esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Therefore, this concentration was used in the present analysis as well. However, a solution with 2.5 mmol seems to improve the boron uptake after trans-stimulation in certain cell lines [28]. Research using longer trans-stimulation times with L-DOPA for rat glioma cells achieved the strongest trans-stimulation effect after 4 h, whereas a small increase in the resulting BPA concentration was seen after two hours of stimulation [20]. Another investigation with shorter trans-stimulation times up to 90 min indicated a decrease in the measured boron uptake after 1 h for some cell lines, whereas for others the uptake did not change between 60 and 90 min [28]. Since this seemed to be a critical time period, the effect of up to two hours of incubation time with L-tyrosine was investigated in the present study without detecting significant differences. However, longer incubation times of up to 4 h, as demonstrated by [PERSON] et al. for glioblastoma and melanoma cells [13], might have resulted in higher BPA concentrations, even if this effect was not seen by the same group in vivo. The timepoint of the highest boron increase without trans-stimulation was seen in the time between 1.5 and 2.5 h and is analogous to the results described in the literature [10,29]. A significantly higher boron uptake without trans-stimulation was reached by administration of high BPA concentrations (20 vs. 50 ppm, \(p<0.012\)). Healthy tissues such as skin also show a higher boron uptake depending on the BPA concentration. In lung cancer metastasis [30] and HNSCC in a hamster cheek pouch model [29], a higher BPA concentration resulted in higher boron uptake. Interestingly, the use of L-tyrosine led to significant differences between the three BPA concentration groups (5 vs. 20 vs. 50 ppm). The effect between the 5 and 20 ppm groups seems to arise from a lower boron uptake in the 5 ppm group. Besides the effect of trans-stimulation, another effect, called trans-inhibition, was described as a negative feedback regulation [31]. It is supposed that the mechanism behind this effect relies on a slowing of entry by a high intracellular content, i.e., of an analogue of the solute (in this case L-tyrosine). The observed lower boron uptake could be the result of this effect. Hence, the use of L-tyrosine in combination with low BPA concentrations could be counterproductive for an increase in boron uptake. Future research might need to focus on boron uptake when comparing more than one cell line that is moderately differentiated with more than one cell line that is weakly differentiated.
# Quantum Compressive Sensing: Mathematical Machinery, Quantum Algorithms, and Quantum Circuitry
## 4 Training
The quantum circuit presented in Figure 3 is a diagrammatic representation of a series of quantum operations acting on the computational basis state \(|000...>\). The individual quantum operations, or _gates_, can be found in any standard quantum information textbook (e.g., [30]); the overall action of the entire quantum circuit prepares the following state:  
\[|\Psi\rangle=\frac{1}{\sqrt{|T|}}\sum_{z=0}^{|T|-1}|z\rangle\sum_{z^{\prime}=0 }^{|T|-1}(-1)^{z\cdot z^{\prime}}|y_{z^{\prime}}\rangle, \tag{6}\]  
where \(|z\rangle\) represents the computational basis state of a \"control register\" with \(\log_{2}|T|\) qubits, \(|y_{z}\rangle\equiv U_{z}|\mathbf{0}\rangle\), and the notation \(z\cdot z^{\prime}\) represents the inner product of the _binary expansions_ of integers \(z\) and \(z^{\prime}\). By measuring all qubits in the control register, we can project the state register into a single linear combination. If we happen to measure all control qubits in the basis state \(0\), corresponding to \(z=0\), we have prepared the desired linear combination modeling Equation (5). The probability \(P(z=0)\) of successfully measuring all control qubits in the basis state \(0\) is given by \(\langle 0|\rho_{z}|0\rangle\), where \(\rho_{z}\) is the \"reduced density matrix\" of the control register obtained by tracing out all degrees of freedom in the state register. We may write it as \(\rho_{z}=\mathrm{Tr}_{y}|\Psi\rangle\langle\Psi|\), where \(\mathrm{Tr}_{y}\) is a partial trace, defined in any standard quantum information textbook. Substituting its definition into \(P(z=0)\), we find the probability to prepare our desired linear combination is \(P(z=0)=\sum_{y}\left(\langle 0|\langle y|\rangle|\Psi\rangle\langle\Psi|( 0)|y\rangle\right)\). Substituting in Equation (5), the probability simplifies to:  
\[P(z=0)=\frac{1}{|T|}+\frac{2}{|T|^{2}}\sum_{i<j}\mathrm{Re}[\langle y_{i}|y_{ j}\rangle]. \tag{7}\]  
In contrast with our previous discussions, this demonstrates a clear _advantage_ of the qubit mappings described in Equations (1) and (2): under these mappings, the second term in this sum is strictly non-negative, meaning \(P(z=0)\geq 1/|T|\). Figure 3: A quantum circuit to prepare the “quantum average” of the signals \(y_{i}\equiv U_{i}|\mathbf{0}\rangle\). The circuits \(U_{i}\) are the state-preparation circuits given in Figure 2. The quantum gate \(H\) is the Hadamard gate. Dots on a wire represent controlled operations: a closed dot has a control value of \(|1\rangle\) while an open dot has a control value of \(|0\rangle\). Meters at the end of a circuit represent measurement. Therefore, the circuit in Figure 3 must be executed only \(O(|T|)\) times in order to successfully prepare the quantum-average state. Since the circuit depth is also \(O(|T|)\), the total run-time of this training procedure is \(O(|T|^{2})\) and requires no gradient descent or numerical optimization.
# Deep Learning-Based Symptomizing Cyber Threats Using Adaptive 5G Shared Slice Security Approaches
## 2 Materials and Methods
### Analytical Engine Selection  
Recently, researchers have been emphasizing DL techniques to build security models, such as DL models of a generative or discriminative nature. Recurrent neural networks (RNN) and convolutional neural networks (CNN) are discriminative models, which are mostly adopted for DL security model design. RNNs models classify data with discriminative power when input data correlates with the models' output labeled sequence explicitly. The RNN model is one of three types: simple RNN, long short-term memory unit (LSTM), and gated recurrent unit (GRU) [23]. The LSTM is one of the most popular RNN systems as well as the most effective in response to temporal data, e.g., speech recognition. The model uses sequencing with two LSTMs, well known as \"Bi-LSTM\", being fed in both forward and backward directions for the process cycle. The CNN model contains one or more \"convolutional\" and \"pooling\" layers in the form of an array to construct a multilayer neural network [23; 24; 25]. For spatial and temporal pattern classification, a hybrid approach combines LSTM and CNN functionality. Conv-LSTM first picks up spatial features from visuals and images, while simultaneously honing temporal features [26]. For the DL model design, we have to evaluate Bi-LSTM, CNN-LSTM, and CNN models for NetFlow analysis on a chosen dataset. The performance of each DL model has been examined using selected configurations and hyper-parameters, as given in Table 2. Additionally, the results are investigated thoroughly for the optimum model selection among the three. The outcomes of each model have been evaluated based on metrics, e.g., recall, precision and F1, and the confusion matrix (CM), that help to assess the best-fit DL model, which can classify the temporal and spatial data features on 5G networks [27; 28].
# Transfer Convolutional Neural Network for Cross-Project Defect Prediction
## 4 Experimental Setup
### Evaluation Metrics  
To compare the predictive performance of different models, we used the F-measure as an evaluation metric [30], which has been widely used in previous CPDP studies. The definition of the F-measure is as follows.  
In this paper, we defined the defective file as a positive instance. In contrast, the clean files were defined as negative instances. Using a dichotomous classifier to perform CPDP, we may capture four outcomes of test data: classifying truly defective instances as defective (true positive, TP); classifying clean instances as defective (false positive, FP); classifying truly defective instances as clean (false negative, FN); and classifying truly clean instances as clean (true negative, TN). These four outcomes could construct a confusion matrix (in Table 4) to help us define the F-measure.  
Based on the confusion matrix in Table 4, we can define the following two interim evaluation metrics.  
Recall, also known as the probability of detection, is a measurement of the integrity that defines the probability of the number of TP instances compared with the number of all positive instances:  
\[Recall=\frac{TP}{TP+FN}\]  
Precision is a measure of exactness that defines the probabilities of the number of TP instances to the number of predictive positive instances:  
\[Precision=\frac{TP}{TP+FP}\]  
In fact, due to the trade-off between recall and precision, it is difficult to evaluate the models' prediction performance effectively by using only one of them. For this reason, we adopted the F-measure to access the prediction performance, which is the harmonic mean of the precision and recall. The F-measure is the most commonly-used evaluation metric in previous CPDP studies [10; 21; 25].  
\[F-measure=\frac{2\cdot Precision\cdot Recall}{Precision+Recall}\]
# Medium Access Control Protocols for Cognitive Radio Ad Hoc Networks: A Survey
## 3 Survey of State-Of-The-Art MAC Protocols
### Time-Slotted Protocols  
These MAC protocols divide time into slots, offering an inherent collision-free scheme for the control channel, as well as for the data transmission. Several of these protocols are based on Time Division Multiple Access (TDMA) and need network-wide synchronization. Similar to the random access protocol, time slotted MAC for CRAHN is also investigated in the literature. For example, energy efficient cognitive radio multichannel (ECR-MAC) [71] uses explicit frequency negotiation and time negotiation to achieve power savings by having an idle node go to sleep mode. The cognitive-radio-based multi-channel MAC (CRM-MAC) protocol [72] exploits the use of double  
Figure 4: AW-MAC basic operation.  
transceivers to improve performance. In the following subsections, we analyze some of the time-slotted MAC protocols in more detail.
## 4 Conclusions  
In this paper, a microstrip dual-band BPF with tunable narrow passbands, miniaturized size, sharp response and low-cost fabrication has been designed and fabricated at C-band frequencies. The proposed filter consists of three subsections including a bended coupling system, flag-shaped resonators and stepped-impedance resonators. The coupling system is based on an end-coupled resonator responsible for creating a weak coupling required for a narrowband response. The quality of the response and the selectivity of the filter is then improved by introduction of flag-shaped and stepped-impedance resonators. The measured results show that the filter has two independent passbands centered at 4.42 and 7.2 GHz with a good isolation level of 24 dB and strong attenuations of 25 and 30 dB in the lower and upper stopbands, respectively. The proposed filter shows excellent selectivity with its sharpness values of 106 and 212 dB/GHz at its lower and upper bands.  
Conceptualization, A.L., A.G., W.M. and K.W.K.; methodology A.L., A.G. and W.M.; software, A.L., A.G. and W.M.; validation, A.L., A.G., W.M., R.B.V.B.S., N.B.-M., M.S.A., G.H.L. and K.W.K.; formal analysis, A.L., A.G., W.M., R.B.V.B.S., N.B.-M., M.S.A., G.H.L. and K.W.K.; investigation, A.L., A.G., W.M., R.B.V.B.S., N.B.-M., M.S.A., G.H.L. and K.W.K.; resources, A.L., A.G., W.M., R.B.V.B.S., N.B.-M., M.S.A., G.H.L. and K.W.K.; data curation, A.L., A.G., W.M., R.B.V.B.S., N.B.-M., M.S.A., G.H.L. and K.W.K.; writing--original draft preparation, A.L., A.G., W.M., R.B.V.B.S., N.B.-M. and M.S.A.; writing--review and editing, A.L., A.G., W.M., R.B.V.B.S., N.B.-M., M.S.A., G.H.L. and K.W.K.; visualization, A.L. and A.G.; supervision, W.M. and K.W.K.; project administration, W.M., G.H.L. and K.W.K.K.; funding acquisition, G.H.L. and K.W.K. All authors have read and agreed to the published version of the manuscript. This research was supported by the National R&D Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (No. NRF-2019M1A7A1A02085630) and also supported by the BK21 Plus project funded by the Ministry of Education, Korea (21A20131600011). The authors declare no conflict of interest.
# AUV Trajectory Planning for Optimized Sensor Data Collection in Internet of Underwater Things
## 2 Materials and Methods
### System Model
#### 2.1.2 AUV Kinematic Model  
Consider an AUV fitted with suitable receivers and deployed to perform data collection tasks in a UASN. The AUV begins its mission at the surface point \(S_{s}=(x_{USV_{s}},y_{USV_{s}},0)\), where the USV is initially stationed. It then descends to a predefined depth \(z_{d}\), navigates along a horizontal plane to collect data from the CHs, and finally returns to the USV at a different surface location \(S_{f}=\left(x_{USV_{f}},y_{USV_{f}},0\right)\). The depth \(z_{d}\) of the horizontal plane must satisfy the constraint:  
\[z_{d}\leq 0, \tag{1}\]  
to ensure the AUV remains below the sea surface. Additionally, \(z_{d}\) must be selected such that its vertical distance from the highest point of the seafloor, denoted by \(\delta(t)\), is greater than a safety distance \(\delta_{safe}\) and within the allowable depth range of the AUV at:  
\[\delta(t)>\delta_{safe}\ \forall t\in[0,T], \tag{2}\]  
where \(T\) denotes the total duration of the mission. This constraint in (2) ensures that the AUV does not descend close to the seafloor, even in the most challenging terrain conditions. The kinematic equations describing the AUV's motion in the horizontal plane are given by [17]:  
\[\dot{x}(t)=u(t)\cos\psi(t)\]  
\[\dot{y}(t)=u(t)\sin\psi(t)\]  
\[\dot{z}(t)=\omega(t)\]  
Figure 1: Overview of the UASN system with sensor nodes (SNs) on an uneven seafloor.  
\[\dot{\psi}(t)=r(t). \tag{3}\]  
The AUV's state at time \(t\) is defined by its position \((x(t),\ y(t),\ z(t))\), where \(z(t)=z_{d}\) and its yaw angle \(\psi(t)\in[0,\ 2\pi)\). Its motion is governed by three key control inputs: the surge speed \(u(t)\in(0,u_{\max}]\), the heave speed \(\omega(t)\in(0,\omega_{\max}]\), and yaw rate \(r(t)\in[-r_{\max},\ r_{\max}]\). When the yaw rate \(\omega(t)\) is nonzero, the AUV follows a curved path with a turning radius defined as:  
\[\kappa_{i}(t)=\bigg{(}\frac{u(t)}{|r(t)|}\bigg{)}. \tag{4}\]
# Urban Vulnerability Assessment of Sea Level Rise in Singapore through the World Avatar
## 3 Methodology
### Sea Level Rise Model  
For this study, a simplistic bathtub inundation model [43] is used to outline the low-lying area, whereby the area below the projected sea level rise is designated as vulnerable. The vulnerable area is derived using the digital elevation model (i.e., NASA SRTM Global 1 arc second elevation model) with the Singapore-specific sea level rise projection height derived from IPCC AR6 [44]. NASA SRTM Global 1 arc second elevation model is used due to its availability as an open-sourced dataset. The consideration for hydrodynamic models through natural or man-made systems such as run-off and drainage is excluded in this study. The affected areas identified in this research refer to the vulnerable, low-lying areas of Singapore that are susceptible to flooding. The simplistic bathtub inundation model used in this study serves as proof of concept for the application of TWA as a sea level rise impact assessment tool. The model can be improved in the future by applying a higher-resolution elevation model (e.g., LiDAR-based elevation model). In this study, an assumption is made that the NASA SRTM Global 1 arc second elevation model and the Singapore-specific sea level rise projection height derived from IPCC AR6 [44] share the same datum to simplify the impact analysis. This assumption may affect the final results; however, this does not compromise the main objective of this paper, which is to demonstrate TWA as a sea level rise vulnerability assessment tool.
# Bearing Fault Diagnosis Using Grad-CAM and Acoustic Emission Signals
## 4 Experimental Results and Discussion  
To validate the performance of Grad-CAM for the bearing fault diagnosis, AE signals from the healthy-state and three types of bearing fault were acquired using the testbed. The length of a measured AE signal was 1 second with a 1 MHz sampling rate and the number of AE signals for each condition was 600. Half of the data instances from the collected dataset were randomly selected for training the CNN. The remaining unseen samples were used for validating the fault diagnosis capabilities of the trained CNN. The trained CNN achieved 99% classification accuracy on the validation dataset, as shown in the confusion matrix depicted in Figure 11.  
Figure 8: Illustration of the bearing, FAG NJ206-E-TVP2.  
Figure 10: The examples of AE signals for each bearing conditions listed as: (**a**) the AE signal of the healthy bearing; (**b**) the AE signal of ORCS; (**c**) the AE signal of IRCS; and (**d**) the AE signal of RECS.  
Figure 9: The seeded bearing faults: (**a**) outer race way with a crack on its surface (ORCS); (**b**) inner race way with a crack on its surface (IRCS); and (**c**) rolling element with a crack on its surface (RECS).  
Figure 12 demonstrates the importance-weight over frequency component of the envelope spectrum. As shown in the figure, the CNN learned that the harmonics of defect frequencies were important information for classifying the states of the bearing. In this study, BPFO, BPFI, BSF, and the shaft speed were 44, 42, 65, and 8.33 Hz, respectively. For the healthy condition, CNN learned that low frequency band components were important since the low frequency band contained the harmonics of the shaft speed frequency that could be clearly observed in the healthy condition of the bearing. Since the defect and shaft speed frequencies are also valuable in traditional bearing fault diagnosis methods, it seems that the CNN was trained without any fault-related information. In the case of outer race fault, the values of 2\(\times\)BSF and BPFO harmonics were too similar that made it difficult to classify the input data based on these characteristic frequencies. Therefore, the CNN chose the sideband of BPFO as useful information instead of BPFO, itself.
# Organic Crystals for THz Photonics
## 3 THz-Wave Generation and Detection via NLO Processes
### Optical Rectification
Besides the NLO coefficient for THz-wave generation \(d_{\rm THz}\), the following NLO material parameters are important: the refractive index \(n_{\rm THz}\) and the absorption coefficient \(\alpha_{\rm THz}\) at the THz angular frequency \(\omega\), as well as the refractive index \(n\), the optical group index \(n_{g}\) and the absorption coefficient \(\alpha\) at the optical wavelength \(\lambda\). The second term in Equation (22) has a unit of length and its maximum is reached at exact phase matching (also called velocity matching) \(n_{\rm THz}=n_{g}\) in case of negligible absorption. Its absolute value is referred to as the effective generation length \(L_{\rm gen}(\omega,\lambda,L)\) and is a useful parameter to analyze the thickness dependence of the generated THz spectrum, which strongly depends on the material dispersion [112]. A simpler figure of merit resulting from the expression (22), which however neglects the optical and THz absorption effects, is again the coherence length \(l_{c}\), which is given by the same Equation (15) as for difference-frequency generation. The particular phase-matching properties at different pump wavelengths and the generated THz frequencies for organic NLO crystals are discussed in detail in Section 3.4 and examples of THz sources based on optical rectification in organic materials are given in Section 4.2. The most common THz-wave generation scheme based on optical rectification employs a short fs pump pulse to produce a broadband THz pulse. Often, however, a single frequency THz source is desired, such as produced by difference-frequency generation for example. A tunable narrowband THz source can be also obtained by using a beating signal of two linearly chirped fs pulses with a variable delay as pump on a THz generator [114]. By controlling the length of the chirped pulse and the delay between the beating pulses, one can tune the frequency and the bandwidth of the generated narrowband THz pulses [114]. This method has been recently also demonstrated using organic THz sources [78; 82; 115].
## 4 Implementation of DFDLOF in Personalizing Educational Pathways
### Adaptive Learning Pathway Design  
#### 4.3.1 Customizing Learning Content Based on Learner Profiles  
The Dynamic Feedback-Driven Learning Optimization Framework (DFDLOF) customizes the learning content based on the learner's profile. This includes developing detailed profiles for each learner based on a wide range of information, such as the learners' educational context and background, learning styles and preferences, and performance data. These data are interpreted by machine learning algorithms, which help to determine each student's significant specific learning needs and interests.  
Content customization in DFDLOF is of multiple natures. It includes adjusting the complexity of the content, the modality (what they see, hear, or perform), and the speed to fit with different learner capacities and preferences. Such a system might present concepts graphically or even illustrate some form of interactive simulation, perhaps illustrating different aspects of the same phenomenon in different ways suitable for different learning styles (e.g., visual or audial). Likewise, the system might provide extra support or change the required complexity for a student having trouble understanding a specific subject.  
Personalization also applies in selecting the learning topics and themes that are relevant and engaging. DFDLOF uses information on student interest and interaction history to present more relevant and interesting content that enhances learning. The framework's ability to persistently update and improve learner profiles from ongoing interaction reflects content customization's dynamic and responsive nature.