esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Method
#### 2.2.2 Multiple Discriminators  
We performed a study based on two discriminators. One discriminator is a conditional GAN which discriminates real segmented images from generated segmented images. Another discriminator is Markovian discriminator (patchGAN) [27].  
For the discriminator based on a conditional GAN, we follow the summarized architecture by [15]. This architecture avoids the use of max pooling. The maximization problem in Equation (1) is solved by training the discriminator network [20]. There are eight convolutional layers increasing from 64 to 512 by a factor of 2; the same as in the VGGarchitecture [30]. To reduce the resolution when the feature numbers are doubled, stride convolutions are used. We use LeakyReLU as the activation function (\(\alpha\) = 0.2). The final feature maps are followed by two dense layers and then by a sigmoid activation function, which provides a probability for the classification. Figure 4a illustrates the architecture of this conditional GAN. The task of the discriminator is to examine the output of the generator and determine whether it is similar to the original ground truth or not. If it is comparably similar to the ground truth, it will predict it as \"real\", otherwise it will be \"fake\". Hence, the generator will consider this loss to generate images similar to the ground truth as much as possible. The discriminator in Figure 4a functions like a binary classifier where real is represented by true and fake is represented by false.  
Figure 4: Architecture of the Discriminator Network of a (**a**) conditional GAN and (**b**) patchGAN with corresponding kernel size ‘k’, stride ‘s’, and a number of feature maps ‘n’ indicated for each convolutional layer.  
Figure 3: Architecture of a Generator Network with the number of channels indicated at the top of each convolution.  
We follow patchGAN architectural guidelines summarized by [27]. The discriminator in patchGAN only discriminates the structure at the scale of patches. A patch of size \(N\times N\) is classified as real or fake. Assuming the pixels separated by more than a patch are independent, this discriminator models the image as a Markov random field [27]. Figure 4b illustrates the architecture of this patchGAN. The structure is similar to the conditional GAN we used. This discriminator also comprises of eight convolutional layers increasing from 64 to 512 by a factor of 2, and LeakyReLU (\(\alpha\) = 0.2) is used as an activation function. In this architecture, the final two dense layers are replaced by a single convolution layer with a single feature map with a kernel size 3 \(\times\) 3. The final result of this discriminator is a of size of 16 \(\times\) 16.
# Study of Force Changes Based on Orthotic Elements Under the First Ray
###### Abstract  
The first ray plays a fundamental role in foot biomechanics, particularly in stabilizing the medial longitudinal arch and enabling efficient weight transfer during the mid-stance and propulsion phases of gait. When dorsiflexed--a condition known as metatarsus primus elevatus--especially in its flexible form, this structure disrupts load distribution, impairs propulsion, and contributes to various clinical symptoms. Despite its clinical importance, the biomechanical impact of orthotic elements placed beneath the first ray remains underexplored. This study aimed to quantify the variations in medio-lateral (Fx), antero-posterior (Fy), and vertical (Fz) force vectors generated during gait in response to different orthotic elements positioned under the first ray. A quasi-experimental, post-test design was conducted involving 22 participants (10 men and 12 women) diagnosed with flexible metatarsus primus elevatus. Each participant was evaluated using custom-made insoles incorporating various orthotic elements, while gait data were collected using a dynamometric platform during the mid-stance and propulsion phases. Significant gait-phase-dependent force alterations were observed. A cut-out (E) reduced medio-lateral forces during propulsion (\(p<0.05\)), while a kinetic wedge (F) was correlated with late-stance stability (\(r=-0.526\)). The foot posture index (FPI)/body mass index (BMI) mediated the vertical forces. The effect sizes reached 0.45-0.42 for antero-posterior force modulation. Phase-targeted orthoses (a cut-out for propulsion, a kinetic wedge for late stance) and patient factors (FPI/BMI) appear to promote biomechanical efficacy in metatarsus primus elevatus, enabling personalized therapeutic strategies.  
foot; first ray; foot orthoses; forces
## 5 Experimental Results and Analysis  
In this section, we analyze the detection performance of our proposed model via various experiments. Specifically, we first investigate the detection performance of the proposed model under a single anomaly scenario. Then, we present the experimental results of the model under mixed anomaly scenarios. Finally, we compare the detection performance with the related research.  
In our study, the specifications of the experimental equipment include an Intel(R) Core(TM) i7-11700K CPU @3.60 GHz and NVIDIA GeForce GTX3080 Ti GPU. For developing the algorithm, we used the continuous wavelet transform analysis tools in Matlab 2020b [49] for the CWTS construction and Python 3.6 with the Pytorch framework for the deep learning methods.  
The evaluation criteria used in this experiment included accuracy (Acc), sensitivity (Sens), precision (Prec), and F1 score (F1), which we calculated from TN (true negatives), TP (true positives), FP (false positives), and FN (false negatives). The evaluation criteria were defined as follows:  
\[\text{Acc}=\frac{\text{TN}+\text{TP}}{\text{TN}+\text{TP}+\text{FN}+\text{FP}}. \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Conv1** & **Conv2** & **Conv3** \\ \hline Filter Size & 11 & 5 & 3 \\ Filter Num & 16 & 32 & 48 \\ Stride & 5 & 3 & 3 \\ Padding & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The model parameters of our CNN architecture.  
Figure 4: The structure of the CNN.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyperparameter** & **Range** & **Value** \\ \hline Learning Rate & 0.0001, 0.001, 0.005 & 0.001 \\ Batch Size & 32, 64, 128, 256 & 128 \\ Epoch Number & 100, 200, 500, 1000 & 500 \\ Dropout Rate & 0.1, 0.2, 0.3 & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameter tuning.  
Accuracy describes the overall ratio of correct detection for normal and anomalous signal readings.  
\[\text{Sens}=\frac{\text{TP}}{\text{TP}+\text{FN}}. \tag{5}\]  
Sensitivity measures the proportion of correctly detected anomalous signal readings from the total number of anomalous signal readings.  
\[\text{Prec}=\frac{\text{TP}}{\text{TP}+\text{FP}}. \tag{6}\]  
Precision assesses the number of true anomalous sensor readings to the total number of anomalies detected by an approach.  
\[\text{F1}=2\times\frac{\text{Prec}\times\text{Sens}}{\text{Prec}+\text{Sens}}. \tag{7}\]  
F1 score is used to balance sensitivity and precision by calculating the harmonic mean of them.  
We chose these metrics because they can evaluate the capability of the detection models to correctly differentiate between normal and abnormal vehicle behaviors.
## 4 Discussion
### Comprehensive Evaluation of Four Scenarios  
Based on the simulation results in Section 3, the four scenarios were ranked on a scale of 1 to 4 for their performance in terms of winter, summer, and indoor wind environment evaluation indicators. A lower score signifies superior performance in external wind conditions and indoor ventilation effectiveness. Figure 19 presents the sorting results using distinct colors to facilitate readers' comprehension. Scenario 1 outperformed the others, achieving a total score of 14. This is mainly because of the implementation of row-by-row floor planning, which enables wind to flow across the entire site, leading to outstanding performance in metrics such as outside wind speed, wind speed amplification factor, vortex areas, and windless regions. Therefore, interior wind environment indicators that have a positive correlation with these factors, such as air exchange rate and indoor wind speed, also showed good performance.  
Scenario 2 performed the worst, mainly due to the adoption of zigzag floor planning. The building form significantly obstructs the wind, creating semi-enclosed spaces on both the windward and leeward sides. This prevents the wind from continuing to flow after entering these enclosed and semi-enclosed spaces. Therefore, Scenario 2 performed poorly in terms of outdoor wind speed, wind pressure difference between the windward and leeward facades of the building, and indoor wind speed.  
Scenario 3 is similar to Scenario 2 but has a floor design with branching that divides the largely enclosed spaces in Scenario 2 into multiple smaller partially enclosed spaces. Therefore, when facing obstacles, the wind is more likely to sustain its flow. Scenario 3 shows a somewhat better performance than Scenario 2.  
Scenario 4 utilizes enclosed floor planning; however, there are holes on the east and west sides to facilitate airflow in and out of the enclosed area. This guarantees that air circulation is maintained within the enclosed area. Scenario 4 outperformed Scenarios 2 and 3 in evaluating inside and outdoor wind conditions. This also offers guidance for  
Figure 19: Comprehensive evaluation of the four scenarios.  
enhancing Scenarios 2 and 3 by creating openings in the building structure to let the wind flow across the area, maintaining wind speed at a comfortable level and promoting airflow.
## 4 Experimental Results
### Setting  
#### 4.1.1 Datasets  
First, we trained our DMFN with the DIV2k [34] dataset and validated it with Set5 [35]. This was used to compare it with other SR methods trained using natural images. Moreover, the corresponding ablation models were trained with the DIV2k [34] dataset and validated with Set5 [35]. Then, as is the case with FAWDN [22], we trained our network with the medical image dataset MRIMP and validated it with MRI13, which was used in [22]. This was named DMFN+. Finally, all the comparison results were tested with three medical image datasets: the MRI13 dataset in [22], ADNI100 [36] dataset and OASIS100 [37] dataset.  
The DIV2k [34] dataset contains 800 training images, which have a resolution of 2K. We increased the number of images 10-fold through rotation and cropping. The medical image dataset MRIMP in FAWDN [22] contains 1444 training images. These were obtained using GoogleMR by crawling the keywords IXI [38], ADNI [36], KneeMR [39], and LSMRI [40]. LR images come from the bicubic down-sampling of HR images. All experimental results were obtained on a GPU under the PyTorch framework.
## 4 Less Regular Connectivity Models
### SINR Model  
The concept of \"transmission range\" used in most connectivity studies is a marked over-simplification. In real networks, the presence of a link depends on both the signal strength and the background interference. One of the main drawbacks of disk-based models is that they do not consider interference, though dense networks produce strong interference.  
To take interference into account, [PERSON] _et al._[58] used another model, the signal-to-interference-plus-noise-ratio (SINR) model, in which two nodes are connected if SINR between the transmitter and receiver exceeds a certain threshold. As signal strength is considered in this model, the connectivity of the network depends on the selection of the attenuation function. It was proved that percolation occurs for the SINR model in a similar way as in the disk models. It was also shown that the commonly used power law attenuation function results in symmetry properties, and attenuation functions with finite support (i.e., a finite transmission range) can also achieve percolation. The authors showed that an efficient CDMA system with small orthogonality factors can improve the connectivity. However, due to the difficulty of designing efficient CDMA codes to achieve a small orthogonality factor, a simple TDMA scheme was proposed, which also percolates.
# Laboratory and Field Experiments on the Effect of Vinyl Acetate Polymer-Reinforced Soil
## Materials and Methods
### Durability Test  
The durability of the soil mainly includes frost resistance, drying and wetting resistance, ultraviolet radiation resistance, and so on. This experiment mainly studies the effect of vinyl acetate polymer on frost resistance and light resistance of soil. The durability of soil is a major property related to climate conditions.  
In the durability test, the dried soil was mixed with the vinyl acetate polymer dilution. The unconfined compression strength increased with the increasing polymer concentration. When the polymer concentration was more than 20%, the unconfined compression strength tended to be stable. Therefore, the concentration of the polymer was 20%. The optimal values (for full mass stabilized soil) were used for all specimens in this test, and were 15.4% in water content and 1.73 g/cm\({}^{3}\) in density. After the preparation, the specimens were kept in a curing box with a temperature of around 20 \({}^{\circ}\)C for 24 h.  
To test the frost resistance of the improved soil, the specimens were first kept in a \(-\)20 \({}^{\circ}\)C environment for 12 h. In addition, they were then placed in a drying box with a temperature of around 20 \({}^{\circ}\)C for 12 h. This is a freeze-thaw cycle. The indoor temperature is 20 \({}^{\circ}\)C. The annual temperature range of Nanjing is 40 \({}^{\circ}\)C, so the temperature cycle of \(-\)/+20 \({}^{\circ}\)C was chosen. In the test, the number of freeze-thaw cycles was set at 0, 2, 4, 6, 8 and 10 (S17, S18, S19, S20, S21 and S22). After the freeze-thaw cycles, the unconfined compression test was carried out with a YYW-2 unconfined compressional strain control device to evaluate the durability of improved soil with different cycles. The loading rate of the unconfined compression test is 2.4 mm/min. The unconfined compression test was based on the ASTM standard (ASTM D2166-00). For the specimens at each relevant parameter, the test was repeated three times.  
To test the light resistance of improved soil, the specimens were kept in an artificial climate box with a temperature of around 40 \({}^{\circ}\)C because the annual maximum temperature in Nanjing is about 40 \({}^{\circ}\)C, and 22,000 Lx intensity of light. In the experiment, the illumination time was set at 0 h, 120 h, 240 h, 480 h, 600 h and 720 h (S23, S24, S25, S26, S27 and S28), respectively. After that, the specimens were also tested for the unconfined compression strength to evaluate the durability of improved soil with different illumination time. For the specimens at each relevant parameter, the test was repeated three times.
###### Abstract
The single-channel MAC focuses on resource allocation whereas the multi-channel MAC deals with collision avoidance and load balancing by providing multiple channel access mechanisms. Some of the challenges tackled by MAC protocol design in VANETs are (1) the provision of low latency safety services and high throughput non-safety services, (2) the elimination of hidden/exposed terminal problems due to rapid mobility of nodes in VANETs and  
Figure 1: VANET architecture [1]. topology changes, (3)proper resource and bandwidth allocation in single-channel as well as multi-channel for load balancing and better network throughput. Various MAC protocols have been reviewed in the past based on different criteria. The authors in [13] reviewed multi-channel MAC protocols in VANETs, with the design of an adaptive MAC protocol to handle topology changes by adjusting the control channel based on a Markov model. In [10], TDMA-based MAC protocols were studied, protocols were classified based on topology, and a comparative analysis along with the advantages of using contention-free access mechanism were provided. In [14], the authors assessed foreground multi-layer challenges for better performing VANETs, with a focus on different layers, and proposed solutions along with the limitations and future work.
# Shot Classification of Field Sports Videos Using AlexNet Convolutional Neural Network
## 3 Results and Discussion
### Evaluation Parameters  
We employed objective metrics to evaluate the performance of the proposed framework. For this purpose, we used precision (P), recall (R), accuracy (A), error rate (E), and F1-score to measure the performance. These metrics are computed in terms of correct/incorrect classification of shot types for each category. Finally, the results of all types of shots are averaged to obtain the final values.  
For shot classification of sports videos, accuracy represents the ratio of correctly classified shots (True Positives and True Negatives) out of the total number of shots. Accuracy is computed as follows:  
\[A=\frac{TP+TN}{P+N} \tag{9}\]  
where true positives (TP) represent the correctly classified shots of a positive (P) class (i.e., long shot if we are measuring the accuracy of long shot). And, true negatives (TN) represent the correctly classified shots of negative (N) class (i.e., medium, close-up and crowd shots in case of measuring long shot accuracy).  
Error rate refers to the ratio of misclassified shots (False Positives (FP) and False Negatives (FN)) to the total examined shots. Error rate is computed as:  
\[E=\frac{FP+FN}{P+N} \tag{10}\]  
where FP represent the N class shots misclassified as positive class shots. Additionally, FNrepresent the P class shot misclassified as the negative class shot.  
Precision represents the ratio of correctly labelled shots over the total number of shots and computed as follows:  
\[P=\frac{TP}{TP+FP} \tag{11}\]  
Recall is the fraction of true detection of the shots over a total number of shots in the video and computed as:  
\[R=\frac{TP}{TP+FN} \tag{12}\]  
F1-score represents the harmonic mean of precision and recall. F1-score is useful metric for performance comparison in cases where some method have better precision but lower recall rate than the other method. In this scenario, precision and recall rates independently are unable to provide true comparison. Therefore, F1-score can reliably be used in such cases for performance comparison. F1-score is computed as:  
\[F1=\frac{P*R}{P+R} \tag{13}\]
# Detection of Defective Features in Cerasus Humilis Fruit Based on Hyperspectral Imaging Technology
## 3 Results and Discussion
### Image Information Detection
#### 3.4.4 Discussion  
In this work, the image information of defective and intact _Cerasus humilis_ fruit was collected by hyperspectral imaging system and combined with chemometrics and image processing methods. The defect features of _Cerasus humilis_ fruits were detected from both the hyperspectral and image information systems, and satisfactory test results were obtained. In addition to discrimination accuracy, classification time also plays an important role in the sorting system. Compared with the machine vision classification system, the hyperspectral imaging system has a significant amount of spectral information and a relatively complex image acquisition method. Therefore, the HSI system has a relatively slow sample defect identification time. In this study, the imaging time is about 25.0 s, and the average analysis time of classifying _Cerasus humilis_ fruit into normal fruit and defective fruit is 37.8 s with the use of a developed image processing algorithm of _Cerasus humilis_ fruit defect features. [PERSON] et al. [9] used hyperspectral imaging to classify the defects of jujube (intact, cracked, brusised, and insect-infested jujubes). The soft independent modeling of class analogies (SIMCA) is the best model based on raw data, and the classification  
Figure 9: Flow chart of the involvement of the cracked feature algorithm in the _Cerasus humilis_ fruit sample: (**a**) hyperspectral image; (**b**) grayscale image; (**c**) PCA; (**d**) mask; (**e**) infill; (**f**) regiongrow; (**g**) bwareaopen; (**h**) canny; (**i**) imadd.  
accuracy was above 95%. [PERSON] et al. [27] used the hyperspectral imaging system to obtain image information of loquat fruit defect features and developed an image processing method to discriminate the defect features with a detection rate of 92.3%. Hyperspectral imaging and machine learning techniques were combined to identify some common defects in \"Algerian\" loquat fruits, with a correct classification rate of 95.9% [28]. [PERSON] et al. [29] analyzed the hyperspectral images of healthy and early decay tomatoes collected using spectral and image processing techniques. The results show that the recognition rates of early decay fruit and healthy fruit were 100% and 97.5%, respectively. When compared with the above research papers, this research has a good effect on recognition, though there is still the phenomenon of false and missed detection. The detection accuracy of our research results is slightly lower (88.57%). This may be due to the small volume of this research object and the small proportion of the defect area, resulting in a low recognition rate. In addition, as a spherical fruit, the uneven lighting distribution on the _Cerasus humilis_ fruit is also one of the main problems causing low accuracy of defect detection. Subsequent research will improve the image detection algorithm to improve the recognition rate.
## 3 Results and Discussion
### Analysis of CIM's Potential for the Calculation of the Indicators
#### 3.2.27 The Indicator to Measure Outdoor Thermal Comfort  
This indicator promotes improving the comfort of inhabitants in outdoor spaces of the site and assessing the percentage of spaces that provide thermal comfort. This is encouraged by SBTool\({}^{\text{PT}}\) Urban, SNTool, BREEAM C, and LEED ND, and also addressed by Levels. Therefore, this indicator is recognized as a high-level environmental concern. According to the SBTool\({}^{\text{PT}}\) Urban Guide, there are two calculation parameters to determine this indicator. The first is the Percentage of Spaces with Thermal Comfort, which is determined by the ratio between the sum of the areas that promote thermal comfort and the total intervened area. This parameter can be calculated using the CIM model, while it is necessary to characterize all the areas and then apply a Dynamo routine to calculate it. Additionally, the second parameter is calculated through the verification checklist, in which the criteria can be assessed using the CIM model, and the points can be attributed using Dynamo.