esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Analytical Method  
The details of sample analysis can be viewed in our previous studies [1,2]. Technically, extraction was performed using a method recommended by the United States Environmental Protection Agency [28]. The sample (10 g) was first lyophilized in an Eyela FDU-1200 freeze dryer (Tokyo Rikakikai, Japan). Anhydroous sodium sulfate was then added and mixed before toluene was added for solvent exchange (40 mL). A sonicator (VCX 750, Sonics Materials, Newtown, CT, USA) was used for the extraction in three cycles (approx. 3 min/cycle). The conditions were set at an amplitude of 62%, pulse on was set at 6.0, and pulse off was set at 3.0. The clean-up was performed using another USEPA method [29]. After ultrasonic extraction, sample was processed through a column with three filtering layers: glass wool, acid silica gel (90 g silica gel mixed with 20 mL sulphuric acid 98.08%), and anhydrous sodium sulfate. The eluate was evaporated in a Turbo Vap II concentrator (Biotage, Charlotte, NC, USA) using nitrogen (pressure 10 psi). The concentrate was solvent exchanged with n-hexane 95% (50 mL) and then added with acid silica gel before being passed through Whatman@filter paper (pore size 110 mm; manufactured by GE Healthcare UK Limited, Amersham Place, United Kingdom). The final extract was added with sulfuric acid before storage at \(-4\)\({}^{\circ}\)C until analysis. PCDD/F analysis was performed according to USEPA [30]. A 6890-5973 gas chromatography-mass spectrometry (GC-MS) system with a column HP-5 MS (30 m \(\times\) 0.25 mm \(\times\) 0.5 \(\upmu\)m) was used to quantify PCDD/Fs. The oven temperature was set at 40 \({}^{\circ}\)C for four minutes before ramping to 300 \({}^{\circ}\)C (10 \({}^{\circ}\)C/min) for twenty minutes. Selective ion monitoring (SIM) mode was employed according to the retention time and characteristic ions. Concentration calculations and calibrations were based on the standard calibration curve. To efficiently interpret removal efficiencies, PCDD/Fs' concentrations were transformed into TEQs as introduced by the World Health Organization at the Stockholm Convention.
# An Electroglottograph Auxiliary Neural Network for Target Speaker Extraction
###### Abstract
However, most of the previous studies have relied on the use of clean speech from the target speaker and they have achieved their targets by training networks on data from the target speaker, thus generating models to extract particular targets. The models are trained on fixed speaker pairs or target speakers. These models also rely on the assumptions that the amount of the data being put into the network is sufficient and substantial, and that speakers without substantial data cannot be extracted. However, clean speech from particular target speakers cannot always be obtained, and sometimes only a few of the utterances from the target speaker can be recorded in a conversation. So, we tried to find a method that could solve the problem of needing substantial clean speech from the target speaker. Some methods have been already proposed to solve this problem. The SpeakerBeam network [13] was proposed to solve the speech extraction problem by training speaker-independent models that are informed by additional speaker information rather than creating particular models for target speakers. However, this method relies on data provided by the additional speaker information and the additional information can only be obtained from conversations without any speech overlaps or the personal devices, which is inadequate for training. To solve the problem of needing additional speech records, we explored some new approaches to speech extraction and investigated EGG signals. These kind of signals come from the vibrations in human throats, and can be recorded without interference from other noises. In addition, the EGG signals of particular speakers can be recorded during conversations in any situation, which increases the amount of additional target speaker information. By utilizing EGG signals from a target speaker, features can be extracted from the signals and applied to deep learning methods for speech extraction. Because EGG signals provide information about particular speakers, designated speech can be extracted from a conversation. Since EGG signals can be obtained in any situation during the process of speaking, they can also be used for real-time speech extraction. This paper is organized as follows: In Section 2, we examine studies related to speech extraction and present our work on the topic. Section 3 introduces the materials and the methods used in our study and illustrates our model in detail. In Section 4, we compare the results from our network to those from previous studies using different datasets and under different signal-to-noise ratios (SNRs). In Section 5, we discuss our work and findings. Finally, Section 6 presents our conclusion and introduces the future directions of our study.
## 1 Introduction-The Emergence of Agile Testing Practices
### Test Driven Development (TDD)  
Agile approaches to software development are based on the understanding that software requirements are dynamic, where they are driven by market forces [3; 4; 5]. An agile approach incorporates shared ideals of various stakeholders, and a philosophy of regularly providing customers with product features in short time-frames [6]. This frequent and regular feature delivery is achieved through a team based approach [7] and by having automated development, testing and release processes [8].  
In many agile development teams, the testing component is based on the philosophy and techniques of TDD, which is an evolutionary approach to development, involving implementing a test before implementing and refactoring the code [9; 10]. These tests should be small, address a small atomic piece of functionality and be executable by a test tool. Tests written this way can also be interpreted as specifications or requirements that the system under test has to meet. There are also tools, (such as Fitnesse), that allow expressing business requirements in such a way that they can be used as part of automated testing. These requirements are also referred to as executable specifications.  
TDD supports the short development cycles of agile methods while ensuring quality requirements are being met. TDD was first used for software development by NASA in the 1960s, but in the context of structured methods with a single development cycle the practice was infrequently used [11]. It was not until the late 1990s that TDD gained wide acceptance as a common practice within agile methods, particularly eXtreme Programming (XP) [12].
## 4 Results and Discussion
### Modified Compact Tension Specim (MCTS)
#### 4.1.2 G3 Specim  
In order to allow the influence of small holes on crack growth path to be investigated, the G3 specimen has an 8 mm diameter hole at a horizontal location h1 = 10.5 mm and a vertical location, v1 = 3 mm. The predicted fatigue crack growth path in both softwares is identical to the experimental path obtained by [32], as shown in Figure 8. The predicted values of the first and second mode of stress intensity factor are shown in Figure 9 with  
Figure 6: Predicted crack growth path for G2 specimen (**a**) ANSYS Workbench, (**b**) FRANC2D/L and (**c**) Experimental [32], (**d**) numerical [33], (**e**) FRANC3D [32].  
Figure 7: Stress intensity factors with corresponding crack length for specimen G2, (**a**) \(K_{I}\) and (**b**) \(K_{II}\).  
a difference of 0.75%. Another two comparisons were made with the numerical data provided by [29] and the FRANC3D results [28], which exhibit an incomplete crack growth path when compared to the results of this study as depicted in Figure 8d,e. As shown in Figure 9a, there were also similar results for K\({}_{\text{I}}\) with the numerical results obtained by [29]. Moreover, as shown in Figure 9b, for the second mode of stress intensity factors, \(K_{II}\) has remarkable values, which influence the crack growth path to propagate in a curved direction toward the hole. These values of KII were also obtained ambiguously by [29] which may be due to the large scale of the curves as a result of merging \(K_{I}\) and \(K_{II}\) in the same curve.  
In particular, the larger hole not only reduces the distance between its edge and the original crack location in the horizontal direction, but also reduces the distance between its edge and the specimen's axis in the vertical direction, making it more effective of transforming the crack path.  
Figure 8: Predicted crack growth path for G3 specimen (**a**) ANSYS Workbench, (**b**) FRANC2D/L and (**c**) Experimental [32], (**d**) numerical [33], (**e**) FRANC3D [32].
# Improved Exponential Phase Mask for Generating Defocus Invariance of Wavefront Coding Systems
## 1 Introduction
Therefore, an improved exponential phase mask is proposed in this paper. Through the Taylor expansion of the exponential phase mask function, the relationship between the expansion term and the wave aberration type is analyzed, and a new phase mask function is devised to eliminate the offset aberration generated by the exponential phase mask. Fisher information is used to construct the optimization equation to optimize the surface parameters of the improved phase mask. By using a numerical analysis method, the performance of the improved phase mask is compared to that of the exponential phase mask, cubic phase mask, sinusoidal phase mask, logarithmic phase mask, and tangent phase mask. To illustrate the phase mask's defocusing imaging capabilities, the invariance of MTF and the shift of PSF are calculated. In addition, imaging simulations were performed to evaluate the suggested phase mask.
# High-Efficiency Closed-Loop Control of a Robotic Fish via Virtual Musculoskeletal Methodology
## 2 Modeling of Two-Joint Robotic Fish
### The Dynamical Model  
The dynamical model of the two-joint robotic fish is established with Lagrange-Euler method shown as follows:  
\[\tau_{i}=\frac{d}{dt}[\frac{\partial L}{\partial q_{i}}]-\frac{\partial L}{ \partial q_{i}} \tag{3}\]  
where \(q_{i}\) is the system variable, \(q_{i}\) is the first derivative of the system variable, \(\tau_{i}\) is the generalized force or torque of the system, and \(i\) is the number of system variables. As we set that the robotic fish only swims horizontally, the gravitational potential energy is constant. Thus, \(L\) is the kinetic energy of the system, which is given in the Appendix A.  
\(q_{i}\) represents the three degrees of freedom in the model: \(\theta_{10}\), the angle of the first joint; \(\theta_{21}\), the angle of the second joint; and \(X_{0}\), the \(x\)-coordinate of the head.  
The \(\tau_{i}\) is expressed as  
\[\left\{\begin{array}{ll}\tau_{1}&=\left(\begin{array}{c}L_{d1}\sin\theta_ {10}\\ L_{d1}\cos\theta_{10}\end{array}\right)\times F_{1}+\left(\begin{array}{c}L_ {1}{\sin\theta_{10}}+L_{d2}\sin(\theta_{21}+\theta_{10})\\ L_{1}{\cos\theta_{10}}+L_{d2}\cos(\theta_{21}+\theta_{10})\end{array}\right) \times F_{2}+M_{1}\\ \tau_{2}&=\left(\begin{array}{c}L_{d2}\sin(\theta_{21}+\theta_{10})\\ L_{d2}\cos(\theta_{21}+\theta_{10})\end{array}\right)\times F_{2}+M_{2}\\ \tau_{3}&=F_{0}\cdot\left(\begin{array}{c}1\\ 0\end{array}\right)+F_{1}\cdot\left(\begin{array}{c}1\\ 0\end{array}\right)+F_{2}\cdot\left(\begin{array}{c}1\\ 0\end{array}\right)\end{array}\right., \tag{4}\]  
where \(\tau_{1}\), \(\tau_{2}\), and \(\tau_{3}\) are the non-conservative forces to generalized coordinates \(\theta_{10}\), \(\theta_{21}\), and \(X_{0}\), respectively. \(M_{1}\) and \(M_{2}\) are the torques generated by the first and second joint. \(F_{0}\), \(F_{1}\), and \(F_{2}\) are the hydrodynamic forces on the head, body, and tail, respectively.  
Neglecting the force of friction, the resistance force to the head is  
\[F_{0}=\frac{1}{2}\rho C_{0}V_{0}^{2}A_{0}\cdot(1,0)^{T}, \tag{5}\]  
where \(\rho\) is the density of the fluid, \(C_{0}\) is the resistance coefficient of the head, \(V_{0}\) is the velocity of the robotic fish, and \(A_{0}\) is the maximum cross-sectional area of the head.  
By simplifying the body as a plane, the hydrodynamic force to the body is  
\[F_{1}=\frac{1}{2}\rho C_{1}(V_
## 2 Material and Methods
### Reflectance Measurements  
Leaf spectral reflectance measurements were collected using a broad range mini spectroradiometer (LR1; ASEQ, Vancouver, BC, Canada) with a fiber-optic of 25\({}^{\circ}\) field of view. The instrument measures spectral reflectance between 300 and 1000 nm with a sampling interval of less than 1 nm. To reduce atmospheric condition changes, the spectroradiometer was calibrated using a white Spectralon reference panel (Labsphere, North Sutton, NH, USA), which can be regarded as a Lambertian reflector. Incident solar irradiance was immediately determined using the same white reference panel prior to the radiance measurements. All spectral measurements were carried out at a nadir view angle ca. 1 cm above the leaf. The ground-projected instantaneous field of view (GIFOV) was thus about 4.4 mm. In each plant, we measured three different leaves at the top of the canopy as replicates. The photochemical reflectance index (PRI, [21; 22]) was calculated from the reflectance data (Rx implies reflectance at x nm) as:  
\[\mathrm{PRI}=(\mathrm{R531}-\mathrm{R570})/(\mathrm{R531}+\mathrm{R570}) \tag{3}\]  
Additionally, we also retrieved the reflectance ratio R690/R630 based on the red-edge region of the spectrum. Because changes in the reflectance at 690 nm (R690) can be attributed to the absorptions of pigments, plant physiological variations and also ChlF which are associated with photosynthetic activity, and R630 is relatively less sensitive to photosynthesis and fluorescence emission [49].  
The differential PRI (\(\Delta\)PRI\({}_{\mathrm{recovery}}\)) and R690/R630 (\(\Delta\)R690/R630\({}_{\mathrm{recovery}}\)) were obtained through nine different drought treatments values subtracted from corresponding recovery treatments.
# Applications of Nanomaterials in RFID Wireless Sensor Components
## 5 Applications
### Environmental Monitoring  
Monitoring the quality of air, water, and other environmental factors is crucial to protect human health and life from external harmful elements [143]. RFID systems are widely utilized in applications related to this field because they can wirelessly and in real time collect and report continuous information about the environment.  
High humidity can generate harmful substances like mold, leading to the corrosion of structures and adversely affecting human health [144]. Additionally, rapid temperature changes can cause expansion or contraction of structures, resulting in damage and causing conditions such as dehydration or hypothermia that hinder normal human body function [145]. From an inventory management perspective, uncontrolled temperatures can degrade the quality of goods transported and stored in large quantities [146]. RFID tags can analyze abnormal behavior in temperature and humidity by collecting real-time information from a distance.  
[PERSON] et al. proposed a chipless RFID humidity sensor based on paper with high sensitivity and quality factors (Figure 10a) [111]. To optimize resonance characteristics, a toroidal-shaped inductor-interdigitated electrode capacitor and double copper-coated paper with a significantly shifted resonance frequency were employed in the fabrication of the RFID humidity sensor. The moisture-sensitive GO-coated electrode achieved a detection range of 60%RH to 90%RH and an improved sensitivity of 6.25 MHz/%RH. This is attributed to the synergy between the moisture absorption capacity of the paper substrate itself and water molecule adsorption in GO.  
[PERSON] et al. fabricated an ultra-high speed and high-sensitivity RFID temperature sensor using ZnO/MoS\({}_{2}\)/RGO nanocomposite material (Figure 10b) [118]. Introducing MoS\({}_{2}\) with a large surface area and rGO with high electrical conductivity to ZnO, a well-known temperature-sensitive material, resulted in a nanocomposite with excellent temperature detection characteristics. The temperature-sensitive microstrip antenna, fabricated using thermal transfer technology, demonstrated outstanding temperature detection performance with a linear relationship in the range of 0 to 200 \({}^{\circ}\)C, a recovery time of less than 10 s, and temperature hysteresis of less than 0.34 \({}^{\circ}\)C.  
[PERSON] et al. produced a functional flexible electronic device capable of detecting both temperature and humidity by printing it with additive-free MXene aqueous ink at room temperature (Figure 10c) [126]. The fabricated RFID sensor undergoes changes in electrical conductivity as the PDMS substrate expands with increasing temperature, stretching the MXene conductive network. Moreover, exposure of MXene to humidity causes swelling in the interlayer structure of the nanosheets, altering electrical conductivity and enabling humidity detection. The flexible sensor, printed using printing technology, effectively detects temperature and humidity when attached to plants or the human body.
## 2 Materials and Methods
### Adsorption Kinetics  
To the kinetics studies, adsorption tests were carried out with 25 mg L\({}^{-1}\) nickel solutions with contact time with the biosorbent varying from 10 to 1440 min. After each evaluation time, the samples were filtered, and the adsorption rates were determined by spectrophotometry. The pseudo-first order, pseudo-second order, Elovich and intraparticle diffusion models were used to describe the kinetics.  
The pseudo-first order equation (Equation (3)) was proposed by [26]. It is generally used in the form proposed by [27]:  
\[\frac{q}{q_{e}}+\ln(q_{e}-q)=\ln(q_{e})-k_{1}t \tag{3}\]  
where the constant \(k_{1}\) is a function of the method. Plotting \(\ln\) (\(q_{e}-q\)) vs. \(t\) provides a value of \(k_{1}\) and \(q_{e}\).  
Pseudo-second order model (Equation (4)) assumes that the rate is second order with respect to the available surface sites. The equation according to [26] is:  
\[\frac{t}{q}=\frac{1}{k_{2}q_{e}^{2}}+\frac{t}{q_{e}} \tag{4}\]  
where \(k_{2}\) is the constant of pseudo-second order found by the plot of \(t/q\) vs. \(t\).  
The [PERSON] equation (Equation (5)) is usually applied to describe chemical adsorption processes and is appropriate for systems with heterogeneous surfaces. A simplified equation was presented by [28]:  
\[q=\left(\frac{1}{b}\right)\ln(ab)+\left(\frac{1}{b}\right)\ln t \tag{5}\]  
where \(a\) and \(b\) are constants estimated by the plot of \(q\) vs. \(\ln t\).  
The intraparticle diffusion as a diffusion-controlled process [29] is represented by Equation (6):  
\[q_{t}=k_{\text{int}}t^{\frac{1}{2}}+C \tag{6}\]  
where \(k_{\text{int}}\) is the constant, a plot of \(q_{t}\) versus \(t^{1/2}\) should be a straight line with a slope \(k_{\text{int}}\) and intercept \({}^{\circ}\)C.
## 5 The Proposed Routing Method
### Routing Tree Based on Dragonfly Algorithm (DA-Tree)
\[F=\sum_{D=1}^{\lfloor\log n_{CH}\rfloor}\frac{1}{D}\left(\sum_{i=1}^{c}\frac{ \left(\frac{E_{i}}{E_{\max}}\right)\times\left(\frac{T_{i}^{total}}{T_{\max}} \right)}{\left(\frac{hop_{count}(n_{i},BS)}{N-1}\right)\times\left(\frac{n_{C_{i }}}{n_{\max}}\right)}\right) \tag{21}\]  
where \(D\) indicates the tree depth, \(n_{CH}\) represents the number of CHs, \(c\) expresses the number of CHs in the current tree depth, \(hop_{count}(n_{i},BS)\) indicates hop count from the \(CH_{i}\) to BS, \(N\) is the number of nodes in the network, \(E_{i}\) represents the remaining energy of \(CH_{i}\), \(nc_{i}\) is the number of CMs in the cluster \(i\), \(n_{max}\) represents the maximum number of CMs in a cluster. It is equal to \(\left\lceil\frac{2N}{n_{CH}}\right\rceil\). * **Stop condition:** This step specifies the stop condition of the DA-Tree algorithm. When it is met, the DA-Tree algorithm ends, and the best response is considered its output. In the DA-Tree, the stop condition is 300 iterations for ending this algorithm. Upon the completion of the algorithm, the BS sends a message to CHs to inform them of their positions in this routing tree. * **Dragonflies update:** At this stage, the values of dragonflies are refreshed in accordance with Equations (1) and (2) related to the DA algorithm. Algorithm 3 expresses the pseudo-code of the DA-Tree algorithm. ```
0:\(n_{i}\): IoT nodes (\(i=1,...,N\))
0: DA-Tree Begin
1: BS: Initialize DA parameters;
2: BS: Form the initial population;
3: for\(i=1\) to 300 do
4: BS: Establish binary routing trees between CHs based on dragonflies;
5: BS: Evaluate routing trees based on Equation (21);
6: BS: Determine the best and worst responses in the population;
7: BS: Update the position of dragonflies using the Equations (1) and (2) of the dragonfly algorithm;
8: endfor
9: BS: Return the routing tree with highest fitness; End
```  
**Algorithm 3** DA-Tree
###### Abstract  
The widespread use of the internet, coupled with the increasing production of digital content, has caused significant challenges in information security and manipulation. Deepfake detection has become a critical research topic in both academic and practical domains, as it involves identifying forged elements in artificially generated videos using various deep learning and artificial intelligence techniques. In this dissertation, an innovative model was developed for detecting deepfake videos by combining the Quantum Transfer Learning (QTL) and Class-Attention Vision Transformer (CaiT) architectures. The Deepfake Detection Challenge (DFDC) dataset was used for training, and a system capable of detecting spatiotemporal inconsistencies was constructed by integrating QTL and CaiT technologies. In addition to existing preprocessing methods in the literature, a novel preprocessing function tailored to the requirements of deep learning models was developed for the dataset. The advantages of quantum computing offered by QTL were merged with the global feature extraction capabilities of the CaiT. The results demonstrated that the proposed method achieved a remarkable performance in detecting deepfake videos, with an accuracy of 90% and ROC AUC score of 0.94 achieved. The model's performance was compared with other methods evaluated on the DFDC dataset, highlighting its efficiency in resource utilization and overall effectiveness. The findings reveal that the proposed QTL-CaiT-based system provides a strong foundation for deepfake detection and contributes significantly to the academic literature. Future research should focus on testing the model on real quantum devices and applying it to larger datasets to further enhance its applicability.  
deepfake detection; quantum transfer learning; vision transformer +
Footnote †: journal: Applied sciences