esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Limitations  
The present systematic review addressed a gap in the scientific literature by including RCT studies, focusing exclusively on children and adolescents, as well investigating both online and face-to-face interventions in all selected studies.  
However, it has some limitations which might affect the results and their generalizability. First, a larger number of articles could have provided more robust evidence regarding the effectiveness of nonpharmacological treatments.  
Second, an area with a potential knowledge gap (and therefore, the topic of future research) lies in the limited data on the simultaneous use of pharmacological therapies [15]. While information on medication for TDs was collected at the beginning of each study, their potential influence on treatment outcomes was not explored. Furthermore, all studies included samples with comorbid conditions such as ADHD and OCD: these comorbidities were not excluded unless their severity required \"immediate treatment\". The inclusion of subjects with other diagnoses or simultaneously treated with drugs may have influenced the treatment outcomes.  
Third, the heterogeneity among the studies included represents a significant limitation. The use of different versions of CBIT and ERP, administered individually or in a group-based format, delivered either face-to-face or online, with different treatment duration and number of sessions, raises relevant challenges in drawing clear conclusions about effective interventions. Moreover, the involvement of different professionals with different levels of training may have introduced a bias effect. Specifically, interventions were administered by clinicians or therapists with masters-level or higher education [15; 30], clinicians or therapists specialized in CBIT training [18; 19; 21; 31], supervised master degree's students [20], young adult patients supported by therapists with at least three years of experience [29], or through online expert support [22].  
Fourth, the generalizability of the findings is reduced by the characteristics of the studies included in the present review. Primarily, the small sample size across the studies reduces the statistical power to address efficacy [18; 19; 20; 31]. Additionally, all studies relied on self-reported measures, susceptible to potential bias. Notably, youths with minimal or marked impairment were excluded from one study [18].  
A fifth limitation concerns the partial lack of assessment of long-term treatment effect. While two studies [19; 30] did not include a follow-up assessment, the others showed differences in the follow-up period: at three and six month post-treatment [15; 20], after three months [21; 29; 31], after four months [18], and after six, twelve, and eighteen months [22]. Due to all these reasons, the findings of the present review should be interpreted with caution.  
Finally, during the selection process, several articles were unretrievable at the full-text level.
## 5 Results and Evaluation  
Figures 9-12 show the training and testing results of the Transformer models. We trained our model in 120 epochs and extracted the accuracy and sparse category cross-entropy loss. The best results for the first layer are 99% accuracy and 0.048 in loss after 63 epochs, and the second layer is 99.4% accuracy and 0.033 in loss after 118 epochs. If we increase the number of epochs, the best results are unchanged. The gaps between the training and validation lines are small, proving that our models are convergent and not overfitted.  
Figure 8: DoH tunneling detection system for SOC in an enterprise network.  
Figure 9: Model accuracy in training and validation phases in first layer.  
To confirm the efficacy of the offered DoH tunneling detection system, we compared our model's evaluation parameters with other research in [15; 16; 17]. We implemented their best algorithms with our dataset. Table 4 shows the comparison between the performance metrics of our work and the previous works. The results show that we obtain better results  
Figure 11: Model accuracy in training and validation phases in second layer.  
Figure 12: Model loss in training and validation phases in second layer.  
Figure 10: Model loss in training and validation phases in first layer.  
with our two-layer Transformer model than with the earlier algorithms if we use the same number of labeled data. These results prove that our model can work more effectively in practicality when labeling the DoH data is very hard. The performance metrics used were:  
* _Precision_: defined as the number of true positives (_TP_) over the number of true positives plus the number of false positives (_FN_) \[Precision\ =\frac{TP}{TP+FN}\] (3)
* _Recall_: defined as the number of true positives (_TP_) over the number of true positives plus the number of false negatives. \[Recall\ =\frac{TP}{TP+FP}\] (4)
* \(F1\)-_score_: the adjustment average of the precision and recall \[F1=2\times\frac{Precision\ \times\ Recall}{Precision\ +\ Recall}\] (5)
## 1 Introduction
### Problem Formulation for Sound Mathematical Representations
\(S_{i}\) is the collection of sub-channel indexes that are allotted to client \(i\). Be aware that the optimization issue in Equation (16) is not arched. Admittedly, it is found that by substituting the discrete solution with the following optimization problem, it might be a continuous one with an upper-bounded time/frequency allocation like this:  
\[\underset{i}{min}\ r_{i}\ \geq\ r^{*}, \tag{2}\]6  
\[\underset{i}{min}\ r_{i}\ \geq\ r^{*}, \tag{2}\]7 \[\underset{i}{min}\ r_{i}\ \geq\ r^{*}, \tag{2}\]8  
The OMA system with the optimization described in Equations (16)-(18) is referred to as OMA-TYPE-II in this paper for notational simplicity. Because the energy conservation \(\sum_{i=1}^{K}\alpha_{i}\frac{P_{i}}{\alpha_{i}}=P\) is created in TDMA throughout all user orthogonal time slots, and the effective noise power becomes \(\alpha_{i}N_{0}\) in FDMA, it should be noted that the optimization difficulties in Equations (14) and (16) apply to both technologies. The definitions of the three different types of OMA systems is suggest in Equation (19)  
\[\underset{i}{min}\ r_{i}\ \geq\ r^{*}, \tag{2}\]9  
Consequently, we must demonstrate that NOMA is superior to OMA to illustrate its superiority as seen in Equation (20). \[r_{i}=ln\left(1+\frac{P_{i}|h_{i}|^{2}}{N_{0}+|h_{i}|^{2}\sum_{j=1}^{i-1}P_{j }}\right) \tag{3}\]0However, OMA-TYPE-I and OMA-TYPE-II are considered in this research to uncover more complex aspects of these OMA systems. Also, distinct mathematical techniques must demonstrate NOMA's superiority over OMA-TYPE-I and OMA-TYPE-II.
## 2 Problem Statement
### Some Typical MUD Algorithms for DS-UWB Systems
For example, in the decorrelating (DEC) detector, its transforming matrix is \(M=R^{-1}\); in the minimum mean square error (MMSE) detector, its transforming matrix is  
\[M=A^{-1}[\text{R}+\sigma_{*}^{2}A^{-2}]^{-1} \tag{7}\]  
Moreover, as the linear detectors, such as CD, DEC, and MMSE detectors, the matrix multiplication is usually followed by a \(-1/{+1}\) decision module:  
\[\hat{b}=\texttt{sgn}(My) \tag{8}\]  
where \(\hat{\mathbf{b}}\) is the preliminary decision vector. Non-linear MUD algorithm is another kind of sub-optimal approach. For example, in the _successive interference cancellation (SIC) detector_, the decision of the \(k\)th user can be expressed as [46]:  
\[\hat{b}_{\iota}=\texttt{sgn}(y_{\iota}-\sum_{j=+1}^{K}A_{j}\rho_{\iota}\hat{b }_{\rho_{j}}) \tag{9}\]  
where the decision of the \(i\)th user, \(i=k+1\),\(k+2\),..., \(K\), should be correctly demodulated, otherwise, its performance will be affected drastically. Thus, the order of demodulating users is the key problem for this detector. Here, we order users through Equation (10), which can be estimated easily from the outputs of CD:  
\[E[(\int_{0}^{T}r(t)S_{\
u}^{(k)}(t)dt)^{2}]=\sigma_{*}^{2}+A_{\iota}^{2}+\sum _{j=k}A_{j}^{2}\rho_{jk}^{2} \tag{10}\]
## 2 Test Method  
### Experimental System  
In this experiment, the SHPB experimental system developed by the China University of Mining and Technology, which is composed of SHPB test system, ultra high-speed strain acquisition system, DIC image analysis system, and temperature control system [36]. The high-speed strain acquisition system consists of an ultra-dynamic strain gauge (LK2107 B, 16 bit, 8-channel), strain gauge junction boxes, and strain gauges, with a sampling rate of 40 MHz. The heating system consists of a heating furnace and a SHIMAX MAC3  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Sources** & **Rock Type** & **Temperature** & **Strain-Rate Regimes (s\({}^{-1}\))** & **Loading Rate (m/s)** & **Peak Incident Stress (MPa)** & **Chamber Pressure (MPa)** \\ \hline [PERSON]; [PERSON]; [PERSON]; [PERSON] (2011) [9] & Sandstone & 25, 200, 400, 600, 800 \({}^{\circ}\)C & 79.05–100.26 & - & - & - \\ \hline [PERSON]; [PERSON] (2013) [6] & Marble & 25, 200, 400, 600, 800, 1000 \({}^{\circ}\)C & - & 3, 5, 7, 11, 12, 13, 14, 15 & - & - \\ \hline [PERSON]; [PERSON] (2013) [8] & Marble & 25, 200, 400, 600, 800, 1000 \({}^{\circ}\)C & - & 11, 12, 13, 14, 15 & - & - \\ \hline [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] (2014) [12] & Sandstone & Room temperature, 800 \({}^{\circ}\)C & 14.17–107.02 & 9.54–22.30 & - & - \\ \hline [PERSON]; [PERSON]; [PERSON] (2017) [14] & Carrara marble & 250, 500, 750 \({}^{\circ}\)C & 10\({}^{0}\)–10\({}^{2}\) & - & - & - \\ \hline [PERSON]; [PERSON]; [PERSON]; [PERSON] (2019) [17] & Granite & 20, 100, 200 \({}^{\circ}\)C & - & - & 160, 180, 205, 250, 300 & - \\ \hline [PERSON]; [PERSON]; [PERSON]; [PERSON] (2019) [16] & Sandstone & 25, 200, 400, 600, 800, 1000 \({}^{\circ}\)C & - & 4.5, 5.5, 6.5, 7.5, 8.5, 9.5 & - & - \\ \hline [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] (2020) [21] & Granite & 200, 400, 600 \({}^{\circ}\)C & 60–220 & - & - & - \\ \hline [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] (2021) [23] & Marble & 25, 100, 200, 400, 600, 800 \({}^{\circ}\)C & 95.12–236.73 & 10, 12, 14 & - & - \\ \hline [PERSON]; [PERSON]; [PERSON]; [PERSON] (2022) [25] & Granite & 25, 100, 200, 300, 800 \({}^{\circ}\)C & 101–102 & 8.1, 12.5, 16.8 & - & - \\ \hline [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] (2022) [28] & Sandstone & 20, \(-\)20, \(-\)30 \({}^{\circ}\)C & - & - & - & 0.6 \\ \hline [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] (2023) [35] & Granite & 25, 100, 175, 250 \({}^{\circ}\)C & - & 2, 3, 5, 7, 8 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Progress of rock dynamics experiments based on temperature and strain rate.  
temperature controller, with a temperature heating range of room temperature to 200 \({}^{\circ}\)C and an accuracy of \(\pm\)1 \({}^{\circ}\)C.
# A Novel Hybrid Approach to Deal with DVL Malfunctions for Underwater Integrated Navigation Systems
## 5 Conclusions  
The DVL which can be easily affected by the environment may not be able to continuously output valid measurements in complex underwater environment, so it is crucial for underwater integrated navigation systems to properly handle the DVL outages. In this paper, a hybrid predictor dealing with short-term DVL malfunctions is proposed. The hybrid approach employs PLSR and SVR to accurately predict the DVL measurements and assist the SINS to obtain accurate velocity and position information during the DVL malfunctions. More specifically, PLSR models the DVL measurements, and SVR models the residual components of the PLSR prediction. PLSR is good at dealing with the situation that strong relevance exists between the independent variables, thus it can enhance the robustness of the predictor. SVR overcomes the shortcomings of PLSR and improves the precision of the predictor. The performance of the proposed hybrid predictor is verified by comparative simulations  
Figure 12: Navigation errors when there is a malfunction for a length of 120s: (**a**) Velocity errors; (**b**) Position errors.  
based on SINS/DVL/MCP/PS integrated navigation system. The results show that the velocity and position errors of the navigation system can be greatly reduced by employing the PLRR-SVR hybrid predictor, compared with the other three solutions (i.e., insulating the DVL without any predictor, using only PLRR predictor and using RBF predictor). Remarkably, the position errors using the proposed hybrid prediction approach is less than 10 m for up to 120 s DVL malfunction. The field test results further validate the applicability of the proposed approach. Therefore, it is indicated that the underwater integrated navigation system with the proposed hybrid predictor can effectively deal with short-term DVL malfunctions, thereby improving the reliability and safety of underwater vehicles in complex underwater environments. Besides, the proposed hybrid prediction approach can be extended to other navigation devices, such as GPS.  
This work was supported by the National Natural Science Foundation of China (No. 61374215). The idea of this work was proposed by [PERSON] and [PERSON]; [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data and wrote the paper.  
The authors declare no conflict of interest.
# A Comparative Study for Control of Quadrotor UAVs
## 3 Simulation Results
### Inner Loop
Considering the attitude controllers that lead to the fastest dynamics (SMC, LQR and FL), the choice of the inner loop control law derives from a trade-off between tracking performances and controller command effort, since the battery consumption, being related to the forces and moments required to act on the quadcopter platform, is a key limitation factor when dealing with multirotor UAVs executing medium-to-long range missions. The SMC-based attitude stabilizer is the most promising one, having minimum \(\mathrm{L_{2}}^{\mathbf{x}}\), intermediate \(\mathrm{L_{2}}^{\mathbf{u}}\) and ensuring fast dynamics to the controlled variables. Besides the norm evaluation, the FL-based controller yields to bigger peaks in the controlled signals. It is worth noting that ad-hoc simulation campaigns may be designed in order to tune the control parameters of each attitude stabilizer such that the system achieves a reference dynamic response. This could enhance performance, but the inherent features of each control approach would not change, as well as the highlighted differences among the methods. Figure 4: Euler angles signals [\(\mathrm{\phi(t)}\), \(\mathrm{\phi(t)}\), \(\mathrm{\psi(t)}\)] of the controlled attitude system (blue): PID-based Attitude Stabilizer (red). Figure 5: Euler angles signals \([\phi(t),\ \vartheta(t),\psi(t)]\) of the controlled attitude system (blue): LQR-based Attitude Stabilizer (red). Figure 6: Euler angles signals \([\phi(t),\ \vartheta(t),\psi(t)]\) of the controlled attitude system (blue): FL-based Attitude Stabilizer (red). Figure 7: Euler angles signals \([\phi(t),\ \vartheta(t),\psi(t)]\) of the controlled attitude system (blue): SMC-based Attitude Stabilizer (red).
## 4 Simulation and Comparison
### Comparison Results
From the above comparison, the ROA and SDA achieved collision-free navigation for multiple robots, but the NSPP provides a shorter average trajectory length for the four- and eight-robot scenarios in the ATL column. In the MTL column of the four-robot case, most results by the proposed NSPP are better than the ROA and SDA. However, in the MTL column of eight robots, it is difficult to tell which is the winner.
# Mechanical and Dielectric Properties of Aligned Electrospun Fibers
## 3 Mechanical and Dielectric Properties of Nanofibers
### Dielectric Properties
#### 3.2.2 Other Electrospun Fibers and Nanoparticles  
The dielectric properties of different nanofibers are shown in Table 4. [PERSON] et al. (2003) studied that dielectric constant strongly depends on solvent content and diameter of PCL electrospun fibers. As the solvent content increases, the fiber diameter decreases and dielectric constant increases [164]. [PERSON] et al. (2014) reported the dielectric characterization of annealed electrospun BaTiO\({}_{3}\) fibers. Crystallized BaTiO\({}_{3}\) nanofibers showed better dielectric permittivity [165]. Electrospun PVDF fibers have higher \(\beta\)-crystalline content which enhances the piezoelectric property and its energy-harvesting application. [PERSON] et al. (2016) reported various electrospun PVDF/PVP mats fabricated with different percentages of carbon black nanoparticles for the applications such as supercapacitor separators and other energy storage devices. The dielectric constant values were increased with the carbon black loading [166]. [PERSON] et al. (2016) studied the effect of Fe and Co mixed with PVP. The analysis of FE-SEM images of electrospun products obtained by using solutions with and without citric acids was carried out. The composite showed excellent electromagnetic (EM) wave absorption properties where the power loss of the FeCo nanofibers increased to 20 GHz [167]. EM waves with frequencies in the microwave range of 12 to 18 GHz are widely used in wireless communication networks, radar systems, military aircraft, and satellite communication devices. [PERSON] et al. (2011) studied that an increase in dielectric constant was achieved in a combination of high aspect ratio barium titanate (BaTiO\({}_{3}\)) and graphene platelets in a silicon rubber matrix compared to their spherical counterparts. Higher volume fractions of ferroelectric particles lead to increased dielectric constant but also to lower mechanical properties. Composites with high aspect ratio fillers at lower loading exhibit higher dielectric constant [168]. [PERSON] et al. (2017) reported an increase in permittivity by the addition of silver nanoparticles (AgNP) in PVDF. This is due to the interfacial polarization associated with entrapment of free charges generated at the interfaces between the AgNPs and PVDF [4]. [PERSON] and [PERSON] (2015) studied the potential of ZnO nanopowders with PVA matrix for the use of UV shielding [169].  
The in situ sol-gel method (ISM) and direct deposition method (DDM) have been discussed to analyze the effect of UV shielding. The ISM-PVA/ZnO composite showed better UV absorption in optical transmission measurements due to the uniform dispersion of the ZnO in the fibrous matrix. [PERSON] et al. (2016) analyzed the EMI-SE of multiwalled carbon nanotube (MWCNT) reinforced polyurethane (PU) in the DMF with tetrahydrofuran solvents and coated with conductive poly(3_A_-ethylenedioxythiophene) (PEDOT) [170]. The EMI-SE from a network analyzer shows 25 dB at the frequency range of 50 MHz-10 GHz.
## 5 Experimental Results and Analysis
### Performance  
#### 5.5.1 Comparison Study on SCBehavior Dataset  
Based on the data presented in Table 3 and Figure 7, it is evident that SBD-Net outperforms the compared models in terms of precision and recall, achieving the highest values of 0.804 and 0.763, respectively. SBD-Net also achieves notable mAP@0.5 and mAP@0.5-0.95 of 0.824 and 0.619, demonstrating its effectiveness in accurately detecting and classifying student behaviors in classroom settings. The integration of focal modulation mechanismsand the incorporation of the DyHead module enhance SBD-Net's ability to detect small objects and handle complex patterns of student behavior. To be specific, the implementation of the DyHead module contributes to the model's superior feature extraction capabilities, enabling it to process high-resolution feature maps while maintaining a manageable number of parameters. SBD-Net's parameter count of 36.5 M strikes a balance between model complexity and computational efficiency, making it suitable for deployment in resource-constrained environments such as classroom cameras. Furthermore, the modification of the loss function to ES Loss improves the model's robustness and accuracy, particularly in handling imbalanced datasets often encountered in real-world classroom scenarios. And in terms of detection speed, SBD-Net demonstrates competitive performance with a FLOPs value of 9.8 G, making it an efficient choice for real-time applications. This efficiency is crucial for practical deployments where both high accuracy and low latency are required. The model's ability to achieve high precision and recall while maintaining computational efficiency underscores its potential for widespread adoption in educational settings for monitoring and analyzing student behavior.  
Overall, SBD-Net's enhancements, including focal modulation, the DyHead module, and the ES Loss function, contribute to its superior performance in classroom student behavior recognition tasks. Its ability to accurately detect and classify various student behaviors with high precision and recall, combined with its efficient computational requirements, makes SBD-Net a robust and practical solution for educational applications.  
Figure 7: From left to right are the original image, the YOLOv8 detection image, and the SBD-Net detection image. We can intuitively observe that SBD-Net achieves better results while assigning more weights to the difficult samples, and it obtains better results.
# PCB Defect Images Super-Resolution Reconstruction Based on Improved SRGAN
## 3 Algorithm Improvement Proposed Algorithm
### Loss Function  
The performance of GAN networks is inextricably linked to the definition of the loss function. One of the advantages of SRGAN is the introduction of the loss perception function, which enhances the realistic detail information after hyper-segmentation. To solve the problem that the traditional pixel loss function MSE optimization lacks high-frequency detail extraction and leads to a lack of realism in the reconstruction effect, the SRGAN network uses a content loss function with perceptual similarity-driven VGG feature mapping. The overall perceptual loss formula of the SRGAN network is shown in Equation (4):  
\[l^{SR}=l^{SR}_{X}+10^{-3}l^{SR}_{Gen} \tag{4}\]  
where, \(l^{SR}_{X}\) is the content loss, \(10^{-3}l^{SR}_{Gen}\) is the adversarial loss, and is \(10^{-3}\) the weighting factor of the adversarial loss. The content loss includes MSE loss and VGG loss, and the  
Figure 8: Structure diagram of the improved discriminator network.  
Figure 7: IRB Network.  
weighting factor of VGG loss is taken as 0.006. The MSE loss, i.e., the pixel-by-pixel loss function between HR and SR, is shown in Equation (5):  
\[l_{MSE}^{SR}=\frac{1}{r^{2}WH}\sum_{x=1}^{rW}\sum_{y=1}^{rH}\left(I_{x,y}^{HR}-G_ {\theta_{G}}(^{LR})_{x,y}\right)^{2} \tag{5}\]  
where \(H\) is the height of the input image, \(W\) is the width of the input image, \(r\) denotes the image reconstruction multiplier, \(I_{x,y}^{HR}\) denotes the value of the pixel point (\(x\), \(y\)) corresponding to the high-resolution image, and \(G_{\theta_{G}}(^{LR})_{x,y}\) denotes the value of the pixel point (\(x\), \(y\)) corresponding to the reconstructed image. The VGG loss, i.e., the pixel-by-pixel loss function between the deep features of the HR image and the SR image, is shown in Equation (6):  
\[l_{VGG}^{SR}=\frac{1}{W_{i,j}H_{i,j}}\sum_{x=1}^{W_{i,j}}\sum_{y=1}^{H_{i,j}} \left(\varphi_{i,j}(I^{HR})_{x,y}-\varphi_{i,j}G_{\theta_{G}}(^{LR})_{x,y} \right)^{2} \tag{6}\]  
where \(W_{i,j}\) and \(H_{i,j}\) denote the dimensions of the feature maps within the VGG network, \(\varphi_{i,j}(I^{HR})_{x,y}\) denotes the eigenvalues of the deep feature map (\(x\), \(y\)) points of the high-resolution image, and \(\varphi_{i,j}G_{\theta_{G}}(^{LR})_{x,y}\) denotes the eigenvalues of the deep feature map (\(x\), \(y\)) points of the reconstructed image, and the loss of the deep features is used as the optimization objective can be complementary to the high-frequency information of the reconstructed image. Generating the adversarial loss function is shown in Equation (7):  
\[l_{Gen}^{SR}=\sum_{n=1}^{N}-\log D_{\theta_{D}}(G_{\theta_{D}}(^{LR})) \tag{7}\]  
where \(G_{\theta_{D}}(^{LR})\) represents the reconstructed image and \(D_{\theta_{D}}(G_{\theta_{D}}(^{LR}))\) represents the probability that the reconstructed image is the true image.