esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Validation
### Flying Wing with Coupled Flight Dynamics and Aeroelasticity
#### 3.2.1 Trim Analysis  
At the flight speed of 12.2 m/s at sea level, the highly flexible aircraft with the varying concentrated payload was trimmed for zero resultant force and moment. Figure 6 shows the variations of the trimmed AOA, flap deflection angle (assuming constant throughout the span) and thrust per motor with the varying payload. It can be seen that the trim AOA increases with the payload, as the heavier is the payload, the higher is the lift required, and thus the higher are the AOAs. When the concentrated payload increases, the aerodynamic loads get higher, which leads to large bending deformations in the highly flexible wings, and the aircraft turns into a \"U\" shape, as illustrated in Figure 7.  
As the aircraft deforms to a \"U\" shape, the aerodynamic center moves backward, which brings about a pitch-down moment, and the flap deflection decreases to reduce the additional pitch-down moment of the flap. It can also be seen that the trimmed thrust does not change significantly with the payload. This phenomenon can be attributed to the simplicity of the drag model, in which the profile drag and frictional drag of the aircraft are constant and the induced drag that is proportional to the lift is not considered.  
Figure 5: Geometry of the flying wing model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Structural Property** & **Value** & **Aerodynamic Property** & **Value** \\ \hline Cross-sectional elastic axis & 25\% chord from L.E. & \(c_{l\alpha}\) for wings (25\% chord) & 2\(\pi\) \\ Cross-sectional center of gravity & 25\% chord from L.E. & \(c_{l\delta}\) for wings (25\% chord) & 1 \\ Torsional stiffness & \(1.65301\times 10^{5}\) Nm\({}^{2}\) & \(c_{d0}\) for wings (25\% chord) & 0.01 \\ Bending stiffness & \(1.03313\times 10^{6}\) Nm\({}^{2}\) & \(c_{m0}\) for wings (25\% chord) & 0.025 \\ Chordwise bending stiffness & \(1.23976\times 10^{7}\) Nm\({}^{2}\) & \(c_{m\delta}\) for wings (25\% chord) & \(-0.25\) \\ Mass per unit length & 8.92898 kg/m & \(c_{l\alpha}\) for pods (25\% chord) & 5 \\ Torsional mass moment of inertia & 4.14765 kgm & \(c_{d0}\) for pods (25\% chord) & 0.02 \\ Flatwise bending mass moment of inertia & 0.691275 kgm & & \\ Chordwise bending mass moment of inertia & 3.45637 kgm & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Properties of the flying wing (after [13]).
# Dynamic Graph Attention Network for Skeleton-Based Action Recognition
## 4 Experiments
### Comparison with State-of-the-Art Methods  
To comprehensively evaluate the effectiveness of the proposed DGAN, we conduct experiments on two widely used benchmark datasets for skeleton-based action recognition, NTU RGB+D 60 and NTU RGB+D 120, and compare our approach with current state-of-the-art methods. Experimental results are summarized in Tables 1 and 2. For objective analysis, we categorize the comparative methods into three groups: CNN/RNN-based methods, GCN-based methods, and Transformer-based methods.  
On the NTU RGB+D 60 dataset, our proposed DGAN achieves accuracies of 93.2% and 96.9% in the Cross-Subject (X-Sub) and Cross-View (X-View) benchmarks, respectively. These results significantly outperform traditional CNN/RNN methods, which typically fail to effectively leverage the structural characteristics of skeleton data. Additionally, our model surpasses classical GCN models, such as 2s-AGCN (88.5%) and CTR-GCN (90.7%), primarily due to DGAN's ability to dynamically adjust node connections, thereby overcoming the limitations of fixed topology in traditional GCN methods and effectively modeling long-range node dependencies. Although ST-DGAT has achieved competitive results on benchmark datasets, it still adopts a fixed human-skeletal topology within each attention head, which limits its ability to model cross-topology dependencies. In contrast, DGAN employs dynamically learned topologies for each attention head and incorporates a partition-aware bias mechanism. This design not only offers superior accuracy but also provides greater structural flexibility and interpretability. Furthermore, DGAN attains an accuracy of 96.7% on the X-View benchmark, matching or slightly exceeding advanced Transformer-based methods such as ST-SA-Net (96.7%) and Hyperformer (96.5%) demonstrate DGAN's effectiveness in integrating local and global information.  
On the more challenging NTU RGB+D 120 dataset, DGAN achieves accuracies of 89.7% and 90.9% in the X-Sub and X-Set benchmarks, respectively. This performance surpasses traditional CNN/LSTM models, such as Ta-CNN+ (85.7%, 90.3%), and exceeds classical graph convolutional models like Shift-GCN (85.9%, 87.6%) and 2s-AGCN (82.9%, 84.9%). These results suggest that, through dynamic attention and node partitioning strategies, DGAN can effectively extract action semantics from different nodes, particularly demonstrating superior performance on the NTU RGB+D 120 dataset, which features larger-scale and more complex actions.  
Overall, the analysis demonstrates that our proposed DGAN, through integrating dynamic graph attention mechanisms and node-partitioning strategies, effectively combines the advantages of GCN and Transformer approaches. It achieves superior accuracy across multiple public benchmark datasets for skeleton-based human action recognition, thereby validating its competitiveness and generalization capabilities in the field.
## 1 Introduction
### Deep Neural Network  
Deep neural networks are the main technological architecture used in deep learning models. They are widely used in different fields of research, such as natural language processing [23], visual and auditory pattern recognition [23; 24], signal processing, and time series processing, among others [15; 25; 26; 27; 28].  
Among the current widely used algorithms for time-series modeling are recurrent neural networks (RNN), which are a classification of artificial neural networks (ANN) [29; 30; 31]. ANNs are a computational model inspired by biological neurons [28; 32]. [PERSON] and [PERSON] in 1943 were pioneers in proposing the computer model of an artificial neuron, called the logical threshold [31; 33].  
The first artificial neural networks were made up of simple neurons called perceptrons, as proposed by [PERSON] in 1959 [28; 31], which operate as a binary classifier (0 or 1) by means of Equation (1) [18].  
\[f(x)=\begin{cases}1&if\ w\cdot x-u>0\\ 0&all\ other\ cases\end{cases} \tag{1}\]  
where \(w\) is a vector of real weights, \(w\cdot x\) is the scalar product, ans u is the threshold, which represents the degree of inhibition of the neuron. The value of \(f(x)\) (0 or 1) is used to classify \(x\) in a binary classification problem [28].  
Later, different network topologies emerged, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), each with its own performance characteristics and therefore with different resulting parameters [28; 33]. Lately, RNN topologies have been widely used to model chaotic behavior in time series [6; 12; 34].  
The RNNs compared in this manuscript were the Elman network, the gate recurrent unit (GRU), and long short-term memory (LSTM) networks. Sections 2-4 describe their operation and main characteristics, respectively. Section 5 will explain the methodology implemented. In Section 6 the results obtained will be shown and a brief discussion will be conducted. Finally, the conclusions will be presented.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Representation** & **Particle Size** & **Designation** \\ \hline PM\({}_{10}\) & \(\leq\)10 \(\upmu\)m & Coarse material \\ PM\({}_{2.5}\) & \(\leq\)2.5 \(\upmu\)m & Fine material \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification of particulate matter [4].
# SoK: Delegated Security in the Internet of Things
## 4 Formalisation of the Security Properties
### Computation Delegation  
Assume that \(\mathsf{id}\in M\), then in the discussed application domain of delegation, an RC system \(\mathcal{P}_{\mathsf{id}}\) delegates costly computations \(\mathsf{m}\) to a delegate \(\mathcal{D}\). \(\mathcal{D}\) processes the request and replies with \(\mathsf{m}^{*}\).  
An adversary \(\mathcal{A}\) tries to obtain secrets of \(\mathcal{P}_{\mathsf{id}}\) or \(\mathcal{D}\) in order to win the security game.  
**Definition 6**.: _Let \(\Pi\) be a Computation delegation protocol, and \(\mathsf{CD}\) be the following experiment:_  
1. _Initialising_ \(M\)_,_ \(F\)_,_ \(H\)_,_ \(S\)_._
2. \(\mathcal{A}\) _receives as input the access matrix_ \(M\) _or_ \(F\)_._
3. \(\mathcal{A}\) _is assigned identifier_ \(\mathsf{id}_{\mathcal{A}}\)_._
4. \(\mathcal{A}\) _can query the following oracles:_ \[\{\mathsf{m}^{*},\bot\} \leftarrow\mathsf{Del}(\mathsf{id},\mathsf{m}),\] \[\{\mathsf{s},\bot\} \leftarrow\mathsf{Cor}(\mathsf{id}),\text{ and}\] \[\{\mathsf{ts},\bot\} \leftarrow\mathsf{Obs}(\mathsf{id}),\] _where_ \(\mathsf{id}\) _identifies an RC system party_ \(\mathcal{P}_{\mathsf{id}}\)_,_ \(\mathsf{m}\) _is an unprocessed message and_ \(\mathsf{m}^{*}\) _is a processed message by a delegate_ \(\mathcal{D}\)_, e.g., encrypted or decrypted._
5. \(\mathcal{A}\) _wins if any of the following cases hold:_ * _Confidentiality:_ \(\mathcal{A}\) _outputs a party identifier_ \(\mathsf{id}\) _and a secret_ \(\mathsf{s}\)_, such that_ 1. \(\mathcal{A}\) _is not_ \(\mathcal{P}_{\mathsf{id}}\) _requested_ \(\mathcal{D}\) _(_\(\mathsf{id}_{\mathcal{A}}\
eq\mathsf{id}\)_), but_ 2. \(\mathsf{s}\) _belongs to_ \(\mathcal{P}_{\mathsf{id}}\)_._ * _Integity:_ \(\mathcal{A}\) _outputs a party identifier_ \(\mathsf{id}\)_, a secret_ \(\mathsf{s}\) _and a modified item_ \(\mathsf{h}_{\mathcal{A}}\) _such that_ 1. \(\mathcal{P}_{\mathsf{id}}\) _legitimately belongs to the interaction process, and_ 2. \(\mathsf{s}\) _belongs to_ \(\mathcal{P}_{\mathsf{id}}\)_. but_ 3. \(\exists\mathsf{h}_{\mathcal{A}}\) _does not match corresponding item_ \(\mathsf{h}\in H\)_._ * _Availability:_ \(\mathcal{A}\) _outputs a party identifier_ \(\mathsf{id}\) _and a secret_ \(\mathsf{s}\)_, such that_ 1. \(\mathcal{P}_{\mathsf{id}}\) _legitimately belongs to the interaction process, and_ 2. \(\mathsf{s}\) _belongs to_ \(\mathcal{P}_{\mathsf{id}}\)_, but_ 3. \(\mathcal{P}_{\mathsf{id}}\) _obtains either no result for its request or an incorrect one._
# Congestion Control and Traffic Differentiation for Heterogeneous 6 TiSCH Networks in IIoT
## 5 Performance Evaluations  
We evaluated the performance of our proposed work and compared it with existing work under different performance metrics. The results were obtained through extensive Monte Carlo simulations in MATLAB. We used the parameters defined in Table 1 to model the simulation environment as close to the real conditions as possible.  
We considered a network of 30 nodes that were randomly deployed in a 200 m \(\times\) 200 m area. Communications were carried out through a predefined TSCH schedule. Constructing and maintaining the TSCH schedule were out of scope of this paper. We considered log-normal shadowing distribution for the channel model with the specified standard deviation selected according to the measurements reported in [41]. Each of the following results were averaged over 10 simulation runs with each lasting for a duration of 1000 consecutive slotframes. The results were produced with a 95% confidence interval based on the t-distribution. In the following text and figures, we refer to our proposed Congestion Control and Traffic Differentiation method as CCTD. We evaluated the performance of CCTD under two scenarios. The first scenario considered a single-traffic network model where nodes generated only T\({}_{3}\) traffic periodically according to a specific rate. The second scenario considered a multi-traffic network model where each node generated both T\({}_{1}\) and T\({}_{2}\) packets according to a Poisson process with parameters \(\alpha_{1}\) and \(\alpha_{2}\), respectively, as defined in Table 1, along with the periodic T\({}_{3}\) packets. For both scenarios, we considered a fixed packet size of 100 B for all traffic types [42]. Since timeliness and reliability were of primary importance to support IIoT applications, our results mainly focused on the Packet Delivery Ratio (PDR) and End-to-End (E2E) delay parameters.
## 5 Discussions and Conclusions
### Theoretical Implications  
The development and application of cross-domain knowledge for RCG is a complex multi-standard decision-making process. Identification of radical innovation opportunities is a prerequisite for related innovative design activities. Previous studies did not have a clear understanding of the formation process for RI opportunities, believing that the RI generation is an accidental event. Technology push and demand pull are the main drivers of innovation opportunities, but the speed of technical performance improvement and the growth of user requirements are not always synchronized. Based on the analysis of the dynamic relationship between customer needs and technical performances in different subsystems, this paper reveals the formation mechanism of radical opportunities. Case studies of airbags show that the analysis is effective in identifying opportunities for RI. Engineers can use this approach to quickly and accurately identify radical innovation opportunities.  
Research in radical innovation management has demonstrated that cross-domain knowledge has an important inspiring effect on the RCG. However, previous studies were limited to how to improve firm's access to cross-domain knowledge; managers and engineers do not know how to accurately search, screen and utilize the cross-domain knowledge for the generation of radical concept. Based on the predictive function of the law of the technological evolution and the advantage of minimum complement distance measurement, this paper establishes an effective method for obtaining cross-domain knowledge to support RCG. The successful radical concept of gas generators in the case responds to the effectiveness of the method. The method allows managers and engineers to select and utilize cross-domain knowledge for RCG.  
It is commonly used in previous studies to obtain radical ideas by establishing evaluation criteria for radical ideas through case summary and screening radical ideas from a large number of ideas with this criterion. The \"screening method\" is a kind of post-analysis, which has a limited guidance for improving effectiveness of the radical concept development and cause a lot of cost during the idea generation stage. The method proposed in this paper considers constraints at an early stage before the ideas are established and helps firms to effectively use cross-domain knowledge to generate radical concept, reduce uncertainty and risk in the process of RCG, and improve success of the RI finally. As shown in the case of airbags, this study does respond to the existing problem about how to development and application of cross-domain knowledge for RCG.
## 5 Modified Chirp Scaling and Azimuth Compression for GTLR-BiSAR System  
In this section, we firstly concentrate our attention on the 2D spectrum deduction of the signal after range chirp scaling operation in RD domain. Then, we complete the azimuth compression and focus the image in the azimuth frequency domain because the signal is aliasing in azimuth time domain after the deramping pre-processing. Finally, we provide a flow diagram to illustrate the entire process of our proposed algorithm visually.
# Survey of Smart Parking Systems
## 3 Types of SPS Reported in the Literature
### Agent-Based Guidance Systems  
In AGS, the agent concept is used. An agent can be any software module capable of perceiving facts or actions through sensors and performing an action in a medium by sending or exchanging information [48]. Agents have characteristics such as autonomy, reactivity, and adaptability. These capabilities are highly necessary to establish automation mechanisms, as in the case of guiding users to a parking place, allowing a highly dynamic and interactive behavior in the system to be established.  
Some AGS solutions propose a collaborative process between agents based on mutual knowledge. Each agent receives information and participates in its updating and regeneration [49]. This process avoids centralization which, as we saw earlier, is costly and makes scalability difficult. Other applications deploy a pool of multi-search agents used in the intelligent parking field, with the contribution of processing a route considering multiple routes and scenarios, on which agents make different decisions in the search for an optimal route to the destination [50; 51].  
This type of SPS is the least used in the studies found. The main reason for this is that they are highly complex algorithmic implementations, which implies a higher cost of development time for the systems against other solutions which are easier to implement. However, an AGS offers solutions that allow decentralizing the processing and adding artificial intelligence in a more natural way to the system.
# Image Retrieval Method Based on Image Feature Fusion and Discrete Cosine Transform
## 4 Experimental Results and Analyses
### Influence of Variable Factors on the Experiment  
#### 4.3.1 Similarity Metrics  
Here, we conduct comparative experiments on the dataset by using different similarity metrics. Table 3 lists the APs and ARs by the proposed methods with different distance or similarity metrics. The Top NR is 20. The deep feature ResNetFc extracted from the ResNet50 fully connected layer and fused shallow features Hld are used for the test.  
Here, in the weighted L1 metric, \(1/(1+p_{i}+q_{i})\) is the weight, as shown in Table 3. When different methods are used, the best similarity metric changes. The Canberra metric appears to be more stable.  
Figure 18: APs bar plot of each query on the Oxford building dataset using the PlaDct method.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Performance**} & \multicolumn{4}{c}{**Distance or Similarity Metrics**} \\ \cline{4-7}  & & & & **Canberra** & **Weighted L1** & **Manhattan** & **Cosine** \\ \hline \multirow{3}{*}{ResNetFc} & \multirow{2}{*}{Corel-1k} & AP (\(\%\)) & 94.09 & 93.87 & 93.99 & **94.47** \\  & & AR (\(\%\)) & 18.82 & 18.77 & 18.80 & **18.89** \\ \cline{1-1}  & \multirow{2}{*}{Corel-5k} & AP (\(\%\)) & 42.30 & 42.35 & 42.36 & **42.72** \\ \cline{1-1}  & & AR (\(\%\)) & 8.46 & 8.47 & 8.47 & **8.54** \\ \hline \multirow{3}{*}{Hld} & \multirow{2}{*}{Corel-1k} & AP (\(\%\)) & 76.28 & **76.91** & 76.59 & 66.24 \\ \cline{1-1}  & & AR (\(\%\)) & 15.26 & **15.38** & 15.32 & 13.25 \\ \cline{1-1} \cline{2-7}  & \multirow{2}{*}{Corel-5k} & AP (\(\%\)) & 30.40 & **30.94** & 30.54 & 23.85 \\ \cline{1-1}  & & AR (\(\%\)) & 6.08 & **6.19** & 6.11 & 4.77 \\ \hline \hline \end{tabular}
\end{table}
Table 3: APs and ARs results using different methods with different distance or similarity metrics, Top NR = 20.
# Strategies for Post-Translational Control of Protein Expression and Their Applications
## 3 Targeted Protein Degradation Using Bifunctional Molecules or Antibodies
### Autophagy/Lysosome-Based Approaches  
Recently, several groups reported alternative approaches using proteolytic machinery including the autophagy-lysosome pathway, which is capable of targeting not only proteins, but also organelles. [PERSON] and coworkers found that \(S\)-guanylation targets substrates for K63-linked polyubiquitination and selective autophagy [32]. They developed autophag-targeting chimeras (AUTACs) consisting of guanine derivatives that mimic the function of endogenous protein \(S\)-guanylation and a specific ligand for a POI (Figure 2c). AUTAC targets the POI for selective autophagy, and mitochondria-targeted AUTAC allows the removal of dysfunctional fragmented mitochondria via mitophagy. In contrast, [PERSON] and colleagues developed a distinct strategy that uses molecular glues, i.e., autophagocentethering compounds (ATTECs) [33,34]. By performing small-molecule-microarray-based screening, they identified compounds that bind to both the autophagosome-resident LC3 protein and the mutant huntingtin protein (mHTT), but not with the wild-type HTT protein. mHTT causes [PERSON]'s disease, which is an incurable neurodegenerative disorder. Besides mHTT, ATTEC can target another disease-causing protein with the expanded polyglutamine stretch, including ataxin-3, and reduce its level.  
The [PERSON] group developed lysosome-targeting chimeras (LYTACs) that direct extracellular and membrane proteins for degradation via the endolysosomal pathway. They designed and synthesized LYTACs that consist of a small-molecule or an antibody that binds to a POI and a chemically synthesized glycopeptide ligand that binds to the cation-independent mannose-6-phosphate receptor (CI-M6 PR), which is a cell-surface lysosome-shuttling receptor (Figure 2d) [35]. LYTAC recruits the POI on the cell-surface to CI-M6 PR and triggers endocytosis, followed by lysosomal degradation. Although CI-M6 PR is ubiquitously expressed in various tissues, there are other classes of lysosome-trafficking receptors that show a tissue-specific expression pattern, inspiring further research to develop a second class of LYTAC technology. The same group focused on the asialoglycoprotein receptor, a liver-specific lysosome-targeting receptor, which induces clathrin-mediated endocytosis [36]. They conjugated binders of the POI to a tritenerrary \(N\)-acetylgalactosamine (tri-GalNAc) motif that engages the asialoglycoprotein receptor to induce the degradation of the target protein. This GalNAc-LYTAC technology achieves tissue-specific degradation, which could be beneficial for future therapeutic applications.