esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Applied Sensors  
K-type thermocouples (TCs) are integrated initially into the specific points of the workspace and were also retrofitted to the system by us for measurement purposes. The construction of the retrofitted TC is a fast-response, general-purpose welded tip PFA (perfluoroalkoxy) insulated sensor. These TCs can be placed inside the workspace in a flexible manner, without workspace and vapour perturbing fixtures (the TC type is shown in Figure 3, left.)  
The applied K-type TCs have 0.2 mm solid conductor covered by insulation PFA layer allowing application in the operating range between \(-\)75 \({}^{\circ}\)C to +250 \({}^{\circ}\)C (ideal for lead-free reflow temperatures) with the precision of \(\pm\)1 \({}^{\circ}\)C (acceptable tolerance, in our case from ambient to 230 \({}^{\circ}\)C). The small 0.5 mm diameter of the welded spot enables fast response. The length of the thermocouple wires was chosen so that they can be immersed to the same level as the pressure probe (presented later in Section 2.4) without perturbing the vapour and leaving any significant impact on the measurement results. A compact thermal profiler (V-M.O.L.E. 3-Channel Thermal Profiler) was used for the TC data acquisition, with a sample rate of 1 s.  
Based on the early approximations, the hydrostatic pressure values of the generated vapour column are in the range of 0-30 Pa. The appropriate pressure sensor was chosen for this purpose from the High-accuracy Silicon Ceramic (HSC) series (Figure 3, right), which has a pressure range of 0-6 kPa (Honeywell, Fort Mill, SC, USA). While the range seems to be large for the purpose, the sensor is considered as highly sensitive among gauge-type products. The HSC Series are piezoresistive gauge silicon pressure sensors, having an analogue output for pressure reading over the full-scale pressure span calibrated for specific ranges. The sensor was acquired in a robust DIP package format (2.54 mm distance between pins). The port has \(\sim\)2.5 mm diameter; we used a silicone hose with 2 mm inner diameter. Under \(\sim\)100 kPa, clamps are not typically required, so we omitted the use of such fixing [31]. With an appropriate analogue-digital converter, the resolution can be kept in the sub-Pa range. NI LabView software and a 16-bit NI MyDAQ hardware (NI, Austin, TX, USA) was used for gauge-type pressure sensor data acquisition. With this setup, the pressure resolution can be kept around 0.1 Pa. The sampling was performed on a per-second basis. We consider this sample rate appropriate for monitoring the \(\sim\)15 min long process.
# The Automatic Algorithm of Optimizing the Position of Structured Light Sensors
## 2 Optimization Method for the Number and Position of Multiple Sensors
### Sensor Number and Pose Optimization  
The sensor optimization algorithm is used to solve the problem of specular reflection in structured light 3D measurements. This algorithm is based on the improvement of the differential evolution algorithm [21; 22; 23]. The main objective of the algorithm is to optimize the sensor system and derive the number and position of projectors and cameras. The algorithm is based on an improved differential optimization algorithm that is able to quickly iterate over the optimal number and position of sensors. This algorithm is efficient for optimizing the sensor layout because it can adaptively adjust the parameters based on the number of iterations, accelerating the convergence of the results to obtain sensor data that meets expectations. The flowchart of sensor number and position optimization is shown in Figure 8.  
To simplify the computational complexity, the specular reflection problem is transformed into a sensor position search problem. The initial data of the algorithm are D rows and N columns of integer-valued parameters. N denotes N sensor combinations, D denotes D sensors, and each combination contains the position information of D sensors. The initial combination is denoted by \(X\). Then, the mutation operation is performed according to Equation (5), where \(i\), \(r_{1}\), \(r_{2}\), and \(r_{3}\) denote the dimensions, and \(r_{1}\), \(r_{2}\), and \(r_{3}\) are inte  
Figure 8: Sensor number and pose optimization.  
gers that are different from each other. F is the variation operator, which is calculated by Equation (6), where \(F_{0}\) is the initial \"mutation\" operator and \(G\) denotes the maximum number of iterations of the optimization algorithm. Next, boundary condition constraints are imposed on the variant combinations to ensure that the newly generated combinations are in the feasible domain. After comparing the objective function values of the initial combination \(X\) and the variant combination \(V\), a crossover operation is performed. Replace the initial combination \(X_{i}\) with the corresponding combination in the variant combination \(V_{i}\), whose function value is better than that of the initial combination \(X_{i}\). Furthermore, determine whether the parameter value \(D\) needs to be changed and whether the termination condition is satisfied. If the termination condition is satisfied, the optimal combination is the output. The objective function value is calculated by Equation (7), where \(F_{fin}\) is the non-covered area and specular reflection area of the nth sensor in a sensor combination, \(F_{sum}\) is the non-covered area and non-specular reflection area of the combination, and the smaller \(F_{sum}\) is the better area. The pseudocode of the algorithm is shown in Appendix A.  
\[V_{i}=X_{r1}+F\bullet(X_{r2}-X_{3}) \tag{5}\]  
\[F=F_{0}*2^{\lambda},\lambda=e^{1-\frac{Cm}{Cm+1-G}} \tag{6}\]  
\[F_{sum}=F_{fei1}\odot F_{fei2}\odot\cdots\odot F_{fin} \tag{7}\]
## 2 Materials and Methods
### Ltc  
The experiments are then repeated using LTC instead of LSTM. The SNR values are now passed as inputs to LTC. LTCs are also a type of Recurrent Neural Networks (RNN). Unlike the traditional RNNs, which process information in discrete steps, LTC networks handle information that is more unpredictable over time. Mathematically, they operate using differential equations, allowing them to model systems that evolve more dynamically [8]. A key feature of LTC is the concept of a \"liquid time constant\", from which LTC gets its name. This refers to the fact that each neuron in the network has its internal timescale for processing information. This flexibility allows the network to capture more granular rates of change in the data even at the time of inferencing.
## 4 Discussion  
This study was conducted in collaboration with the Thailand Department of Highways and is an example of high-impact digital transformation that can improve public service and safety. We developed a prototype mobile application to assist field technicians to conduct visual bridge defect inspection and improved collaboration among team members. To develop a successful user driven application, we applied the unified theory of acceptance and use of technology (UTAUT) to identify the key factors that influence behavioral intention to use of the mobile application. The fundamental specifications were derived from our previous research on mobile applications for construction process quality control [6], AI for visual bridge defect-inspection system [7], and related literature reviews in the area of teamwork, project management, and visual inspection. Building on this body of work, the current study proposes a modified technology acceptance model UTAUT research framework, which identifies parameters that influence user intention to use the product by adding a new VIS factor that leads to either performance expectancy or effort efficacy.  
As a result, two valuable insights were obtained. First, the user expects the technology to improve their routine task performance. Second, the intention to use will occur when prospective users trial the product and see the potential benefit of the technology. This  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Log in** & **Project Overview** & **List of Bridge under** & **Notification** \\ \hline
**Add/Edit Project** & **Add/Edit Bridge** & **Add New Task with** & **Add Task Detail** \\ \hline
**Task Detail for** & **Assigned Person** & **Edit Task** & **User Setting** \\
**comment** & & & \\ \hline
**Addi** & **Assigned Person** & **Edit Task** & **User Setting** \\ \hline  
suggests that allowing the user to test the product in a free trial could be an effective strategy. Next, the product concept was developed including the system architecture, use case diagram, user interface, and backend design. This process was repeated several times according to the stage-gate process \"Triple A\" concept. After the prototype was constructed, it was used for a field trial in a controlled environment and a user satisfaction survey was conducted. The overall satisfaction score of 14 users was 4.024 of 5.00, reflecting the high satisfaction for user behavioral intention to use the product. However, satisfaction scores were lowest (3.714 of 5.00) with the user interface design section and so the UXUI will be improved with fine-tuning after the analyses of additional user feedback.  
Although, few aspects of the new system need to be improved before replacing traditional methods, there is an agreement that this new system can significantly enhance the performance of the overall inspection processes. It might be slowly replaced or parallelly implemented with the traditional methods but eventually will be a main tool for bridge inspection as a government digital transformation policy.
## 4 Discussion  
In this study, an ML classification model showed a higher classification performance with dual FBB than with dFBB. This is because eFBB and dFBB have complementary properties as biomarkers [25]. dFBB provides information on amyloid deposition and eFBB provides information on neurodegeneration [5; 6; 7]. Some patients with AD have at least one important neurodegenerative marker, including reduced FDG metabolism, but no detectable amyloid deposition [26]. Dual FBB is more likely to detect cognitive impairment at follow-up by identifying both markers of amyloid deposition and neurodegeneration [26; 27]. In addition, dual FBB has advantages, such as cost savings, reduced radiation exposure, improved convenience for patients and caregivers, and reduced social costs [28]. In this regard, the preceding studies classified using multimodal data are as follows. [PERSON] et al. (2021) [29] reviewed 116 studies that applied machine learning to neuroimaging data from 2010 to May 2021. In this paper, to predict the progression from MCI to AD, or early prediction, we analyzed the existing classification method based on ML algorithms applied to neuroimaging data together with other variables. Most of the studies used data such as MRI and PET, and multimodal data achieved better classification accuracy than one image. SVM, the most used algorithm, showed an average accuracy of 75.4%, and the average accuracy of the convolutional neural network was 78.5%, which was higher than that of SVM. [PERSON] et al. (2021) [30] classified AD, MCI, and HC using RF with multimodal data (including MRI images, FDG PET, and cognitive scores). As a result, cross-validation accuracy of 93.95% and F1 score of 93.94% were obtained. [PERSON] et al. (2022) [11] classified subjective cognitive decline (SCD) and HC using SVM for multimodal data including MRI images and genetic information. When only MRI images were classified, the classification accuracy was SCD 79.49% and HC 83.13%, and when classified as multimodal data, the classification accuracy improved to SCD 85.36% and HC 82.52%. [PERSON] et al. (2022) [31] classified multimodal data including FDG, PiB PET, and cognitive test data using adaptive hyper parameter tuning random forest ensemble classifier. Among the three classes (AD, MCI, and HC), two classes were selected and classified. In the case of binary classification of AD and HC, it showed accuracy of 100%. In the case of binary classification of MCI and HC, it showed accuracy of 91% and specificity of 100%. In the case of binary classification of AD and MCI, it showed accuracy of 95%, specificity of 100%, and sensitivity of 80%. Recent studies used ML algorithms to classify images of Alzheimer's dementia, MCI, and HC with very high accuracy. In general, by adding data rather than a single image, it was classified as multimodal data to increase the classification accuracy. When multimodal data is used, classification accuracy increases, but tests for data increase. However, addi
# Correlation of Optical Constants and Morphologies with Mueller Matrix for Micro-Rough Surfaces
###### Abstract  
This paper focuses on the coupling relationships between the optical constants (\(n\): refractive index; \(k\): extinction coefficient) and Mueller matrix elements, as well as between the morphological parameters (\(\sigma\): root mean square roughness; \(\tau\): correlation length) and Mueller matrix elements, of randomly micro-rough surfaces. The electromagnetic response of randomly micro-rough surfaces was simulated by the finite-difference time-domain method, so that the rough surfaces' reflection coefficients of incident light in the p and s directions could be obtained. According to the formula for the Jones-to-Mueller matrix conversion, we obtained a \(4\times 4\) Mueller matrix of rough surfaces. The simulation method was validated with experimental results measured by Mueller matrix spectroscopic ellipsometry. It was found that the Mueller matrix element \(m_{12}\) has great potential to invert the optical constants of the rough surfaces, whose refractive indices, \(n\), and extinction coefficients, \(k\), are in the ranges of \(0\leq n\leq 4\) and \(0\leq k\leq 10\), respectively. The Mueller matrix element \(m_{34}\) is proportional to the morphological parameters \(\sigma/\lambda\) (\(\lambda\): incident wavelength) or \(\sigma/\tau\). Moreover, the expressions \((S+\beta_{2})\propto\sigma/\lambda\) and \((S+\beta_{2})\propto\sigma/\tau\) can be applied to predict the morphologies of rough surfaces within morphological parameter ranges of \(0.003\leq\sigma/\lambda\leq 0.015\) and \(0.125\leq\sigma/\tau\leq 0.75\). This research signifies a key step toward the ability to invert the morphological parameters or optical constants of micro-rough surfaces through a Mueller matrix.  
Mueller matrix; morphological parameters; optical constants; rough surfaces Article
# Partial Fake Speech Attacks in the Real World Using Deepfake Audio
## 9 Discussion  
**The Importance of Ground-Truth Data.** Our experiments show that even when participants know the speaker's voice, they are fooled by partial fake speech. As a result, it is necessary to develop detection models that efficiently detect fake and partial fake speech. The availability of real speech samples for all target individuals is essential so the attacker can launch fake and partial fake speech attack. At the same time, a detection mechanism having the target individual speech can improve the protection against fake and partially fake speech attack [76]. For example, if an organisation provides a service via a call centre, they have to obtain a short speech from an individual during the enrolment process. Later, the organisation will have a real recording of all enrolled users, and when someone impersonates any enrolled user, the system can identify the caller based on the voice. All existing audio datasets do not contain both real and fake speech from the same speaker [77].  
**Various Techniques for Fake Generation Speech Exist.** These including TTS and VC, online commercial services, and open-source models. Yet, all detection methods target specific techniques for fake generation speech. We believe there is a significant need for a universal method that can detect all types of fake speech regardless of generation methods, as well as a need for robust detection models that have the ability to detect data that are not included in the training model.  
**Real-World Proactive Defence against Synthesis Attacks.** Content creators, educational platforms, and similar media content providers can increase the protection of the voices in their media before publishing them, like the proactive defence approach leveraging adversarial examples to disrupt unauthorised speech synthesis proposed in the Anti-Fake study [76]. That approach ensures that the synthesised DeepFake audio does not sound like the victim's voice to either humans or machines.
## 4 Results and Discussion
The benchmark guidelines introduce a protocol that has independent procedures to access the medium for uplink and downlink transmissions adapted to their use cases. The downlink access scheme is contention-free as a means to ensure that collision of command information to the sensor nodes could be minimized. A hybrid access scheme is adopted in the uplink to improve the network throughput and meet the transmission demand generated due to monitoring the data observation of sensor nodes. This keeps transmission resources flowing in a more efficient and just manner. The newly established communication benchmark [42] also introduces, into the MAC protocol, the design of a special propagation time imperfection that exists only in underwater acoustic communication. The numerical results and performance comparison show that the new proposed protocol outperforms all existing state-of-the-art MAC protocols in UWSNs in terms of the throughput, end-to-end time delay, and transmission fairness. Benchmark 3  
The third benchmark [43] focuses on the unmanned exploration and exploitation of the oceans of the Earth, compelling interests in developing more robust methods for unmanned underwater mobile vehicles management and relevant task scheduling. But, for these unmanned vehicles as nodes, due to their displacement, a number of methods are struggling to minimize the amount of signaling, and thus delays [43; 44]. As such, they have not produced an adaptive solution that is highly efficient in multiple conditions-based scenarios. Due to the mobility of nodes and specific characteristics of the underwater channel, the signaling of free MAC design introduced in a new manner already adapts to regularly changing conditions as signaling incurs delays. With multi-agent reinforcement learning, based on the UW-ALOHA-QM protocol [43], this benchmark gives nodes the ability to interact with the dynamic underwater wireless environment in a trial-and-error manner. This provides additional resilience and flexibility to the network. In order to assess our approach, they carried out multiple simulations based on four greatly varying scenarios, it is a known fact that each where nodes move differently [45; 46; 47]. This demonstrated an improvement of 300% in the channel utilization of the UW-ALOHA-QM Protocol compared to existing mobile network protocols.
## 3 Datasets and Methods
### Methodology  
The task of sign recognition from a given hand gesture is essentially an image classification problem. Table 3 represents the operations involved in sign recognition. The proposed system takes as input an annotated dataset of sign images/hand gestures from which it extracts five features followed by dimensionality reduction on the convolutional-related features. Lastly, it trains these features on supervised classifiers in an attempt to achieve maximum recognition accuracy which is a crucial metric in evaluating the performance of the trained models.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Size** & **No. of** & **Samples** & **Test Size** \\  & & **Classes** & **per Class** & **Test Size** \\ \hline
**ASL alphabet** & 72,000 & 24 & 3000 & 20\% \\
**ASL finger-spelling dataset** & 65,774 & 24 & \textgreater{}2600 & 20\% \\
**Cambridge hand gesture dataset** & 63,188 & 9 & \textgreater{}6000 & 20\% \\
**NUS I dataset** & 240 & 10 & 24 & 10.4\% \\
**NUS II dataset** & 2000 & 10 & 200 & 12.5\% \\
**ISL digits** & 2000 & 10 & 200 & 12.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dataset details–ASL alphabet, ASL finger-spelling dataset, Cambridge dataset, NUS I and II, ISL digits.  
Figure 1: Datasets–ASL alphabet, ASL finger-spelling dataset, Cambridge dataset, NUS I and II, ISL digits.
## 2 Materials and Methods
### UHPLC Equipment  
UHPLC analyses were carried out on an Agilent 1290 Infinity chromatograph (Agilent Technologies, Santa Clara, CA, USA) equipped with a photodiode array detector (PDA), thermostat and autosampler. The column used in the separation was Zorbax Eclipse XDB-C18 column (1.8 \(\upmu\)m, 100 mm \(\times\) 3.0 mm I.D.; Agilent Technologies, Santa Clara, CA, USA). The mixture 90:10 (\(v/v\)) of methanol and 1% (\(w/v\)) aqueous orthophosphoric acid was used as the mobile phase for isocratic elution. The injection volume was 3 \(\upmu\)L. The flow rate and the temperature of the analysis were optimised as described below to the values of 0.44 mL/min and 18 \({}^{\circ}\)C, respectively. For quantitative purpose, the analytes were monitored at 215 nm. The investigated compounds were identified by comparison of their retention times and UV spectra with that of reference standards.
## 2 Computational Model
### Governing Equations
Concurrently, turbulent viscosity is shaped as a product of turbulent velocity and turbulent length scale. The \(k-\epsilon\) turbulence model is predominantly economical computationally and effective in solving the turbulence in the core flow region. However, at the same time, it does not solve the boundary, rather it estimates it using the built-in wall functions. Consequently, the \(k-\epsilon\) turbulence model is not appropriate for flows with the boundary layer separation, flows with sudden changes in the mean streamflow, flows in revolving fluids, and flows over curved surfaces. On the other hand, the shear stress turbulence (SST) model combines two models [19; 20; 21; 22; 23; 24; 25], i.e., \(k-\omega\) and \(k-\epsilon\) turbulence models according to the distance from the wall. Wilcox \(k-\omega\) is utilized to resolve the flow in the vicinity of the walls for accurate prognostication of the boundary layer, whereas the \(k-\epsilon\) turbulence model is solving the flow in fully developed regions to gain advantage from its robustness, economy, and free stream independence [13; 19]. Therefore, the shear stress turbulence (SST) model accounts for the transport of turbulent shear stress and avoids over-prediction of eddy viscosity. Numerous researchers [26; 27; 28; 29; 30; 31] have adopted the shear stress transport (SST) turbulence model established by [PERSON] [19] and achieved accurate aerodynamic predictions of rotating machines and complex flows. Based on the above discussion, the SST turbulence model was adopted for the current work as the flow was rotating. The accurate prediction of the boundary layer is critical for the precise evaluations of erosion. Thus, the turbulence model that does not resolve the boundary layer and uses wall functions instead will not determine the flow feature near the walls. Many researchers [31; 32; 33] in the literature have employed the shear stress transport model (SST model) [19] to predict the near-wall phenomenon accurately for rotating machines. Thus, \(the\)\(SST\)\(k-\omega\) turbulence model was adopted in the current study for turbulenace modeling. Details on the model could be found in [19].