esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
MFCCs and Cepstral Peak Prominence (CPP) have been used to discriminate between modal, breathy, strained and other voice types based on microphone data [11; 26]. The difference between the amplitudes of the first two harmonic components on microphone and NSA spectra, \(H1\)-\(H2\), was found to be correlated with perceived speech breathiness [13; 14]. Glottal flow waveforms obtained using the Glottal Inverse Filtering (GIF) method have been used for differentiation between modal and pathological voice through the estimation of the MFDR and other residue quantities [27; 28]. However, the accuracies of processing close rounded vowels using the GIF method were unacceptable and would limit the use of the GIF for continuous speech. Thus far, no single metric could show a good statistical separation between different voice types, such as modal, breathy and pressed. A second level of analysis that uses multiple features is usually needed to obtain effective evaluation. Secondary processing tools, such as expert systems or machine learning algorithms, require that the mathematical algorithms first be calibrated through the adjustments of model parameters that are data-specific. The Hidden Markov Model (HMM), Support Vector Machine (SVM) and Artificial Neural Network (NN) were used to improve the performance on classification between different voice qualities based on microphone features [29; 30]. A Gaussian Mixture Model (GMM) using Mel-Frequency spectral Coefficients (MFCCs) extracted from neck-surface vibration signals achieved 80.2% and 89.5% accuracies in classifying modal, breathy, pressed and rough types at the frame level and utterance level, respectively [11]. Machine learning algorithms critically require, for accurate results, that the calibration be done against a \"ground truth\", or gold standard. In the case of voice quality classification, the \"ground truth\" consists of subjective evaluation by a panel of trained listeners, with intra- and inter-rater analysis [11]. The NSA technology has recently been applied for voice type discrimination with the use of a Gaussian mixture model [31]. However, the work reported so far in this area has not used a comprehensive listener panel dataset with inter- and intra-rater analysis as the ground truth. The present study attempted to bridge this gap. The overarching idea is to provide automatic detection of voice type and provide long-term monitoring of voice routines based on accumulated datasets. The objective of the present study was to investigate the feasibility of using NSA signals and supervised learning techniques to discriminate between three voice types, namely modal, breathy and pressed. The paper is organized as follows: Section 2 describes the NSA hardware and the data acquisition process. Section 3 describes the subjective auditory-perceptual voice type rating task using an online system. Section 4 describes how features were extracted and used for machine learning classification. Section 5 shows the classification and analysis results based on different feature sets. The conclusions, the limitations of this study and future work are discussed in Section 6.
## 3 Methodology
### Image Preprocessing
#### 3.2.4 Extraction of Patches  
Small segments of size 50 \(\times\) 50 pixels are obtained from the images of the entire mounted slide and then classified into two categories, namely, IDC-positive or IDC-negative. This step is performed to provide the model with smaller and more focused inputs that contained the regions of interest (IDC regions). The extraction of patches from an image can be formalized as a process of selecting and cropping regions of a larger image into smaller, overlapping, or non-overlapping segments.  
If \(f(x,y)\) is the pixel intensity value at \((x,y)\) position in the original image and \(P\) is the patch length, then the equation for extracting a patch centered at location \((x,y)\) is given as  
\[f^{\prime}(i,j)=f\bigg{(}x-\frac{(P-1)}{2+i},y-\frac{(P-1)}{2+j}\bigg{)} \tag{10}\]  
where \(i=0,1,\dots,P-1\) and \(j=0,1,\dots,P-1\).  
This equation crops a patch of size \(P\times P\) centered at location \((x,y)\) from the original image. The patches can be extracted in an overlapping or non-overlapping manner depending on the desired level of spatial resolution. If the patches are extracted in an overlapping manner, then the equation for the next patch can be given as  
\[f^{\prime\prime}\left(i,j\right)=f\bigg{(}x-\frac{\left(P-1\right)}{2+i+d},y- \frac{\left(P-1\right)}{2+j+d}\bigg{)} \tag{11}\]  
where \(d\) is the overlap distance between the patches.  
This equation crops a patch of size \(P\times P\) centered at location \(\left(x+d,y+d\right)\) from the original image which overlaps with the previous patch. The extraction of patches can be performed in a sliding-window manner to ensure that all regions of the image are covered. Figure 6 shows the preprocessed data sample, while the class-labeled samples are shown in Figure 7.  
Figure 5: Dataset samples after the application of data augmentation methods.  
Figure 6: Preprocessed extracted patches from the data samples.
# Analysis of Wafer Warpage in Diamond Wire Saw Slicing Sapphire Crystal
## 2 Theoretical Analyses  
### Heat Flux  
Figure 1 is the schematic diagram of diamond wire saw cutting. The diamond wire is wound around a guide wheel and driven by power to move back and forth. The sapphire crystal is fed in the direction perpendicular to the diamond wire, and the diamond abrasives fixed on the surface of the wire achieve a cutting effect to remove material and form the kerf. As the processing progresses, the wafer is cut to form. The sawing process generates heat in the sawing area, and its heat flux density is the heat passing through the cross-sectional area of the workpiece per unit area per unit time. The heat flux \(q_{\mathrm{V}}\) into the workpiece in the sawing process can be described as follows [7]:  
\[q_{\mathrm{V}}=\frac{\varepsilon P}{A} \tag{1}\]  
where \(\varepsilon\) represents the energy partition of the heat transfer into the workpiece in slicing. [PERSON] et al. [5] suggested that approximately one-third to one-half of the heat during sawing is absorbed by the crystal, and \(\varepsilon\) was defined as 0.667 by [PERSON] et al. [8]; therefore, the same numerical value is used for \(\varepsilon\) in the calculation of this paper. \(P\) is the consumed power for the wire sawing process, and \(P\) can be calculated as the product of tangential sawing force Ft exerted on the workpiece by the diamond wire and the wire speed \(v_{\mathrm{c}}\)[7,13], as \(P=F_{t}\times v_{\mathrm{c}}\). And \(F_{t}\) can be calculated by using the formula \(F_{t}=0.6224(v_{\mathrm{w}})^{0.9355}(v_{\mathrm{c}})^{-0.8785}(\eta)^{0.9355} \,DL_{\mathrm{x}}\)[15], where \(v_{\mathrm{w}}\) is workpiece feed rate, \(\eta\) is the abrasive density on diamond wire surface, and \(D\) is the wire outer diameter. \(A\) is the cross-sectional area of the kerf, which can be approximately expressed as \(A=L_{\mathrm{x}}\times d\), where \(L_{\mathrm{x}}\) is the length of contact between the diamond wire and the workpiece during cutting, \(d\) is the kerf width, and the approximate relationship with the wire outer diameter \(D\) is: \(d=D+20\)\(\mu\)m [16]. The length of the diamond wire (indicated by the blue dashed line) cutting into the interior of the crystal shown in Figure 1 is \(L_{\mathrm{x}}\). According to the relationship between \(L_{\mathrm{x}}\) and the width \(L\) of the cut crystal shown in Figure 1, the approximate relationship is as follows: \(L_{\mathrm{x}}=L_{\mathrm{x}}\times\cos\alpha\), and \(\alpha\) is the wire bow angle, and the value is \(1.5^{\circ}\)[13]. In the finite element model, when the workpiece feed rate is \(v_{\mathrm{w}}\), the length of material cut along the feed direction in each analysis step \(t_{\mathrm{inc}}\) is \(v_{\mathrm{w}}\times t_{\mathrm{inc}}\). The heat flux density entering the workpiece during this process is:  
\[q_{\mathrm{V}}=\frac{\varepsilon P}{A}v_{\mathrm{w}}f_{\mathrm{inc}} \tag{2}\]  
Based on the above equation, the heat flux into the workpiece can be described as follows:  
\[q_{\mathrm{V}}=\frac{\varepsilon F_{t}v_{\mathrm{c}}v_{\mathrm{w}}f_{\mathrm{ inc}}}{L_{\mathrm{x}}d} \tag{3}\]
# 3. Create cryptographic proof return zk_snark_proof  
function submit_to_contract(submission):  # Submit transaction to Ethereum smart contract contract contract = load_contract()  tx = contract.submitZKP(  submission[\"fileID\"],  submission[\"proof\"],  submission[\"thresholdSetID\"]  )  return tx.hash  
function verify_on_chain(file_id, proof, threshold_profile):  thresholdSetID = hash_threshold_profile(threshold_profile)  return contract.verifyZKP(file_id, proof, thresholdSetID)  
Here is a **Solidity smart contract design** for implementing the BMVS-ZKP* validation scheme. This contract allows the submission and verification of software metric compliance using **zero-knowledge proofs (ZKPs)** while supporting flexible threshold profiles, submission metadata, and on-chain auditability.  
// SPDX-License-Identifier: MIT pragmas solidity ~0.8.18;  
/// @title BMVS-ZKP Validator Contract /// @notice Stores and verifies ZKPs for software metric compliance contract BMVSZKPValidator {  
struct Submission {  address submitter;  bytes32 fileID;  bytes zkProof;  bytes32 thresholdProfileID;  uint256 timestamp;  bool verified; }  
mapping(bytes32 => Submission) public submissions;  
event SubmissionReceived(  bytes32 indexed fileID,  address indexed submitter,  bytes32 thresholdProfileID,  uint256 timestamp );  
event VerificationResult(  bytes32 indexed fileID,bool success,  uint256 timestamp );  
/// @notice Submit ZKP proof for a source file and metric compliance  function submitProof(  bytes32 fileID,  bytes calldata zkProof,  bytes32 thresholdProfileID  ) external {  require(submissions[fileID].timestamp == 0, \"File already submitted\");  
submissions[fileID] = Submission({  submitter: msg.sender,  fileID: fileID,  zkProof: zkProof,  thresholdProfileID: thresholdProfileID,  timestamp: block.timestamp,  verified: false  });  
emit SubmissionReceived(fileID, msg.sender, thresholdProfileID,  block.timestamp);  }  
/// @notice Verify ZKP off-chain result (simulated on-chain hook)  function markVerified(bytes32 fileID, bool result) external {  require(submissions[fileID].timestamp!= 0, \"No such file\");  require(!submissions[fileID].verified, \"Already verified\");  
/// In a real implementation, this would call a verifier  contract  // For demo: instructor or validator confirms ZKP validity  externally  if (result) {  submissions[fileID].verified = true;  }  
emit VerificationResult(fileID, result, block.timestamp);  }  
/// @notice Retrieve proof metadata (for audit)  function getSubmission(bytes32 fileID)  public  view  returns (  address submitter,  bytes memory zkProof,bytes32 thresholdProfileID,  uint256 timestamp,  bool verified  )  {  Submission memory sub = submissions[fileID];  return (  sub.submitter,  sub.zkProof,  sub.thresholdProfileID,  sub.timestamp,  sub.verified  );  }  } ```  
In this way, our validation process will utilize the full metric suite, detect threshold violations prior to submission, and integrate a zero-knowledge proof (ZKP) backend.
## 2 Materials and Methods
### Procedures
#### 2.2.6 Trunk Strength  
Trunk strength assessment was done according to [PERSON] et al. [26] protocol. A trunk dynamometer (S2P Ltd., Ljubljana, Slovenia) with a bending beam load cell (model 1-Z6 FC3/200 kg, HBM, Darmstadt, Germany) was used to measure trunk flexion, extension, and lateral flexion isometric strength. Isometric strength was measured as peak torque (Nm/kg) of the best one second interval during maximal voluntary contraction. All of the output variables were normalized with subject's body mass. During trunk extension measurement, subjects were standing with back turned towards the dynamometer with sensors on level of scapular spine and hands crossed on their shoulders. During trunk flexion measurement, subjects were standing turned face towards dynamometer with sensors on the same level as during extension and arms floating in the air to prevent their contribution. During trunk lateral flexion, subjects were standing sideways to the dynamometer, positioned so that their spine was in neutral position. The hand closer to the sensor was placed on their opposite shoulder and the other one was placed on opposite hip. During all trunk strength measurements, subjects were in their training shoes, standing with feet hip width distance apart, while a rigid strap was tightly fastened across pelvic girdle to achieve good fixation. During every trial subject was verbally encouraged to reach his best performance and hold it for 3-5 s. Each task was done three times with 60 s of rest in between. The best result for each task was taken for further analysis.
# An Energy-Efficient Routing Protocol for Reliable Data Transmission in Wireless Body Area Networks
## 2 Related Works
[23] proposed a reliable energy efficient multi-hop routing protocol, and this protocol constructs a cost function which considers the parameters of the residual energy, the distance to the sink, the failure probability and the communication count to realize reliable and efficient data transmission. Similarly, there are more methods to find the best next hop node by constructing cost function [24; 25; 26; 27; 28; 29; 30]. In multi-hop routing, the selection method of the next hop node is the core of the research, which is related to the comprehensive performance of the network [31]. Through the study of the above routing protocols, there are still some shortcomings in the next hop node selection method. For example, the constructed cost function considers single parameters, and the proposed protocol does not consider data classification and priority processing. Of course, it cannot meet the requirement of multiple Qos in a wireless body area network. Therefore, based on the above background, this paper proposes an energy-efficient routing protocol for reliable data transmission in WBAN. Unlike the current research results, this protocol takes a number of parameters into account and constructs a maximum benefit function by normalization, which is used to dynamically select the next hop node with a good state. It can not only achieve reliable and efficient routing transmission of data, but also improve the energy efficiency of network, and thereby prolong the network lifetime. Following is a detailed description of the system model, construction of the maximum benefit function, routing process and performance evaluation of the proposed routing protocol.
## 4 Model for Calculating the Roll Stability Boundary
### Analysis of Model Training Results
#### 4.2.2 Root Mean Square Error (RMSE):  
RMSE is the square root of MSE. It is also used to measure the error between the predicted values and the true values. Compared with MSE, RMSE is more sensitive to the magnitude of the errors because it considers the square of the errors, amplifying the impact of larger errors. When evaluating the Transformer model, RMSE can intuitively reflect the average error magnitude between the model's predicted values and the true values. A smaller RMSE value indicates that the maximum LTR predicted by the model is closer to the actual situation, the prediction accuracy of the model is higher, and it has higher reliability in determining the vehicle roll-stability boundary. Its calculation formula is as follows:  
\[RMSE=\sqrt{\frac{1}{n}\frac{n}{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}} \tag{26}\]
## 4 Discussions and Comparative Analysis
### Sensitivity  
A good MPPT technique must be sensitive enough to operate under all abnormal (uniform and nonuniform) conditions or atmospheric changes. It must be able to react quickly and track the GMPP of the specific PV system at the given condition. The comparative assessment of all the MPPT techniques is presented in Table 6.  
The offline MPPT techniques track MPP under uniform irradiance with fast tracking speed and fewer oscillations. These methods fail to track the GMPP under PSCs. Moreover,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Offline** & **Online** & **Intelligent** \\
**Type of MPPT Technique** & **MPPT** & **MPPT** & **MPPT** \\ \hline Tracking speed & High & High & Medium \\ Tracking accuracy & Less & Moderate & High \\ Tracking efficiency & Poor & Medium & Very good \\ Steady-state Oscillations & Less & High & Less \\ GMPPT tracking under PSC & Yes & No & Yes \\ Suitability for high-efficiency operations & No & Yes & Yes \\ Suitability for environmental changing conditions & No & No & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 6: A comparative assessment of MPPT techniques.  
they fail to track accurate MPP for uniform irradiance. The drawback of offline techniques is that they require pre-estimation of the parameters. Therefore, to overcome these issues, offline MPPT techniques must be combined with the heuristics techniques for tracking GMPP. Similarly, online techniques offer fast tracking speed, less steady state oscillations, and better tracking efficiency. However, these techniques fail to track GMPP under PSCs. Hence, they require modifications and/or to be combined with swam techniques. Unlike the offline and online MPPT methods, intelligent techniques track the GMPP under PSCs. The intelligent MPPT techniques improve tracking accuracy, tracking efficiency, steady state oscillations, and suitability with changing climatic conditions. However, the convergence speed of these techniques is less with high starting oscillations. Therefore, more research needs to be carried out to improve the convergence speed and reduce the starting oscillations.  
The existing reviews provide the MPPT techniques based on uniform and partial shading conditions. They provided the details of a few conventional and swarm algorithm-based MPPT techniques. In addition, they performed a comparison of these techniques. The existing MPPT methods have drawbacks, such as being trapped at local MPP under PSCs, high initial oscillations, and slow tracking speed. Furthermore, to overcome these issues, hybrid intelligent MPPT techniques can be formed by combining the conventional and swarm algorithms or combining two heuristic methods.
## 2 Materials and Methods
### Development and Design Process of the Control System Hardware
#### 2.2.3 EMG Digitalising  
The EMG signal is an analogue voltage signal, so the AD7606 chip was chosen to convert this signal into a digital one. The AD7606 chip has a specialised 16-bit simultaneous sampling and can operate on a single supply of 5 V. Data collection is a very important process, because the precise collection of EMG signals is a good foundation for the control system to understand the patient's intentions. It has eight simultaneously sampled channel inputs with a sampling rate of 200 KSPS. Internally, AD7606 has a digital filter (a second-order Butterworth filter), which has a cut-off of 3 dB with a frequency of 22 kHz and  
Figure 3: The process of the interactive interface system.  
provides an antialiasing rejection of 40 dB. The time taken for information to be passed to the STM32 chip is 2730.6 us.
## 2 Materials and Methods  
### Process-Integrated Embedding of Weld Inserts into TPC  
The technology of process-integrated embedding of weld inserts into TPC uses the principle of moulding holes by a pin tool and simultaneously places weld inserts in the moulded hole [15]. Thereby, the reinforcing fibres are not cut by punching or drilling but shifted aside by a tapered pin tool while the TPC is hot and formable [16]. The integration of the embedding process of the weld inserts into the composite component manufacturing process provides the possibility of reducing the number of process steps and manufacturing costs.  
The process is schematically illustrated in Figure 1. At first, a pre-consolidated TPC sheet is warmed up above melting temperature of the matrix polymer by an infrared heating device (Figure 1a). Subsequently, the TPC sheet is transferred into the open compression mould. Immediately after mould closing (Figure 1b), a pin tool is shifted forward, forming a hole by displacing the reinforcing fibres and the still molten thermoplastic matrix (Figure 1c). The pin tool consist of a pin retainer containing a magnet to fix the steel weld insert and the tapered pin. After the pin movement, a ring shaped counterpunch recompresses the squeezed-out material whereby the undercut of the weld insert is filled with fibres and matrix material (Figure 1d). The embedding of the weld insert (steps c and d) takes less than one second. After cooling and solidification of the TPC specimen, the pin retainer is retracted and the tapered pin is separated. Finally, the TPC specimen with integrated weld insert is demoulded (Figure 1e).  
The process parameters for the embedding of weld inserts in TPC were identified based on the experience of moulding holes [17] and process-integrated embedding of metal inserts into TPC [16]. A tempered steel mould (in this case 40 \({}^{\circ}\)C, cf. Table 1) on laboratory scale with vertical flash face is applied to produce plane test specimens with embedded weld inserts. The pin tool is pneumatically actuated, such as the counterpunch. The feed forces have been set at approximately 2.5 kN and 5 kN, respectively (cf. Table 1). A schematic illustration of the temperature curve during process-integrated embedding of weld inserts in TPC with the heating temperature of the TPC set to 210 \({}^{\circ}\)C and photos of TPC test specimens with integrated weld inserts are shown in Figure 2.
# A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications
## 5 Discussion and Future Directions  
### Combining Feature Selection with Ensemble Learning  
Many studies have shown that using selected important features or variables instead of using all extracted variables can result in more accurate and robust predictions [105; 106; 107].  
Ensemble algorithms are essentially black box models that carry the risk of overfitting, and the underlying physical mechanisms can be obscure [108]. It is therefore critical to identify important variables by selecting features before training a model; filter, wrapper, and embedded algorithms are just a few of the feature selection techniques that have been proposed [106; 109].  
Some studies have highlighted the importance of combining feature selection with ensemble learning algorithms in practical applications. For example, [PERSON] et al. [43] found that recursive feature elimination (RFE) for feature selection in combination with a Cated-Boost model produced the most accurate prediction of forest biomass for all forests in Jilin province, China with RMSE = 25.77 t/ha. There have also been studies that used a variance inflation factor (VIF) to quantify multicollinearity between independent variables, and only variables with VIF values \(<10\) were finally included for modeling [62; 110]. Of the three types of ensemble learning methods, tree-based bagging and boosting algorithms have identified important variables mainly by permutation importance [111; 112]. Some other indicators, such as mean decrease in accuracy and mean decrease in impurity, have also been used in association with tree-based algorithms (e.g., RF) [113]. In contrast, stacking ensemble algorithms appear to have difficulty in selecting important variables due to working with a set of models rather than an individual model, and it can be difficult to interpret the ensemble results [88; 114]. Feature selection should thus be implemented with care when stacking, but this aspect of the technique has been little explored in published studies.
# Joint Optimization of Data Transmission and Energy Harvesting in Relay Satellite Networks
## 3 System Model and Problem Formulation  
In this section, we design a stochastic system model that includes the RSN network, dynamic models of data queue, and energy queue. Then, a system utility maximization problem is formulated with multiple constraints. The key notations with descriptions used in this paper are summarized in Table 1.