were identified from initial images using a CNN methodology [191]. Another study [192] proposed employing a DBN and random forest (RF) in conjunction with myelin and T1-weighted images to recognize MS pathology in brain tissue, which showed as normal on an MRI. They utilized a four-layer DBN to learn latent feature representations from 3D image patches of normal-appearing white matter (NAWM) and normal-appearing gray matter (NAGM) in this approach [192].
* Cancer detection: In medical diagnostics, deep learning has shown significant potential to revolutionize healthcare. One study explored a computer-aided diagnosis system that utilizes deep learning algorithms for detecting, segmenting, and classifying masses in mammograms [162]. It explored several deep learning methods for mass detection, including CNN, Deep Belief Network (DBN), and Conditional Random Fields (CRFs). Previous studies [193; 194; 195] have shown that classical learning techniques (SVM, RF, decision tree) perform well in terms of predicting breast cancer survival.
* Heart disease detection: Some studies have demonstrated that CNNs can detect heart failure and arrhythmia from image collections obtained via IoT devices analyzing ECG data [170]. They facilitated faster training and testing processes by categorizing ECG data into two distinct heartbeat signals. Their simulation evaluated the CNN's efficacy with ECG image datasets, utilizing cloud resources for swift and lag-free classification. The results showed an improved accuracy rate, surpassing existing ECG image classification algorithms and highlighting CNN's superior performance in identifying instances of heart conditions [196].
* Alzheimer's disease detection: Some studies [197] proposed a CNN-driven method for conducting early-stage Alzheimer's disease (AD) diagnosis using structural MRI [198]. A study [199] developed a CNN-based procedure to diagnose AD from structural MRI, demonstrating superior performance compared to SVM in their study. Ref. [200] recommended an RNN-based model to forecast the progression from mild cognitive impairment (MCI) to AD, extracting temporal variables from multimodal data.
## 4 Results and Discussions
This method is based on the observation that genuine AE signals with high amplitude should have long duration and vice versa [11]. Therefore, false signals in this method are categorized by long duration with low amplitude and short duration with high amplitude. The data is presented in terms of duration versus amplitude distribution. Signals which did not comply with the characteristics of genuine signals were deleted based on visual observation of the waveforms. The rejection limits for the internal and external sensors are presented in Table 1. In addition, suspicious signals were removed by inspection of waveforms and the chamber activity timetable provided by the University of Tennessee, Knoxville. The filtered AE data from 50 to 195 days after casting for all sensors is presented in Figure 5. The figure illustrates the amplitude and cumulative signal strength (CSS) in terms of specimen age. The time window of 50 to 195 days was selected because the highest strain change rate occurred during this period. The gap in the data from 65 to 100 days is missing data due to a difficulty with the data acquisition system caused by an unexpected energy surge. The amount of data in the control specimen is much lower than the data for the reactive specimens. The difference between AE data for the control and reactive specimens is also observable from plots of cumulative signal strength versus time (Figure 5). Therefore, the relatively high AE activity for the reactive specimens can likely be associated with expansion caused by the alkali-silica reaction.
## 5 System Engineering Analyses
### Data Budget  
A significant and noteworthy amount of data are produced by the VHF receiver-based payload that is utilized to receive information from the AIS and the autonomous ground station connected to the fire detection sensor network. For the AIS application, a message of 256 bits is transferred at a rate of 9600 bits per second utilizing a binary Gaussian minimum shift keying (GSMK) modulation during each of the brief 26.67 millisecond slots that the AIS uses to broadcast information. The ships can transmit their message bursts in any of the 2250 slots that make up each frame.  
For fire detection application, the data budget is dominated by the measurement of Global Positioning System (GPS) coordinate (24 bits, which are enough for good accuracy) to locate where the fire is propagating with some measurements of smoke, temperature, wind speed, and direction (10 bits of resolution for each measurement). Given these data specifications and numbers, the maximum data rate requirements are close to 64 bits for one autonomous ground station.  
According to the analysis and platform specifications, it is feasible to access the ground after five or six orbital rotations, and OBC sends requests for telemetry data every 5 s. These parameters should be considered when planning the estimation of the data budget as shown in Table 7.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**AIS Data Budget** & \\ \hline
**Maximum payload frame size** & 72 Kbyte \\ \hline
**Data rate download of communication radio** & 9600 bps \\ \hline
**Time to download 1 payload frame** & 60 s \\ \hline
**Fire Detection data budget** & \\ \hline
**Maximum payload frame size (from** & 800 byte \\
**100 nodes)** & 9600 bps \\ \hline
**Data rate download of communication radio** & 9600 bps \\ \hline
**Time to download 1 payload frame** & 666.67 ms \\ \hline \hline \end{tabular}
\end{table}
Table 7: Data budget of the proposed nanosatellite mission.  
Figure 18: ADCS block diagram.  
As mentioned in Section 5.2, the size of the OBC and transceivers technology was chosen to support data sizes and data rates up to 64 MByte and 9600 bps, respectively, which allows the mission to have a large safety margin in the data budget. This data budget estimate is adaptable to the mission strategy and open to various revisions.
## 2 The Forecasting Model
### Ensemble Empirical Mode Decomposition
#### 2.1.2 Ensemble Empirical Mode Decomposition (EEMD)  
Modal aliasing refers to the condition that one intrinsic mode function contains the characteristic time scale with great difference, or the similar characteristic time scales are distributed in different intrinsic mode functions, which causes two adjacent intrinsic mode functions' waveforms to be mixed and unrecognizable. Empirical Mode Decomposition may introduce the modal aliasing problem. To solve this problem, EEMD was proposed by [PERSON] and [PERSON] [39]. Based on the traditional EMD, the Gaussian white noise is introduced to eliminate the intermittent phenomenon in the original signal to solve the modal aliasing problem by eliminating the intermittence of the original signal usingthe statistical characteristics of the uniform distribution of white noise. The principle of EEMD is as follows: when additional white noise is averagely distributed in time frequency space, the time frequency space is composed of different scales segmented by filter banks.  
The detailed steps of EEMD are as follows:  
1. Add random Gaussian white noise sequence to the signal \[X_{m}(t)=X(t)+k\cdot n_{m}(t),\] (1) where \(k\) is the amplitude factor of added white noise; \(n_{m}(t)\) is the white noise sequence.
2. Decompose the signal into a group of intrinsic mode functions using Empirical Mode Decomposition with white noise;
3. Add a different white noise sequence each time, and then, repeat the steps of (1) and (2);
4. Calculate the mean value of decomposed IMFs, and take the average value of each IMF obtained by decomposition as the final result. \[\overline{c}_{i}=\frac{1}{N}\sum_{m=1}^{N}c_{i,m},\] (2) where \(N\) is the integration number using EMD; \(c_{i,m}\) is the \(i\)th IMF in the \(m\)th decomposition.  
The process of EEMD is shown in Figure 1.
## 5 Results  
### Pricing Methods and Experimental Setup  
In this section, the results of applying the three pricing methods--BOPM, LSM, and RL--to the following cases are presented as follows:  
* The underlying price is modeled using GBM (Section 5.1).
* The underlying price is modeled using GARCH (Section 5.2).
* The underlying price is modeled using EGARCH (Section 5.3).  
The parameters for each pricing model, including their calibration (for GARCH and EGARCH models), are provided within each respective section.  
It is worth mentioning that the Binomial Option Pricing Model (BOPM) is appropriate for the Geometric Brownian Motion (GBM) price model because GBM assumes constant volatility and a log-normal price distribution, fitting well with BOPM's discrete time steps and constant volatility assumptions. However, stochastic volatility models like GARCH and EGARCH feature time-varying volatility, which complicates the BOPM framework to be applicable. Therefore, BOPM is not suitable for these models, and only the RL and LSM methods have been applied to GARCH and EGARCH price models.
# Wind-Wave-Current Coupled Modeling of the Effect of Artificial Island on the Coastal Environment
## 2 Materials and Methods
### Data Source and Model Setup
#### 2.2.4 Suspended Sediment and Bed Sediment Conditions  
The suspended sediment concentration affected the regional sediment movement trend and had a great influence on regional erosion and sedimentation. The data used in this study were based on the suspended sediment concentration in the middle and lower surface waters measured around the artificial island in 2020 and 2021. In addition, different types of seabed sediment also affected regional flow, suspended sand, and other indices. In this simulation, bed sand conditions were also taken into account. The data were based on bed sand and quaternary bed sand thickness surveys conducted using three shallow holes (depth \(<\) 60 cm) and 1 deep hole (about 20 m deep). Then, the suspended sediment was  
Figure 4: Changes in water level and suspended sediment concentration at the Wanquan River Jiaji Station (2020).  
Figure 5: Tidal level and harmonic analysis recorded at Gangbei Town tidal level Station ((**a**) tidal level; (**b**) harmonic analysis).  
divided into 5 groups according to particle size and interpolated into the calculation grid by inverse distance to form the initial distribution conditions of the suspended sediment concentration, bed sand gradation, and quaternary bed sand thickness [21].
# Performance Evaluation of IEEE 802.11 ah Networks With High-Throughput Bidirectional Traffic
## 4 Performance Evaluation and Discussion  
To evaluate TIM and RAW performance with UL/DL traffic, we analyzed two scenarios: the first considers reliable and high-throughput traffic of IP camera streaming applications (cf., Section 4.2), whereas the second assesses the scalability of a reliable sensor network that sends measurements at specific time intervals (cf., Section 4.3). Exact simulation setups, obtained results, and their interpretations are presented in the corresponding sections. The ratio between uplink and downlink traffic amounts to 92/8% in the first scenario and 67/33% in the second scenario (in bytes). The general simulation setup is presented in Section 4.1, whereas Section 4.4 presents the discussion of the theoretical limits regarding throughput and latency in the simulated network (cf., Section 4.4). Finally, Section 4.5 discusses TCP behavior when the link layers introduce long delays.  
Figure 4: RAW slot assignment procedure when (**a**) RAW is not restricted to paged stations; (**b**) RAW is restricted to paged stations.
## 7 The Effect of the Conditional Probabilities in this Method
### Comparative Analysis  
In this section, we applied several existing AOs to the information provided by the decisionmakers in Tables 5-8 to evaluate the validity and feasibility of our proposed methodologies. We compared our approach with IFWA [19], IFWG [18], IvIFAAWA [31], IvIFAAWG [45], HFAAW [25], IFDWA [46], FDWG [46], IFRAAWA [24], IFRAAOW [24], IFRAAHA [24], IHPPA [14], and IHPG [14] methods. The results of existing AOs operators are shown in Table 12.  
1. The analysis of Table 12 suggests that our proposed approach is more general than existing models.
2. Our proposed model also provides more flexible acceptance results compared to [PERSON]'s IFPWA [14].  
Figure 3: Effects of conditional probability on the participants.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Environment**} & \multicolumn{4}{c}{**Classification**} \\ \cline{3-5}  & & **Accepted** & **Boundary** & **Rejected** \\ \hline
**IFWA**[19] & IFNs & X & X & x \\
**IFWG**[18] & **IFNs** & X & X & x \\
**IvIFAAWA**[31] & IvIFNs & X & X & x \\
**IVIFAAWG**[45] & **IVIFNs** & X & X & x \\
**HFAAW**[25] & **HFNs** & X & X & x \\
**IFDWA**[46] & **IFNs** & X & X & x \\
**IFDWG**[46] & **IFNs** & X & X & x \\
**IFRAAWA**[24] & **IFRNs** & X & X & x \\
**IFRAAOWA**[24] & **IFRNs** & X & x & x \\
**IFERAHAHA**[24] & **IRRNS** & **X** & x & x \\
**HIFWA**[14] & **IHFNs** & \(\{A_{1},A_{2}\}\) & \(\{A_{3},A_{4}\}\) & \(\{\varnothing\}\) \\
**HIFWG**[14] & **IHFNs** & \(\{A_{1},A_{2},A_{3}\}\) & \(\{A_{3},A_{4}\}\) & \(\{\varnothing\}\) \\
**IHFAWA (Proposed)** & **IHFNs** & \(\{A_{1},A_{2},A_{3}\}\) & \(\{A_{4}\}\) & \(\{\varnothing\}\) \\ \hline \hline \end{tabular}
\end{table}
Table 12: Comparison of proposed and existing techniques.  
Additionally, we observed that IFWA [19], IFWG [18], HFAAW [25], IFDWA [46], IFDWG [46], IFRAWA [24], IFRAAOW [24], and IFRAHAHA [24] effectively handle intuitionistic fuzzy and hesitant fuzzy data. However, there are certain situations where these approaches may not be suitable. This demonstrates the dependability and effectiveness of the proposed model for decisionmakers.  
Table 12 also highlights that [PERSON] et al. developed IvIFAAWA [31] and IvIFAAWG [45] operators for interval valued intuitionistic fuzzy information, but comparison studies have shown that these approaches are not effective for intuitionistic hesitant fuzzy data. Therefore, our proposed approach provides a solution to address more complex and vague situations.  
Figure 4 provides a visual representation of the comparison studies presented in Table 12.
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 4 Proposed SLAM Classification Based on UWSNs
### Traditional SLAM Classifications
#### 4.1.1 Sensor Modalities  
Underwater SLAM techniques are often categorized based on the types of sensors deployed. Acoustic sensors, such as sonar, Doppler velocity logs (DVL), and acoustic beacons, are widely used due to their effectiveness in environments where light penetration is limited [43]. Optical sensors, including monocular and stereo cameras, facilitate VSLAM but face challenges due to light scattering and absorption in water [28]. Laser sensors, while providing high-resolution measurements, are constrained by water turbidity and absorption properties [44]. To overcome the limitations of individual sensors, sensor fusion approaches combine multiple sensor types, leveraging the strengths of each modality to improve overall SLAM performance [45].
## 3 Data Protection Risks of ML Systems and Relations with GDPR Provisions
### Model Inversion Attacks  
There are also cases where the feature vectors are not stored in the ML models themselves, e.g., in the case of neural networks. In such cases, an adversary can try to construct feature vectors that mimic the actual feature vectors used to construct the ML model in question using the response received from the ML model. This type of attack is known as a model inversion attack. In other words, the adversary tries to infer personal information about a data subject by exploiting the outputs of an ML model. Such attacks could use, e.g., support vector machines (SVMs) and confidence values (e.g., probabilities or the decision value of the SVM) along with a classification result to recover feature vectors that were used to build the model [44]. These attacks aim to produce an average value that represents a particular class of data, so it constitutes a privacy risk, for example, when a particular class of data is associated with a particular individual, as in facial recognition applications [45]. It is worth noting that an inversion attack can be used together with a reconstruction attack, according to the researchers [PERSON] et al., in the case of face recognition [44], where the input sample is identical to the feature vector. Such attacks are mainly related to the security requirements stemming from the GDPR since the feature vectors need to be protected.
## 2 Pentacyclic Triterpenoids (PCTTs)
### Ursanes  
Ursanes differ structurally from oleananes only by the position of methyl group 29, which is attached to carbon 19, in a \(\beta\) position. In the structure of ursanes, methyl group 30 is found in \(\alpha\) position. Rings A/B, B/C and C/D have _trans_ configuration, while rings D/E have _cis_ configuration, like oleananes. The most common ursanes also present a double bond between carbons 12 and 13 [19]. There were 88 ursanes (**U**) isolated from Celastracaee species, triterpenoids **U1** to **U88**, were reported (Figure 10).  
Figure 8: Structures of lupane-type pentacyclic triterpenoids isolated from Celastracae species (2001–2021). (**a**) Compounds **L1**, **L2**, **L4**, **L6**, **L7**, **L9**, **L10**, **L13**, **L15**, **L17**, **L19**, **L22**, **L28**, **L30**, **L33**, **L39**, **L40**, **L44**, **L49**-**L51**, **L56**, **L61**, **L67**, **L74**-**L77**, **L83**, **L84**, **L87** and **L88**. (**b**) Compounds **L3**, **L5**, **L8**, **L11**, **L12**, **L14**, **L16**, **L18**, **L20**, **L21**, **L23**–**27**, **L31**, **L32**, **L34**–**L38**, **L41**–**L43**, **L45**–**L48**, **L52**–**L54**, **L57**–**L60**, **L62**–**L66**, **L68**–**L73**, **L78**–**L82** and **L85**. (**c**) Compounds **L29**, **L55**, **L86** and **L89**.  
Figure 9: _Cont._  
Figure 9: Structures of oleanane-type pentacyclic triterpenenoids isolated from Celastraceae species (2001–2021). (**a**) Compounds **O1**–**O10**, **O12**–**O15**, **O17**, **O25**–**O27**, **O29**, **O30**, **O33**, **O35**, **O36**, **O38**, **O39**, **O43**, **O46**, **O48**, **O50**, **O55**, **O57**–**O59**, **O63**, **O64**, **O66**, **O68**, **O70**, **O71**, **O73**, **O74**, **O76**, **O79**–**O82**, **O84**–**O87** and **O89**. (**b**) Compounds **O11**, **O16**, **O18**–**O20**, **O22**, **O23**, **O28**, **O31**, **O32**, **O34**, **O37**, **O40**–**O42**, **O44**, **O47**, **O49**, **O51**, **O52**, **O54**, **O56**, **O60**–**O62**, **O65**, **O67**, **O69**, **O83**, **O88** and **O90**–**O100**. (**c**) Compounds **O21**, **O24**, **O45**, **O53**, **O72**, **O75**, **O77**, **O78**, **O101**, and **O102**.  
Figure 10: _Cont._  
\({}^{13}\)C-NMR spectrum of ursanes differ from the spectrum of oleananes by the chemical shift signals of the olefinic carbon atoms, which are observed around \(\delta_{\rm C}\) 124 (C-12) and \(\delta_{\rm C}\) 139 ppm (C-13). In ursanes, the proximity of methyl group 29 with the double bond promotes a steric effect on these carbons, causing a shielding effect on C-13 and deshielding on C-12 [27; 34]. This effect can be observed by comparing the \({}^{13}\)C-NMR data of **O3** and **U6**, for example. Additionally, the number of quaternary carbon signals also represents a distinction parameter between these two skeletons, since 6 signals are observed in the oleananes spectrum and 5 signals in the ursanes spectrum.
# Design and Analysis of Systematic Batched Network Codes
## 3 Systematic Outer Codes
### General Approach to Systematic Outer Codes
Denote by \(\text{ENC}_{n_{s}}^{*}\) the encoder that generates only the \(M_{i}\) non-redundant coded packets in the \(i\)th batch, where \(i=1,\ldots,n_{s}\). For any \(K\) packets \(\mathbf{B}\), \(\text{DEC}_{n_{s}}^{*}(\text{ENC}_{n_{s}}^{*}(\mathbf{B}))=\text{DEC}_{n_{s}} \big{(}\text{ENC}_{n_{s}}(\mathbf{B})\big{)}=\mathbf{B}\). Note that \(\text{ENC}_{K}\) and \(\text{DEC}_{K}\) can be expressed as square matrices that are inverse to each other, and hence their order can be interchanged without changing the output, i.e., \(\text{ENC}_{n_{s}}^{*}\big{(}\text{DEC}_{n_{s}}^{*}(\mathbf{B})\big{)}=\text{ ENC}_{n_{s}}^{*}\big{(}\tilde{\mathbf{B}}\big{)}=\mathbf{B}\). The computation cost of the third step of encoding can be simplified as not all the packets in the systematic batches need to be regenerated. Let \((\mathbf{X}_{1},\ldots,\mathbf{X}_{n_{s}})=\text{ENC}_{n_{s}}(\tilde{\mathbf{ B}})\). We have \(\hat{\mathbf{X}}_{i}\subset\mathbf{X}_{i}\) and \(\mathbf{X}_{i}\setminus\hat{\mathbf{X}}_{i}\) includes only the redundant packets for \(\text{DEC}_{n_{s}}\) in the \(i\)th batch. As \(\hat{\mathbf{X}}_{i}\) is a subset of the message packets, it is not necessary to generate it again. Denote by \(\text{ENC}_{n}^{-}\) the encoder of \(\tilde{\mathbf{B}}\) that generates only \(\mathbf{X}_{i}\setminus\hat{\mathbf{X}}_{i}\) for \(i=1,\ldots,n\). Let \((\hat{\mathbf{X}}_{1},\ldots,\hat{\mathbf{X}}_{n})=\text{ENC}_{n}^{-}(\tilde{ \mathbf{B}})\). Then, the \(n\) systematic batches are \(\mathbf{X}_{i}=\hat{\mathbf{X}}_{i}\cup\hat{\mathbf{X}}_{i}\). The batches generated by the above systematic encoding process will be further transmitted through a network and processed by the inner code. Let \(\mathbf{Y}^{\prime}\) be the codedpackets received by the destination node. To decode, first, DEC is applied on \(\mathbf{Y}^{\prime}\) to output \(\mathbf{\tilde{B}}\). Then, we apply \(\text{ENC}_{n_{s}}\) on \(\mathbf{\tilde{B}}\) to recover \(\mathbf{B}\). See Figure 2c for an illustration of the decoding process.