esa-sceva/satcom-chunk-collection huggingface dataset:
# Toward Improving the Reliability of Discrete Movement Recognition of sEMG Signals
## 3 Methods
### Signal Processing and Feature Extraction  
The sEMG signals of Ninapro database 1 have already been amplified, band pass-filtered, and rectified. So, there is no signal preprocessing further applied to the sEMG signals in this study. The signals have been labeled so that the movement-related signalscould be directly obtained. For feature extraction, a sliding window method is applied. The width of the window is 200 ms, and the increment is 20 ms. For each sliding window, two time-domain feature extraction methods, root mean square (RMS) and waveform length (WL), have been applied due to their computational simplicity and relatively better performance than the other features from my previous study [5]. The definitions of the feature extraction algorithms RMS and WL are described below.  
* RMS \[\text{RMS}_{j}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\mathbf{X}_{ij}^{2}}\] (1)
* WL \[\text{WL}=\sum_{i=1}^{N}|\mathbf{x}_{i}-\mathbf{x}_{(i+1)j}|\] (2) where \(j\) is the column; \(N\) is the number of observations in the current window; and \(\mathbf{X}_{ij}\) represents the data point.
# Consolidating ICESat-2 Ocean Wave Characteristics with CryoSat-2 during the CRYO2 ICE Campaign
## 2 Data
### Cryo2 ICE Orbit Matching  
The extent of the study is limited to the coincident orbits following the orbit change of CryoSat-2 in 2020. The matching of the coincident orbits between ICESat-2 and CryoSat-2 has been carried out using the (cryo2 ice.org, accessed on 10 August 2021) coincident data explorer [21]. 547 coincident orbits were found from 1 August 2020 to 1 August 2021, in the region from 0\({}^{\circ}\) to 70\({}^{\circ}\) N, with 168 orbits containing ocean observations within the set limits. The rejected orbits were primarily short-time (<3 s) measurements containing land observations, with only the long-time coincident orbits reaching the open ocean for the most part.  
We decrease the 168 orbits to 136 orbits containing observations from the Pacific or Atlantic oceans, including the Bering Sea, that are at least 40 km from land. This limits shallow-water wave effects [22], as well as noise from photons reflected by the ocean floor [23]. A map showing the orbits used in this study can be seen in Figure 1. The location for each measurement has been designated using the National Oceanic and Atmospheric Administration (NOAA) basin codes, which were obtained from the RADS database [24]. The matching orbits have been limited to allow for a maximum separation of 50 km between ground tracks, with generally larger separations at lower latitudes; however, no significant increase in the RMSE was found with larger separation distances below the 50 km limit. Due to the configuration of the orbits, the time difference of the observations is between 2.3 h and 3 h, with an average time difference of 2.8 hours.
# RST Resilient Watermarking Scheme Based on DWT-SVD and Scale-Invariant Feature Transform
## The Proposed Scheme
### RST Attack Correction Based on SIFT
#### 3.3.3 Translation Attack Correction  
The definition of the translation attack is that all points of one image are moved towards the same linear direction for the same distance. The translation attack cannot change the shape and size of the attacked image, but result in image information destruction. The match between horizontally translated image (128 pixels) and watermarked image is shown in Figure 7.  
Translation correction is implemented with the help of matching points' coordinates. Similar to scaling attack correction, coordinates of the watermarked image and translated image are set as \((x_{\mathrm{w}},y_{\mathrm{w}})\) and \((x_{\mathrm{t}},y_{\mathrm{t}})\), and the size of the image is \(N\times N\). The corrected horizontal and vertical coordinates, \(x_{\mathrm{c}}\) and \(y_{\mathrm{c}}\), can be calculated by Equation (14):  
\[x_{\mathrm{c}}=\left\{\begin{array}{ll}x_{\mathrm{t}}-x_{\mathrm{w}}+N,&x_{ \mathrm{t}}<x_{\mathrm{w}}\\ x_{\mathrm{t}}-x_{\mathrm{w}},&\mathrm{otherwise}\end{array}\right.,\ y_{ \mathrm{c}}=\left\{\begin{array}{ll}y_{\mathrm{t}}-y_{\mathrm{w}}+N,&y_{ \mathrm{t}}<y_{\mathrm{w}}\\ y_{\mathrm{t}}-y_{\mathrm{w}},&\mathrm{otherwise}\end{array}\right.. \tag{14}\]  
After that, \(x_{\mathrm{c}}\) and \(y_{\mathrm{c}}\) are used to correct the attacked image on the horizontal and vertical location of coordinates in the inverse direction to achieve the translation correction.  
The corrections of RST attacks are demonstrated, respectively, as above all.  
Figure 6: Matching between scaled image and watermarked image: (**a**) scaled image (0.5); (**b**) feature matching.  
Figure 7: Matching between translated image and watermarked image: (**a**) horizontally translated image (128 pixels); (**b**) feature matching.
# Real-Time Guitar Amplifier Emulation with Deep Learning +
## 2 WaveNet-Style Model
### Real-Time Implementation  
The WaveNet-style model was implemented in C++. The real-time application was built using the open source JUCE framework. JUCE allows building cross-platform audio applications as well as VST, AU, and AAX plugins from a single source code. The Eigen library was used for matrix and vector operations. The source code is available at [[https://github.com/damskaggep/WaveNetVA](https://github.com/damskaggep/WaveNetVA)]([https://github.com/damskaggep/WaveNetVA](https://github.com/damskaggep/WaveNetVA)).  
The real-time performance of the C++ code was estimated for several model configurations. The models were tested using an Apple iMac with an 2.8 GHz Intel Core i5 processor, using a sample rate of 44.1 kHz. During the test, all other applications were shut down and the computer was disconnected from the internet. This was done to minimize the effect of other processes in the test.  
Figure 4 shows the processing speed of the model with different numbers of layers and different numbers of convolution channels. In this case, the models use a gated activation function.  
Figure 3: Visualization of three convolutional layers in series and the resulting receptive field of \(N=8\). The figure has been adapted from [24].  
Figure 2: Block diagram of a single convolutional layer.  
The processing speed is expressed as a factor of the requirement for real-time application. Clearly, the computational load increases as the number of layers and channels is increased. The largest model running in real time uses 18 layers and 16 channels in the convolutional layers. With 24 layers, a model with 8 convolutional channels can be run in real time.  
Figure 5 shows the processing speed of the 18-layer model using different activation functions. The activation functions are detailed in Section 2.6. The rectified linear unit (ReLU) is the computationally cheapest and the gated activation is the most computationally expensive activation.
## Abbreviations and Symbols  
The following abbreviations and symbols are used in this manuscript:  
\begin{tabular}{l l} TMY & typical meteorological year \\ PV & photovoltaic, photovoltaics \\ BIPV & building-integrated photovoltaic, building-integrated photovoltaics \\ \(PR\) & performance ratio \\ AOI & angle of incidence \\ LID & light-induced degradation \\ GIS & geographic information system \\ \(NOCT\) & nominal operating cell temperature (K, \({}^{\circ}\)C) \\ \(NOCT_{\text{eff}}\) & effective nominal operating cell temperature (K, \({}^{\circ}\)C) \\ \(NMOT\) & nominal module operating temperature (K, \({}^{\circ}\)C) \\ \(Y_{\text{f}}\) & net output energy of the PV system per unit of installed PV array power (kWh/kW) \\ \(Y_{\text{r}}\) & number of hours at the peak irradiance of 1 kWh/m\({}^{2}\) (h or (kWh/m\({}^{2}\))/(kW/m\({}^{2}\))) \\ \(E_{\text{out}}\) & output energy (kWh) \\ \(P_{0}\) & nominal power (W) \\ \(G_{\text{STC}}\) & irradiance at standard test conditions (W/m\({}^{2}\)) \\ POA & plane-of-array \\ \(G_{\text{POA}}\) & plane-of-array irradiance (kWh/m\({}^{2}\)) \\ \(H_{\text{POA}}\) & plane-of-array irradiation (kWh/m\({}^{2}\)) \\ STC & standard test conditions (1 kW/m\({}^{2}\) irradiance, 25 \({}^{\circ}\)C cell temperature and AM1.5G \\ \(IAM\) & incidence angle modifier \\ \(\gamma\) & PV temperature power loss coefficient (\%/K, \%/^{\circ}\)C) \\ \(T_{\text{c}}\) & cell temperature (K, \({}^{\circ}\)C) \\ \(T_{\text{c,0}}\) & cell temperature at standard test conditions (K, \({}^{\circ}\)C) \\ \(T_{\text{m}}\) & module temperature (K, \({}^{\circ}\)C) \\ \(T_{\text{a}}\) & ambient air temperature (K, \({}^{\circ}\)C) \\ \(W_{\text{s}}\) & wind speed (m/s) \\ \(a\), \(b\) & empirical coefficients of SAM or King's model [a dimensionless, b (m/s)\({}^{-1}\)] \\ \(u_{0}\), \(u_{1}\) & empirical coefficients of [PERSON]'s model [u\({}_{0}\) (W/m\({}^{2}\)C), u\({}_{1}\) (W/m\({}^{3}\)Sc)] \\ \(\alpha\) & optical absorption coefficient of solar irradiance \\ \(\eta_{\text{m}}\) & PV efficiency related to the module area \\ \(C\) & thermal capacitance (J/\({}^{\circ}\)C) \\ \(\tau\alpha\) & effective transmittance-absorptance product \\ \(\eta_{\text{m}}\) & efficiency of the PV module \\ \(B\) & direct irradiance (W/m\({}^{2}\)) \\ \(D\) & diffuse irradiance on the horizontal plane (W/m\({}^{2}\)) \\ \(A\) & ground-reflected albedo irradiance (W/m\({}^{2}\)) \\ \(\theta\) & angle of incidence (\({}^{\circ}\)) \\ \(a_{\text{r}}\) & angular losses coefficient \\ \(\beta\) & tilt angle of the module (\({}^{\circ}\)) \\ \(c_{1}\) & first fitting parameter of [PERSON] model (4/3\(\pi\)) \\ \(c_{2}\) & second fitting parameter of [PERSON] model \\ \end{tabular}
###### Abstract  
Initially, electrofacies were introduced to define a set of recorded well log responses in order to characterize and distinguish a bed from the other rock units, as an advancement to the conventional application of well logs. Well logs are continuous records of several physical properties of drilled rocks that can be related to different lithologies by experienced log analysts. This work is time consuming and likely to be imperfect because human analysis is subjective. Thus, any automated classification approach with high promptness and accuracy is very welcome by log analysts. One of the crucial requirements in petroleum engineering is to interpret a bed's lithology, which can be done by grouping a formation into electrofacies. In the past, geophysical modelling, petro-physical analysis, artificial intelligence and several statistical method approaches have been implemented to interpret lithology. In this research, important well log features are selected by using the Extra Tree Classifier (ETC), and then five individual electrofacies are constructed by using the selected well log features. Finally, a rough set theory (RST)-based whitebox classification approach is proposed to classify the electrofacies by generating decision rules. These rules are later on used to determine the lithology classes and we found that RST is beneficial for performing data mining tasks such as data classification and rule extraction from uncertain and vague well log datasets. A comparison study is also provided, where we use support vector machine (SVM), deep learning based on feedforward multilayer perceptron (MLP) and random forest classifier (RFC) to compare the electrofacies classification accuracy.  
electrofacies classification; extra trees classifier; lithology interpretation; rough sets; knowledge acquisition; decision making +
Footnote †: journal: Applied Sciences
## 3 Multi-Source Information Fusion Method  
In this paper, the statistical analysis method SPRT is used to analyze and process the relay valve output pressure signal to identify latent leakage faults in the relay valve. In addition, data fusion technology is used to find effective data in redundant information and evaluate the target through certain algorithms and evaluation strategies. Data fusion can occur at different levels of information, including the data layer, feature layer, and decision layer [19]. The machine learning method of SVM, which is widely used in pattern recognition, is used to fuse multiple fault features, and the combined fault features are used for the internal and external leakage diagnosis of the relay valve.
# Tomographic Inversion Methods for Retrieving the
###### Abstract  
In this paper, we deal with the problem of retrieving maps of tropospheric Water Vapor (WV) concentration by means of a set of Low Earth Orbit (LEO) satellites orbiting in the same plane and along the same direction. It is assumed that a number of microwave links is deployed between a group of satellites with microwave transmitters onboard and another group with receivers. It is also assumed that the Normalized Differential Spectral Absorption (NDSA) approach is used to provide time series of Integrated Water Vapor (IWV) along each link. The set of links scans an annular region belonging to the orbital plane of the LEO satellites, so that the time series of the IWV measurements can be exploited to retrieve the WV concentration in such a region. This is a typical tomographic inversion problem. The geometry of the acquisition system and the path-integrated nature of measurements well fit the application of the Exterior Reconstruction Tomographic Algorithm (ERTA), which was proposed several decades ago in contexts different from remote sensing. In this paper, we investigate the potential of ERTA for the WV retrieval and compare its performance with that of a least square inversion approach already presented in the literature. The compared analysis has been carried out through simulations that have highlighted the peculiarities and retrieval capabilities of the two tomographic methods, as well as the impact of the richness of the satellite constellation on the overall performance.  
tropospheric water vapor; LEO satellites; tomographic inversion methods Article
# Go Source Code Vulnerability Detection Method Based on Graph Neural Network
## 4 Methodology  
### System Framework  
Existing detection models and methods for Go source code fail to comprehensively extract lexical, syntactic, and semantic features, limiting their ability to identify various types of vulnerabilities. To address this issue, we propose a hybrid semantic-based graph neural network vulnerability detection method for Go programs, named GoVulDect.  
Figure 1 illustrates the detailed design of GoVulDect, which consists of three main modules: **(1) Graph-Level Feature Extraction Module**: This module represents potentially concurrent Go functions as code property graphs (CPGs) and utilizes GraphSAGE, a graph  
neural network based on random walks, to extract graph-level features that incorporate multiple types of semantic information. **(2) Token-Level Feature Extraction Module:** This module extracts token sequences using taint analysis and SpanBERT, a pre-trained model, to embed them into vectors. A Transformer model with multi-head attention is then employed to extract fine-grained token-level features. **(3) Detection Module:** This module fuses the source code features extracted by the previous two modules. Specifically, the graph-level features obtained via GraphSAGE are concatenated with the token-level features extracted by a Transformer-based model, forming a comprehensive representation of the source code. This fused feature vector is then passed to a pre-trained XGBoost classifier [13] to detect vulnerabilities in Go source code. XGBoost, as a scalable end-to-end tree boosting system, allows us to efficiently handle large-scale imbalanced data.  
In contrast to existing hybrid detection approaches such as HyVulDect, which are primarily designed for traditional programming languages like C/C++, GoVulDect is specifically tailored to the concurrency semantics and structural characteristics of the Go language. During preprocessing, we enhance the static modeling of goroutines and channels to more accurately reconstruct potential concurrent execution paths. For feature extraction, GoVulDect incorporates a pretrained SpanBERT model with a span boundary objective (SBO) to generate semantically rich token representations. In addition, GraphSAGE is employed to capture global contextual features from the structural graphs of source code, while the Transformer further models deep semantic dependencies over long token distances. These design choices significantly improve the model's capacity to represent and detect vulnerability patterns in Go source code. Detailed explanations of each component are provided in the following sections.
## 3 Characterization of Materials and Systems  
Impedance spectroscopy provides interesting possibilities to characterize materials based on their complex electrical behavior. The characterization of pharmaceutical materialsis essential for drug discovery, development, and evaluation. In [3], [PERSON] et al. carried out an EIS-based analysis in a wide frequency range from 20 Hz to 30 MHz of four drug formulations (trimethoprim/sulfamethoxazole C14H18N4O3-C10H11N3O3, ambroxol C13H18 Br2N2O.HCl, metamizole sodium C13H16N3 NaO4S, and ranitidine C13H22N4O3S.HCl). Based on arrays of linear R-C models, the different compounds could be differentiated.  
For continuous production in the pharma industry, additional instrumentation to optimize material flow will be needed. Tableing manufacturing has high requirements concerning changes in flow conditions of raw materials, which may lead to a lack of reproducibility. In [4], [PERSON] et al. show how, with impedance spectroscopy supported by an online identification of generalized order parametric models, changes in material properties can be tracked by correlation of the model parameters. The proposed solution is easy to implement and provides significant added value to the pharmaceutical industry.  
Battery cell characterization is also a classical use case for impedance spectroscopy. Due to the advancement of electro-mobility, the need for online diagnostic methods for batteries is increasing. State-of-health (SoH) estimation can be carried out based on a modeling approach. The most common method is thereby to model the acquired impedance spectrum with equivalent circuits and focus on the most sensitive parameters, namely the charge-transfer resistance. In [5], [PERSON] et al. introduce a detailed model of a battery cell. Based on the physical meaning of the model parameters, a novel approach is proposed for SoH assessment combining parameters of the impedance spectrum by building the ratio of the solid electrolyte interphase (SEI) resistance to the total resistance of SEI and the charge transfer. This ratio characterizes the charge-transfer efficiency at the electrodes' surfaces and should decrease systematically with SoH and show a systematic correlation to the number of charging cycles on individual cell parameters, providing the basis of a novel online SoH assessment method.
# Effect of Rock Abrasiveness on Wear of Shield Tunnelling in Bukit Timah Granite
## 4 Discussion  
### Wear Coefficient of Disc Cutters  
As can be seen from Table 4, the wear coefficient of the disc cutter is influenced by the disc cutter position and the weathering grade of Bukit Timah granite. The wear coefficient for the mixed zone was similar to that of G(III) grade ground. The effect of disc cutter position on the wear coefficient has already been described above. As the degree of weathering increased, the wear coefficient of disc cutters decreased. This trend coincides with the variation in the average values of _CAI_ based on the degree of weathering, as shown in Table 1. Therefore, the wear coefficient of disc cutters and _CAI_ are closely correlated. Figure 11 displays the relationship between the _CAI_ values and the wear coefficient of 19 inch disc cutters with respect to the cutter position. For comparison with existing studies, the results of the models mentioned in Figure 1 are also plotted. The geomechanical properties of rocks, except _CAI_, were insufficient to be considered for comparison with the wear coefficient; thus, the relationship between the geomechanical rock properties and the wear coefficient was not investigated in this study. Similar to Figure 1, the wear coefficient increased with _CAI_.  
Empirical equations for the wear coefficient relationship with the _CAI_ values are recommended as follows:  
\[wc\;[\mathrm{mm}/\mathrm{km}]=\frac{1}{50.054-7.03 CAI}\;\mathrm{for\;center\; cutter} \tag{7}\]  
\[wc\;[\mathrm{mm}/\mathrm{km}]=\frac{1}{74.589-8.432 CAI}\;\mathrm{for\;face\; cutter} \tag{8}\]  
\[wc\;[\mathrm{mm}/\mathrm{km}]=\frac{1}{54.303-5.673 CAI}\;\mathrm{for\;transition\; cutter} \tag{9}\]  
\[wc\;[\mathrm{mm}/\mathrm{km}]=\frac{1}{57.754-7.915 CAI}\;\mathrm{for\;gage\; cutter} \tag{10}\]  
Figure 11: Relationship between the _CAI_ values and the wear coefficient.
# Securing Construction Workers' Data Security and Privacy with Blockchain Technology
## 3 Methodology
### Experimental Design of the Proposed Blockchain  
#### 3.1.1 Blockchain System Architecture of the Case Study  
In this architecture, the biodata and safety data of the tilers are stored in a decentralized manner on the blockchain network. Each worker's information is encrypted and stored in blocks, thus forming an immutable and tamper-proof ledger. Data encryption aids in preventing unauthorized users from accessing data stored within a blockchain network and its data storage system [92]. Access to the data is strictly controlled through cryptographic keys, thus ensuring confidentiality and privacy. Unlike traditional centralized systems, where a single point of failure can compromise the privacy of workers, the distributed nature of the proposed blockchain provided an added layer of protection against data  
Figure 2: Overall framework of this study.  
breatches and unauthorized access and allowed for real-time updates and synchronized data [93] across all relevant stakeholders, including employers, regulatory bodies, and project managers. Smart contracts or chaincodes are implemented to automate key processes related to construction workers' management, such as verifying trade type or skills, certifications, high-risk-level workers, and tracking training programs.
# Impact of Patient Size and Positioning on Radiation Dose in Multiphase Liver CT Examinations
## 3 Results
#### 3.5.2 Group with Abdomen and Pelvis Imaging  
The analysis of the 'Abdomen + Pelvis' group encompassed 146 patient observations. Patients in this group presented an average height of 1.68 m, a weight of 73.80 kg, and a BMI of 26.26. Here, weight and BMI showed a more pronounced impact on the X-ray tube output's mA settings across the three operational phases. Particularly, the correlation between BMI and the mA settings in the third phase was statistically significant (Figures 16 and 17).
## 3 Results and Discussion
### Heat Recirculation for VAM and PSM  
#### 3.3.1 Definition of Solid-To-Gas Preheating Zone and Heat Loss Zone  
In micro-combustion, the conjugate heat transfer calculation among gas mixture, wall and solid matrix is helpful to gain more in-depth insights into the heat transfer processes of the VAM and PSM combustors.  
Referring to the definition of a preheating zone and heat loss zone in the VAM combustor, \(T_{g}\) is lower than \(T_{s}\) in the \(q_{g-s}\) < 0 region, meaning that heat is transferred from the solid matrix to the gas mixture. In the \(q_{g-s}\) > 0 region, heat released from chemical reactions leads to higher \(T_{g}\) and lower \(T_{s}\). Hence, the contour with \(q_{g-s}\) = 0 is the boundary to separate the preheating and heat loss zones. The contour with H\({}_{2}\) = 5% is identified as the end of  
Figure 5: Comparison of temperature obtained by the PSM and VAM.  
the heat loss zone. Therefore, the region determined by \(q_{g-s}<0\) and H\({}_{2}>5\%\) is called the \"preheat zone,\" and the \"heat loss zone\" and is identified as the region determined by \(q_{g-s}>0\) and H\({}_{2}>5\%\), as shown in Figure 6.  
In our previous study [39], the contour with \(q_{g-s}=0\) and H\({}_{2}=5\%\) was continuous for the VAM combustor. The reaction zone in the PSM combustor displays a number of scattered regions because of multiple small flames, and the contours thus divided multiple scattered areas.