esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results and Analysis
### Evaluation Metrics  
In view of the satellite attitude estimation results, relevant evaluation indexes are provided by SPEC2019, in which the scoring standard of the orientation quantity is the angle between the orientation vectors:  
\[E_{\mathbb{R}}=2\text{arccos}(|\langle q_{est},q_{gt}\rangle|), \tag{14}\]  
where \(q_{est}\) and \(q_{gt}\) are the quaternion representations of the estimated value of orientation and the true value, respectively, and the scoring index of translation is the \(L_{2}\) norm of the error between the estimated value and the true value.  
\[\xi_{\mathbb{T}}=\left\|t_{gt}-t_{est}\right\|_{2}, \tag{15}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Description** & **Value** \\ \hline \(f_{x}\) & Horizontal focal length & 17.6 mm \\ \(f_{y}\) & Vertical focal length & 17.6 mm \\ \(n_{u}\) & Number of horizontal pixels & 1920 \\ \(n_{v}\) & Number of vertical pixels & 1200 \\ \(d_{u}\) & Horizontal pixel length & 5.86 \(\times\) 10\({}^{-3}\) mm \\ \(d_{v}\) & Vertical pixel length & 5.86 \(\times\) 10\({}^{-3}\) mm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Camera parameters.  
where \(t_{est}\) and \(t_{gt}\) are estimated and true values of translation, respectively.  
This is normalized to obtain \(E_{T}\),  
\[E_{T}=\frac{\xi_{T}}{\left\|t_{gt}\right\|_{2}} \tag{16}\]  
Finally, we denote the total score using \(ESA_{score}\):  
\[ESA_{score}=\frac{1}{N}\sum_{i=1}^{N}{(E_{\mathbb{R}}+E_{T})}, \tag{17}\]  
where \(N\) denotes the number of images.
# Dynamic Online Label Distribution Feature Selection Based on Label Importance and Label Correlation
## 4 Proposed Method
### Dynamic Algorithm for Online Label Distribution Feature Selection
#### 4.3.1 Importance Analysis  
Letting \(F_{i}\) be the new feature arriving at the \(i\)-th moment, \(S_{t_{i-1}}S_{t_{i-1}}\) is the reduced set reduct at the \(i\)-th moment. For the new features that arrive, the first step is to perform redundancy analysis on \(F_{i}\). The purpose of redundancy analysis is to determine whether the newly arrived feature \(F_{i}\) is beneficial to the label set \(L\), that is, to determine the importance of \(F_{i}\) to the entire label set \(L\).  
Using Equation (17), \(\text{SIG}\big{(}F_{i},S_{t_{i-1}},D^{L}\big{)}=\gamma_{F_{i}\cup S_{t_{i-1}} }\big{(}D^{L}\big{)}-\gamma_{S_{t_{i-1}}}\big{(}D^{L}\big{)}\) is set to measure the importance of \(F_{i}\). If \(\text{SIG}\big{(}F_{i},S_{t_{i-1}},D^{L}\big{)}<0\), we consider \(F_{i}\) to be redundant and unimportant for the current label set \(L\), and hence \(F_{i}\) can be omitted.
## 6 Simulation Evaluation of the Dynamic VWSN
### Discussion of Memory Utilization
However, on the contrary, in decentralized systems, more powerful resources are required for any node in the network. [PERSON] et al. mentioned that research of the virtualization of WSNs is getting more pertinent because WSNs' nodes are becoming more powerful [16]. It is expected that this trend will continue, and WSNs' nodes will get more powerful resources in the future. In our architecture, therefore, sensor nodes process their tasks in a decentralized manner after the VWSN provider deploys the constructed VWSN. Moreover, some techniques with small overheads can be applied to our architecture. For example, only high-spec connected nodes hold the entire \(N\)-tier routing tables and are responsible for routing between higher tier modules, while a low-spec node holds only zeroth-tier table and sends its sensing data to only the nearest connected node. Any routing algorithm will do in the zeroth-tier, not only any-to-any routing, but also converge routing to a high-spec node. Many efficient converge routing algorithms for WSNs have been proposed [31]. Here, a method of resource management or an energy-efficient solution is out of the scope of this paper and relies on other research. Another important aspect is Internet compatibility. It is natural to consider that the VWSN providers or users access the virtualized resources through the Internet. Therefore, we need to address compatibility or connectivity between WSNs and the Internet. As mentioned by many researchers [32], the gateway-based strategy can be one solution to this problem. Our architecture can also gain the compatibility to the Internet by using gateway-based solutions. In our architecture, connected nodes can be seen as gateways between modules or networks. As mentioned in Section 1, we consider that users can select or configure protocols that they use in their applications. Standardized protocols, such as 6 LoWPAN, can be also included. Moreover, this idea can be applied to each tier in a VWSN network deployed for an application. As mentioned above, because there are some low-spec nodes in WSNs, an energy-efficient and low overhead routing algorithm can be the first choice in zeroth-tier networks. Then, each connected node, behaving as a gateway, aggregates data packets whose destination is out of the module, encapsulates them for routing in higher tier networks and translates protocols as necessary. In this scenario, although the amount of energy consumption is heterogeneous, the heterogeneity of the nodes' spec is more general in the future IoT environment because of the existence of multiple vendors and providers. How to manage such heterogeneity is out of our scope, but should be future work.
# Variable Delayed Dual-Core Lockstep (VDCLS) Processor for Safety and Security Applications
###### Abstract  
Dual-Core Lockstep (DCLS) is one of the most commonly used techniques in applications requiring functional safety. As the semiconductor process nodes keep shrinking, the DCLS technique is also more and more frequently seen in industrial or even consumer electronics. The paper presents the novel approach to the DCLS technique. While the typical approach is to set the slave core delay as a fixed number of clock cycles, we allow the checker core to run freely behind the main core within the constrained boundaries of clock cycles. This increases the temporal diversity needed for common mode failure mitigation. The system integrity provided by DCLS may also be used in the area of security applications. In this paper, we show that the proposed Variable Delayed Dual-Core Lockstep technique can flatten the power consumption correlation between the running cores, essential for a wide range of attacks. The proposed technique was implemented in the RSC-V processor core and verified in the Xilinx VCU108 FPGA platform.  
Keywords:dual-core lockstep; functional safety; security; fault tolerance; embedded processor; RISC-V
## 1 Introduction
### Key Contributions  
This study proposes a method for efficiently distributing the node tasks in the network while increasing the throughput of smart contracts using sharding. Specifically, this study proposes a hierarchical multi-block system (HMBS), in which multiple sidechain and dual sidechain pairs are branched from and connected to a single blockchain (called _mainchain_ in this work) with a prespecified rule, where each pair works as a shard. The contributions of the proposed method are as follows.  
* One sidechain and dual sidechain pair form one shard, in which cryptocurrency transfers and smart contract executions are processed independently from those in other shards.
* The mainchain determines the block at which the results of cryptocurrency transfers or smart contract executions in shards are synchronized to HMBS.
* A modifiable blockchain, which we will call the _accountchain_, securely records the result of smart contract execution so that they are trusted by all HMBS network participants.
* When recording the state changes made by smart contract executions, the proposed method uses the accountchain to keep the database as small as possible, while existing UTXO-based blockchain models require an ever-growing database.
* The existing account-based blockchain model demands that all nodes record state changes in their local database by executing all smart contracts themselves. The proposed method allows sharded nodes to execute smart contracts and record the resulting state changes in the accountchain, to share them with all other nodes.
* The proposed method uses the mainchain to execute the smart contract that cannot be executed independently in one shard.
* Unlike existing parallelization approaches to smart contract executions, the proposed method does not require a group of privileged nodes to allocate jobs or check results inadvance. Instead, it uses a predetermined rule for the parallelization of smart contract executions. Moreover, in the proposed method, all nodes can securely share execution results without resorting to the help of some privileged nodes.
# Intelligent Dynamic Data Offloading in a Competitive Mobile Edge Computing Market
## 6 Results
### Operation of the DO-MECS Framework  
Towards illustrating the successful operation of the DO-MECS framework, we performed detailed simulations considering two main cases regarding the end-users that reside within the MEC environment: (a) homogeneous end-users; and (b) heterogeneous end-users, with reference to their sensitivity on the pricing imposed by the MEC servers (i.e., end-user dynamics \(d_{u}^{(t)}\) in Equation (2)). In our simulations, we considered \(S=5\) MEC servers and \(U=100\) end-users, while for demonstration purposes the weights \(w_{1},w_{2},w_{3}\) in Equation (4) were considered of same importance, and each one equal to \(1/3\). We considered a business perspective with respect to the MEC servers, in the sense that they present different characteristics with respect to parameters such as cost, discount factor, etc. The parameters that characterize the different MEC servers are presented in Table 1. Regarding the communication part of the network operation, each MEC server was assumed to receive data from the users via its own subcarrier. Thus, each user sensed the interference only from the users that were offloading to the same MEC server. It is noted however that, in this study, the transmission power control problem was not treated, and it was assumed that users transmit with fixed power.
## 5 Challenges and Future Directions
### Heterogeneity  
Although NDTs can collect network configuration data in the form of graphs, effectively processing these data for training machine learning models, such as GNNs, necessitates thoughtful consideration and meticulous engineering. One of the crucial challenges in this process is identifying the suitable entities to be represented as nodes and determining the relationships between them as edges. Real-world graphs such as network topologies are often heterogeneous, containing various types of nodes and edges, each with their unique attributes and typically situated in different feature spaces. The evolving nature of these graph structures and information makes the application of GNNs to Next-generation networks a complex task. Consequently, careful consideration is required during the preprocessing step to accommodate graph heterogeneity. Nevertheless, most existing GNN-based models primarily handle homogeneous graphs with a singular node type and edge type.  
Recently, innovative solutions have emerged to address these issues; for example, studies such as those proposed in [137; 138]. In particular, the authors in [137] proposed a new model named _MAGNN_, which employed three major components, (i) the node content transformation to encapsulate input node attributes, (ii) the intra-meta path aggregation to incorporate intermediate semantic nodes, (iii) and the inter-meta path aggregation to combine messages from multiple meta paths. Experimental results demonstrate that MAGNN can significantly outperform state-of-the-art models on various heterogeneous graph mining tasks, including node classification, link prediction, and recommendation. The results indicate that the model successfully captures both the semantic and structural information in heterogeneous graphs, demonstrating the potential for handling complex real-world graph data. Moreover, the work in [138] proposed an innovative Contrastive Pre-Training strategy for GNNs on Heterogeneous Graphs (CPTHG). This strategy captures both the semantic and structural properties of graphs in a self-supervised manner. Contrastive learning is a form of self-supervised learning where the model is trained to identify which samples in the dataset are similar (or contrastive) to each other. The proposed model contains three main components, (i) a semantic-preserving contrast that aimsto maximize the similarity between the node pairs that have the same type or share the same semantic information, (ii) a structure-preserving contrast that helps to maximize the similarity between the node pairs that have similar structural roles in the network, and (iii) negative sampling to reduce the similarity between the nodes that do not share similar structural roles or semantic information. These advancements suggest a promising direction for future work involving these techniques, particularly in the domain of network traffic prediction.
# An Overview of CubeSat Missions and Applications
## 8 The Use of CubeSats in Novel Technology Demonstration Missions  
In this final chapter, the use of CubeSat in technology missions will be explored. As already mentioned, their low-cost, rapid development, and inherent ability to reduce risks associated with the deployment of new technologies in space make CubeSats the perfect platforms for demonstrations. Given that traditional satellites have a much higher development cost and require significantly more time, it is of great challenge to use them for demonstration of emerging technologies efficiently. Before the in-space demonstration, CubeSats undergo several testing, verification, and validation procedures, to ensure the proper operation of their components under space environment conditions. One notable testing facility is the Zero-G Lab at the University of Luxembourg, which utilizes robotic manipulators to emulate zero gravity conditions [293; 294]. It usually observed that after successful in space demonstrations in CubeSats, a lot of those technologies are deployed to larger satellites. Some missions mentioned in the previous sections were also demonstration missions. Missions such as ASTERIA, EQUULEUS, GomX-4, ISARA, MarCO, and Artemis I CubeSats for Lunar Demonstration are only some examples of them. However, in order to not persevere, those missions will not be further analyzed. This section will be further subdivided into subsections for better categorization of the missions. Those categories are Propulsion and Maneuvering Demonstration, Communications, AI and Automation Technology Demonstration, Quantum Technology Demonstration, Space Environment and Radiation Testing, AI and Autonomy Demonstration, and Novel Mission Architectures and In-Orbit Demonstration.
## 4 Understanding Centralised Identity Systems
### LDAP: The Directory-Based Approach
#### 4.5.2 LDAP Authentication Process  
The LDAP authentication procedure can be broken down into the following steps [21; 23; 25]:  
1. **Client Request Initiation**: The process starts when the client sends a bind request to the LDAP server, providing the Distinguished Name (DN) and the password.
2. **Bind Operation**: * **Simple Bind**: The client sends the DN and password in plaintext. This method is insecure unless SSL/TLS is used.
* **SASL Bind**: The Simple Authentication and Security Layer (SASL) framework is employed to provide secure authentication mechanisms.
3. **Server Verification**: The LDAP server verifies the credentials by comparing the provided password with the one stored in the directory.
4. **Search Operation**: Upon successful verification, the server searches the Directory Information Tree (DIT) for the corresponding entry of the authenticated user.
5. **Response to Client**: The server responds to the client indicating whether the authentication was successful or not. If successful, the client is granted access to the directory services requested.
6. **Post-Authentication Actions**: Once authenticated, the client may perform actions like reading, modifying, or deleting entries in the directory, as allowed by their permissions.
# Ultrawideband Low-Profile and Miniaturized Spoof Plasmonic Vivaldi Antenna for Base Station
## 5 Discussion  
The quantitive comparison of similar antennas has been given in Table 2. For the 2G/3G/4G/5G base station applications, the wide bandwidth is necessary to cover the 2G, 3G, and 4G bands and the new sub-6 GHz 5G frequency bands. Hence, only sub-6 GHz frequency band of the proposed antenna is shown here. Besides, the gain, HPBW, and the electrical size should be considered simultaneously for the base station antenna. From Table 2, we can see that the lowest operating frequency of the proposed plasmonic AVA is the lowest (1.8 GHz). The electrical size is 0.56\(\lambda_{0}\), which is a little larger than that of Refs. [31; 32; 33], where \(\lambda_{0}\) is the wavelength in the air corresponding to the lowest operating frequency. The minimum gain (5.5 dBi) is the highest, since high gain is necessary for the base station antenna.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Frequency** & **Typical AVA** & **Plasmonic AVA** \\ \hline
1.8 GHz & 113.5 & 52.3 \\
2.0 GHz & 66.6 & 54.7 \\
3.0 GHz & 61 & 52.9 \\
4.0 GHz & 52.2 & 52.6 \\
5.0 GHz & 48.1 & 47.1 \\
6.0 GHz & 41 & 66.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulated half-power bandwidth (HPBW) of the AVA and plasmonic AVA.  
Figure 8: Simulated and Measured radiation patterns of the plasmonic AVA at (**a**) 1.8 GHz, (**b**) 4 GHz, and (**c**) 6 GHz.
## 3 Results and Discussion
of EI, and eventually reaches saturation. However, the enhancement ratio of Alq\({}_{3}\) (Ag NWs) exhibits an approximately uniform growth behavior, which is almost independent of the EI value. In addition, it can be found that Ag NWs lead to PL quenching at EI values below 1.7 mW/cm\({}^{2}\); when EI is higher, the PL begins to show enhancing behavior, while the incorporation of Ag NPs consistently exhibits PL enhancement. In the asymmetric Alq\({}_{3}\)-Ag NWs structure studied here, the PSPs coupling is only possible through the scattering process [40]. In order to intuitively observe the scattering feature induced by Ag NWs, the distribution of detection angle-dependent PL peak intensity is plotted in Figure 8. The normal line of samples is along the horizontal direction, and the incident light and reflected light are located below and above the normal line, respectively. As the angle between the detection direction and the normal line changes in the range of \(-150^{\circ}\) to \(150^{\circ}\) in steps of \(15^{\circ}\), the PL intensity will also change. We found that on the upper side where the reflected light is located, the PL intensity is relatively large when the detection angle is \(0^{\circ}\)-\(45^{\circ}\) and \(120^{\circ}\)-\(135^{\circ}\), while on the lower side where the incident light is located, the PL is generally very weak. In particular, the PL from the detection angle of \(120^{\circ}\) is over 37.6 times stronger than that from \(-90^{\circ}\). Figure 7: (a) EI-dependent IPLI of Alq\({}_{3}\) (pure), Alq\({}_{3}\) (Ag NPs) and Alq\({}_{3}\) (Ag NWs); (b) IPLI enhancement ratio as a function of EI.
## 3 Survey of Techniques for Updating
### Traffic Reduction
#### 3.2.4 AdapCode  
AdapCode [46] is a dissemination protocol, using broadcasts to benefit from redundant links, and adaptive network coding to reduce network traffic. Linearly independent packets are coded on each node for propagation, and decoded using Gaussian elimination. When a node receives a packet, it uses Gaussian elimination to see if it can now decode the entire _page_. If so it determines its coding scheme, based on the number of neighbours, and following a random backoff, starts broadcasting the coded packets. Periodically, with progressively doubling timer value, receiving nodes transmit a NACK giving the number of additional packets required. Nodes that can respond now delay for a random time, and only respond if no other node has during this delay. The timer is reset once a response is received. Nodes wait for an inter-page time \(T\) before moving to the next page--selection of this value has a significant impact on efficiency and latency. The Gaussian elimination takes 1433 of RAM space with their selected coding parameters (number of messages \(M\) and maximum coefficient _p_). Their results show a traffic reduction of 30%-40%, a reduction in latency of up to 15%, and a better balancing of the load between the nodes, compared to Deluge.  
AdapCode has no specific features relevant to autonomous updating, apart from the advantages of traffic reduction through network coding, but it is an open question whether the use of network coding might provide some additional flexibility for autonomous updates, perhaps in environments with limited connectivity or high noise.
# Improving Structural Power Content Analysis Robustness for Satellite Navigation Applications +
## 2 Structural Power Content Analysis  
The SPCA method is a pre-despreading spoofing detection technique with low computational complexity that was first proposed in [7]. Using the spectral properties of GNSS signals, the method uses filtering techniques to obtain an estimate of the received signal power. Since spoofing signals intentionally mimic the structure of GNSS signals, the estimated signal power increases in the event of a spoofing attack, and the signal power can therefore be effectively used to detect the occurrence of such an event.  
In the following section, the basic algorithmic steps of the SPCA method are briefly reviewed, based on the description presented in [7], as a basis for the further development and analysis following later in this paper.
# Audio-Visual Speech Recognition Based on Dual Cross-Modality Attentions with the Transformer Model
## 4 Experimental Results and Discussions
### Comparison with Simple Concatenation of Audio and Video Encoder Outputs  
In order to show the effectiveness of DCM attention, Table 4 compares the WERs of our model with those for decoding on simple concatenation of audio and video encoder outputs. For all the experimented SNRs, our model outperformed the model using simple concatenation of audio and video information, which demonstrated the effectiveness of DCM attention.  
Figure 8: WERs according to the numbers of words in sentences for the models using audio–visual modality on the test sets of LRS2-BBC and LRS3-TED datasets with (**a**) clean and (**b**) noisy reverberant audio data.  
Figure 9: WERs on out-of-sync data for the models using audio–visual modality on the test sets of LRS2-BBC and LRS3-TED datasets with (**a**) clean and (**b**) noisy reverberant audio data. The video frames were shifted by the numbers of frames on the horizontal axes with audio data fixed. The positive offset meant that the audio preceded the video, and the negative offset meant vice versa.