esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Analysis
### Results and Analysis
#### 4.3.2 Compare Experiment
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classification Model** & **ResNet-18** & **ResNet-34** & **ResNet-50** & **ResNet-101** \\ \hline Not CEFL loss & 75.21\% & 74.17\% & 78.47\% & 77.24\% \\ CEFL loss (\(\gamma=1\lambda=4\)) & 87.42\% & 89.91\% & 90.25\% & 92.70\% \\ Improved CBAM module & 83.52\% & 85.81\% & 87.22\% & 85.64\% \\ Improved CBAM module + CEFL loss (\(\gamma=1\lambda=4\)) & 89.04\% & 90.53\% & 90.75\% & 93.28\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Accuracy rates for each classification model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classification Model** & **ResNet-18** & **ResNet-34** & **ResNet-50** & **ResNet-101** \\ \hline Not CEFL loss & 72.39\% & 73.28\% & 72.98\% & 75.42\% \\ CEFL loss (\(\gamma=1\lambda=4\)) & 89.87\% & 85.25\% & 82.66\% & 84.61\% \\ Improved CBAM module & 84.21\% & 85.42\% & 86.29\% & 85.51\% \\ Improved CBAM module + CEFL loss (\(\gamma=1\lambda=4\)) & 90.26\% & 89.84\% & 90.17\% & 91.71\% \\ \hline \hline \end{tabular}
\end{table} 
Table 12: F1-score for each classification model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classification Model** & **ResNet-18** & **ResNet-34** & **ResNet-50** & **ResNet-101** \\ \hline Not CEFL loss & 73.77\% & 73.72\% & 75.61\% & 77.74\% \\ CEFL loss (\(\gamma=1\lambda=4\)) & 89.87\% & 85.25\% & 82.66\% & 84.61\% \\ Improved CBAM module & 84.21\% & 85.42\% & 86.29\% & 85.51\% \\ Improved CBAM module + CEFL loss (\(\gamma=1\lambda=4\)) & 91.14\% & 90.68\% & 91.44\% & 92.49\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Accuracy rates of classification models under each attention mechanism. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classification Model** & **ResNet-18** & **ResNet-34** & **ResNet-50** & **ResNet-101** \\ \hline No attention module & 75.21\% & 74.17\% & 78.47\% & 77.24\% \\ CEFL loss (\(\gamma=1\lambda=4\)) & 87.42\% & 89.91\% & 90.25\% & 92.70\% \\ Improved CBAM module & 83.52\% & 85.81\% & 87.22\% & 85.64\% \\ Improved CBAM module + CEFL loss (\(\gamma=1\lambda=4\)) & 89.04\% & 90.53\% & 90.75\% & 93.28\% \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Recall rates for each classification model. From the results presented from Tables 10-12, it can be observed that after using the CEFL loss function and the improved CBAM module, the performance of each classification model is significantly enhanced, which is better than only introducing one improved scheme. Further, validation accuracy is also compared with other models named as AAE (87.3%) and GAN (82.8%). After comparison, we can find that the classification accuracies of similar architectures are not as good as the architecture mentioned in this paper. Therefore, the effectiveness of the encrypted traffic classification model proposed in this paper is confirmed.
## 3 Results and Discussion
### Efforts and Strategies for Improvement of SWM in Lahore
#### 3.1.12 Outsourcing of SWM Services to Turkish Companies  
A consultant designed the solid waste collection model in the outsourcing mode, i.e., M/s. ISTAC. Two international firms were hired in November 2011 through a competitive bidding process in accordance with Punjab Procurement Regulation Authority (PPRA) rules to perform the tasks of waste collection and its transportation at a designated disposal site. The total cost of outsourcing services was USD 320 million for seven years. The scope of services included manual sweeping, mechanical sweeping, mechanical washing, DtD collection, CBC, and waste transportation to the disposal site, and these are depicted in Figure 6a. 138 Union Councils (UCs) out of 150 were handed over to both contractors to perform a function under contractual arrangements, and the remaining area (12 UCs) was managed by LWMC by its resources, excluding catchment areas (Figure 6b).  
Figure 5: Administrative hierarch of LWMC.  
About 8000 waste storage containers of 0.8 m\({}^{3}\) were placed in the city to store the waste--machinery deployed by contractors, as depicted in Figure 7. On average, 4350 tons of waste per day was collected by contractors, and the year-wise total tonnage collected and disposed of at designated sites is depicted in Figure 4. A decline in waste collection and disposal was observed from 2017 to 2020 (Figure 7) due to the COVID-19 epidemic and some other issues with the international contractors, i.e., audit observation and inquiries [108; 109; 110]. However, daily 1300 km mechanical sweeping and 100 km washing were performed by both contractors [112].  
Extension in international contracts was granted for one year until 31 December 2020. Meanwhile, LWMC attempted two times to outsource the waste collection services for the transition period locally but attempts needed to be matured due to higher rates received from local contractors [112].  
Figure 6: Outsourcing of waste collection services: (**a**) Scope of outsourced services; (**b**) Map of Lahore showing outsourced service area to contractors.  
Figure 7: Status of resources deployed and tonnage collected: (**a**) Fleet deployed for waste collection and haulage to disposal site; (**b**) Tonnage collected and disposed at Lakhodair site.  
3.1.13. Lesson Learned from SWM Outsourcing  
Lesson learnt in previous outsourcing contracts are described in Table 2.
## 3 Results
### Comparison of Input Variable Combinations Based on RF
#### 3.2.2 RF Performance in NE1, NE2, and NE3 Sites with Different Input Variable Combinations  
Input variable combinations in Table 2 were applied to test the performance of the RF model (\(mtree=500\), \(ntry=3\)) in three NE sites (70% of data for training, and the other 30% for testing with random selection) by running it 1000 times. The test results are shown in Figure 5. In terms of mean value for comparison, A0 had the lowest accuracy (NSE = 0.9574, RMSE = 1.8671 gC\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\), Bias = \(-0.0174\) gC\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\), CV = 0.1805). In contrast, when SLN and NMP alone or their combination were added as model inputs, the distribution plot of NSE gradually moved to the right side while the plots of RMSE and CV shifted to the left side with lower uncertainty. A3 obtained the highest accuracy (NSE = 0.9703, RMSE = 1.5596 gC\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\), Bias = 0.0029 gC\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\), CV = 0.1508). Thus, owing to the direct influence of SLN and NMP on carbon fixation such as maize leaf's photosynthetic rate, they both boosted the performance of models.  
Data combinations of A0 and A3 (the minimum and maximum in Figure 5) were selected to further validate the effectiveness of NMP and SLN for RF among three NE sites (one site for training with another site for testing, as described in Section 2.5.3). In Figure 6, the estimated value was close to the ground-measured value when NMP and SLN  
Figure 5: Distribution of evaluation metrics of test results for different combinations (A0: (**a**,**e**,**i**,**m**); A1: (**b**,**f**,**j**,**n**); A2: (**c**,**g**,**k**,**o**); and A3: (**d**,**h**,**l**,**p**)) using RF in NE1, NE2, and NE3 sites for 1000 running times. \(\mu\) is the mean value and \(\sigma\) is standard error. The line is the fitted normal distribution curve. The column represents the data number.  
were introduced in the model, whether for the NE1, NE2, or NE3 sites. They had higher NSE and lower RMSE and CV values. Moreover, the slope of the fitting line was closer to 1, and the intercept was closer to 0. In terms of NSE, RMSE, and CV, Figure 6a had the best model performance, as both NE1 and NE2 sites were contiguous and irrigated maize cropland and had similar growing environments. Figure 6e,f demonstrates the lowest accuracy in performance for A0 and A3 combinations, primarily due to the difference in growing environments (e.g., soil moisture) between the rainfed NE3 site and the irrigated NE2 site. Another contributing factor to the weak correlation in Figure 6e is the relatively small sample size for the NE3 site with a narrow GPP range (N = 987), making it difficult to accurately estimate GPP for the NE1 site with a wide GPP range (N = 1945).
## 7 Challenges and Limitations
### Dataset Availability and Quality
#### 7.1.1 Textile Characterization  
Many of the studies examined in this review did not provide sufficient details on the types of textiles included in their datasets. The variability amongst textiles in commercial use is almost infinite, so the details of the types of textiles included are essential information to understand the usefulness and applicability of the AI models. At the very minimum, the fabric structure and fiber types should be included, though ideally the characterization details of all fabrics within the dataset would be available (fiber, yarn, structure, mass per unit area, and thickness).
# SnoreLab Application in the Assessment of Obstructive Sleep Apnea Syndrome: A Pilot Study
## 2 Materials and Methods
### SnoreLab (Reviva Softworks Ltd., London, UK)  
SnoreLab, developed by Reviva Softworks Ltd., is a mobile app designed for recording, measuring, and tracking patients' snoring, utilizing the smartphone's microphone. While its primary function involves screening for snoring patterns, it also allows the comparison of snoring trends over time, thereby offering potential utility in follow-up assessments and the evaluation of the efficacy of remedies and lifestyle modifications. Notably, the app is classified as a medical device and is ranked among the top 50 in the Apple app store. For this study, the premium version of SnoreLab (EUR 5.99/monthly) allows multiple registrations and was used in this study.  
Utilizing an algorithm, a \"Snore Score\" is generated, reflecting the intensity and frequency of snoring. The median Snore Score among SnoreLab users is 25, with a score \(\geq 50\) indicative of an advanced snoring category. As per the developer's recommendations, individuals with scores \(\geq\) 100 should seek medical advice, whereas a score \(\leq\) 10 is considered a favorable target. Moreover, snoring severity is further categorized as \"light\" when unlikely to disturb a bed partner, \"loud\" when likely to disturb a bed partner, and \"epic\" when highly likely to disturb a bed partner.
## 2 Model Principles and Methods
### The Basic Principles of the Long Short-Term Memory (LSTM) Network  
LSTM networks are an improved type of recurrent neural network (RNN) that addresses the issues of gradient explosion and vanishing gradient encountered in traditional RNNs. This is illustrated in Figure 2.  
Figure 1: Rock burst prediction flowchart.  
The first step of the LSTM involves the \"forget gate\", which determines the degree of influence of the previous time step's cell state \(C_{t-1}\) on the current time step's cell state \(C_{t}\). \(f_{t}\) represents the output of the forget gate, with the input being the hidden state \(h_{t-1}\) from the previous sequence and the current sequence data \(x_{t}\). The activation function \(\sigma\) (commonly the sigmoid function), the bias vector \(b_{f}\), and the weight matrix \(W_{f}\) are used in the computation of the forget gate [25].  
The computational formula is as follows:  
\[f_{t}=\sigma(W_{f}h_{t-1}+W_{f}x_{t}+b_{f}) \tag{1}\]  
The second step, \"input gate\", consists of two parts, and the computational formula is as follows:  
\[i_{t}=\sigma(W_{i}h_{t-1}+W_{i}x_{t}+b_{i}) \tag{2}\]  
\[C_{t}=f_{t}\times C_{t-1}+i_{t}\times\tanh(W_{c}h_{t-1}+W_{c}x_{t}+b_{c}) \tag{3}\]  
In the equation, \(i_{t}\) determines the necessary information to be updated into the cell state; \(C_{t}\) represents the new cell state at time \(t\); \(W_{c}\) is the weight matrix for the input gate; \(b_{c}\) is the bias vector for the input gate; and the activation function \(\tanh\) denotes the hyperbolic tangent function.  
The third step, \"output gate\", controls the influence of \(C_{t}\) on \(h_{t}\), and its update formula is as follows:  
\[o_{t}=\sigma(W_{o}h_{t-1}+W_{o}x_{t}+b_{o}) \tag{4}\]  
\[h_{t}=o_{t}\times\tanh(C_{t}) \tag{5}\]  
In the equation, \(o_{t}\) determines the output portion of the cell state; \(b_{o}\) is the bias vector for the output gate; \(W_{o}\) is the weight matrix for the output gate; and \(h_{t}\) represents the hidden layer state value of the corresponding unit at time \(t\).
# Research on the Robustness of Active Headrest with Virtual Microphones to Human Head Rotation
## 3 Simulations  
Based on the physical model and optimization methods described above, the robustness of the active headrest system to human head rotation is investigated in this section. The angle of human head rotation ranges from \(-90^{\circ}\) to \(90^{\circ}\), where the positive sign denotes the clockwise rotation and the negative sign denotes the counterclockwise rotation from the top view. An active headrest system with two secondary sources is shown in Figure 3, with the \(y\) coordinates of the secondary sources and the physical microphones at \(\pm 30\) cm and \(\pm 25\) cm respectively, while the virtual microphones are on the \(y\)-axis, \(2\) cm away from the surface of the rigid sphere with a radius of \(8.5\) cm, which are also the evaluation points for measuring the noise reduction performance. Assuming weak coupling effects between the transmission channels of the secondary sources, a single channel system on only the left side of the head can be discussed, so this is hereafter referred to as a single channel active headrest system.  
As for an active headrest system with four secondary sources, two different layouts are discussed as shown in Figure 4. Again, due to the weak coupling effects of the transmission channels of the secondary sources on either side of the human head, only the dual-channel system on the left side of the head is considered. The physical system of layout A is symmetrical about the \(y\)-axis in the \(xy\)-plane, with two secondary sources on each side of the head and corresponding physical microphones near each secondary source. The \(y\) coordinates of secondary sources and physical microphones are \(\pm 30\) cm and \(\pm 18\) cm respectively, while each secondary source, its corresponding physical microphone and the origin are aligned on the same line, which has an angle \(\alpha\) of \(30^{\circ}\) with the \(y\)-axis. The evaluation points are located at the midpoint of the two virtual microphones spaced 2 cm apart, as in the single channel control system, which are on the \(y\)-axis and 2 cm from the surface of the rigid sphere. For layout B, the virtual microphone positions remain the same, and the relative positions of the secondary sources and physical microphones to the rigid sphere are the same as in layout A, except that they are arranged in the \(yz\)-plane.  
Since active headrest systems are generally used at low frequencies, simulations are carried out in a tonal sound field in this paper, with the tonal frequencies of 125, 250 and  
Figure 4: Dual-channel active headrest system: (**a**) layout A (top view); (**b**) layout B (front view).  
Figure 3: Single channel active headrest system (Top view).  
500 Hz. Simulation results for these frequencies show similar patterns, with only results for 250 Hz provided in detail due to the length of the article.
## 2 Related Work  
### Emotion Recognition Dataset  
Current research in emotion analysis primarily focuses on constructing and evaluating datasets with emotion labels. In recent years, six important datasets have advanced the field. For example, the IEMOCAP (Interactive Emotional Dyadic Motion Capture) dataset contains information in three modalities, audio, video, and text, covering angry, happy, sad, neutral, and other emotion categories [12]. The MELD (Multimodal EmotionLines Dataset) extends the EmotionLines dataset with more conversational and multimodal information covering the emotion categories of anger, happiness, sadness, neutrality, surprise, disgust, and fear [13]. The CMU-MOSEI (Multimodal Opinion Sentiment and Emotion Intensity) dataset contains more than 23,000 video clips covering the six basic emotions, with each clip containing audio, text, and video information [14]. The AFEW (Acted Facial Expressions in the Wild) dataset contains video clips of facial expressions extracted from movies covering the emotion categories of anger, disgust, fear, happiness, neutrality, sadness, and surprise [15]. The RAVDESS (Ryerson Audio-Visual Database of Emotional Speech and Song) dataset contains emotionally rich speech and song segments covering the eight emotion categories of neutral, happy, sad, angry, scared, disgusted, surprised, and calm [10]. In addition, the EmoReact dataset focuses on children's emotional responses while watching videos and contains rich facial expression and body language information for studying children's emotion recognition and behavior analysis [16]. However, the existing datasets are not only primarily categorical datasets with less emotion evaluation, but also for some specific tasks, the dataset's information such as audio and video cannot be better applied.  
Based on the existing dataset, this study added data on the evaluation of students' emotional expressions in their second language. The evaluation data were obtained from a team of professional English teachers, who evaluated the students' audio and video for emotional fullness. In addition, this study generates audio and video data that match the needs of this task by integrating audio and video generation techniques for the students' second language emotional expressions learning scenarios. This method gives second language learners standard practice materials for emotion expression, while also being able to generate training data that is more compatible with the tasks of this paper. This method focuses on the improvement of students' language skills and emphasizes their emotional expressions, which helps to improve students' learning outcomes and overall educational experience through an accurate evaluation of and feedback on their emotional expressions.
# Optimization Design for Sparse Planar Array in Satellite Communications
## 2 Sparse Planar Array Optimization Design Model
### Array Antenna Mathematical Underpinnings
For linearly polarized waves, the phase difference between the two components of the electric field vector is an integer multiple of \(\pi\). For circularly polarized waves, the amplitude of the two components of the electric field vector is equal, and the phase difference is \(\pi/2\). For elliptical polarization, the relationship between the amplitude and phase difference of the electric field vector can be arbitrary. In spaceborne phased arrays, linear polarization typically corresponds to the Ku frequency band, while circular polarization is utilized in the L, S, and Ka frequency bands, among others.
## 2 Materials and Methods
### Input Parameters Selection
Therefore, it is necessary to further analyze the degree of influence between related factors and NOx formation and describe the correlation quantitatively. Since this paper takes the 6 EX340 EF engine as the research object, its relevant structural parameters will not change during use, so the correlation with NOx is not considered. A certain number of data samples for the working parameters and performance parameters of the engine under different control parameters are designed by the simulation experiment, and the influencing factors present a normal distribution. Therefore, the commonly used Pearson correlation coefficient and grey relational analysis correlation methods were used to correlate the relevant variables; the results using the Pearson correlation coefficient are shown in Table 3, and the results of the grey relational analysis are shown in Figure 4.  
It can be seen from the correlation coefficients that P_CR, torque, IMEP, PFP, HRR, Q_HR, T_max, and P have a significant positive correlation with NOx. The positive correlations of T_oil, Ex_o, and T_sca are weak. The correlations of Inj, Inj_D, Ex_c, P_sca, T_exh, P_exh, CA50, and CA10 are negative. Figure 4: Gray relational analysis comparison graph. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Pearson** & **Sig.** & & **Pearson** & **Sig.** \\ \hline P\_CR & 0.485 ** & 0.001 & Tor & 0.701 ** & 0.000 \\ Inj & \(-\)0.122 & 0.416 & IMEP & 0.716 ** & 0.000 \\ Inj\_D & \(-\)0.264 & 0.073 & CA50 & \(-\)0.609 & 0.000 \\ T\_oil & 0.097 & 0.516 & CA10 & \(-\)0.868 & 0.000 \\ Ex\_o & 0.020 & 0.892 & PFP & 0.639 ** & 0.000 \\ Ex\_c & \(-\)0.102 & 0.496 & PFP\_C & 0.254 & 0.123 \\ T\_sca & 0.075 & 0.615 & HRR & 0.851 ** & 0.000 \\ P\_sca & \(-\)0.043 & 0.772 & Q\_HR & 0.841 ** & 0.000 \\ T\_exh & \(-\)0.031 & 0.834 & T\_max & 0.353 * & 0.030 \\ P\_exh & \(-\)0.007 & 0.963 & P & 0.701 ** & 0.000 \\ \hline \hline \end{tabular} Note. * The correlation is significant at the 0.01 level (two-tailed). ** Correlation is significant at the 0.01 level (two-tailed).  
\end{table} 
Table 3: Table of Pearson correlation coefficients between variables and NOx. From the grey relational analysis correlation graph, it can be seen that all of the analyzed variables are strongly correlated with NOx, and this result has similarities and differences with the Pearson results. To make full use of the information for the relevant parameters and to reduce the number of model input parameters, principal component analysis was used to perform dimensionality reduction for the above 20 parameters. After principal component analysis, the resulting scree plot is shown in Figure 5, and its component coefficient is shown in Table 4. As can be seen from Figure 5, after the 7 th principal component, the data tends to flatten out, that is, 20 principal components can be abbreviated to 7 principal components by using the coefficients in Table 4.
## 2 Model Improvement
### Upper-Level Model Improvement  
The reserve capacity coefficient actually reflects the expansion multiple of the basic OD matrix. In the original model (Equation (4)), the constraint condition is that the link flows which have been expanded should be under capacity limits. Considering that when the road service level is lower than a certain value, travelers cannot arrive at the destination in the given time, which is \"unreliable\". On the basis of capacity limitation, this article improves the model by adding service level coefficient \(\partial\), and the value can be between 0.8-1.0 [24], as shown in Equation (5):  
\[\begin{array}{ll}\max\ \mu_{r}\\ \ s.t.&x_{a}(\mu_{r}q)\leq\partial C_{a}\end{array} \tag{5}\]  
At the same time, with the increase of traffic flow, the road impedance increases and the travel time of users increases. After the increase to a certain extent (relative to the original travel time), the road network is actually \"unreliable\" for users. \(t_{a}(q)\) represents the user's travel time, \(\tau\) is defined as the travel time reliability threshold, if possible, it can be comprehensively selected according to the field survey questionnaire. The meaning of other variables and parameters is the same as above, and the model is improved to Equation (6).  
\[\begin{array}{ll}\max\ \mu_{r}\\ \ s.t.&x_{a}(\mu_{r}q)\leq\partial C_{a}\\ &t_{a}(\mu_{r}q)\leq\tau t_{a}(q)\end{array} \tag{6}\]  
It can be found that the improvement of the upper-level model is essentially a restriction on the traffic assignment flow. To a certain extent, the reliability analysis of the traffic capacity and travel time can be unified.