esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Model Construction
#### 3.2.3 Training the Student Model with Knowledge Distillation  
Knowledge distillation is a cutting-edge method used to transfer information from a more complicated teacher model to a simpler student model. Generally, larger models have higher classification accuracy than lighter ones [61]. By constricting a larger model with higher performance and a light model with lower performance, we force the lighter model to imitate the behavior of the larger one, while focusing on cross-entropy loss to improve its classification performance. This process is generally referred to as knowledge distillation. In knowledge distillation, the larger model is known as the teacher model, and the lighter model is the student model. In this experiment, a hybrid ResNet152V2 and Swin Transformer architecture is employed as the teacher model. The main objective is to transfer the teacher model's high-level decision boundaries and feature representations to the student model, Xception, so that it can imitate them and gain access to its discriminative power.  
As presented in Figure 8, both the teacher and student models receive the same input data during training, and the student model is tuned to produce predictions that are both accurate and consistent with the softer, more nuanced outputs of the teacher model. This alignment is accomplished by combining supervised loss, which guarantees accurate class predictions, and distillation loss, which incentivizes the student to match the probability distributions of the teacher, typically by using a temperature parameter to regulate the output softness of the teacher. Knowledge distillation produces a more compact model with competitive performance by successfully transferring the teacher model's comprehensive understanding and generalization abilities to the student. To train both models, we used  
Figure 6: The proposed student model.  
the categorical cross-entropy loss function and Adam optimizer. The training process lasted for 100 epochs. The hyperparameter temperature value is 10, making the probability distribution of the teacher model more soft.
## 5 Fair Training
### Pre-Processing Fairness  
Preprocessing fairness applications involve modifying training data before feeding them into an AI model to reduce bias and promote fairness. These techniques focus on addressing biases in the data themselves, which can lead to fairer model results. Common  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Fair Training Method** & **Definition** & **Implementation** & **Key Features** & **References** \\ \hline Pre-processing Fairness & Modifying training data before feeding into the model & Re-sampling, re-weighting data augmentation & Addresses bias at the data level & [136; 139; 140] \\ \hline In-processing Fairness & Modifying learning algorithms or objective functions & Adversarial training, adversarial debiasing & Simultaneously optimizes for accuracy and fairness & [137; 141; 142] \\ \hline Post-processing Fairness & Adjusting the model’s predictions after training & Re-ranking, calibration & Does not require access to the model’s internals & [46; 143; 144; 145] \\ \hline Regularization-based Fairness & Adding fairness constraints to the optimization process & Penalty terms in the loss function & Can be combined with various learning algorithms & [43; 146; 147] \\ \hline Counterfactual Fairness & Measuring fairness based on changes in sensitive attributes & Counterfactual reasoning & Focuses on individual-level fairness & [45; 148; 149] \\ \hline \hline \end{tabular}
\end{table}
Table 5: Fair training method comparison.  
methods include resampling, reweighting, data augmentation, fairness-aware clustering, and synthetic oversampling techniques.  
Resampling techniques, such as oversampling and undersampling, adjust data distribution to alleviate bias by equalizing group representation. Reweighting assigns higher weights to underrepresented instances during model training, reducing bias against marginalized groups. Data augmentation generates synthetic data to bolster underrepresented groups, enhancing fairness. Fairness-aware clustering ensures equitable grouping, while the Synthetic Minority Oversampling Technique (SMOTE) generates synthetic samples to balance class distribution, promoting fairness in classification tasks. These methods collectively counteract bias and enhance fairness in AI models.  
In Table 6, it can be seen that re-sampling techniques handle class imbalance, re-weighting adjusts data importance, data augmentation enhances diversity, attribute swapping equalizes sensitive attributes, fairness-aware clustering ensures equitable grouping, and SMOTE addresses class imbalance. By selecting and applying the appropriate pre-processing fairness method based on the specific dataset and fairness goals, AI practitioners can develop models that prioritize fairness and equitable outcomes. Continued research and experimentation with these techniques will advance the pursuit of unbiased AI applications across various domains.
# A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks
## 4 Adversarial Attack Generation Methods for IoT Networks
### Exploratory Attack Methods
#### 4.1.1 Fast Gradient Sign Method  
Fast Gradient Sign Method (FGSM) is a straightforward and efficient method for generating adversarial examples (AEs) [15]. Those AEs are inputs that have been intentionally modified in a way that optimizes the maximum quantity of perturbation applied to each pixel (i.e., image) to induce incorrect predictions by an ML model. The FGSM works by taking the gradient of the loss function relative to the input data and subsequently perturbing the input data in the direction of the sign of the gradient. The magnitude of the perturbation is established by a hyperparameter known as epsilon (\(\varepsilon\)), which controls how much the input data are modified. The output result is called the AE and its formula can be formalized by the Expression (1):  
\[X_{adversarial}=X+\varepsilon.Sign(\
abla x\ J(\theta,X,Y)) \tag{1}\]  
where \(\varepsilon\) represents a small value and \(\
abla\) denotes the gradient of loss function \(J\) relative to the original input data (i.e, image) \(X\), the original input class label \(Y\), and the model parameters \(\theta\).  
The FGSM algorithm can be summarized in three steps. The first step computes the gradient of the loss relative to the inputs, the second step scales the gradient to have a maximum magnitude of \(\varepsilon\), and the third step adds the scaled gradient to the input data (i.e., image) \(X\) to create the adversarial example \(X_{adversarial}\).  
Although this method is fast for generating AEs, its effectiveness is lower than that of other state-of-the-art methods for generating adversarial attacks because it generates only one AE per input data point and may not be able to explore the full space of possible AEs. Additionally, being a white-box attack is that it assumes full knowledge of the targeted model. This requirement limits its applicability in scenarios where the adversary possesses restricted access to the model's internal details, but it remains useful for manufacturers to assess the resilience of their ML models against adversarial attacks as well as in scenarios of insider attacks [36].
## Results and Discussion
### Influence of PAW on Sensory Properties  
The sensory properties of thin sheets of bean curd were evaluated immediately after different treatments. There was no significant difference among different groups with respect to the attributes of appearance, flavor, brittleness, as well as the overall acceptance of thin sheets of bean curd (Table 3). Although the total color differences were clearly visible from the PAW treatments according to the color measurements, the attribute of appearance acceptance was not significantly influenced. Therefore, this means that PAW treatment would not affect consumers' acceptance of thin sheets of bean curd. This is consistent with studies that found that PAW did not affect most sensory properties of mung bean sprouts, lettuce, and meat products [13; 16; 29].  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Springiness (g/s)** & **Cohesiveness** & **Chewiness (\(\times\)1000)** & **Resilience** & **Hardness (kg)** \\ \hline Control & \(1.43\pm 0.20\)\({}^{\text{a}}\) & \(0.93\pm 0.03\)\({}^{\text{a}}\) & \(16.05\pm 2.53\)\({}^{\text{a}}\) & \(0.85\pm 0.06\)\({}^{\text{a}}\) & \(12.19\pm 1.35\)\({}^{\text{b}}\) \\ SDW-30m & \(1.14\pm 0.43\)\({}^{\text{a}}\) & \(0.94\pm 0.01\)\({}^{\text{a}}\) & \(16.13\pm 1.70\)\({}^{\text{a}}\) & \(0.89\pm 0.04\)\({}^{\text{a}}\) & \(14.96\pm 2.09\)\({}^{\text{a}}\) \\ PAW15-20m & \(1.19\pm 0.45\)\({}^{\text{a}}\) & \(0.94\pm 0.01\)\({}^{\text{a}}\) & \(17.57\pm 2.21\)\({}^{\text{a}}\) & \(0.86\pm 0.03\)\({}^{\text{a}}\) & \(15.79\pm 1.64\)\({}^{\text{a}}\) \\ PAW30-20m & \(1.24\pm 0.58\)\({}^{\text{a}}\) & \(0.93\pm 0.01\)\({}^{\text{a}}\) & \(16.60\pm 1.07\)\({}^{\text{a}}\) & \(0.85\pm 0.04\)\({}^{\text{a}}\) & \(14.30\pm 0.78\)\({}^{\text{a}}\) \\ PAW60-20m & \(1.03\pm 0.56\)\({}^{\text{a}}\) & \(0.94\pm 0.01\)\({}^{\text{a}}\) & \(14.30\pm 2.01\)\({}^{\text{a}}\) & \(0.88\pm 0.03\)\({}^{\text{a}}\) & \(13.93\pm 1.10\)\({}^{\text{a}}\) \\ PAW90-10m & \(1.31\pm 0.56\)\({}^{\text{a}}\) & \(0.93\pm 0.01\)\({}^{\text{a}}\) & \(14.58\pm 1.62\)\({}^{\text{a}}\) & \(0.85\pm 0.03\)\({}^{\text{a}}\) & \(11.98\pm 0.84\)\({}^{\text{b}}\) \\ PAW90-20m & \(1.56\pm 0.41\)\({}^{\text{a}}\) & \(0.94\pm 0.01\)\({}^{\text{a}}\) & \(16.13\pm 1.98\)\({}^{\text{a}}\) & \(0.88\pm 0.03\)\({}^{\text{a}}\) & \(14.70\pm 1.28\)\({}^{\text{a}}\) \\ PAW90-30m & \(1.03\pm 0.54\)\({}^{\text{a}}\) & \(0.94\pm 0.01\)\({}^{\text{a}}\) & \(14.43\pm 1.87\)\({}^{\text{a}}\) & \(0.89\pm 0.04\)\({}^{\text{a}}\) & \(14.86\pm 1.06\)\({}^{\text{a}}\) \\ \hline \end{tabular} Note: Data are presented as the mean \(\pm\) standard deviation. Values in the same column with the same letters are not significantly different (\(p>0.05\)).  
\end{table}
Table 2: Influence of plasma-activated water treatment on the textural properties of thin sheets of bean curd.  
Figure 3: The total isofluavone content of the thin sheets of bean curd with and without treatments. The same letters in the bars indicate no significant difference with each other (\(p>0.05\)).
# Improved Remote Sensing Image Classification Based on Multi-Scale Feature Fusion
## 2 Datasets
### The GF-6 Images Dataset
#### 2.1.2 Remote Sensing Data  
Gaofen 6 (GF-6) is a low-orbit optical remote sensing satellite launched by China in 2018, which provides images with high resolution, wide coverage, high quality, and high efficiency. GF-6 has a design life of eight years and two cameras: a full-color 8 m (high-resolution) multi-spectral camera with an image swath of 90 km and a 16 m (medium-resolution) multi-spectral wide-format camera with an image swath of 800 km. The GF-6 satellite will operate with the Gaofen 1 satellite network, reducing the time resolution of remote sensing data acquisition from 4 d to 2 d. In-depth analysis of these remote sensing images and the establishment of appropriate image segmentation methods will allow the acquisition of high-precision global land-use information, which is of great significance for improving the application level of GF-6.  
We selected images from different seasons to increase the anti-interference abilities of the WFCNN and mitigate potential complications, such as the change in seasons, and thus enhance applicability. We collected a total of fifty-two GF-6 remote sensing images (Figure 1d). These images were divided into four groups according to the image acquisition time. The first image group was captured in autumn 2018, the second in winter 2018, the third in spring 2019, and the fourth in summer 2019. Together, the images of each group covered the study area. Specifications are given in Table 1.  
Figure 1: Geographic location of the city of Tai’an (red boundary) within Shandong Province, China: (**a**) Geographic location of China; (**b**) Geographic location of Shandong Province; (**c**) Terrain of Tai’an and (**d**) remote sensing images used in this study.  
Image preprocessing included geometric correction, radiation correction, and image fusion. We used Python to develop a program for geometry correction that captured control points from geometrically corrected Gaofen 2 (GF-2) remote sensing images. These geographically corrected GF-2 images have a spatial resolution of 1 meter, which is suitable for selecting control points from them.  
Atmospheric correction was performed using the fast line-of-sight atmospheric analysis of spectral hypercubes module in the environment for visualizing images (ENVI) software. The multi-spectral and panchromatic images were fused using the Pan-sharping module in ENVI. The resulting image included four bands (blue, green, red, and near-infrared) with a 2-m spatial resolution.
## 5 Results
### Bridge Navigation Items Related to Required Information for SA (Q1 in Table 2)
For instance, a magnetic compass gives only magnetic compass course information. Some information resources consist of a combination of information from multiple pieces of items. For example, the information resource to precipitate the location of navigation hazards is gathered from seven items (ECDIS, ARPA, Gyro Compass, AIS/Radar, VHF, NAVTEX and Chart). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Required Information**} & **Bridge Navigation** & \multirow{2}{*}{**No.**} & **Bridge Navigation** \\  & & **Items** & & **Items** \\  & & **(No. in Table 3)** & & **(No. in Table 3)** \\ \hline
1 & Ship’s position & 1, 2, 3, 4, 16 & 23 & Speed of tidal current & 1, 2, 5, 7 \\ \hline
2 & Speed through the water & 2, 3, 5, 7, 11 & 24 & Direction of tidal current & 1, 2, 4, 7 \\ \hline
3 & Speed over the ground & 1, 2, 3, 7, 16 & 25 & Direction of wave & 4, 18 \\ \hline
4 & Gyro compass course & 2, 3, 4 & 26 & Height of wave (wind, swell) & 18 \\ \hline
5 & Magnetic compass course & 17 & 27 & Target location & 2, 3, 4, 6 \\ \hline
6 & Heel & 15 & 28 & Target speed & 2, 3, 6 \\ \hline
7 & Rudder angle & 12 & 29 & Target distance & 2, 3, 6 \\ \hline
8 & Rate of turn & 4 & 30 & Target course & 2, 3, 6 \\ \hline
9 & Pitching & - & 31 & Target bearing & 2, 3, 4, 6 \\ \hline
10 & Yawing & 4 & 32 & Number of targets & 2, 3, 6 \\ \hline
11 & Rolling & - & 33 & TSS to be followed & 2, 4, 8, 16 \\ \hline
12 & Swaying & - & 34 & VIS communication & 2, 8, 16,19 \\ \hline
13 & Surging & - & 35 & VIS standing instructions & 8 \\ \hline
14 & Under keel clearance & 2, 9, 16 & 36 &
\begin{tabular}{c} Location of navigation \\ hazards \\ \end{tabular} & 2, 3, 4, 6, 8, 10, 16 \\ \hline
15 & Visibility & - & 37 & Anchorage areas & 2, 3, 4, 8, 16 \\ \hline
16 & Temperature & 18 & 38 &
\begin{tabular}{c} Location of wreck, shoals, \\ underwater rocks \\ \end{tabular} & 2, 3, 4, 10, 16 \\ \hline
17 & Sea Temperature & 18 & 39 & Density of traffic & 2, 3, 6 \\ \hline
18 & Moisture & 18 & 40 & Planned route & 2, 16, 19 \\ \hline
19 & Amount of cloud & - & 41 & Distance to waypoints & 2, 3, 4, 16 \\ \hline
20 & Sky condition & - & 42 & Planned speed for each leg & 2, 16,19 \\ \hline
21 & Speed of wind (relative and absolute) & 13 & 43 & Air draft & 2, 16, 19 \\ \hline
22 & Direction of wind (relative and absolute) & 4, 13 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Bridge navigation items. Table 5 extracts the required information to which two or more bridge navigation items are related from the agreed linkage sheet. This suggests that OOWs acquire required information in multiple ways utilising various bridge navigation items.
# DTS-Net: Depth-to-Space Networks for Fast and Accurate Semantic Object Segmentation
## 3 Proposed Method
### Nearest Label Filtration  
As we use an image reconstruction technique, the reconstructed segmentation map introduces a reconstruction noise, particularly at the borders of the objects in the image. Hence, mislabeled pixels are introduced. In detail, these noisy pixels are usually mislabeled with a label neighboring the true label. During the training stage, the network attempts to learn the true label as accurately as possible; however, an abrupt color change occurs at a border, causing mislearning of the border pixels. This problem is particularly notable when the CNN down-samples the image using max-pooling, and some details disappear from the final feature maps. To solve this problem, we propose Nearest Label Filtration (NLF), which aims to correct those noisy pixels by mapping them to the nearest classification label using a patch-by-patch technique. We train the network for multi-label classification besides segmentation; thus, we use the classification labels of the image (global labels) to filter the noisy pixels in the segmentation map. We divide the image into patches (imperially, \(10\times 10\) and \(8\times 8\) are the best patch sizes). For each patch, we obtain the unique labels that exist in the patch (patch labels). Then, we find the intersection between the patch labels (\(PL\) ) and global labels (\(GL\) ) of the image to obtain the true labels (\(TL\) ):  
\[TL=GL\cap PL \tag{2}\]  
By measuring the distance between each \(PL\) and the \(TL\), we can find the distance array for all \(PL\) as follows:  
\[dist\_arr=|PL_{i}-TL_{j}| \tag{3}\]  
for \(i\) in \(\Omega_{PL}\) space and \(j\) in \(\Omega_{GL}\) space. Then we can estimate the nearest label in \(TL\) corresponding to the minimum distance in \(dist\_arr\) and overlay the noisy pixel with the nearest label (\(NL\) ), as follows:  
\[NL_{i}=TL[argmin(dist\_arr)] \tag{4}\]  
This filtration method efficiently removes the noise from the image and provides clear edges. However, performance degradation occurs in extremely dense cases where the segmentation mask has a large number of classes and almost all pixels have widely differing labels (e.g., for the Cityscapes [4] dataset, in which the scenes are very crowded with small labels and many different classes). However, the method performs very well on the Pascal VOC2012 and NYUV2 datasets, in general. Figure 1 presents sample images that clearly show the difference before and after filtration. Figure 2 presents a visual illustration of the filtration step as a component of the overall proposed method.
# Data Security-Based Routing in MANETs Using Key Management Mechanism
###### Abstract  
A Mobile Ad Hoc Network (MANET) is an autonomous network developed using wireless mobile nodes without the support of any kind of infrastructure. In a MANET, nodes can communicate with each other freely and dynamically. However, MANETs are prone to serious security threats that are difficult to resist using the existing security approaches. Therefore, various secure routing protocols have been developed to strengthen the security of MANETs. In this paper, a secure and energy-efficient routing protocol is proposed by using group key management. Asymmetric key cryptography is used, which involves two specialized nodes, labeled the Calculator Key (CK) and the Distribution Key (DK). These two nodes are responsible for the generation, verification, and distribution of secret keys. As a result, other nodes need not perform any kind of additional computation for building the secret keys. These nodes are selected using the energy consumption and trust values of nodes. In most of the existing routing protocols, each node is responsible for the generation and distribution of its own secret keys, which results in more energy dissemination. Moreover, if any node is compromised, security breaches should occur. When nodes other than the CK and DK are compromised, the entire network's security is not jeopardized. Extensive experiments are performed by considering the existing and the proposed protocols. Performance analyses reveal that the proposed protocol outperforms the competitive protocols.  
cryptosystems; MANET; routing; security; key management +
Footnote †: journal: Applied  
11 December 2021  
Accepted: 17 January 2022  
Published: 20 January 2022
## 3 Results and Discussion
### Antioxidant Analysis in the Vegetables  
We also measured the concentrations of antioxidants, such as vitamins C and E, in all samples in the experimental groups (Figures 4 and 5). The respective vitamin C concentration in groups 1 and 2 with Cd exposure were significantly higher in sweet potato leaves (25.3 vs. 13.6 g/kg) and water spinach (8.72 vs. 9.94 g/kg) than in the control group (16.5 and 6.63 g/kg, respectively) (Figure 4a). In addition, significantly higher levels of vitamin C were found in the sweet potato leaves (28.4 vs. 20.8 g/kg) and water spinach (11.9 vs. 7.26 g/kg) in groups 1 and 2 with Cu contaminant as compared to the control group (Figure 4b). When comparing Vitamin C levels in Cd and Cu contaminant groups,  
Figure 3: BAF of Cd and Cu in different vegetables in contaminant soils.  
vitamin C was active in sweet potato leaf and in water spinach in both contaminated scenarios.  
Figure 5 presents the distribution of vitamin E concentrations in all vegetables of the contaminated soil groups for both Cd and Cu. The respective vitamin E concentrations in groups 1 and 2 with Cd exposure were similar, but higher in tomato (17.1 vs. 18.0 mg/kg) and sweet potato (17.0 vs. 27.3 mg/kg) than in water spinach (14.2 vs. 13.6 mg/kg), sweet potato leaves (9.61 vs. 23.2 mg/kg), and potato (5.58 vs. 3.78 mg/kg) as compared to the control group. [PERSON] et al. [55] found 15 local crops that were rich in various active natural substances, such as vitamin C (3.46-109 mg/100 g) and vitamin E (0.10-4.35 mg/100 g), in different types of leafy vegetables, as well as vitamin C at 15.2-101 mg/100 g in leaf vegetables [56], and vitamin E at 0.43-12.85 mg/kg in fruits [25], in order to establish a dietary guide for local people in Thailand. In another study, [PERSON] et al. [26] carried out a pot experiment using artificial additives heavy metal (Cd and Cu) treated soils planted two hydroponic potted plants and analyzed the antioxidant levels for chlorophyll, carotenoids, vitamin C, and vitamin E after harvesting. In the Cd-contaminated soils, the concentrations of vitamin E increased significantly. Meanwhile, in the Cu-contaminated soils, the concentrations of vitamin C and vitamin E increased simultaneously. However, another study mentioned that there was a 70.58% reduction in vitamin C content in pakchoi shoots that were exposed to a mixture of Cd (20 mg/kg)  
Figure 4: The comparison of Vitamin C levels in different vegetables of control, group 1 and group 2: (**a**) Cd level; (**b**) Cu level (g/kg F.W.).  
Figure 5: The comparison of Vitamin E levels in different vegetables of control, group 1 and group 2: (**a**) Cd level; (**b**) Cu level (g/kg F.W.).  
and Cu (400 mg/kg) [27]. In the present study, we also found that vitamin C increased in sweet potato leaf and water spinach plants exposed to heavy metals, as well as an increase in vitamin E in most crops. Our results were in agreement with [PERSON] et al. [26], who speculated that vitamin C and E aided in the tolerance to metals, and in particular that vitamin E was better than vitamin C during exposure to Cd.