esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Device Design
### Coupling Rotation and Transmission Mode  
The motion mode and structural schematic of the coupled structure rotor are shown in Figure 3.  
The blades are driven by a rotating shaft, causing the electret and the electret support layer to rotate. Due to resistance, the rotation speed of the blades is not equal to the wind speed, but there is a theoretically corresponding relationship in Equation (1):  
\[\lambda=\frac{\omega_{r}R}{V} \tag{1}\]  
where \(\lambda\) is the tip speed ratio, \(R\) is the maximum rotational radius of the wind wheel or the blade tip radius, \(\omega_{r}\) is the angular velocity of the wind wheel (rad/s), representing the rotational speed, and \(V\) is the wind speed. In ideal cases, once the blade and rotor structure are determined, the tip speed ratio can be established. In practice, the tip speed ratio may vary with the range of rotational speeds, and it is generally obtained through measurements to create a speed-tip speed ratio curve.  
Figure 2: Topology diagram of the rotational energy/wind-energy-harvesting system.  
The coupling method of the piezoelectric and electrostatic transducers during rotor rotation, as well as the vibration of the magnetically actuated piezoelectric components due to stress deformation, can be described as shown in Figure 4.  
In the coupled structure, the piezoelectric component is of the magnetically actuated type and is subjected to intermittent magnetic excitation. When the magnet is rotated close to the free end of the piezoelectric cantilever beam, the magnetic force \(F\) attracts the cantilever beam, causing it to bend, thereby exciting the oscillation decay process based on the natural frequency of the piezoelectric cantilever beam, which gradually decays to stop before the next excitation arrives [22; 23]. Correspondingly, the electrostatic plates experience changes in induced charge \(\Delta Q\) due to the movement of the field source electret, leading to an external output.
## 3 Results
### Far-Distance VA
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**ANOVA Test for Eye Chart Size**} & \multicolumn{3}{c}{**Tukey HSD Comparisons for Eye Chart Size (\(p\)-Value)**} \\ \hline  & **Df** & **Mean Square** & **F-Ratio** & \(p\)**-Value** & **AVmax/Avmax-1** & **AVmax/Avmax-2** & **AVmax-1/Avmax-2** \\ \hline Test duration & 2 & 725.671 & 55.82 & \textless{}0.001 * & \textless{}0.001 * & \textless{}0.001 * & \textless{}0.001 * & \textless{}0.001 * \\ Fixation time & 2 & 606.413 & 61.16 & \textless{}0.001 * & \textless{}0.001 * & \textless{}0.001 * & \textless{}0.001 * \\ Fixation count & 2 & 628.898 & 17.07 & \textless{}0.001 * & \textless{}0.001 * & \textless{}0.001 * & 0.1476 \\ \hline \multicolumn{6}{c}{**ANOVA test for gaze direction**} & \multicolumn{3}{c}{**Tukey HSD comparisons for gaze direction (\(p\)-value)**} \\ \hline  & **Df** & **Mean square** & **F-ratio** & \(p\)**-value** & **Centered/dominant** & **Centered/Non** & **Dominant/Non** \\ \hline Test duration & 2 & 31.62 & 6.09 & 0.040 * & 0.066 & 0.079 & 0.996 \\ Fixation time & 2 & 16.24 & 3.45 & 0.039 * & 0.172 & 0.2069 & 0.995 \\ Fixation count & 2 & 252.45 & 12.17 & \textless{}0.001 * & 0.003 * & \textless{}0.001 * & 0.782 \\ \hline \multicolumn{6}{c}{**ANOVA test for lens design**} & \multicolumn{3}{c}{**Tukey HSD comparisons for lens design (\(p\)-value)**} \\ \hline  & **Df** & **Mean square** & **F-ratio** & \(p\)**-value** & **Balance/Distance** & **Balance/Near** & **Distance/Near** \\ \hline Test duration & 2 & 96.87 & 13.82 & \textless{}0.001 * & 0.066 & 0.065 & \textless{}0.001 * \\ Fixation time & 2 & 74.62 & 14.86 & \textless{}0.001 * & 0.163 & 0.031 * & \textless{}0.001 * \\ Fixation count & 2 & 211.79 & 8.18 & \textless{}0.001 * & 0.047 * & 0.331 & \textless{}0.001 * \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Detailed statistics for Figure 4. Three-way repeated measures ANOVA test with pos-hoc comparisons using Tukey HSD method. * Shows significance at the 0.05 level.
## 2 Summary of the Paper
### Compressed Sensing  
Compressed sensing builds upon a core tenet of signal processing and information theory: that signals, images, and other data often contain some type of _structure_ that enables intelligent representation and processing. For example, in imaging compressing, a correlated signal's energy can be compacted into just a few essential coefficients. Such transform coders exploit the fact that many _natural_ signals have a _sparse_ representation in terms of some basis (e.g., Fourier, wavelet).  
Fortunately, by the first author [22, 29, 30], transient electromagnetic signals and acoustic signals are rigorously shown to have such a structure, called compressibility. UWB signals (used for communications and radar) belong to this class of transient signals. This theoretical work paves the way for the practical application, since the prerequisite for using the CS principle is that channel impulse response is compressible in some basis, either frame or dictionary. Using singular value decomposition (SVD) --an orthogonal basis--of the channel impulse response, the first author has proved that the SVD coefficients follow a power-law decay and thus satisfy the definition of compressibility [22, 30]. The focus of this proposal is to demonstrate the real-world UWB system, designed by using a CS principle.
# Blockchain-Based UAV-Assisted Mobile Edge Computing for Dual Game Resource Allocation
## 3 Model of a Drone-Assisted Mobile Edge Computing System
### Model Safety Analysis  
In traditional centralized systems, the resource allocation process can be controlled and interfered with by a single central node, which can lead to problems such as unfair resource allocation and data tampering. In contrast, the decentralized nature of blockchain allows every node in the system to participate in the data recording and verification process, avoiding the risks of single point of failure and data tampering. Using distributed ledger technology, blockchain stores a record of every transaction and resource allocation at every participating node. Each node can verify the legitimacy of the transaction and work together to maintain a tamper-proof ledger, increasing the transparency and trustworthiness of the system. The process of resource allocation and transactions between drones and mobile users involves a large amount of sensitive data, which must be protected from leakage and unauthorized access. Blockchain uses cryptographic algorithms and consensus mechanisms that can effectively secure data. Blockchain uses cryptographic algorithms (e.g., SHA-256) to encrypt data and consensus mechanisms (e.g., PoW, PoS, etc.,) to ensure the authenticity and reliability of data [33]. Only verified transactions are recorded on the blockchain, ensuring data security. In the UAV-assisted MEC resource allocation system, the resource allocation and transaction process must be performed efficiently and accurately. Traditional manual operation is not only inefficient, but also prone to errors. Smart contracts, on the other hand, can automatically execute transactions according to pre-set rules, eliminating human intervention and improving efficiency and accuracy. A smart contract is a self-executing code running on the blockchain that automatically executes transactions and allocates resources based on pre-set rules. Between drones and servers and between drones and users, resource allocation and transaction processes can be automated through smart contracts.
# Acoustic Voice Analysis as a Tool for Assessing Nasal Obstruction: A Systematic Review
## 5 Limitations  
Several studies in this review included a small number of participants, which generally reduced their statistical power and the applicability of the results. There is a lack of standardized speech tasks and recording protocols across the studies. Additionally, there are limited controls for confounding factors, such as coexisting laryngeal conditions, which are rarely taken into account. In many studies, there is no correlation with physiological assessments.  
All these aspects detract from the possibility of a considerable amount of research having concentrated on time-based perturbation measures, including jitter, shimmer, and noise-to-harmonic ratio, while neglecting other potentially important measures for the assessment of hyponasal speech, such as long-term average spectrum (LTAS), nasal murmur amplitude, cepstral peak prominence (CPP), or spectral tilt. Moreover, the parameters discussed above are mainly designed to evaluate the stability and consistency of vocal fold vibration during phonation; however, they are not as effective for assessing resonance characteristics. Most studies depended exclusively on vowel prolongations, missing continuous speech and potentially diminishing the ecological validity of the findings. Several studies lacked control groups, which are important for separating the effects of nasal obstruction and its treatment on acoustic parameters from other influencing factors. Additionally, numerous studies did not provide essential information regarding the conditions under which voice samples were recorded, such as calibration, specifications of microphones and interfaces, and the recording environment, all of which can substantially affect the quality of voice signals. Ultimately, only a limited number of studies have investigated the correlation between acoustic voice alterations and physiological assessments of nasal obstruction, such as rhinomanometry or acoustic rhinometry, hence constraining the comprehension of their interaction. Additionally, this review has its limitations. Only articles published in English were included, potentially introducing language bias. A meta-analysis could not be conducted due to methodological heterogeneity. Grey literature and conference abstracts were omitted, potentially leading to publication bias. Future systematic reviews may improve through preregistration, the inclusion of non-English studies, and the consideration of unpublished data to augment comprehensiveness and reproducibility.
# Secure Delivery Scheme of Common Data Model for Decentralized Cloud Platforms
## Materials and Methods  
### Materials  
#### 2.1.1 Distributed Ledger  
A distributed ledger is a new trend in the field of information technology. A lot of financial and non-financial applications has made the worth of distributed ledger. Blockchain is the latest leading technology. It brings a solution for challenges like traceability, transparency, trust, and accountability [4; 5]. Distributed ledgers are multi-purpose technology in the digital world that are specifically designed to be shared across network of multiple sites, geographies, or institutions.  
A blockchain is a linear form of a distributed ledger composed of immutable blocks of data, with each block containing a list of transactions and a unique reference to its predecessor block [6]. It is commonly considered as a specialized form of a distributed database [7]. Blockchain offers the ability to create unique (sometimes scarce) digital assets. Blockchain can solve the problem of Single Point Failure (SPF) [8]. Bitcoin's presenter proposed a mechanism to ensure digital data rights management [9].  
Blockchain has features that can be utilized for transparent distribution of CDM data. The CDM provider functions as a peer and performs sequencing and validity determination of transactions on the blockchain. It also guarantees high availability through a peer-to-peer (P2P)-based file sharing system.
## 4 Discussion
### Comparison of Classifiers  
Although previous studies have proposed various classifiers that can be applied to hand pose pattern recognition (the LDA classifier [42], the k-NN classifier [27], the SVM classifier [32] and the ANN classifier [33]), few comparative studies have been conducted on them. In this study, three classification algorithms (k-NN, SVM and ANN) were applied separately for hand movement recognition. As shown in Figure 5B, for the overall dataset with or without sex labeling, the classification effectiveness of the classifier is ANN, SVM and kNN in descending order. However, for half of the overall dataset, as shown in Figure 5A, the overall classification effectiveness of the classifier is ANN, kNN and SVM in descending order. This also illustrates the claim that there is no optimal classifier but only the most suitable one. Taken together, ANN classifiers have the best classification prediction accuracy when comparing kNN and SVM classifiers. Deep learning (ANN) outperforms the classification results of machine learning (kNN and SVM).
## 4 Results And Discussion  
This section illustrates the simulation results of the proposed methodology for both RFI recognition and AMC, using MATLAB. For this purpose, we evaluate the performance of the four pretrained CNNs (AlexNet, GoogleNet, VGG 16, and ResNet18) in the classification phase using the deep learning toolbox. The results show a comparative analysis of these pretrained CNNs with respect to the accuracy in transfer learning and the consumed training time. The architecture of the FC for each classifier includes a layer with four neurons, followed by a Soft-Max classifier. The highest classification results are achieved in the experiments using SGD with momentum (SGDM) and Adam optimizer for the RFI classification and AMC phases, respectively.
# Identification of Impact Frequency for Down-the-Hole Drills Using Motor Current Signature Analysis
## 2 Materials and Methods
### Characterization of Drilling Stages  
Before obtaining the impact frequency from current signal analysis, it is necessary to characterize the dynamic behavior of the pressure and current signals during the different stages of drilling. This characterization allows us to determine ranges of values associated with no percussive and stable percussive action of drilling over a sample rock specimen. For this study, the rock specimen is a high-hardness tonalite (granite) phaneritic crystalline formed by plagioclase (large crystals), quartz, biotite, and hornblende. The approximate dimensions of these testing samples are 300 mm \(\times\) 700 mm \(\times\) 400 mm (height \(\times\) width \(\times\) depth). Figure 3 shows the behavior of the hammer pressure (P\({}_{h}\)) and motor current (I\({}_{m}\)) signals captured before and during the perforation of the rock sample, with the main stages of the drilling process separated with dashed vertical lines. In this experiment, the pressurized air was distributed to the DTH hammer pipes by the pneumatic console compressor at t = 20 s while the induction motor started 10 s later at t = 30 s.  
The first stage, defined as no percussive (NP) operation, starts when P\({}_{h}\) is stabilized around 3.6 bar while I\({}_{m}\) is kept stable, reaching peaks around 3.12 A. The current signal during the NP stage represents the no-load current, i.e., the magnetizing current required to set up magnetic flux inside the motor air gap. The second stage is called the transition (TR) stage and starts when the DTH hammer makes contact with the rock surface, showing the presence of oscillations in the hammer pressure and motor current due to the activation of hammer percussion. The P\({}_{h}\) signal shows an exponential increase in its magnitude as the drill bit gets deeper, to be stabilized around t = 75 s with a mean value of 6.9 bar. During the TR stage, the amplitude of the motor current increases until reaching peaks around 4.2 A. The third and last stage represents the drilling operation with stable percussion (SP), which starts once the hammer bit is entirely inside the rock specimen and ends at t = 118 s when the drill bit traverses the rock sample. During the SP stage, P\({}_{h}\) shows a stable oscillation around the mean value of 6.9 bar, and the peak current oscillates between 2.6 and 4.2 A due to the DTH hammer percussion. After the drill bit traverses the rock, both hammer pressure and motor current signals rapidly decrease until the previous values observed for the NP stage are reached.  
Figure 3: Characteristic profile of monitored signals during operation (NP: no percussion, TR: transition, SP: stable percussion): (**a**) hammer pressure; (**b**) motor current.
## 3 Fluctuation Function and Decoding of the Proposed System
### RS Decoder  
In the output of data extraction, there might exist errors in the estimated codewords, for which the RS decoder can be used for error correction. Among RS decoding algorithms, the BM algorithm [28] and Forney algorithm [30] are considered in the proposed systems. Let \(\mathbf{r}\) be one of \(\hat{\mathbf{c}}_{v}\) for \(v=0,\cdots,N_{\mathrm{c}}-1\) and \(r(X)\) be the polynomial form of \(\mathbf{r}\). Then, the polynomial \(r(X)\) can be expressed as:  
\[r(X)=r_{0}+r_{1}X+r_{2}X^{2}+\cdots+r_{n-1}X^{n-1},\]  
where \(r_{l}\) is an element of \(\mathrm{GF}(2^{q})\). The received polynomial can be regarded as the summation of a codeword polynomial \(c^{\prime}(X)\) and an error polynomial \(e(X)\):  
\[r(X)=c^{\prime}(X)+e(X).\]  
To determine error locations and values, syndromes \(S_{i}\) for \(i=1,2,\cdots,2\tau\) are calculated as:  
\[S_{i}=c(\alpha^{i})+e(\alpha^{i})=e_{0}+e_{1}\alpha^{i}+\cdots+e_{n-1}\alpha^{ i(n-1)}, \tag{18}\]  
where \(\alpha\) is a primitive element in \(\mathrm{GF}(2^{q})\), \(c(X)=0\) for \(X=\alpha^{i}\) and \(i=1,2,\cdots,2\tau\). Let the error location polynomial be defined as:  
\[\Lambda(X)=\Lambda_{0}+\Lambda_{1}X+\cdots+\Lambda_{u-1}X^{u-1}, \tag{19}\]  
where \(u\) is the number of errors and \(u\leq\tau\). There is a relation between syndromes and the coefficients of error location polynomials:  
\[S_{i}=-\sum_{j=1}^{i-1}\Lambda_{j}S_{i-j}-i\Lambda_{i},\ \ \text{for}\ i\leq u \tag{20}\]  
\[S_{i}=-\sum_{j=1}^{u}\Lambda_{j}S_{i-j},\ \ \text{for}\ i>u. \tag{21}\]  
In the BM algorithm, coefficients of the error location polynomial are determined from (20) and (21). LFSR can be used for the implementation to calculate the coefficients. Then, error locations can be found by solving the roots \(\alpha^{i_{0}},\alpha^{i_{1}},\cdots,\alpha^{i_{n-1}}\) of the polynomial.  
[PERSON]'s algorithm [30] utilizes each error location to determine corresponding error values. Before the error values are computed, two necessary definitions, syndrome polynomial, \(S(x)\), and error evaluator polynomial, \(\Omega(x)\), are established using (18), and they are defined as:  
\[S(X) =S_{1}+S_{2}X+\cdots+S_{\tau}X^{2\tau-1}=\sum_{j=0}^{2\tau-1}S_{j +1}X^{j},\] \[\Omega(X) =S(X)\Lambda(X)\ \ (\text{mod}X^{2\tau}).\]  
The error values, \(e_{i_{k}}\), are computed as:  
\[e_{i_{w}}=-\frac{\Omega(\alpha^{-i_{w}})}{\Lambda^{\prime}(\alpha^{-i_{w}})}, \tag{22}\]  
where \(\Lambda^{\prime}(X)\) is the derivative of (19) and \(w\) is from zero to \(u-1\). The error polynomial, \(e(X)\), is computed using (20), (21) and (22), and then, we determine the RS codeword, \(c(X)\), using the error polynomial.  
\[\tilde{c}(X)=r(X)-e(X) \tag{23}\]After decoding of \(N_{\text{c}}\) codewords, recovered codewords \(\tilde{\mathbf{c}}_{v}\) for \(v=0,\cdots,N_{\text{c}}-1\) are calculated. Since systematic RS codes are considered, the recovered messages are obtained by concatenating \(kq\) bits of the recovered codewords.
# A Three-Port Power Electronic Transformer Based on Magnetic Integration
## 6 Economic Benefits Analysis
### Device Cost Comparison
The device cost of the other two existing typical PETs (CHB + common HF bus PET and MMC + ISOP DABs PET) can be obtained by the same manner discussed above, and all the results are shown in Figure 13. It can be found that the proposed PET has a minimum device cost (total cost of $0.27 million ) compared with the CHB + common HF bus PET (total cost of $0.35 million), MMC + distributed DABs PET (total cost of $0.46 million) and MMC + ISOP DABs PET (total cost of $0.32 million). It can be concluded that the proposed PET saves more than 19% of the device cost compared with the existing typical PETs. Furthermore, although the cost of magnetic components is unavailable here, it can be predicted that the total costs of magnetic components in the proposed PET will be lower, according to listed magnetic volume in Table 2.
## 3 Problem Formulation and Solutions
### Solution: Hybrid Precoder  
The maximization of sum-rate can be achieved by lowering inter-beam interference as well as by increasing effective channel gain. With fewer hardware restrictions, the HP scheme can utilise the mmWave-mMIMO system to its full potential. Motivated by [3; 4; 46], the hybrid precoder designing step is divided into two segments, and the analogue RF precoder as well as the digital baseband precoder are discussed separately. Here, the AP algorithm (for \(\text{F}^{RF}\)) is inspired by the work in [4]. As part of the low-dimensional DP design, the authors have introduced a low complexity algorithm (for \(\text{F}^{BB}\)) and compared its performance with those of current digital precoders.
## 2 IoT and Opportunistic Networks--State of the Art
### Applications  
* Disaster Management: Advanced routing protocols in IoT-enabled opportunistic networks facilitate resilient data dissemination and coordination in disaster scenarios. Recent research emphasizes the development of delay-tolerant routing algorithms and message ferrying strategies to ensure the timely delivery of critical information [4].
* Wildlife Monitoring: Routing protocols optimized for energy efficiency and adaptability enhance data collection and transmission in wildlife monitoring applications. Recent studies explore opportunistic routing schemes, content-centric protocols, and cooperative forwarding strategies to extend network coverage and reduce communication overhead [5].
* Rural Connectivity: Advanced routing protocols address connectivity challenges in IoT deployments in rural areas. Research focuses on geographic routing, social-aware forwarding, and multi-path selection techniques to optimize message delivery and mitigate the impact of network partitions [6].