esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method
### Architecture of Improved Swin-Unet  
As shown in Figure 3, the network framework of the improved Swin-Unet consisted of the following parts: encoder (left part), decoder (right part), bottleneck block (bottom part), and skip connection (middle jumper section). In each part, the Swin Transformer model was the core module.  
The encoder split the HRRS image into non-overlapping blocks of size 4 \(\times\) 4 and converted the input image into sequence embeddings. The encoder consisted of multiple modules to generate the hierarchical representations of HRRS image, and each module contained two Swin Transformers and one Patch Merging layer. The Swin Transformer  
Figure 3: Architecture of improved Swin-Unet.  
module [38] was built on a shifted window and was responsible for learning features. The Patch Merging [39] was responsible for downsampling operation and increasing the feature dimension.  
The decoder also consisted of multiple modules. Except for the last module, each module contained two Swin Transformers, one Patch Expanding and one Concatenating. Patch Expanding [39] was used to perform feature dimension and upsampling. Similar to the skip connection of original Swin Unet, Concatenating was responsible for feature fusion, to alleviate the lost spatial information caused by Patch Merging. It should be pointed out that there was no Patch Expanding on the last module.  
The most obvious differences between our improved Swin-Unet and the original Swin-Unet were:  
1. In order to improve the algorithm's robustness, the patch expanding layer of the last module of the Swin-Unet decoder was canceled. After all, higher-level Transformers focus on encoding relatively complex high-level semantic information, and overly complex information could weaken algorithm robustness;
2. The position of the Skip connection between the first module of the encoder and the last module of the decoder was changed to cater to the cancellation of the Patch Expanding layer of the last module of the decoder, which reduced the impact of extraneous features on the tampering sensitivity of the algorithm;
3. Due to the network structure, the output image size of the improved Swin-Unet was 128 \(\times\) 128 pixels, while the output image size of the original Swin-Unet was 224 \(\times\) 224 pixels--the input image size for both models was 224 \(\times\) 224 pixels. This input-output asymmetry helped to reduce the impact of redundant information and improve the performance of hashing algorithm.  
Although the network was structurally different from Swin-Unet, we did not rename it. Instead, we called it the improved Swin-Unet.
## 4 Experimental Results  
This section of the document delves into the experimental results derived from the study, structured to thoroughly evaluate the performance of the proposed models under various settings and conditions. Initially, the technical specifications and configurations of the embedded devices used are detailed. The dataset and metrics that form the basis of the evaluation strategy are then discussed. Following this, the methods and benchmarks used for comparative analysis are explored, emphasizing the adaptability and efficiency of the models in realistic operational environments. Comprehensive performance evaluations offer insights into the models' capabilities in handling real-world scenarios, particularly focusing on their responsiveness and accuracy in dynamic traffic conditions.
# Technology Identification, Evaluation, Selection, and Optimization of a HALE Solar Aircraft
## 4 General Methodology
### Performance Evaluation  
#### 4.6.1 Performance of the Reference Aircraft  
Figure 20 shows the solar power profile for 24 h according to the previously set reference aircraft. During the summer season (6/21), the solar power that can be obtained according to the main wing area of the aircraft is the largest, and the least on the winter solstice (12/22).  
The total solar power that can be obtained during each season is shown in Figure 21a. The total solar power was also the largest in the summer and the smallest value in the winter solstice. Accordingly, as shown in Figure 21b, the energy margin in the summer solstice was 17.27% that is the only possible flight seasons.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Independent Design Variable** & **Desirability** & **Actual Value** & **Unit** \\ \hline Aspect ratio & \(-\)0.0024 & 19.98 & – \\ Wing area & \(-\)0.0098 & 34.85 & m\({}^{2}\) \\ Airframe weight adjustment factor & 0.0023 & 0.85 & – \\ Battery specific energy density & \(-\)0.0002 & 200 & Wh/kg \\ Solar cell specific mass & 0.0037 & 0.45 & kg/m\({}^{2}\) \\ Solar cell efficiency & \(-\)0.0014 & 0.195 & – \\ Payload weight & 0.0024 & 7.51 & kg \\ \hline \hline \end{tabular}
\end{table}
Table 13: Optimized independent design variables.  
Figure 19: Desirability profile for independent design variables.  
We also analyzed how many days long-endurance is possible during the summer (6/21) as shown in Figure 22. The energy margin over 10% was assumed and on 5/24 and 7/19, the energy margin was 9.9%. Therefore, it was shown that the reference aircraft could fly for a total of 56 days, from 5/24 to 7/18.  
Figure 21: (a) Total solar power for the reference aircraft; (b) energy margin for the reference aircraft.  
Figure 22: Long endurance performance analysis according to the energy margin for the reference aircraft.  
Figure 20: Solar power profile for the reference aircraft.
# Reduction of Phase Shifters in Planar Phased Arrays Using Novel Random Subarray Techniques
## 2 Random Subarray Design Methodology  
The array factor for the planar array is defined as a function of \(\theta\) and \(\phi\) by  
\[AF(\theta,\phi)=\sum_{n=1}^{N}\sum_{m=1}^{M}I_{n,m}e^{i[kd(n-1)\phi_{x}+kd(m-1 )\phi_{y}+\beta_{x}+\beta_{y}]}, \tag{1}\]  
where  
\[\psi_{x}=\sin\theta+\cos\phi \tag{2}\]  
\[\psi_{y}=\sin\theta+\sin\phi \tag{3}\]  
\[\beta_{x}=-kd(n-1)\sin\theta_{0}\cos\phi_{0} \tag{4}\]  
\[\beta_{y}=-kd(m-1)\sin\theta_{0}\sin\phi_{0}. \tag{5}\]  
The planar array consists of \(N\) columns and \(M\) rows, with a total number of \(N\times M\) antennas. The separation between antenna elements is given by \(d=0.5\lambda\) and the amplitude of the \(nm\)-th antenna element is represented by \(I_{n,m}\). The steering vectors \(\beta_{x}\) and \(\beta_{y}\) denote the cophasal excitation values in the \(x\)-axis and \(y\)-axis, respectively. In this work, the section of RF (radio frequency) hosts the phase shifters.
## 2 Problem Description--Energy Monitoring in Industries
### Turret Punch Press  
The machine analyzed in this research is an aging legacy Wiedemann Turret Punch Press (TPP), a crucial machine used for fabricating sheet metal components in air conditioning units. While reliable and versatile, this older model lacks modern features such as PLC-based control, factory network connectivity, and integrated data collection. Instead, it relies on outdated programmed diskettes, preventing automatic monitoring of runtime, energy usage, and efficiency.  
Like many legacy machines, the Wiedemann TPP (Figure 1) suffers from poor documentation and limited configuration control, making maintenance and optimization difficult. Investigations revealed that the machine frequently remains in standby mode due to uncertainties in production scheduling. While this ensures readiness, it also leads to significant resource waste, unnecessarily consuming electricity and compressed air.  
With rising energy costs and a commitment to green initiatives, M-ACE recognizes the need to modernize its production process. Enhancing communication about machine downtime and optimizing product flow could reduce waste and improve efficiency. Toaddress these challenges, a sensor-based monitoring system was introduced to track energy and air consumption, analyze usage patterns, and support data-driven decision-making.
# Malware Collusion Attack against SVM: Issues and Countermeasures
## 1 Introduction
According to our experiments, ColluDroid-Sec-SVM has the best performance against Collusion Attack and Evasion Attack. To summarize, this paper makes the following contributions:  
* Introducing Collusion Attack on SVM: By splitting one app into two, malware developers can easily evade the detection of current SVM based methods. We also write four example apps to demonstrate the idea of splitting. * Proposing a method to detect the Collusion Attack: We present a method called ColluDroid to deal with Collusion Attack. In our proposed method, we analyze all the possible communication links to detect collusion. * Evaluating the performance of ColluDroid with different learning methods. We develop a prototype, and present results from experiments running on our data set. The result shows the effectiveness of ColluDroid in dealing with collusion attacks and evasion attack. ColluDroid with Sec-SVM has the best performance. Furthermore, we analyze the efficiency of ColluDroid. The result shows that our ColluDroid is fast enough and can be applied on a large scale. ColluDroid is implemented as a tool that can be deployed on both market-level (e.g., Google Play) and end-user-level. When it is deployed on the market-level, it can be used to examine whether there are collusion apps across the market or a certain app set (e.g., the apps that have been downloaded by an end-user from this market.). When it is deployed on the end-user-level, it will be implemented in two parts. One part is implemented as an app and installed on the user's mobile devices. Once installed, it will collect the installed application list and send the list to the other part. The other part is implemented as a back-end server, which detects whether there is collusion behavior within the app list. The remainder of this paper is organized as follows. Section 2 summarizes the basic terminologies in Android and also its communication channels. Section 3 presents a example that motivate our work. Section 4 provides ColluDroid architecture and details of it. Section 5 presents the evaluation results. Finally, we conclude the paper and discuss the future works.
## 4 The Improved Self-Collision Detection Technology
### Comparison of the Performance of Self-Collision Detection
#### 5.3.1 Comparison of the Approximation Results
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Name** & **Physical Model** & **Typical Method** & \multicolumn{2}{c}{**Optimized Method**} \\ \hline \multirow{2}{*}{**Number**} & **Original Volume** & **Typical Volume** & **Degree** & **Optimized Volume** & **Degree** \\  & \(\mathbf{V_{1}}\) (mm\({}^{3}\)) & \(\mathbf{V_{2}^{4}}\) (mm\({}^{3}\)) & \(\mathbf{n_{1}}\) & \(\mathbf{V_{2}^{6}}\) (mm\({}^{3}\)) & \(\mathbf{n_{2}}\) \\ \hline
1–Platform & \(1.6993\times 10^{8}\) & \(3.8956\times 10^{8}\) & \(43.62\%\) & \(3.8956\times 10^{8}\) & \(43.62\%\) \\
2–Load & \(4.9637\times 10^{6}\) & \(4.7080\times 10^{7}\) & \(10.54\%\) & \(4.7080\times 10^{7}\) & \(10.54\%\) \\
3–Motor1 & \(2.0390\times 10^{6}\) & \(4.1888\times 10^{6}\) & \(48.68\%\) & \(4.1888\times 10^{6}\) & \(48.68\%\) \\
4–Motor2 & \(2.0390\times 10^{6}\) & \(4.1888\times 10^{6}\) & \(48.68\%\) & \(4.1888\times 10^{6}\) & \(48.68\%\) \\
**5–Link1** & \(1.7107\times 10^{6}\) & \(1.2566\times 10^{7}\) & \(\mathbf{13.61\%}\) & \(3.6191\times 10^{6}\) & \(\mathbf{47.27\%}\) \\
**6–Motor3** & \(1.1786\times 10^{6}\) & \(4.1888\times 10^{6}\) & \(\mathbf{28.14\%}\) & \(2.1447\times 10^{6}\) & \(\mathbf{54.95\%}\) \\
**7–Link2** & \(1.3585\times 10^{6}\) & \(8.5786\times 10^{6}\) & \(\mathbf{15.84\%}\) & \(4.0532\times 10^{6}\) & \(\mathbf{33.52\%}\) \\
8–Motor4 & \(7.0638\times 10^{5}\) & \(1.4368\times 10^{6}\) & \(49.17\%\) & \(1.4368\times 10^{6}\) & \(49.17\%\) \\
9–Motor5 & \(7.0638\times 10^{5}\) & \(1.4368\times 10^{6}\) & \(49.17\%\) & \(1.4368\times 10^{6}\) & \(49.17\%\) \\
10–Motor6 & \(7.0638\times 10^{5}\) & \(1.4368\times 10^{6}\) & \(49.17\%\) & \(1.4368\times 10^{6}\) & \(49.17\%\) \\
**11–Tool** & \(9.0120\times 10^{5}\) & \(4.3103\times 10^{6}\) & \(\mathbf{2.09\%}\) & \(1.5268\times 10^{6}\) & \(\mathbf{5.90\%}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Approximation degree of each component.
Controllers designed using rotation matrices or quaternions control the full attitude, and therefore cannot be directly applied to fixed-wing aircraft using banked turn maneuvers. One approach could be to feedback the true yaw angle into the desired rotation matrix and as such use a rotation error representation for roll and pitch only. However, this representation is highly redundant, as 9 parameters are used to parametrize a two-dimensional subspace. A simpler approach, that does not require the full machinery of working in SO(3), is to consider a reduced-attitude representation, evolving on the two-sphere, \(\mathbb{S}^{2}\subset\mathbb{R}^{3}\)[45]. In this space of reduced attitude, all rotations that are related by a rotation about some fixed axis, are considered the same [27]. Control systems with reduced attitude evolving on \(\mathbb{S}^{2}\) have previously been studied in the context of spin-axis [45] and boresight-axis [46] control for satellites, pendulum stabilization [31], path-following control of underwater vehicles [47], thrust-vector control for multirotor UAVs [48; 49] and for general rigid bodies [50; 51; 52]. Controllers developed on \(\mathbb{S}^{2}\) are relatively simple compared to those developed using rotation matrices and require fewer matrix operations. It is well known that a desired attitude (full or reduced) cannot be globally stabilized using continuous state-feedback control laws [26]. This stems from the topological properties of SO(3) and \(\mathbb{S}^{2}\), which are compact, boundaryless manifolds that are not diffeomorphic to any Euclidean space. The largest possible attraction basins under continuous feedback are almost global, i.e., excluding a zero-measure set, which corresponds to the stable manifolds of additional unstable equilibrium points [53]. However, global asymptotic stability can be achieved by using tools from hybrid dynamical systems, where hysteresis-based switching ensures that all trajectories converge to the desired equilibrium [49; 50; 52; 54; 55; 56; 57].
## 1 Introduction
### Evaluating the Protocol's Security and Performance  
We evaluated our proposed protocol in both formal and informal security assessments. Additionally, we compared the presented method with similar protocols, considering security, efficiency, and computational cost. The results of the evaluations show that the proposed protocol provided better performance than similar designs in terms of security, operation calculations, and communication cost.  
Following this, the three main theories of elliptic curve cryptography, specifically ECC, ECDLP, and ECDH, and their advantages are utilized within the suggested approach, and the safety concerns that have affected the Internet of Things are explained. Figure 1 shows the communication infrastructure and components of the network.
## 5 Experimental Setup Development  
Pertaining to the practicability of the proposed scheme, an experimental setup was developed regarding lightning impulse and power frequency disruptive discharge tests for the proposed scheme, as per the IEC 60060-1:2010 standard [70]. FGIL models, fabricated respectively with simple and SCF-coated stranded conductors, were used in this experimental investigation. Concerning the lightning impulse discharge tests, U\({}_{50}\) for different GIL specimens was determined by Up-Down method, where for power frequency discharge an average of ten disruptive discharges was considered. A compressor and a pressure control unit were incorporated in order to create a vacuum in the developed GIL model along with the injection of dielectric gas at the desired pressure. Gas-insulated equipment normally utilizes pure SF\({}_{6}\) or a mixture of SF\({}_{6}\) and N\({}_{2}\) at a ratio of 20:80, but the required gas pressure in the latter case was almost doubled as compared to the prior case. Considering the security concerns associated with the high-pressure containment of SF\({}_{6}\)/N\({}_{2}\) mixture at the laboratory level, pure SF\({}_{6}\) gas was used, as it would result in a significant reduction of the required gas pressure without compromising the insulation characteristics. A block diagram of the experimental setup is shown in Figure 18. Figure 19a represents the different components used in the high-voltage experimentation, while Figure 19b represents the experimental setup placed in a high-voltage laboratory.  
Figure 19: (**a**) Different components used in the discharge tests and (**b**) the experimental setup for lightning impulse and disruptive discharge tests.  
Figure 18: Experimental setup for the dielectric strength testing of the developed FGIL models.
# Joint User-Slice Pairing and Association Framework Based on H-NOMA in RAN Slicing
## 6 Discussion  
In the future, further research can be pursued to investigate the following open issues. First, we can use one of the proposed ways to prove its effectiveness in minimizing co-channel interference, such as (a) a hybrid overlay-underlay spectrum access scheme in heterogeneous networks to improve energy efficiency [60]. (b) cooperative relaying networks with NOMA-HetNet [61]. A relay uses a decode-and-forward (DF) scheme to improve the performance of the far user in terms of the outage and throughput. (c) a downlink underlay cognitive radio-NOMA (CR-NOMA) HetNet to reduce CCI and enhance the performance of spectrum sharing [55]. Second, we can combine HetNets with aerially controlled networks such as Unmanned Aerial Vehicles (UAVs) are required to overcome these challenges. UAV-aided HetNet shows lower transmission delay (lower latency) and better average jitter compared to without UAV-based HetNet, because UAVs control the transmission of packets with efficient utilization of available bandwidth from source to destination [62]. Third, we can use FANETs [63], in which multiple UAVs cooperate and establish an ad hoc network in a multi-UAV scenario to enhance network performance. Finally, we will leverage potential deep learning approaches to provide coexisting radio resources for various services. Using a learning-based strategy for flexible joint UE association and pairing will prevent coexistence concerns in 5G and wireless networks.
# A Graph-Based Approach to Recognizing Complex Human Object Interactions in Sequential Data
## 4 Experimental Results
### Experiment II: Computational Complexity  
While determining the efficiency of an algorithm, its computational complexity should also be considered. The most common way of doing this is in terms of the big-O notation. The big-O notation represents the upper bound of an algorithm. For neural networks, the complexity depends on their layers. Hence, the total complexity of a neural network is the sum of the complexity of each layer. There are three types of layers in the used GCN model: convolutional layers, pooling layers, and a fully connected layer. A convolution is the sum of the row-wise dot products of a filter with a region matrix. For a filter of size \(k\), the cost of the dot product is \(O\big{(}k.d^{2}\big{)}\), where \(d\) represents the depth dimension. Since the filter is applied over the input \(n-k+1\) times, where \(n\) is the length of the input or the input nodes in this case, the final complexity of a convolutional layer is \(O\big{(}k.n.d^{2}\big{)}\). A max pooling layer basically finds the maximum value in each region. For search in an unsorted array, where each element of the array is visited at least once, the complexity is \(O(n)\). Finally, in a fully connected layer, the input row vector is multiplied with the weight matrix. Hence, its computational cost is \(O(k.n.d)\). Table 2 provides an overview of the running times of each layer.
## 1 Introduction
The methods of producing CRAMs are mainly wet and dry processes [5, 30]. In the wet process, CR is added to a traditional sharpener and mixed into virgin asphalt with the characteristics of modified asphalt, which can be used as a binder for asphalt mixtures. In the dry process, CR with relatively coarse particles is added to the aggregate and then sprayed into the hot asphalt to form asphalt mixtures [31, 32]. Compared to the wet process, the dry process is simpler, does not require special equipment, and can consume more used tires [33, 34]. [PERSON] et al. [35] found from a microscopic point of view that the addition of CR to the dry process increases the hardness of the asphalt, improves the rutting resistance, and changes the shape and porosity of the CR due to the interaction between rubber and asphalt. The optimum size (<200 \(\upmu\)m) for rubber particles in the dry mixing process was obtained. [PERSON] et al. [36] found that CR can expand to three times its original size during rubber-asphalt interactions and may resist subsequent compaction. This expansion effect prevents the mixture from reaching its target density, leading to premature failure, such as cracking and loosening. Moreover, many studies have proven that skeleton dense texture can reserve enough space to accommodate the volume of CR expansion, which prevents CR from interfering with asphalt mixture gradation [37, 38, 39]. In addition, skeleton dense texture lowers the asphalt dosage, and the asphalt dosage is saved compared to a virgin asphalt mixture. The oil savings are generally 0.3-0.6% [34, 35]. Although previous studies have demonstrated that the addition of TOR could improve the performance of CRMA and become a viable option in asphalt pavement, it has also been proven that the preparation of CRAMs through the dry process could better solve the expansion problem of CR. However, the combination of TOR activator and the dry process has not been studied. In order to obtain a CRAM with excellent performance, a new direction for research and the application of CRAMs was developed. On the basis of TOR-activated CR, a new type of CR was developed, and a new type of CRAM with a skeleton dense texture was designed through the dry process (abbreviated as CRTAM). First, using the traditional design method, optimized gradation and the compact gradation design method were adopted to make the CR play a role in the asphalt modification and at the same time fill the aggregate. In addition, road performance, i.e., high-temperature stability, low-temperature crack resistance, and water stability, was analyzed through experiments. It is anticipated that the research results of this study will benefit the engineering practices of dry-processed CRTAM and promote its wider application.
## 5 Test Systems and Simulation Cases  
The MI-SOCP formulation is validated in two widely-used test systems for the problem of optimal location and sizing of DERs in power distribution networks. The test systems are the IEEE 33- and 69-bus systems. The most relevant information about these test systems is presented below.