esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Therefore, the network results of the YOLO series for remote sensing object detection need to be improved further to achieve better detection performance. To address the problems of complex scenes in remote sensing images, multi-scale objects in different categories, and large scenes with small objects, we propose DFPN-YOLO, a dense feature pyramid network structure based on YOLO. Since the YOLO series became more integrated after version v3, the structure changes of network were not significant. Therefore, we use YOLOv3 as a baseline to easier compare the accuracy before and after altering the structure of network. First, we add a spatial groupwise enhancement [34] (SGE) attention module to the residual block [35] of the backbone to increase the efficiency of the backbone in extracting meaningful semantic information from complex scenes; then, we add a large detection layer to improve the accuracy in detecting small objects in remote sensing images; and finally, we propose Dense-FPN, a dense feature pyramid network structure that combines the semantic information of the feature layers to improve the ability to detect objects at different scales. The remainder of this paper is organized as follows: related work on YOLO, in particular the framework structure of YOLOv3, is discussed in Section 2. In Section 3, our methodology is described in detail. In Section 4, an experimental validation is presented, introducing the datasets used as well as the relevant evaluation metrics. Finally, the conclusions are given in Section 5.
# Off-flavors in Aguacultured Fish: Origins and Implications for Consumers
## 1 Introduction  
Aquaculture has grown worldwide annually; the sector has expanded significantly in recent years, and total production, trade, and consumption have reached all-time highs in recent years [1]. The world's aquaculture fish production inland was 47 million tons of live weight in 2018. The Asian continent has dominated the world's fish production, with an 89% share of aquaculture production in the last two decades. Among the main producing countries, China, India, Indonesia, Vietnam, Bangladesh, Egypt, Norway, Chile, and Brazil stand out [1,2].  
Different aquaculture systems have been used for fish production, such as water-based systems (e.g., onshore/offshore), land-based systems (e.g., rainfed ponds, flow-through systems, tanks, and raceways), integrated farming systems (e.g., livestock-fish, agriculture, and fish dual-use aquaculture and irrigation ponds), recycling systems (e.g., high control enclosed systems, more open pond-based recirculation) [3].  
However, recirculating aquaculture systems (RAS) have gained prominence, as they are indoor systems that permit fish farmers to manage environmental conditions year-round; they characterize an ideal alternative to open fish culture systems [4]. These systems may contribute to reducing environmental problems by minimizing water demand and managing effluent discharge [5]. However, several factors, such as the presence of phosphorus, micronutrients, and organic matter, can affect the quality of the water. Studies have shown that these compounds are associated with the heightened growthof phytoplankton that can produce off-flavors as secondary metabolites [6; 7; 8]. These off-flavors are easily bioaccumulated by fish and reduce their market value [9].  
Some microorganisms are responsible for synthesizing secondary metabolites that may alter the taste, odor, and color of foods, which are known as off-flavors and taste/odor compounds (TOCs). These metabolites are a major concern in a wide range of environments as they make end products undesirable [9; 10; 11]. Thus, it is important that the fish production system incorporates approaches that enable preventive actions, instead of having a large production of fish with off-flavor with reduced market value [12; 13]. Therefore, the general objective of this review is to aggregate information about the main TOCs produced by cyanobacteria and other phytoplankton, how these compounds affect aquaculture, fish consumption and commercialization, and the main detection and purification methods.
## 2 Theoretical Background for Spherical Localization Based on Attenuation of EM Waves
### Particle Filtering  
As an alternative to the EKF, a PF can be used to estimate the position from the measured signal strengths (8). Particle filters (also referred to as sequential Monte Carlo filters) perform better than an EKF if the problem variance is large and the measurement model highly non-linear [15]. The sampling importance resampling (SIR) PF [16] is recapped in this subsection.  
The distribution of the receiver position is approximated by a particle set consisting of \(M\) particles \(\left\{\boldsymbol{p}^{[m]}(k)\right\}\) indexed by the integer \(m\). In addition, the particles are associated with weights \(w^{[m]}(k)\), which indicate the importance of each particle. The weights are positive and are enforced to always sum up to unity, i.e., \(\sum_{m=1}^{M}w^{[m]}=1\).  
At time step \(k\), a temporary particle set \(\left\{\hat{\boldsymbol{p}}^{[m]}(k)\right\}\) is created from the particle set of the previous time step \(k-1\) according to the random walk model (10):  
\[\hat{\boldsymbol{p}}^{[m]}(k)=\boldsymbol{p}^{[m]}(k-1)+\boldsymbol{w}(k). \tag{11}\]  
The unnormalized weight \(w^{[m]}_{\mathrm{u}}(k)\) is computed as the probability of the measurement \(\boldsymbol{\tau}(k)\) for particle \(\hat{\boldsymbol{p}}^{[m]}(k)\):  
\[w^{[m]}_{\mathrm{u}}(k)=\mathcal{N}(\boldsymbol{\tau}(k)\mid\boldsymbol{\mu} (\hat{\boldsymbol{p}}^{[m]}(k)),\,\boldsymbol{C}). \tag{12}\]  
In order to avoid particle degeneration, the temporary particle set is resampled. The weights \(w_{\mathrm{u}}\) are normalized to sum to unity, and all particles are drawn with replacement \(\boldsymbol{p}^{[m]}(k)\sim\left\{\hat{\boldsymbol{p}}^{[i]}(k)\right\}\) with probability \(\propto w^{[i]}(k)\), \(i=1,\ldots,M\). The position estimate \(\hat{\boldsymbol{p}}(k)\) can be obtained by averaging the resampled particles set:  
\[\hat{\boldsymbol{p}}(k)=\frac{1}{M}\sum_{m=1}^{M}\boldsymbol{p}^{[m]}(k). \tag{13}\]
# Median U-Turn Intersection Critical Parameter Research and Operational Performance Evaluation
## 7 Comparative Discussion of Proposed Findings and Relevant Case Studies
### Comparison of Operational Efficiency and Safety  
MUT, RCUT, and DLT designs exhibit distinct advantages and limitations regarding intersection safety and capacity. The MUT design, in particular, is highly effective in improving traffic flow and safety at high-volume intersections. The findings from this study support these conclusions, particularly in complex urban environments. The simulation results from the cellular automata model used in this study align with previous studies that show a remarkable improvement in the operational performance and safety of MUT intersections.  
RCUT intersections also improve operational efficiency by minimizing left-turn congestion. However, RCUTs do not completely eliminate left-turn traffic; instead, they redirect it to a secondary U-turn location. While this strategy reduces direct left-turn conflicts, it may still result in some delays for left-turn vehicles, especially during peak traffic periods. According to the Federal Highway Administration (FHWA), RCUTs can achieve a 25-45% reduction in crash rates [53]. [PERSON] et al. found that RCUT intersections improved intersection safety by more than 30% by reducing the likelihood of left-turn accidents [54]. However, RCUT intersections may experience higher delay times than MUT intersections due to the need for vehicles to perform a U-turn at a distant location.  
DLT intersections, by relocating left-turn lanes away from the intersection, reduce delays and congestion associated with left-turning vehicles. [PERSON] et al. and [PERSON] et al. conclude that DLT intersections can provide significant safety and traffic efficiency improvements [55; 56]. However, DLT does not fully address the potential for congestion caused by the remaining left-turn traffic, which may still conflict with other movements in the modified left-turn lanes.  
Overall, the MUT design stands out as the most effective in improving both traffic flow and safety, particularly at high-volume intersections, by completely eliminating left-turn movements and significantly reducing conflict points.
# The Challenges of Recognizing Offline Handwritten Chinese: A Technical Review
## 3 Handwritten Chinese Character Recognition
### Recognition Methods Based on Traditional Technology
They used a density-based clustering method to obtain substructures and converted the problem of recognizing a single character consisting of multiple substructures into the recognition of substructure strings, with the final extracted features fed into the MQDF classifier. It is worth noting that [PERSON] et al. [30] proposed a feature learning method used to do HCCR experiments in the same year. The method is called discriminative quadratic feature learning (DQFL), which mainly uses the statistical and spatial correlation of features to increase the dimensionality of features, and then discriminative feature extraction (DFE) is used to reduce the dimensionality. The combination of dimensionality enhancement and dimensionality reduction makes the feature representation more discriminative and nonlinear, thus significantly improving the recognition accuracy. Both methods above use MQDF to complete the classification task at the end, but it has a high storage requirement for MQDF. In 2018, [PERSON] et al. [31] used sparse coding to compact the parameters of MQDF, constructed two compact MQDF classifiers with maximum likelihood-based and K-SVD methods, and recognized unconstrained handwritten Chinese characters efficiently. These methods are summarized and compared in Table 3. In 2021, [PERSON] et al. [32] attempted to use the proposed efficient 2D-GMM-HMM based on the Kaldi toolkit for HCCR experiments. This system is not only an improvement of the 1D-GMM-HMM system in recognition accuracy but also differs from the 1D-GMM-HMM, which does a one-dimensional alignment. 2D-GMM-HMM can segment Chinese characters into basic components in horizontal and vertical directions according to hidden states. The traditional HCCR framework has not received much attention in recent years, but this does not mean that the traditional techniques are obsolete. Some research works still show that traditional feature extraction methods and classifiers can further improve the performance of widely used neural networks in this field. These approaches we will present in Section 3.3.
## 3 Results  
Descriptive statistics for all participants are provided in Table 2. The main analysis revealed that the estimated model fitted reasonably well with the data (\(\chi 2[5]=7.244\), \(p=0.203\); NFI = 0.972; TLI = 0.961; CFI = 0.991, RMSEA = 0.071 [0.000, 175], \(p=0.315\)). All unstandardized path coefficients proved to be significant at \(p<0.05\), indicating that (a) PA significantly predicts MC and (b) MC predicts all the EFs variables in the model. Based on the standardized path coefficients, it was shown that the associations between the variables were low to medium (Figure 1). According to squared multiple correlations (Figure 1), PA explained 4.5% of variance in MC, whereas MC accounted for 4.3-14.5% of variance in EFs.  
Except for the effect of MC on NSwitchRT, all other direct effects in the model (Table 3) were significant (\(p<0.05\)). Similarly, all the indirect effects of PA on EFs through MC (Table 3) were significant, confirming the hypothesis that MC is an intervening variable in these relationships.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variables** & **M**\(\pm\)**SD** \\ \hline PA & 12,461 \(\pm\) 3449 \\ MC & 73.0 \(\pm\) 4.7 \\ BMI & 18.3 \(\pm\) 3.1 \\ \hline EFs & \\ WMcorrect & 14.29 \(\pm\) 4.065 \\ SwitchAcc & 0.858333 \(\pm\) 0.1044384 \\ SwitchRT & 2042.828508 \(\pm\) 610.5183967 \\ NSwitchAcc & 0.875926 \(\pm\) 0.0893287 \\ NSwitchRT & 1976.520956 \(\pm\) 628.4286279 \\ Switch costs & 66.307552 \(\pm\) 390.6293040 \\ \hline \hline \end{tabular} PA: physical activity, MC: motor competence, BMI: body mass index, EF: executive functions, M = Mean, SD = Standard Deviation.  
\end{table}
Table 2: Descriptive statistics (M \(\pm\) SD) for all study variables.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **PA** & **MC** & **BMI** & **SwitchAcc** & **SwitchRT** & **NSwitchAcc** & **NSwitchRT** & **Switchcosts** & **WMcorrect** \\ \hline PA & 1000 & & & & & & & \\ MC & 0.211 * & 1.000 & & & & & & \\ BMI & 0.000 & \(-\)0.068 & 1.000 & & & & & \\ SwitchAcc & \(-\)0.095 & 0.324 ** & \(-\)0.044 & 1.000 & & & & \\ SwitchRT & \(-\)0.098 & \(-\)0.270 * & 0.062 & 0.410 ** & 1.000 & & & \\ NSwitchAcc & \(-\)0.053 & 0.381 ** & \(-\)0.034 & 0.763 ** & 0.328 ** & 1.000 & & \\ NSwitchRT & \(-\)0.071 & \(-\)0.208 * & 0.045 & 0.257 * & 0.802 ** & 0.206 * & 1.000 & \\ Switch costs & \(-\)0.039 & \(-\)0.087 & 0.024 & 0.227 * & 0.273 ** & 0.084 & 0.356 ** & 1.000 & \\ WMcorrect & \(-\)0.125 & 0.347 ** & \(-\)0.067 & 0.160 & \(-\)0.201 & 0.257 * & \(-\)0.278 ** & 0.133 & 1.000 \\ \hline \hline \end{tabular} PA: physical activity, MC: motor competence, BMI: body mass index, SwitchAcc: accuracy in switch trials, SwitchRT: reaction time in switch trials, NSwitchAcc: accuracy in non-switch trials, NSwitchRT: reaction time in non-switch trials, switch costs: SwitchRT-NSwitchRT, WMcorrect: correct responses in the task Digits Backwards, *_p_ < 0.05, **_p_ < 0.001.  
\end{table}
Table 1: Bivariate correlations of the study variables.
## 3 Results
### Rq05  
The evaluation metrics used by authors are:  
* For classification problems: Prediction Accuracy Rate (PAR)/Accuracy, True Positive Rate (TPR)/Sensitivity/Recall, False Positive Rate (FPR)/Fall-Out, F1 Score, and Area Under Curve (AUC);
* For regression problems: Mean Absolute Error (MAE), Mean Relative Error (MRE), Root Mean-Square Error (RMSE), and Mean Squared Error (MSE). Of all these metrics, the more commonly used are:
* For classification: PAR, TPR, and F1 Score;
* For regression: RMSE and MAE.
## 4 Antenna Feed Network  
The design, optimization of the antenna feed network, as well as full-wave simulations were conducted in ANSYS HFSS. A novel feeding configuration, integrating a ring-shaped slot and an L-shaped microstrip conductor is employed to excite the antenna. The specific structural layout of this feeding network is illustrated in Figure 1b,c. Notably, the L-shaped microstrip line incorporates a quarter-wavelength impedance-transforming section, which facilitates good impedance matching between the microstrip feed line and the ring-shaped slot, thereby ensuring efficient power transmission and optimal antenna performance. By observing Figure 3, it can be found that the maximum currents of \(\mathbf{J}_{1}\) and \(\mathbf{J}_{2}\) as well as the minimum currents of \(\mathbf{J}_{3}\) and \(\mathbf{J}_{4}\) are concentrated at the central region of the MTS. Consequently, the feeding network should be designed beneath the center patch of the MTS to effectively excite the desired modes while suppressing the undesirable ones.  
To validate the CMA and clearly establish the connection between it and the practical design analysis of the proposed structure, we conducted simulations using HFSS to simulate the current density distribution on the MTS after excitation. At the 6.9 GHz, the current density distribution in HFSS closely matches the characteristic current distribution in CST. Specifically, at the 0\({}^{\circ}\) phase, the current direction was predominantly along the \(y\)-axis, as illustrated in Figure 6a, whereas at the 90\({}^{\circ}\) phase, it shifted to the -x direction, as shown in Figure 6b. These current orientations closely align with the predictions of the characteristic modes presented in Figure 3, indicating that the feeding network successfully introduces the 90\({}^{\circ}\) phase difference required for circular polarization. This outcome not only validates the correctness of the theoretical analysis but also highlights the effectiveness of our proposed feeding configuration in achieving the desired CP radiation.  
Figure 5: Modal significance of different MTS structures. (**a**) Initial structure; (**b**) structure with parasitic patches; (**c**) structure with mode-suppression patches.
# Secure Transmission in mmWave Wiretap Channels: On Sector Guard Zone and Blockages
## 2 System Description and Performance Metrics
### Performance Metrics  
In the following, we use the SOP, the COP and the secrecy throughput to measure secrecy performance [37].
## 4 Parametric Analyses
### Influence of the Bending Moment  
Based on the parameters in Table 1, the influences of the magnitude of bending moment on the LEBS and the effective ratio of LEBS are analyzed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(\varphi\) & \((EI)_{eq}\) & \(\eta\) \\ \hline Yukio [12] & 55.03\({}^{\circ}\) & 6.76 \(\times\) 10\({}^{7}\) kN\(\cdot\)m\({}^{2}\) & 0.0713 \\ The present study & 55.09\({}^{\circ}\) & 6.71 \(\times\) 10\({}^{7}\) kN\(\cdot\)m\({}^{2}\) & 0.0707 \\ Calculation error & 0.11\% & 0.74\% & 0.84\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Errors between the two solutions.  
Figure 3: Relationship between LEBS and the number of bolts.  
As shown in Figure 4, the LEBS and the effective ratio of LEBS both are constants with values of (\(EI\))\({}_{\text{eq}}\) = 6.71 \(\times\) 10\({}^{7}\) kN\(\cdot\)m\({}^{2}\) and \(\eta\) = 0.0707, respectively, when the residual jacking force (i.e., N = 0) is not considered. The LEBS of the shield tunnel increases nonlinearly with an S-curve for the residual jacking force, which eventually tends to stabilize. When the entire cross-section of the tunnel is under pressure, the LEBS of the shield tunnel reaches the maximum value, which is equal to the longitudinal bending stiffness of the shield tunnel without the joints between the rings. Under the same residual jacking force, the larger the bending moment, the smaller the LEBS and the effective ratio of LEBS, but the maximum and minimum LEBS are not affected by the bending moment because the maximum and minimum LEBS are only related to tunnel parameters. If the LEBS of the shield tunnel remains unchanged, the larger the bending moment, the greater the residual jacking force applied. Similar results are also presented by [PERSON] et al. [22] and [PERSON] et al. [25], who analyzed the LEBS of the tunnel by performing model tests and theoretical analyses, respectively.  
The study indicates that the residual jacking force significantly affects the contact state between tunnel segmental linings and, thus, the characteristics of LEBS. The field-measured data show that the residual jacking force decreases with time and eventually tends to a stable value [9], which changes the LEBS and the effective ratio of LEBS. If the residual jacking force is not considered, there is a large difference between the calculated LEBS and the actual LEBS of the shield tunnel, and it will affect the prediction of tunnel deformation.
# Mobile Learning and Its Effect on Learning Outcomes and Critical Thinking: A Systematic Review
## 6 Conclusions and Recommendations  
The present study analyzed 50 empirical studies that provided evidence of the impacts of m-learning integration. Overall, it is concluded that m-learning has the potential to improve both learning outcomes and critical thinking skills of students.  
In terms of learning outcomes, the analysis revealed that most of the research on m-learning is concentrated in Asia, with Taiwan standing out as the leader in scientific production in this area. The studies reviewed evidence successful application of m-learning in fields such as English language learning, health and biological sciences, and computer science. These findings reinforce the claim that m-learning provides effective tools for improving various academic competencies.  
In terms of critical thinking, the results indicate that strategies facilitated by mobile devices, such as peer assessment, inquiry, and gamification, have a positive impact on the development of critical skills. Also, m-learning has been found to foster self-regulation, which is crucial for critical thinking. However, most studies rely on self-perception questionnaires to measure these skills, which may not accurately reflect actual improvements. Therefore, more research is needed to more accurately assess the impact of m-learning on the development of critical thinking.  
Information and Communication Technologies (ICT) have profoundly transformed teaching and learning, facilitating a more dynamic and interactive education through digital media that allow the active participation of students [91]. In this context, it is first suggested that university educators and managers not only integrate these technologies into their teaching methods, but also foster an environment that favors pedagogical innovation, adapting to the specific needs of students and the educational context.  
Successful implementation of m-learning, however, requires more than mere technology adoption; it is the result of strategic planning and careful consideration of multiple factors. As [PERSON] [5] points out, educators play a key role in ensuring students' readiness, infrastructure, course content, learning objectives, the learning environment, internet connectivity, and the suitability of learning applications. Moreover, the alignment of these activities with the context and content, and the establishment of a robust feedback system are essential for monitoring and evaluating the impact of m-learning.  
Second, it is critical that teachers become familiar with technology-enhanced learning environments early in their training. M-learning has proven to be a valuable approach, with a positive impact on both students' experiences and teachers' professional development [92]. Therefore, it is essential that teacher training includes the teaching of specific strategies for the use of mobile devices, in order to optimize the pedagogical advantages that this technology provides and create more dynamic and effective learning experiences.