esa-sceva/satcom-chunk-collection huggingface dataset:
# Sensor Micro and Nanoparticles for Microfluidic Application
## 2 Construction Principles of Sensor Particles
### Combination of Particle-Based Oxygen Sensing with Flow Velocimetry  
[PERSON] et al. [61] used a conventional microparticle image velocimetry method for performing simultaneous measurements of dissolved oxygen concentration (DOC) and velocity fields. DOC was determined by applying oxygen-sensitive particles, which were produced by the dispersion polymerization method. Polystyrene (PS) particles were doped with PtOEP dye, whose phosphorescence was quenched by oxygen molecules. Therefore, formed oxygen-sensitive particles were applied not only as tracer particles but also as oxygen sensors. In order to observe a  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Analyte**} & \multirow{2}{*}{
\begin{tabular}{c} **Detection** \\ **Method** \\ \end{tabular} } & \multirow{2}{*}{**Particle Type**} & \multirow{2}{*}{**Size**} & \multirow{2}{*}{**Application**} & \multicolumn{2}{c}{**Fluidic**} & \multirow{2}{*}{**Main Advantage**} & \multirow{2}{*}{**Ref.**} \\  & & & & & & & Less than 0.2 s \\ \multirow{4}{*}{O\({}_{2}\)} & Phosphore & \multicolumn{2}{c}{Pt (II) benzopyphyrin} & Glucose & & response time, & \\  & & & & oxidase & Microfluid & optimum sensitivity, & \\  & & & & enzymatic & glass chips & excellent stability, & \\  & & & polymer NPs & & activity & & possibility to reuse \\ \multirow{4}{*}{O\({}_{2}\)} & Epifluorescence & \multicolumn{2}{c}{PSVP-} & \multirow{4}{*}{245 nm} & Glucose & \multicolumn{2}{c}{Y-channel} & \multirow{4}{*}{High measurement accuracy (0.03)} & \multirow{4}{*}{[58]} \\  & microscopy & & & & &  
diffusion of dissolved oxygen in a Y-shaped microchannel, oxygen-sensitive particle were dispersed in de-ionized water with DOC values of 0 or 100%. Particles were excited at 385 nm, and their phosphorescence intensity distribution was recorded. As a result, due to different dissolved oxygen concentrations in water, it was possible to observe a qualitative visualization of two different intensities of phosphorescence. It was found that the maximum velocity of the water streams was 3.5 mm/s, and a parabolic velocity profile was obtained. Additionally, it was determined that after 3 s exposure to excitation light, photobleaching of particles was less than 2%.
# Channel Characteristics and Link Adaption for Visible Light Communication in an Industrial Scenario
## 3 Analysis of Channel Characteristics  
### Channel Impulse Responses  
The output file, including the information on the received rays (e.g., received power and the path lengths), is generated by the non-sequential ray-tracing tool. By using this information, the channel impulse response which was processed in Matlab(r) can be expressed as [27]:  
\[h(t)=\sum_{i=1}^{N}P_{i}\delta(t-\tau_{i}), \tag{2}\]  
where \(N\) is the number of rays received by PD, \(P_{i}\) denotes the received power of the \(i^{th}\) ray, \(\delta(t)\) is the Dirac delta function, and \(\tau_{i}\) is the propagation time of the \(i^{th}\) ray.  
The CIRs under the single TX (\(S_{8}\)) case are investigated, which PD \(D_{10}\) (Road 1) and \(E_{10}\) (Road 2) are selected to analyzed. According to Figure 1b and the previous simulation parameters, the LOS distances from these two points to the TX (\(S_{8}\)) are equal (6.9 m). With which they divide by the speed of light (\(3\times 10^{8}\) m/s), the propagation delay of the LOS component is 23 ns. Hence, only the NLOS components of CIRs for the IIoT scenario are drawn in Figure 2, for \(D_{10}\) and \(E_{10}\), respectively. It is observed that the delays of the first and second peaks (36.0 ns and 44.4 ns) in the Figure 2a are equal to those in the Figure 2b, which are close to the delay of the LOS component. Hence, it can be inferred that those mainly come from the same main reflectors (equipment group B and the floor) shared for both two PDs in Figure 1b. Furthermore, we also find that CIR for \(D_{10}\) have more peaks than those for \(E_{10}\), which can also be found at other points, i.e., the third peak (66.2 ns) in Figure 2a. This result indicates that the light rays can be received at \(D_{10}\) through the reflections of more scatters surrounding Road 1 but only a few reflections by specific scatters can reach the \(E_{10}\) in Road 2. It can be explained by the density of surrounding objects. As depicted in Figure 1, the scatter density surrounding \(D_{10}\) is larger than that of \(E_{10}\), i.e., the more light rays are received. Hence, the third peak in Figure 2a is generated by composing the stronger multipath components (MPCs).  
In addition, the VLC channel in the IIoT scenario presents a larger delay fluctuation (70 ns) than that of the VLC channels (20 ns) of the conference room in the [15], which can be explained by the complexity of IIoT scenarios. As discussed in [17], the IIoT scenarios with large physical size metal machine tools make the reflected light rays take longer paths to the RX. Hence, the larger delay of the peak shown in the CIR can be observed.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline QAM modulation order & 64 \\ Number of IFT/FFT (\(N_{i}\)) & 128 [25] \\ Number of pilots (\(N_{p}\)) & 4 [26] \\ Information symbols(\(N_{i}\)) & 48 [26] \\ Cyclic prefix of length (\(N_{cp}\)) & 32 [26] \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parameters of DCO-OFDM system.
# Optimized Detection Algorithm for Vertical Irregularities in Vertical Curve Segments
## 2 Virtual Track Inspection Technology  
### Multibody Dynamic Simulation Model  
To investigate the relationship between dynamic detection accuracy of track vertical irregularities and variations in core parameters--such as train speed, vertical curve radius, length, and gradient--a comprehensive vehicle multibody dynamic model was developed using Simpack multibody dynamics simulation software, Simpack 2018.1-build66, 64 bit (as shown in Figure 1) to obtain virtual dynamic response signals. Subsequently, the virtual signals were processed using the inertial reference method, followed by digital high-pass filtering to obtain the track vertical irregularities, referred to in this study as \"virtual vertical irregularities\". Based on observed anomalies in dynamic inspection and the practical research requirements for long-wavelength irregularities, the spatial cutoff frequency of the filter was set to 120 m.
# Semantic Segmentation of Packaged and Unpackaged Fresh-Cut Apples Using Deep Learning
## 2 Materials and Methods
### Training of the Network  
The DeepLabV3+-based neural network model was created in MATLAB using the ResNet-18 as backbone. The image size was set to 1544 \(\times\) 2064 \(\times\) 3.  
The same network underwent two separate trainings: one for unpackaged pieces (randomly disposed and orderly disposed with the pulp oriented upward) and another for randomly disposed pieces packaged in the plastic bag. A different number of classes was used in the two cases because packaged apples need a further class for glitters. Each training set was composed by augmented image data, each with the corresponding label data. The two training sets were independent and separated.  
The trainings of the networks were done using the following options identified by the Adam optimizer [35]: the maximum number of epochs was 10 and the mini batch size was 8 at each iteration. A piecewise schedule was used for the learning rate. The learning rate drop period was 5 and the drop factor was 0.2. Thus, after every 5 epochs, the learning rate was reduced by a factor of 0.2.
# The Numerical Study for the Effect of Stiffness Matching on Wheel-Rail Curve Squeal Noise
## 3 Results
### Frequency Response Analysis of Wheel and Rail
#### 3.2.3 Frequency Response Analysis of The Rail  
The modal analysis of the rail involves the extraction of rail modal characteristics within the frequency range of 0 to 5000 Hz. Similar to the wheel analysis, the \"Steady-state dynamics, Modal\" method is employed to calculate the rail's frequency response when subjected to transverse excitation at the contact point between the wheel and rail. Given the high-frequency nature of wheel-rail curve squeal noise, the frequency sweep range is established from 1000 Hz to 5000 Hz. The modal damping for the rail is set to 0.01, based on empirical rail damping characteristics. The frequency response analysis yields the vertical and lateral displacement responses of the rail at the contact point, as depicted in Figure 7.  
The analysis of Figure 7 reveals that when the rail is subjected to lateral forces, numerous wave crest frequencies emerge. Notably, due to the structural characteristics of the rail, it is more prone to excitation than the wheel, making it susceptible to resonance. Within the 1000 Hz to 2000 Hz frequency range, amplitudes are notably larger. The vibration patterns at the contact point primarily manifest as transverse vibrations, complemented by a vertical vibration component. Figure 8 illustrates selected modal shapes of the rail at wave crest frequencies.  
Figure 5: Displacement responses of wheel: (**a**) radial displacement response; (**b**) axial displacement response.  
Figure 8: Partial modal shapes of rail at the wave crest frequency: (**a**) 1180.7 Hz; (**b**) 1442.2 Hz; (**c**) 2469.7 Hz; (**d**) 3806.4 Hz.  
Figure 6: Summary of mode shapes at wave crest frequency: (**a**) 1072.60 Hz; (**b**) 1890.56 Hz; (**c**) 2038.69 Hz; (**d**) 2468.33 Hz; (**e**) 2811.14 Hz; (**f**) 3785 Hz; (**g**) 4786.55 Hz.  
Figure 7: Displacement response of rail: (**a**) vertical displacement response; (**b**) lateral displacement response.
# Optimal Harmonic Mitigation in Distribution Systems with Inverter Based Distributed Generation
## 5 Applications  
The proposed method is applied according to two cases studied as follows:  
Case 1: Optimal planning of single-tuned filters without considering the harmonic spectrum of the DG units.  
Case 2: Optimal planning of single tuned filters with considering the harmonic spectrum of the DG units.  
The type of nonlinear load is assumed to be a six-pulse inverter that has a harmonic spectrum as shown in Table 1[30]. The harmonic spectrum of the inverter-based DGs is also considered as shown in Table 2[28]. DGs are placed nearby the heavy loads to reduce the power losses and improve the voltage profile [9]. For considering a high penetration of DGs, the total capacity of the DGs is assumed to be 40% of the total demand and divided equally into four units. The proposed method considered all the odd harmonic orders from 3 rd up to the 25 th harmonic order.  
The weighting factors \(k_{1}\), \(k_{2}\), and \(k_{3}\) are considered equal to 0.5, 0.3, and 0.2, respectively. The WCA is implemented in MATLAB software using m-file code taking the total initial number of raindrops equals to 100 (one sea, 9 rivers, and 90 streamers). The evaporation condition \(d_{max}\) is considered to be \(1\times 10^{-4}\), and the iterations number is 50.
## 3 Method
### Proposed LSTM Network
#### 3.4.3 Regularization in Loss Function  
There are several types of loss functions: mean squared error (MSE), root mean squared error (RMSE), binary cross entropy (BCE), and categorical cross entropy (CCE). Among them, the CCE with softmax used as a common loss function for multi-class classification [41] is selected. One requirement for using CCE is that the labels of the output should follow the one-hot encoding method. The CCE is expressed as [42]  
\[L_{CCE}=-\frac{1}{N}\sum_{i=1}^{N}\sum_{j=1}^{C}p_{ij}\log\big{(}q_{ij}\big{)} \tag{14}\]  
Figure 4: The typical structure of long-short term memory (LSTM). \(\sigma_{h}\) and \(\sigma_{g}\) denote the hyperbolic tangent and sigmoid functions, respectively.  
Figure 5: Fall detection model architecture for LSTM.  
where \(N\) is the size of the data set, \(C\) is number of classes, \(p_{ij}\) is the true probability distribution which the \(i\)th training pattern belongs to \(j\)th category, and \(q_{ij}\) is the predicted probability distribution for \(i\)th observation belonging to class \(j\). L2 regularization [43] is added to the CCE loss function to address overfitting issues. The regularized cost function, \(J(W)\), is expressed as [44,45]  
\[J(W)=L_{\text{CCE}}+\frac{\lambda}{2}\|W\|_{2}, \tag{15}\]  
where \(W\) is the connection weight between all layers, \(\lambda\) is the regularization rate, and \(\|W\|_{2}\) represents the L2 norm. Parameter \(\lambda\) determines a trade-off between the training error and the generalization ability [44].
## 5 The Concept of IoV
### Security Threats and Attacks in IoV Environments
#### 5.6.3 Dissimulation of a GPS Attack  
This refers to the act of intercepting and modifying GPS signals sent by a vehicle within a particular network. These signals are then sent to an unsuspecting receiver after being modified. As a result, the driver and passenger of the targeted vehicle make wrong decisions based on the modified message [60]. In a GPS attack, GPS signals are attacked and the exact location of the vehicle is manipulated. As such, when searching for a particular vehicle, a user may be directed to another location with a fake vehicle. A GPS spoofing attack is a common name for this kind of attack [57].
# A Multiscale Approach to the Numerical Simulation of the Solid Oxide Fuel Cell
## 4 Mathematical Model
### Thermo-Electric Model  
The thermo-electric model describes the thermodynamics and the electrochemistry of the SOFC. The hydrogen-fueled SOFC realizes the following reactions:  
* the anodic reaction--hydrogen oxidation: H\({}_{2}\)+ O\({}^{2-}\)\(\rightarrow\) H\({}_{2}\)O + 2 e\({}^{-}\);
* the cathodic reaction--oxygen reduction: \(\frac{1}{2}\)O\({}_{2}\)+ 2 e\({}^{-}\)\(\rightarrow\) O\({}^{2-}\);
* the overall cell reaction: H\({}_{2}\)+ \(\frac{1}{2}\)O\({}_{2}\)\(\rightarrow\) H\({}_{2}\)O. These reactions may occur only at the sites, where the constant supply and drain of the reactants and products are possible. These reaction sites are called the triple phase boundaries (TPBs) on the anode and the double phase boundaries (DPBs) on the cathode. Please mind that the cathodic TPB is neglected in this model. The open circuit voltage of such a cell may be described by the Nernst equation. For the cell working in any temperature and pressure, the Nernst equation may be written as [3]:  
\[E_{\text{OC}}=-\frac{\Delta_{\text{H${}_{2}$CO${}_{2}$}}g^{\circ}}{2F}+\frac{ \Delta_{\text{H${}_{2}$CO${}_{2}$}}g}{2F}\left(T-T^{\circ}\right)-\frac{RT}{2F} \ln\left(\frac{p_{\text{H${}_{2}$CO${}_{2}$}}}{p_{\text{H${}_{2}$}}p_{\text{O${}_ {2}$}}^{1/2}}\right), \tag{2}\]  
where \(E_{\text{OC}}\) (V) is the open circuit voltage of the SOFC, \(\Delta_{\text{H${}_{2}$CO${}_{2}$}}g^{\circ}\) (J mol\({}^{-1}\)) is the specific molar Gibbs free energy of the water formation in the standard state (here, the standard state temperature is  
Figure 8: Computational domain for the SOFC simulations \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Reference Microstructure** \\ \hline \(\psi_{\text{pore,ano}}\) & 0.26 \\ \(\psi_{\text{Ni}}\) & 0.33 \\ \(\psi_{\text{YSZ}}\) & 0.41 \\ \(\psi_{\text{pore,cath}}\) & 0.39 \\ \(\psi_{\text{LSCF}}\) & 0.61 \\ \(\text{pore,ano}\) & 24.534 \\ \(\text{T${}_{\text{Ni}}}\) & 3.647 \\ \(\text{T${}_{\text{YSZ}}}\) & 2.240 \\ \(\text{T${}_{\text{porc,cath}}}\) & 2.419 \\ \(\text{T${}_{\text{LSCF}}}\) & 1.850 \\ \(d_{\text{pore,ano}}\) (\(\upmu\)m) & 0.71 \\ \(d_{\text{pore,cath}}\) (\(\upmu\)m) & 1.02 \\ \(\rho_{\text{TPB,ano}}^{L}\) (\(\upmu\)m\({}^{-3}\)) & 4.44 \\ \(\rho_{\text{DPB,cath}}^{A}\) (\(\upmu\)m\({}^{2}\)m\({}^{-3}\)) & 2.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Reference microstructure for the numerical calculations\(T^{\circ}=298\,\mathrm{K}\) and the standard state pressure is \(P^{\circ}=101,\mathrm{325}\) Pa = 1 atm), \(\Delta_{\mathrm{H_{2}xO_{2}}S}\) (J mol\({}^{-1}\) K\({}^{-1}\)) is the change of the specific molar entropy associated with the water formation reaction, \(T\) (K) is the temperature and \(p_{i}\) (atm) stands for the partial pressure of species \(i\). \(F\) is the Faraday constant (\(F=96,\mathrm{485.32289}\) C mol\({}^{-1}\)) and \(R\) is the universal gas constant (\(R=8.314\,\mathrm{459}\,\mathrm{8}\,\mathrm{J mol^{-1}\,K^{-1}}\)).
# Soil Classification from Piezoone Penetration Test Using Fuzzy Clustering and Neuro-Fuzzy Theory
## 4 Verification of Suggested Neuro-Fuzzy Model
### Incheon Trade Center Site  
Incheon is located on the northwestern coast of Korea, where strong tidal action exists. The maximum tide difference is almost 9 m, and fine-grained soil is almost \"ML\". According to the boring log, fill reclaimed up to G.L-8.5 m and was layered by clayey silt up to G.L-19 m. Sandy silt and silty sand existed below the clayey silt. A piezocone test was performed to a depth of G.L-18.6 m through a fill into a casing tube. Soil classification  
Figure 12: Verification results at Pusan new port support area site.  
Figure 13: Verification results at Jeonnam Dojang new port site.  
results according to USCS from undisturbed samples in the clayey silt layer were mainly \"ML\" due to tidal action, except for \"CL\" at around G.L-17 m to 18 m. Water contents ranged from 28.2% to 32.8%, liquid limits ranged from 34.2% to 38.9%, and plastic limits ranged from 21.9% to 29.5%. According to the results shown in Figure 14, [PERSON] and [PERSON]'s method uniformly estimated all layers as silt and sand. The \(B_{q}\) chart proposed by [PERSON] et al. [3] also classified clayey silt into silt or sand. Some misclassification was observed when considering the classification of USCS as \"ML\" or partially \"CL\". [PERSON]'s method estimated the mixture of clay, silt, and sand with depth and did not show any difference in detecting the thin clay layer at GL-17 m. The suggested neuro-fuzzy model also classified the clayey silt layer into mainly silt and succeeded in detecting the thin clay layer at around 17 m with \"CL\".
# Emerging Drivers of Adoption of Generative AI Technology in Education: A Review
## 2 Research Approach
### Inclusion and Exclusion Criteria  
This review focused on empirical studies that apply theoretical models or acceptance frameworks to investigate the adoption of generative AI (GenAI) technologies in educational contexts, particularly those supporting teaching and learning. GenAI technologies, as defined here, include advanced tools such as large language models (LLMs) like GPT and image generators like DALL-E that produce human-like outputs across modalities.  
To ensure methodological rigor, the following inclusion criteria were applied:* Empirical, peer-reviewed studies published in English (journal articles and conference proceedings),
* Application of theoretical models (e.g., TAM, UTAUT) to examine GenAI adoption or acceptance in education,
* Clearly formulated hypotheses and empirically tested relationships between constructs.  
These criteria ensured the selection of studies providing validated, model-based evidence on predictors such as perceived usefulness, ease of use, and behavioral intention--critical for understanding adoption dynamics in educational settings.  
The exclusion criteria were as follows:  
* Purely theoretical, conceptual, or review papers,
* Meta-analyses or studies without primary data analysis,
* Studies using surveys alone, even when employing validated instruments, without deeper model-driven examination of interrelationships between constructs,
* Non-peer-reviewed works, such as book chapters.  
In particular, studies that lacked explicit modeling or did not operationalize constructs within a theoretical framework were excluded due to concerns about the reliability and interpretability of their findings. While survey-based studies offer preliminary insights, their omission ensures consistency in analytical depth and alignment with the review's concept-centric approach. Among the excluded works--despite their use of validated scales--are studies by [PERSON] et al. [19], [PERSON] et al. [20], [PERSON] et al. [21], [PERSON] and [PERSON] [22], [PERSON] et al. [23], [PERSON] [24], [PERSON] and [PERSON] [25], [PERSON] et al. [26], and [PERSON] et al. [27].
###### Abstract  
The prediction of earth pressure balance (EPB) shield performance is an essential part of project scheduling and cost estimation of tunneling projects. This paper establishes an efficient multi-objective optimization model to predict the shield performance during the tunneling process. This model integrates the adaptive neuro-fuzzy inference system (ANFIS) with the genetic algorithm (GA). The hybrid model uses shield operational parameters as inputs and computes the advance rate as output. GA enhances the accuracy of ANFIS for runtime parameters tuning by multi-objective fitness function. Prior to modeling, datasets were established, and critical operating parameters were identified through principal component analysis. Then, the tunneling case for Guangzhou metro line number 9 was adopted to verify the applicability of the proposed model. Results were then compared with those of the ANFIS model. The comparison showed that the multi-objective ANFIS-GA model is more successful than the ANFIS model in predicting the advance rate with a high accuracy, which can be used to guide the tunnel performance in the field.  
Keywords:advance rate; shield performance; principal component analysis; ANFIS-GA; tunnel +
Footnote †: journal: Applied Sciences
## 2 Related Work
To be able to provide the best device capabilities, mobile service providers work on presenting high-quality video service in a bandwidth-efficient manner. For LTE broadcast/evolved multimedia broadcast multicast service and terrestrial broadcast TV/ATSC 3.0 services, an open-loop multi-input multioutput (MIMO) scheme was proposed with considering the SVC. A well known video compression technique SVC supports simulcasting. A high-quality video sequence is divided by SVC into bit-stream layers. Thus, a better service degradation will be decoded under variable reception scenarios. These scenarios may be H.264/SVC and H.265/SHVC. In SVC, the QoS is based on the highest enhancement layer, which will be mainly processed. The main aim of the presented scheme was to provide the minimum vital information together with gaining additional information to develop the quality of video [22]. In this work, we propose to (i) mitigate significant interference for MUE victims in a HetNet scenario where its SINR is unsatisfied; (ii) to match the data rate of the interfered channel with the video rate in a way that the best quality video service is delivered by an adaptive resource allocation scheme; and (iii) to allocate RBs in an optimized way to deliver higher QoS (Table 1 describes all the acronyms used in the article). \begin{table}
\begin{tabular}{l l} \hline
**Acronym** & **Meaning** \\ \hline BL & Base Layer \\ CMT-DA & Distortion-Aware Concurrent Multipath Transfer \\ CQI & Channel Quality Indicator \\ CSG & Close Subscriber Group \\ DASH & Dynamic Adaptive Streaming over HTTP \\ DL & Downlink \\ EL & Enhancement Layer \\ ELBA & Energy-Quality Aware Bandwidth Aggregation \\ EVIS & Energy-Video Aware Multipath Transport Protocol \\ FBS & Femtocell Base Station \\ FDD & Frequency Division Duplex \\ FUE & Femtocell User Equipment \\ HAS & HTTP Adaptive Streaming \\ HetNet & Heterogeneous Networks \\ HTTP & Hypertext Transfer Protocol \\ ILP & Integer Linear Programming \\ LTE & Long-Term Evolution \\ LTE-A & Long-Term Evolution Advanced \\ \hline \end{tabular}
\end{table} 
Table 1: Table of acronyms.