esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this paper, we analyzed some special orbits around Jupiter considering only the \(J_{2}\) and \(J_{4}\) terms of the non-spherical gravitational potential. First, stationary orbits were investigated in spherical coordinates. The radius of stationary orbits was found to be 2.2414 Jupiter radii. The longitude drift due to the non-spherical component of the gravity field  
Figure 9: Inclinations of sun-synchronous repeating ground track orbits as a function of the semi-major axis, for different values of the repetition parameter \(Q\).  
did not occur for the lack of \(J_{22}\) terms. Moreover, a small perturbation of \(r\) and \(\phi\) would lead to the oscillation phenomenon in the radial and North-South directions. We also showed that the perturbations of the Sun and the Galilean satellites, the main ring and the magnetic field are negligible on short time scales compared to those represented by the terms \(J_{2}\) and \(J_{4}\). However, the obtained solutions may be incorrect on long time scales. Then, sun-synchronous orbits, orbits at critical inclinations, and repeating ground track orbits were discussed based on the mean-element theory. The results showed that only one meaningful inclination exists for sun-synchronous orbits when \(a\) and \(e\) are fixed in a suitable range. Additionally, only two critical inclinations exist for the critical orbits. Repeating ground track orbits, which are also sun-synchronous, were also calculated here. The traditional frozen orbits were not discussed because the \(J_{3}\) term was not taken into account in our gravity model. However, we remark that there may exist some non-traditional frozen orbits around Jupiter, which will be left for our future investigations.  
The dynamics around Jupiter are very complicated and even if some perturbations can be neglected for preliminary orbit design, other effects should be considered. For example, orbits at low-to-moderate inclinations within a few Jovian radii may loiter inside Jupiter's radiation belts. This can lead to considerable risk for the integrity of a satellite. However, we believe that our work can help to reduce or eliminate the need for station keeping in future space mission around Jupiter.  
Conceptualization, H.Z. and Y.L.; methodology, H.L. and Y.J.; software, Y.J.; validation, H.L., Y.J. and Y.L.; formal analysis, H.L.; investigation, H.Z.; resources, Y.J.; data curation, H.Z. and Y.L.; writing--original draft preparation, Y.L.; writing--review and editing, H.L. and Y.J.; visualization, H.Z. and Y.L.; supervision, H.L. and Y.J.; project administration, H.L. and Y.J.; funding acquisition, Y.J. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (Grant No. 11772356). The authors gratefully acknowledge the reviewers for their careful work and thoughtful suggestions that have helped improve this paper substantially.  
The authors declare no conflict of interest.
## 6 Future Research Directions
This will help users from non-technical backgrounds to better understand how the algorithms work and the results, thus increasing the acceptance and application of the algorithms. * Application Prospects:  
By improving the interpretability of intelligent algorithms, researchers and engineers will be able to better understand the internal working mechanisms of algorithms, thereby optimizing their performance and application effectiveness. In addition, this enhanced interpretability will increase user trust in the algorithm results and promote the wider application of intelligent algorithms in hydrodynamic noise optimization. (3) Optimization of low-noise design  
Low-noise design is an important goal in engineering design. Future research can explore how to use intelligent algorithms to optimize the low-noise design of engineering structures, such as ship propellers and underwater equipment. Through the optimization of intelligent algorithms, the noise radiation of a structure can be minimized and the noise performance and environmental friendliness of equipment can be improved under the premise of meeting engineering performance requirements. * Research Direction:  
Multi-objective optimal design: research on how to use intelligent algorithms to carry out multi-objective optimal design, taking into account multiple performance indicators such as noise level, energy efficiency, structural strength, and cost-effectiveness at the same time. Through multi-objective optimization, solution offering the best trade-off between different performance metrics can be found, to achieve the optimization of low-noise designs. Application of novel materials and structures: explore how novel materials and structures can be applied to low-noise design, such as the use of sound-absorbing materials and optimization of structural shape and layout. Intelligent algorithms can help researchers to evaluate the noise performance of different material and structural solutions, so as to select the optimal design solution. Interdisciplinary co-operation: strengthen interdisciplinary co-operation and integrate knowledge and technology from the fields of fluid dynamics, acoustics, materials science, and control engineering, to jointly solve complex problems in low-noise design. Through interdisciplinary cooperation, more efficient low-noise design methods and technologies can be developed. * Application Prospects:  
By optimizing low-noise design using intelligent algorithms, the noise radiation from structures can be significantly reduced and the noise performance and environmental friendliness of equipment can be improved, while meeting engineering performance re quirements. This will help reduce the impact of hydrodynamic noise on the environment and ecosystem and promote sustainable engineering practices.
# URSIM: Unique Regions for Sketch Map Interpretation and Matching
## 6 Experiments
### Stability with Respect to the Number of Time Samples  
Two intrinsic parameters of URSIM, present in Equation (11), are the sampling rate \(n\) used to calculate the values of \(t\), and the number of samples \(n_{l}\). While the sampling rate \(n\) is closely related to the scale of the global information, there is no direct way to choose it. We investigated the stability of URSIM to those parameters by using between \(n_{l}=1\) and \(n_{l}=25\) time samples, and increasing by \(n=0.2\) between subsequent samples. For each map and value of \(n_{l}\), the matching algorithm was run with different values of \(m\), the threshold under which two vertices are considered similar, ranging from 0 to 1 by steps of 0.025. The highest \(F_{1}\) score over all possible \(m\) was kept as the final matching \(F_{1}\) score. The stability of URSIM to the threshold \(m\) is presented in Section 6.4.  
Over the sketch map dataset, the \(F_{1}\) scores obtained are fairly independent on \(n_{l}\), as shown in Figure 8. While using more time samples leads to slightly better results, the \(F_{1}\) mean score is always above 0.69 and stays fairly stable when using more than 10 time samples. Hence, while the value of the threshold \(m\) matters, as shown in Section 6.4, the number of time samples does not have much  
Figure 7: Regions in one map may have several ground truth correspondences in another map. In this example, one region on the metric map on the left side has ground truth correspondences to three regions in the sketch map. Those three correspondences form a clique.  
influence on the matching accuracy. Accordingly, in the rest of the experiments, we used a sampling rate \(n=0.2\) and used \(n_{l}=15\).
## 7 Defense Strategies
### Changing the Training Procedure and Input Data
#### 7.1.1 Adversarial Training  
The fundamental goal of adversarial training is to increase the regularity and robustness of a DNN [37]. Moreover, in training, adversarial samples are used, and fresh adversarial samples are generated at every stage of the process [41,77,78]. More precisely, the adversarial training that is accomplished on some models can improve the accuracy of pre-trained models.  
The researchers in [79] suggested a solution for AML detection. This paper measured the performance of intrusion-detecting algorithms after being exposed to four different attack methods: the fast gradient sign, the primary iterative method, the Carlini and Wagner attacks, and the projected gradient descent created by the researchers by putting five different ML classifiers under the test. Then, they implemented a method for detecting such attacks as a new way of dealing with adversarial attacks on artificial neural networks (ANN). As a result, this study recalled 0.99 for adversarial attacks using random forest and the nearest neighbor classifier. Nevertheless, significant reductions in the false positive rate are critical for the method's future development.  
Moreover, we have listed a few defensive techniques that fall under the adversarial training scope as follows:  
1. ZK-GanDef  
The authors in [33] proposed a defense strategy called zero-knowledge adversarial training defense (ZK-GanDef) to defend against adversarial attacks in neural networks (NN). Additionally, this approach enhanced the accuracy by 49.17% against adversarial attacks compared to other attacks.  
2. AFR  
To assess the resilience of ML-based NIDS, the authors in [80] performed the first comprehensive investigation of gray-/black-box traffic-space adversarial assaults. Moreover, they implemented an attack on NIDS and suggested an adversarial feature reduction (AFR) method, which reduced the attack's efficacy by reducing adversarial feature development. This study also demonstrated the need to consider an attacker's capacity to mutate traffic. To sum up, the attackers can affect NIDS even if they do not have a precise understanding of the characteristics utilized by them. The findings of this experimental research clarified that the creation of adversarial features could be reduced via adversarial feature development (AFR). Moreover, the attack achieved a rate of more than 97% in half of the cases, and the proposed defense technique might successfully minimize such attacks. In addition, AFR could not prevent attackers from exploiting the vulnerable feature during traffic mutation. According to the authors, this attack technique was intended to evade NIDS without paying attention to the payload. Therefore, it was ineffective for systems that use payload-based detection. Additionally, this attack is now unavailable online.
## 4 Calculation Example of Slope Stability under Freeze-Thaw Conditions
### Analysis of Calculation Results  
#### 4.4.1 Influence of Freeze-Thaw Cycles on Slope Stability  
Figure 3 shows the effect of freeze-thaw cycles on slope stability. It can be seen that the safety factor of the cutting slope gradually decreases as the number of freeze-thaw cycles increases, then the reduction amplitude decreases gradually and finally remains at a stable value. After 50 freeze-thaw cycles, the safety factor of the slope is 0.74. When the slope has not undergone a freeze-thaw cycle, the slippage of the slope U\({}_{\text{N}=0}\) is zero before the slope reaches the limit equilibrium state; and when the slope reaches the limit equilibrium state, the slope begins to slip. As time goes on, the amount of slippage U\({}_{\text{N}=0}\) gradually increases and eventually stabilizes. Before and after the freeze-thaw cycle, the change of slope slip is obviously different. As can be seen in Figure 3, when the slope does not reach the limit equilibrium state, both U\({}_{\text{N}=10}\) and U\({}_{\text{N}=50}\) are greater than zero, indicating that slippage has begun before the slope reaches the limit equilibrium state, and the greater the number of freeze-thaw cycles, the greater the slippage and its rate. Therefore, the freeze-thaw cycle plays a role in accelerating the slope slippage, and the influence of the freeze-thaw cycle on the slope cannot be ignored.  
Figure 3: Trend graph of slope safety factor \(Fr\) with freeze-thaw cycles \(N\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Density/(kg/m\({}^{3}\))** & **Water Content/(\%)** & **Cohesion/(kPa)** \\ \hline
1967 & 6.92 & 2.88 \\ \hline
**Internal Friction Angle/(\({}^{\circ}\))** & **Linear Expansion Coefficient/(10–6)** & **Thermal Conductivity/(W/m \(\times\) K)** \\ \hline
27.3 & 9.73 & 0.48 \\ \hline
**Specific Heat Capacity/J/(g \({}^{\circ}\)C)** & **Elastic Modulus/(GPa)** & **Compressive Strength/(GPa)** \\ \hline
0.856 & 2.51 & 38.92 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Physical and mechanical parameters of specimens in slope.
## 5 Application of Our Proposed Approach
### Case Study-2: Average Sales Data of Dantfoss District Heating  
The average sales data of 4247 products of a Slovenian company named Danfoss District Heating, which was originally presented by Tratar [36], is adapted in this research for validating and assessing the strength of our proposed collaborative approach. The time series, as shown in Figure 7, represents the monthly average sales data for the time span of four years ranging from 2005 to 2008. The data set also depicts the varying means and the overall uprising trend with multiplicative seasonal patterns.  
However, to assess the strength of our proposed collaborative approach, initially the starting values of the level, the growth rate and the seasonal components are estimated. These initial values are incorporated with the conventional multiplicative Holt-Winters method to determine the time-series parameters such as the level (\(L_{T}\)), growth rate (\(b_{T}\)), seasonality (\(SN_{T}\)) as well as the forecast (\(Y_{T}\)). A fuzzy logic-based level prediction model is also developed to attain the output level (\(L_{T\!pred}\)). To do so, the Mamdani fuzzy inference system is chosen to contemplate the judgmental knowledge and the relationships established between the variables by means of 39 IF-THEN rules and the logical operator 'AND' as shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Components**} & \multicolumn{10}{c}{**Time Period**} \\ \cline{2-13}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** \\ \hline \(D_{T}\) & 14 & 14 & 16 & 18 & 18 & 20 & 23 & 24 & 21 & 19 & 17 & 15 \\ \(L_{T}\) & 15.66 & 15.91 & 17.08 & 17.71 & 17.71 & 16.74 & 20.34 & 20.43 & 21.20 & 20.11 & 18.25 & 16.86 \\ \(L_{T\!pred}\) & 15.4 & 15.4 & 16.4 & 17.4 & 17.4 & 17.4 & 19.5 & 19.5 & 22.4 & 21.4 & 19.5 & 17.4 \\ \(Y_{T}\) & 22.35 & 14.01 & 15.15 & 17.63 & 18.28 & 21.48 & 19.23 & 24.21 & 20.50 & 20.28 & 18.99 & 16.48 \\ \(Y_{T\!c}\) & 14.24 & 14.24 & 16.25 & 18.27 & 18.27 & 20.32 & 23.30 & 24.31 & 21.27 & 19.25 & 17.25 & 15.24 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parametric values and forecasts for the test period.  
Figure 7: Monthly average sales data of Slovenian company. Adapted from [36].  
Finally, the de-fuzzification is performed by using Equation (8) to obtain the non-fuzzy levels (\(L_{Tpred}\)) which are embedded into the multiplicative Holt-Winters method to get the Coll-MHW model-based forecasted demand (\(Y_{TC}\)) through Equation (9).
# SARFNet: Selective Layer and Axial Receptive Field Network for Multimodal Brain Tumor Segmentation
## 3 Methodology  
### Network Architecture  
The suggested U-shape structure with the encoder on the left, the decoder on the right, and the skip connections in the middle achieves good segmentation performance in many image segmentation tasks. As shown in Figure 1, the network we proposed is similar to the UNet structure. Firstly, the images of the four brain tumor modalities (T1, T1 ce, T2, and FLAIR) are processed using two 3 \(\times\) 3 \(\times\) 3 convolutions with a stride of 1, and the dimensions are adjusted from 4 \(\times\) 128 \(\times\) 128 \(\times\) 128 to 32 \(\times\) 128 \(\times\) 128. Group Normalization (GN) and Gaussian error linear unit (GeLU) are employed after each convolution. Empirically, a residual block is used in the first layer. Sequentially, the SL\({}_{i}\)RF module is used for downsampling. Four layers are used in the encoder to perform the feature extraction operation. In the bottleneck stage, the three AAM modules and squeeze-and-excitation (SE) are used to improve the model performance by explicitly constructing the interdependence between feature channels. The four upsampling blocks in the decoder path alter the image resolution via deconvolution with a stride of 2 based on the previous image pixels. Each layer in the decoder contains two convolutions, a GN and a GeLU, and the size after convolution is the same as that of the equivalent encoder. There is a residual block between convolutions. The SE module, used in the residual block, can dynamically adjust the weights of different channels by learning the relationships between different channels. Skip connections are constructed that can integrate high-level semantic information and low-level semantic information between the encoder and decoder layers to accurately locate the target image. In the third and fourth layers of the skip connections, we applied CAM [58], which is shown in Figure 2, to guide high-level features to the selection of low-level semantic features. Naturally, deep supervision is introduced to solve convergence problems and improve training performance. At the end of the network, a 1 \(\times\) 1 \(\times\) 1 convolution is utilized to restore the dimensions of the obtained feature map to the original 4 \(\times\) 128 \(\times\) 128 \(\times\) 128.  
Figure 1: An illustration of the proposed SARFNet for brain tumor image segmentation.
# Additive Manufacturing in Underwater Applications
## 5 Areas of Application
### Membranes  
One of the most promising areas of application for AM technology is in the different types of membranes used for water treatment purposes [77; 87]. In this case, AM technology helped to obtain a lattice structure for membrane elements. It was combined with the hydrothermal method and liquid phase deposition (LPD) to obtain multi-functional and multi-material structures for oil-water separation and wastewater treatment [87]. In particular, such materials as TiO\({}_{2}\) and Cu\({}_{2}\)O were used to achieve suitable efficiency in organic pollutant removal [87].  
The main goal of most of the membranes created with the use of AM technology is water purification, especially removing organic dyes, acids, and alkalis, as well as metal ions [88]. The 3D printing technology in this case allows the creation of more efficient and cheaper systems--for example, those that are used in solar water evaporation [88]. It is also worth noting that this type of membrane could have wider application also in desalination systems [78] and as an element for gas-water separation [89].  
Among the many materials dedicated to effective 3D-printed solutions for water treatment [90; 91; 92; 93], a work that is particularly interesting, from a material point of view, is that of [PERSON] et al. [94]. In this case, a 3D printing ceramic material-alumina was combined with in-situ-grown aluminum borate whiskers [94]. This advanced technological solution allowed them to obtain high-efficiency separation, a high flow rate, increased durability in cycling tests, and resistance to harsh environments. Moreover, the obtained solution was easy to regenerate through high-temperature heat treatment [94].  
Another interesting possibility is the automatization of water treatment, especially for the specific needs of the identification and deactivation of pollutants that are present underwater. For this purpose, [PERSON] et al. [95] developed a specific approach by using small robots with green-fuel-driven thumb-sized motors produced using 3D printing methods, for the identification of underwater pollutants and elimination of contamination [95]. This type of solution has the potential to enable in situ underwater remediation [95].
# Small Target Radiometric Performance of Drone-Based Hyperspectral Imaging Systems
## 5 Discussion  
In this section, we discuss three main aspects of our results. Firstly, we compare the standard deviation of all eight point targets to all Lambertian targets across all 18 overpasses. This provides the first glimpse into the radiometric stability or instability between the two methods. This is then followed by examining how a point target (i.e., convex mirror) manifests itself before and after orthorectification across all 18 overpasses. Next, we focus our attention on radiometric accuracy concerns for a handful of point target observations. Lastly, we investigate the spectral inconsistencies introduced into the point target spectral radiance after orthorectification.
# Electromagnetic Radiation Shielding Using Carbon Nanotube and Nanoparticle Composites
## 2 Overview of Shielding Approaches for Different Types of Radiation
### Why CNT-Nanoparticle Materials Need to Be Considered for Radiation Shielding  
Materials for radiation attenuation are often formed as hybrid or composite materials [38; 39; 40; 41; 42; 43]. Metal powders such as lead, tin, and bismuth can be mixed with poly(vinyl chloride) or CNT fabric to create a flexible matrix, which can then be cut into different shapes and layered to make protective clothing. CNT-based textiles can be good alternatives to conventional shielding approaches. Incorporating heavy metals like bismuth, zinc, iron, gadolinium, or tungsten can enhance the shielding performance of carbon nanostructures, as suggested by different studies.  
Materials can be custom designed to maximize the attenuation or transmission loss of incoming waves. Shielding is important because radiation wave transmission can cause harmful health impacts [5] and interfere with or damage electronic equipment. Additionally, wave reflection could reveal the location of a defense system. CNTs have emerged as compelling candidates for radiation shielding due to their excellent physical, mechanical, and chemical properties. Nanostructured materials such as CNTs are good candidates for radiation shielding because their nanostructure or nanoparticle size approaches the wavelength of waves like microwaves. Also, the compact, thin-layer hexagonal structure of CNTs with incorporated nanomaterials increases the likelihood of interactions with photons as they pass through the material.  
Hybrid CNT materials are a convergence of diverse materials, including polymers, metals, and ceramics, with CNTs. This combination leverages the synergistic properties of the different materials to enhance radiation attenuation performance. CNT hybrid fabrics, known for their excellent mechanical properties, are being explored as innovative materials for photon attenuation in radiation-shielding applications. These fabrics are created through various techniques like weaving and knitting. Non-woven fabric can also be formed. Embedding CNTs into polymer matrices can provide good electromagnetic interference (EMI) shielding capabilities, which is crucial for protecting electronic devices. However, challenges such as enhancing CNT dispersion within polymers, optimizing production for scalability, and assessing long-term material stability under radiation remain.