esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Introduction to MobileNet V2  
MobileNet V2 is a lightweight convolutional neural network improved by the Google team in 2018 based on MobileNet V1. It has efficient architecture that balances high accuracy with low computational cost, which is essential for deployment on embedded and mobile devices with limited resources. Its use of depthwise separable convolutions significantly reduces model parameters and computation, enabling faster inference and smaller model size without sacrificing performance. The inverted residual blocks with linear bottlenecks enhance feature extraction by preserving important information and capturing fine-grained facial details necessary for accurate expression recognition. Furthermore, MobileNet V2 has widely available pretrained weights, facilitating transfer learning and improving generalization on facial expression datasets. Compared to V1, V2 has about 20% fewer trainable parameters and performs better in the 1000 classification of the ImageNet competition with higher accuracy. Compared to MobileNet V3 proposed by Google team in 2019, although MobileNet V3 is slightly better in terms of accuracy, the parameters of the model are about 1.5 times of MobileNet V2. As the sample of the facial expression dataset is small, and too many parameters of the model are easy to cause overfitting, so the algorithm chooses MobileNet V2, which combines both accuracy and small parameters, as the main network to improve. The recognition rate and parameter count (P/M) comparison of MobileNet series network in ImageNet competition 1000 classification are shown in the Table 1.  
In Table 1, \"224\" indicates that the input image size is 224 \(\times\) 224, \"recognition rate/%\" is the top1 accuracy rate, and \"P/M\" is the parametric size of the model.  
The main features of MobileNet V2 include: the use of 3 \(\times\) 3 depth separable convolution instead of conventional convolution, which can significantly reduce the number of trainable parameters and speed up the training speed of the model. The use of the inverted residual structure first expands the input feature map and then reduces its dimensionality, allowing residual connections after feature extraction. This increases the overall network depth, enabling the model to capture higher-dimensional features and enhancing its feature representation capability. In addition, replacing the ReLU activation function used in V1 with a linear activation function helps reduce the loss of low-dimensional features.
# Performance Analysis of Hybrid PDM-SAC-OCDMA-Enabled FSO Transmission Using ZCC Codes
## 3 Weather Conditions and OCDMA Code
### Attenuation Caused by Fog and Snowfall Conditions  
The various changes in weather conditions have an impact on the information signal in the FSO channel while it is propagating through it. In this study, CW, LF, MF, HF, WSE, and DSF weather conditions were considered. When tiny smoke particles are suspended in the air, fog is formed, and it varies from LF to HF depending on particle size and visibility (\(R\)) range. The fog attenuation coefficient, \(\gamma\) (dB/km), can be given as in [27]:  
\[\gamma=\frac{3.912}{R}\bigg{(}\frac{\lambda}{550\ \mathrm{nm}}\bigg{)}^{-p} \tag{1}\]  
where \(\lambda\) indicates the wavelength in nm and \(p\) is the size distribution of the scattering particle. According to the Kim model, this can be calculated as in [27]:  
\[p=\left\{\begin{array}{ll}1.6&R>50\\ 1.3&6<R<50\\ 0.16\ R+0.34&1<R<6\\ R-0.5&0.5<R<1\\ 0&R<0.5\end{array}\right. \tag{2}\]  
Table 1 shows attenuation coefficients caused by different fog conditions.  
Snowfall has a significant impact on the LOS link and causes attenuation that differs according to the snowfall rate. The attenuation caused by snowfall, \(\propto\) (dB/km), is mathematically calculated as in [29]:  
\[\propto=mS^{n} \tag{3}\]  
where \(S\) in mm/hr refers to the snowfall rate. Alternatively, \(m\) and \(n\) are parameters that are given as in [29]:  
\[\begin{array}{ll}n=1.38,\ \mathrm{and}\ m=5.42\ \times\ 10^{-5}\lambda\ +\ 5.49\ \mathrm{in}\ \mathrm{the}\ \mathrm{case}\ \mathrm{of}\ \mathrm{DSF}\\ n=0.72,\ \mathrm{and}\ m=1.02\ \times\ 10^{-4}\lambda\ +\ 3.78\ \mathrm{in}\ \mathrm{the}\ \mathrm{case}\ \mathrm{of}\ \mathrm{WSF}\end{array} \tag{4}\]  
Table 2 shows attenuation coefficient for different snowfall types.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fog Conditions** & **LF** & **MF** & **HF** \\ \hline Attenuation coefficient (dB/km) & 10 & 18 & 27 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Different fog conditions with their corresponding attenuation coefficient [27; 28].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of Snowfall** & **WSF** & **DSF** \\ \hline Attenuation coefficient (dB/km) & 13.73 & 96.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Different snowfall conditions with their corresponding attenuation coefficient [30].
## 4 Evaluation of Performance and Accuracy of Different Implementations  
The described options in Section 3 regarding the implementation of the two notch root approximations are to be evaluated in terms of the following two aspects:  
* The accuracy with which the notch root approximations are carried out affects the results of the fatigue strength assessment.
* The calculation resources required for the application or the required computing time matter for an efficient implementation. When performing single calculations on a single or a few individual locations of a component to be verified, this aspect is not important. However, in regard to performing many assessments, e.g., when applied to a whole FE surface mesh of a component, the calculation time becomes a decisive factor.  
For this purpose, a large number of artificial examples are considered in the following, which contain all necessary input variables for a notch root approximation with the two notch root approximations described above. For these examples, notch root approximations are performed with the different implementation variants, and the calculation results of the different variants are compared with each other in terms of accuracy and efficiency.
## 4 Creep Deformation Calculations and Predictions for Jinping Diversion Tunnels at Different Depths  
### Engineering Geological Overview [25; 32]  
The Jinping-II Hydropower Station leverages a 310 m natural head from the 150 km long Yalong River bend to generate power through a diversion tunnel. A group of world-class ultra-deep tunnels has been constructed beneath the Jinping Mountain, comprising seven tunnels traversing the mountain: four diversion tunnels, two auxiliary traffic tunnels, and one drainage tunnel. With a total length exceeding 120 km, tunnel diameters of 13 m, and overburden depths generally exceeding 1500 m, this project represents the deepest and most challenging underground tunnel system globally. The Jinping diversion tunnel follows a gentle longitudinal gradient of 3.65%, with an average length of 16.67 km. The surrounding rock exhibits a unit weight of 26.5 kN/m\({}^{3}\), an elastic modulus of 20-25 GPa, and a Poisson's ratio of 0.26-0.32.  
This section employs the validated numerical subroutine to simulate the time-dependent deformations in the Jinping diversion tunnel at two representative cross-sections of different depths (Figure 7):  
Figure 7: Long-term monitoring points in cross-sections at different depths in Jinping diversion tunnels [33].  
Section 2: Chainage 1#K9 + 960, overburden depth \(\approx\) 2400 m.  
The numerical results are compared with field monitoring data from both sections, followed by 20-year post-excavation deformation predictions for the Jinping diversion tunnel.
## 6 Discussion of Results
### CEF-ARPO with Uncertain Inputs
* Scenario 5 (S5): the maximum number of assets in the portfolio is decreased from 10 to 9. * Scenario 6 (S6): Scenarios S4 and S5 are jointly considered. * Scenario 7 (S7): the minimum quantity for all assets is increased from 0.01 to 0.015. * Scenario 8 (S8): the maximum quantity for all assets is decreased from 1 to 0.995. * Scenario 9 (S9): Scenarios S7 and S8 are jointly considered. Observe that in Scenarios S4-S9 the vector of asset returns and the variance and covariance matrix are calculated from historical data and, in contrast to Scenarios S1-S3, do not involve any random inputs. Table 10 presents the calculated CEF-ARPO variances and APLs for Scenarios S4-S6. The calculations show that increasing the lower bound of the cardinality constraint does not have any effect on APLs or computational times. The APL is always 0 irrespectively of the CEF-ARPO position, including the higher end of the CEF. Similarly, decreasing the number of assets from 10 to 9 under S5 poses no significant challenge to portfolio diversification, since the resulting APLs--mainly at the lower end of the CEF--are very low. Scenario S6 that combines the two preceding scenarios shows some insignificant departures from S0, essentially due to the lower maximum number of assets that can be included in a portfolio. Finally, Scenarios S7-S9 show that small variations in the minimum and maximum quantities are followed by similarly unimportant changes in the CEF. Indeed, an increase in the minimum weight attributed to each asset leads to the average APL of 0.013% (S7 and S9) relative to the optimal solution under S0 (see also Table 9). However, a decrease in the maximum weight does not appear to alter the optimal solution. All in all, additional computational experiments indicate that the ARPO algorithm shows a reasonable level of stability when dealing with constrained portfolio optimization problems. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenario** & **Average APL** & **Non Feasible Portfolios** & **Time (s)** \\ \hline Scenario 1 & 3.324\% & 3 & 0.173 \\ Scenario 2 & \(-\)0.109\% & 0 & 0.141 \\ Scenario 3 & 3.274\% & 3 & 0.196 \\ Scenario 4 & 0.000\% & 0 & 1.460 \\ Scenario 5 & 0.042\% & 0 & 0.177 \\ Scenario 6 & 0.042\% & 0 & 0.540 \\ Scenario 7 & 0.013\% & 0 & 0.256 \\ Scenario 8 & 0.000\% & 0 & 0.192 \\ Scenario 9 & 0.013\% & 0 & 0.070 \\ \hline \hline \end{tabular} Notes: Table 9 shows the average APL (column 2), the number of non feasible portfolios (column 3), and the average computational times (column 4) obtained for 9 different scenarios on Instance 1 (Hang Seng).  
\end{table} 
Table 9: Different scenarios based on Instance 1 (Hang Seng).
## 4 Discussion
In previous studies addressing position effects, [PERSON] et al. used sEMG and accelerometer sensors attached to 10 subjects with normal arms to reduce the average classification error from 18% to 5% [21]. [PERSON] et al. presented their proposed spectral moments to achieve significant reductions in the classification error rates of up to 10% on average across subjects and five different limb positions [22]. [PERSON] et al. proposed a cascade classification scheme using a position classifier and multiple motion classifiers responsible for different arm positions using multichannel EMG signals and tri-axial accelerometer mechanomyography signals to reduce the interpositional classification error of amputees by up to 10.8% [19]. [PERSON] et al. used time domain power spectral descriptors as the feature set to train a Deep Neural Network classifier, obtaining 98.88% recognition accuracy on five limb positions [36]. The aim of our study was to address TH amputees' problem of decreased recognition performance due to arm position changes, and few studies have focused on this group. However, TH amputees similarly and inevitably need to make arm position changes while using their prostheses to complete daily activities [39]. Our proposed algorithm, which does not require the introduction of other inputs or computation of features, simply uses two action repetitions for retraining to achieve stable recognition at different arm elevation positions, which is in line with the actual usage scenario of myoelectric prostheses. Our proposed DAIDA is able to achieve accurate upper arm movement recognition while realizing adaptation at different arm elevation positions, which is useful in practical applications, by improving the model performance from the source domain to the target domain through knowledge migration. Second, the deep domain adaptation network is able to capture the shared features between the source and target domains, thus achieving better model generalization capabilities. Therefore, DAIDA is uniquely suited to address motion recognition at different arm elevation positions. Although the DAIDA presented in this paper enables the stable detection of multiple limb positions in translumeral amputees, we have only covered the most common limb positions in daily life and not all degrees of freedom of the upper arm. At the same time, we were able to achieve a limited number of movement patterns. Practical prosthesis use may also involve factors such as electrode displacement and muscle fatigue, which will be a direction for future refinement.
## 3 Results and Discussion
### Analysis of Starch Gelatinization Characteristics  
Starch gelatinization represents a complex transformation of starch granules from an ordered state to a disordered state [47]. A comparison of the gelatinization characteristics of C, US, and US-Q is presented in Table 1. Compared to the US, the peak viscosity and breakdown value of the C following ultrasonic treatment exhibited a significant increase, whereas the increase in the setback value was not pronounced. Notably, the peak viscosity, trough viscosity, and final viscosity of the modified starch composite all decreased significantly. After the addition of quercetin, the breakdown value decreased from 1116 to 685, indicating that the composite modification of broken rice starch by the combination of ultrasound and quercetin led to a marked enhancement in the starch's thermal stability and a reduced likelihood of starch granule fragmentation. The setback value of the C, which was 1021, was significantly higher than that of the US-Q, which was 407. A lower setback value implies better anti-aging properties of the starch. After ultrasonic modification, the setback value was relatively higher than that of the unmodified broken rice starch. However, it  
Figure 4: Chain length distributions of broken rice starch, ultrasonically modified starch, and modified starch composite.  
decreased significantly upon the addition of quercetin, demonstrating that the addition of quercetin led to a reduction in the setback value, thereby significantly delaying the starch's retrogradation and substantially improving the anti-aging properties. The decline in gelatinization performance might be attributed to the fact that the hydroxyl groups on the surfaces of polyphenols exist in the form of hydrogen bonds and form a complex with the side chains of starch molecules under the action of van der Waals forces [48]. This, in turn, hindered the interactions between starch molecules and inhibited the recrystallization of the starch after cooling, as well as the leaching of amylose, thereby achieving the effect of delaying the starch's retrogradation.
## 1 Introduction
### Audio Classification and Machine Learning  
A sound classification method has been proposed that uses convolutional neural networks (CNNs) and Mel frequency cepstral coefficients (MFCCs) as sound conversion methods, in order to obtain spectrograms suitable to be used as inputs to CNNs. This method improved the classification accuracy on the dataset from 63% to 97% while offering many benefits, such as faster training [19]. The acoustic quantification diversity of vocal audio has been utilized in a generalizable method with an unsupervised Random Forest model. It was demonstrated that this method could classify acoustic structures with high accuracy, offering a standard tool for music variation comparison [20]. Moreover, the use of Mel spectrograms has been applied with convolutional neural network (CNN) and Long Short-term Memory (LSTM) network architectures. Results show that a hybrid method, using compression techniques, Taylor pruning, and 8-bit quantization, exhibited high accuracy against traditional methods with the minimum of computational resources [21]. A systematic review compared machine learning techniques with limited data augmentation in audio-based sound classification, i.e., voice and speech. This paper presented common challenges, such as low or high noise data, and how they impact classification performance.  
This review of the literature sheds light on sound analysis to derive features [22], a survey with an emphasis on different deep learning models in audio classification, and emphasizing five best architectures: CNNs, Recurrent Neural Networks (RNNs), Autoencoders, Transformers, and Hybrid Models. Popular audio transformation techniques, such as spectrograms and MFCCs, were included [23]. Evaluating small data and augmentation techniques exhibited the potential for improving classification using deep learning on sound features and problems such as noise or data scarcity that complicate and make feature extraction difficult and unreliable. Recommendations were proposed for enhancing the performance of sound classification [22]. A recent study explored repeated training of an existing convolutional neural network to analyze the impact of retraining parameters that will impose on accuracy and processing time in sound datasets [24].  
Further, researchers improved their previous findings on the prediction of aggressive behaviors in dementia patients by integrating audio-based violence detection with their present visual-based analysis of body motion. Noise-filtering algorithms such as MFCCs, frequency filtering, and speech prosody were used in an audio recording dataset as a means of extracting pertinent features. Using a Late Fusion Rule, predictions of both models were integrated into a single meta-classifier as a way of achieving better early aggression detection accuracy. The results proved that multimodal enhancement further improved predictive capability, yielding a more accurate model for detecting and predicting aggressive behaviors in clinical populations, thereby facilitating caregivers in interventions [25].
## 5 Discussion  
### GPP Estimated at 30 m Spatial Resolution  
While urban areas are highly fragmented and heterogeneous, most models simulate GPP in urban areas at coarse resolutions (e.g., 1-2 km) [5; 6; 7]. In this study, vegetation GPP in Wuhan was estimated at 30-m spatial resolution with land cover maps derived from Landsat images, the synthetic NDVI generated with STARFM, and meteorological measurements. It is worth mentioning that since the NDVI that was used to calculate fPAR in the GPP estimation reflected the combined effect of LUCC, meteorological variations and phenology, it was extremely difficult to completely eliminate the impact of meteorological variations on the GPP estimation.  
The STARFM has been found to be able to produce an accurate interpolated NDVI time series in many studies [21; 25]. In this study, the variations in the accuracy of the synthetic NDVI may be due to different atmospheric conditions when MODIS data and Landsat data were obtained on different days. GPP estimated with the synthetic NDVI demonstrated more spatial details and complexities in the urban area (Figure 11). The greater level of details provided a better representation of fringe areas and small LUCC than the models with 1-2 km spatial resolutions [26].  
Directly validating GPP estimates in the urban area is difficult, due to the absence of EC flux towers in cities [7; 10; 41]. In this study, we proved the reliability of vegetation GPP estimates by comparing GPP_act with [PERSON] et al.'s [40] results and MOD17 GPP. Overall, values of GPP_act were within the reasonable range. The slightly higher values of GPP_act compared with the MOD17 GPP values were due to several factors. In the MOD17 GPP algorithm, fPAR was estimated with atmospherically corrected daily bidirectional reflectance and land cover classification [29], whereas GPP_act was based on the linear relationship between fPAR and NDVI. In addition, fPAR used in GPP_act was smoothed, free of peaks and valleys in the curve. In the mid-growing season of crops and EBF, the abrupt low values of MODIS fPAR may have great impacts on estimations of GPP. Another reason attributed to the difference between GPP_act and MODIS GPP may be the difference between the ground meteorological data used in GPP_act, and the GMAO surface meteorology used in MODIS GPP.  
The study was based on the assumption that air pollutants did not affect the GPP of urban vegetation. Air pollutants originating in a city and transporting outside can adversely affect atmospheric chemistry, as well as the vegetation in that region [42]. Vegetation productivity in the urban area may decrease or increase, depending on the type of pollution and its effect on vegetation productivity [43]. Besides, the air temperature in the urban area may be more heterogeneous than air temperature that is estimated with observations at limited weather stations. Although the complex impacts of air pollutants and heterogeneous air temperature on vegetation GPP in the urban areas are very important issues, they are beyond the scope of this paper.
# Advanced Thermal Management for High-Power ICs: Optimizing Heatsink and Airflow Design
## 4 Results  
Our results demonstrate that optimizing fan velocity and heatsink geometry leads to significant reductions in IC temperature. Specifically, increasing the heatsink surface area by 20% resulted in a 15% reduction in the maximum operating temperature. These findings underscore the effectiveness of relatively simple design modifications in achieving thermal management goals, without the need for more complex and expensive solutions like liquid cooling. The practical implications of this work are particularly relevant for telecommunications equipment and high-power integrated circuits used in data centers, where efficient heat dissipation is critical to maintaining system reliability.  
Beyond simple temperature measurements, we incorporated detailed statistical analyses to evaluate heat dissipation across various configurations. The heat dissipation efficiency of each model was analyzed, revealing that the optimal configuration resulted in a 30% improvement in heat dissipation compared to that in traditional setups. Addi  
Figure 4: The surfaces around the designed box exchange heat with the surrounding environment through radiation.  
tionally, time-varying temperature comparisons between different heatsink designs were introduced to illustrate how fan speed and geometry interact over time to influence overall cooling performance.  
In this section, our research presents comprehensive findings from a series of simulations and experiments designed to optimize thermal management solutions for high-power-density integrated circuits (ICs). We systematically explore multiple configurations and modifications to the cooling systems, emphasizing the interplay between heatsink geometry and cooling efficiency.  
Our approach introduces a new dimension to existing research by specifically optimizing the heatsink and airflow design for ICs. While previous studies have addressed broader thermal management strategies, our work tackles the unique challenges in heat dissipation by refining geometric configurations and fan speed adjustments. These optimizations demonstrate measurable improvements in both efficiency and scalability, particularly within the context of high-power 5G systems.