esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Saving Strategy of UAV in MEC Based on Deep Reinforcement Learning
## 3 System Model
### Energy Transfer Model  
In the system, we use TDMA to offload and schedule computing tasks. The system time distribution is shown in Figure 2. The system time is divided into consecutive time blocks with length \(T\), which is set smaller than the channel mutual interference time--for example, only on the scale of seconds [37]. At this time, wireless power transmission and task offloading are performed in the same frequency band. Let \(c_{n}\) denote the wireless channel gain of the AP and the \(n-\)th UAV in the same time block. The communication speed and energy acquisition between the AP and the UAV are both related to the wireless channel gain. Since the mutual interference between wireless power transmission and communication is avoided, the wireless channel gain remains constant within each time block, but may change between different time blocks [38]. At the beginning of each time block, the duration of AP transmitting energy to UAV is \(a\ T\), \(a\in[0,1]\). Therefore, the energy captured by the \(n-\)th UAV is:  
\[E_{n}=\mu\ P\ c_{n}\ a\ T,n=1,\ldots,N \tag{1}\]  
where \(\mu\in(0,1)\) denotes the energy harvesting efficiency and \(P\) denotes the AP transmit power. Using the harvested energy, each UAV needs to complete the computing task before the end of a time block, and the calculation of the task is executed immediately from the beginning of the time block. The offloading time of the \(j-\)th UAV is \(b_{j}\ T\), \(b\in[0\,\ 1]\). Here, we assume that the computing speed and transmit power of the AP are much larger than those of resource-constrained UAVs, such as more than three orders of magnitude [28; 30], so we can safely ignore the time for AP computing tasks. Since the calculation result data of UAVs are usually much smaller than those for the offloaded calculation task, we can also safely ignore the calculation result download time.  
Since each time block is only occupied by WPT and task offloading, there is  
\[a+\sum_{j\in U_{1}}b_{j}\leq 1 \tag{2}\]
## 3 Results
### Structural Characterization
#### 3.2.3 Brunauer-Emmett-Teller Analysis  
Specific surface area was investigated by nitrogen physisorption measurements. Based on the BET results, both EGCD and PEGCD1 have a low BET specific surface area, namely 3.31 m\({}^{2}\cdot\)g\({}^{-1}\) and 4.37 m\({}^{2}\cdot\)g\({}^{-1}\), respectively. It should be noticed that the BET analysis of PEGCD2 was not recorded due to its gel form. According to [PERSON] [66], \(\beta\)-CD presented a micropore structure with a surface area of 2.45 m\({}^{2}\cdot\)g\({}^{-1}\). PEGCD1 exhibited a higher surface compared to EGCD. This fact can be attributed to the effect of the crosslinker used [65]. Therefore, this short difference is probably due to the more pronounced hydrophobic character of the PEGDE compared to the EDGE linker. However, as described by [PERSON] [67], the conditions for N\({}_{2}\) adsorption in the BET method can disturb the surface properties of the adsorbent and skew the results of a specific surface area. Thereby, monolayer MB adsorption following the Langmuir model, as described in Section 3.3.3, provides an alternative method to calculate the BET surface.  
Estimated specific surface area (\(A_{S}\)) was calculated using Equation (2):  
\[A_{S}=\frac{10^{-3}Q_{m}N_{A}A_{m}}{M} \tag{2}\]  
where \(N_{A}\) is [PERSON]'s number (6.022 \(\times\) 10\({}^{23}\) mol\({}^{-1}\)); \(A_{m}\) the effective molecular area for MB (\(A_{m}\) = 1.3.10\({}^{-8}\) m\({}^{2}\)); and \(M\) is the molecular weight of MB (319.89 g.mol\({}^{-1}\)).  
The maximum adsorbed amount of MB (\(Q_{m}\)) was 10.68 mg.g\({}^{-1}\) for EGCD and 6.37 mg.g\({}^{-1}\) for PEGCD1. Therefore, the estimated specific surface area was 26.14 m\({}^{2}\cdot\)g\({}^{-1}\) and 15.60 m\({}^{2}\cdot\)g\({}^{-1}\) for EGCD and PEGCD1, respectively.
## 5 Discussion
### RQ3: Context-Specific Mitigation Strategies for Different Blockchain Applications  
Different blockchain applications require tailored security approaches:  
* Smart Contracts (DeFi and Financial Transactions):
* Pre-deployment formal verification to identify coding errors.
* Multi-signature authentication to prevent unauthorized withdrawals.
* Real-time transaction monitoring for Ponzi scheme detection.
* Enterprise and Government Blockchains:
* Hybrid blockchain integration balancing transparency and confidentiality.
* Regulatory compliance mechanisms such as AML and KYC frameworks.
* Decentralized governance models using DAOs for decision-making.
* IoT and Industrial Blockchain Applications:
* Lightweight blockchain security protocols for resource-constrained devices.
* Edge and fog computing integration to secure IoT data.
* Zero-knowledge proofs for securing IoT-generated data.
* Privacy-Critical Applications (Healthcare, Identity Management):
* Decentralized identity management to prevent unauthorized access.
* Confidential transactions with advanced encryption mechanisms.
* Blockchain-Based Voting and Governance:
* Cryptographic vote verification for secure elections.
* Resilient consensus mechanisms to prevent rollback attacks. By implementing application-specific security strategies, blockchain networks can achieve better security, compliance, and efficiency.
# MB-OFDM-UWB Based Wireless Multimedia Sensor Networks for Underground Coalmine: A Survey
## 4 Other Research Issues
### Information Processing
Accordingly, it is necessary to deal with these application-specific querying and processing, and to develop distributed filtering and in-network processing architectures, to allow real-time retrieval of useful information [4]. Besides, architectures that allow data fusion or other complex in-network processing operations should be proposed. For the purpose of gaining useful information which is more accurate and concise, information processing techniques in different level, aiming at different network structure, MB-UWB wireless transmission mode and specific application requirements, should be proposed. By introducing information processing, the overall monitoring performance of coalmine WMSNs will be sufficiently improved.
## 4 Experimental Evaluation  
### Experimental Environment and Parameter Configuration  
To validate the ERA-MADDPG algorithm, this paper employs the Mininet network simulation platform [15] and Ryu SDN controller [22], which are widely recognized in the SDN research community. Mininet is an open-source tool for rapid prototyping of software-defined networks, enabling lightweight emulation of large-scale network topologies with real kernel forwarding and interactive Command-Line Interface (CLI) management. The Ryu controller, as a leading open-source SDN controller, provides a robust framework for implementing network control logic through its north-bound Application Programming Interface (API) and supporting dynamic topology management. The data layer adopts the basic topology of the GEANT network [23], as shown in Figure 5, which contains 23 network nodes and 37 bidirectional links. The GEANT network topology is divided into multiple autonomous domains. Intra-domain links connect nodes within the same domain, while inter-domain links connect different domains. The bandwidth of the intra-domain and inter-domain links is set to 100 Mbps. The controller layer of the 5 domains is controlled by the Ryu controller simulation, and the multi-agent layer adopts the ERA-MADDPG algorithm.  
The experimental environment software consists of Ubuntu 18.04.1, Python 3.6.5, Torch 1.4.0 for building neural network models and performing efficient numerical calculations, Gym 0.17.1 task environment, Numpy 1.18.2 numerical calculation dependency library, and Statsmodels 0.11.1 library. The experimental hardware environment consists of an I5-13600K-CPU, 32 GB DDR5 memory, and an RTX 4060 Ti 16 G. The ERA-MADDPG algorithm introduces randomness through two primary mechanisms: (1) Gaussian exploration noise \(N_{i}\) added to deterministic actions during training. (2) Randomly sampling of experience tuples from the replay buffer. To mitigate the impact of this randomness on results, all experiments were repeated 30 times with different initial random seeds. Set the ERA-MADDPG algorithm training steps, learning rate, discount factor, experience replay pool, and other parameter sizes as shown in Table 1.  
Traffic load intensity (80%) represents the ratio of actual network traffic to the maximum capacity, simulating a high-load scenario to test the algorithm's robustness under heavy traffic conditions. The experience replay pool is updated every 200 iterations to balance training stability and convergence speed, reducing the correlation between consecutive training samples.
# A Review on Head-Related Transfer Function Generation for Spatial Audio
## 4 Physical Modelling  
We saw in Section 2 that an HRTF is a measure of a physical process, the scattering of a sound wave by a rigid body (the head and torso of an individual) in an anechoic, boundless environment. This process is completely governed by the wave equation with suitably chosen boundary conditions. This makes it possible to generate HRTF through a numerical solution of a well-defined mathematical problem defined by Equations (4), (6) and (7) in the time domain, or Equations (8)-(10) in the frequency domain.  
This section focuses primarily on the boundary element method (BEM), which is one of the most popular approaches to modelling scattering problems in acoustics, but other approaches are also considered.
## 2 Related Work
Furthermore, FS techniques may be useful in bridging gaps in study results caused by ambiguity in the exact parameters that cause ASD [17]. Identifying significant disease-related features is also important in medical diagnosis. Some studies used no FS approach [16; 28], while others used sample techniques such as F1-score, recursive feature elimination (RFE), and principal component analysis (PCA) [20; 21]. A few studies have yielded promising results [29] using advanced technologies like Boruta or bio-inspired algorithms like the GWO and particle swarm optimization (PSO). For example, four bio-inspired methods were used in [30] to create optimized ML models using gene expression data to predict ASD. In [30], four bio-inspired methods were employed to develop optimized ML models using gene expression data for ASD prediction. The GWO-SVM model achieved an accuracy of 99%. In [31], a model for Alzheimer's detection was proposed, where the Boruta and information gain filter feature selection methods were independently tested. The authors achieved an accuracy rate of 99.06% using a GBC and Boruta. However, to the best of our knowledge, optimized feature selection approaches have not been extensively explored for ASD categorization using sMRI. Additionally, comparing the performance of different ML models applied to the same data is challenging due to variations in participant groups, preprocessing steps, CV procedures, and other factors, which is in line with the \"no free lunch\" theory [5]. The reviewed studies (as presented in Table 1) provide an analysis of various aspects, including datasets, preprocessing tools, evaluation metrics, dimensionality reduction and FS methods, participant characteristics, imaging modalities, and identified features/biomarkers for ASD diagnosis. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \ \begin{tabular}{c} **Features** \\ **Statistic** \\ \end{tabular}  & \begin{tabular}{c} **Feature Selection** \\ **Method** \\ \end{tabular}  & **Medicity** & **Ref. Year** & **Biomarker** & **Dataset** & **Subjects** & **App** & **Perp  
Given the foregoing, the purpose of this study was to optimize and evaluate seven ML algorithms with varying degrees of complexity used to diagnose 5- to 10-year-old children with ASD or TD using sMRI-based morphological features, as well as to identify important biomarkers associated with ASD to aid in early and accurate diagnosis. This work aimed to shed a light on the role of the RFECV, Boruta and GWO-based algorithms for FS, and the random search and GWO algorithm for hyperparameter tuning. This study aims to provide guidance for selecting acceptable ML models for classifying ASD based on morphometry data because it has significant benefits in aiding accurate diagnosis.
# A Security-Oriented Data-Sharing Scheme Based on Blockchain
## 3 A Security-Oriented Data-Sharing Scheme Based on Blockchain
### Formal Model
#### 3.2.1 A Storage Architecture That Separates Data from the Proof of Ownership
```
Input:  File address: address, File name: filename, Sender publickey address:  spublickey, Recipient publickey address: rpublickey, File ID: TokenId,  Maximum supply: turnover Output:  IPFS File address: CID, the block number in the blockchain where the file is saved: number
1 spublicKeyBytes=Get spublickey
2 rpublicKeyBytes=Get rpublickey
3 Unlock=string (rpublicKeyBytes)
4 Holder-string (spublicKeyBytes)
5 //The shared key for encrypting and decrypting the file is generated using the Elliptic Curve Key Exchange (ECDHE) algorithm  Sharekey=ECDHE (spublicKeyBytes, rpublicKeyBytes)
6 CID=uploadFile(address)
7 //The ciphertext hash value is obtained using Base64 encoding  HASH=base64(Encryption (Sharekey, CID))
8 Holder=rpublickey; Owner=spublickey
9 Set up blockchain
10 //The file-related information
11 A = [fileName, Holder, Unlock, turnover, Hash, Owner, TokenId]
12 //Initialize the blockchain, store information in it, and then return the count of stored blocks along with any potential errors encountered
13 Number, err=APPI.InitLedger(A)
14 B = [fileName, Holder, \"ipfs\", spublickey]
15 //Mine new blocks on the blockchain, store the information in B, and return the number of mined blocks along with any possible errors encountered
16 Number, err=APPI.mine(B)
17 return CID, Number, Sharekey
```  
**Algorithm 1**Data-Proof separation algorithm
## 4 Discussion
### The Evaluation  
For our initial evaluation, we invited five participants of the LADR laboratory network to partake in a video conference where we demonstrated the capabilities of LUMA. Presenting the software in this way meant that our evaluation focused on the general premise of LUMA and not primarily on aspects of usability, although some input regarding that was generated nonetheless. It additionally highlighted the fact that the development of a web-based interface is the right way forward for future developments. Although one could bundle the software components and data in a way that does not rely on a FHIR terminology server and capable UCUM tool to exist as a requirement for using LUMA, it is a much more sensible approach to encapsulate all of it in a platform-agnostic way that is also easier to maintain due to having one central back-end to take care of. Additionally, although the participants were only loosely familiar with UCUM, we nonetheless inquired what would make adopting the standard more attractive on a user basis. One requirement was deemed to be very important. There was an interest in a verified and easy-to-use reference that would ideally also be able to be used in a standardized way within a digital context. Having worked with the UCUM standard extensively, there is something to be said about the accessibility as presented on the official web presence. The specification document is quite dense and very detailed, which makes sense considering the fact that its main purpose is to lay out the framework for software implementations. There is no doubt that end-users wanting or needing to acquaint themselves with the standard without the intention of implementing it would likely be intimidated by this. As of March 2022, the official web presence is undergoing changes regarding its appearance and composition, so it remains to be seen what future developments are going to occur in this regard.
# Image Mosaicing Applied on UAVs Survey
## 4 Aerial Panorama Applications
### Feature-Based: Global Single Transformation
#### 4.1.3 Fast  
In comparison with Harris Corner, the FAST algorithm can detect more features at the same time, yet, compared with SIFT, the number of features detected is less than half. This can lead to the wrong assumption that SIFT is better than FAST. Nevertheless, in processing time, FAST accomplishes feature detection with simple operations that make it faster than SIFT [90]. This has to be considered to choose the best fit implementation according to the application. A SIFT variation using FAST in each pyramid instead of DoG is proposed  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Author** & **Advantage** \\ \hline [PERSON]. [PERSON] et al. (2013) [2] & The SR algorithm improves in effectiveness. \\ \hline [PERSON] et al. (2018) [83] & The speed estimation performs the aerial panorama in a short time with appropriate aspect ratios and good visual quality. \\ \hline [PERSON] et al. (2019) [4] & A positioning system based on image stitching and top-view transformation is proposed, relating it to the GPS data to calculate the relative UAV position for distance measurements and object localization. \\ \hline [PERSON] et al. (2018) [84] & Stitching region prediction based on IMU and GPS information is used for image stitching using SIFT. \\ \hline [PERSON]. [PERSON] et al. (2018) [87] & A FAST 3D modeling of fully or partially collapsed buildings using images from UAVs for the Urban Search and Rescue task is proposed. \\ \hline \hline \end{tabular}
\end{table}
Table 3: SIFT applied on UAVs.  
in [91]. Such an approach achieves a robust and faster algorithm with more features than what is achieved just using FAST and faster than SIFT. As aforementioned, FAST has the advantage of speed computation; for this case, [92] proposes a real-time application using the FAST feature detector with the correspondence algorithm Bag-of-Word (BoW) to improve the time correspondences compared to the brute-force matching algorithm. Sometimes the stitching process uses multitemporal images, which present more changes in lighting and contrast than when applying any of the feature detection methods. The process will have errors due to the change in the grayscale. Reference [93] applies the use of phase congruency (PC) to maintain the image structure, regardless of the change in the grayscale, once the PC images are obtained. A crowd density estimated by jointly clustering analysis is presented in [94], where two versions of FAST are tested to detect the crowd features. The filtering procedures are used to eliminate the feature points which did not belong to crowd features. Some applications of the algorithm used with drone images are presented in Table 4.
## 6 Performance Evaluations
### Performance Evaluation Criteria  
Four quantitative measures are taken into account to provide a balanced assessment of the effectiveness of the proposed method. Here is a breakdown of the standards we will be using to evaluate the candidates:  
* Network Lifetime [8] is the amount of time that has passed since the network's first node failed because of a dead battery.
* Packet Delivery Ratio (PDR) [8]. The ratio of the number of messages successfully received by the sink node to the total number of messages sent by all source nodes.
* Deadline miss ratio [8]. This is the number of packets whose deadlines were missed because they never reached the sink node.
* Average end-to-end delay [8]. This is the meantime for a data packet to transit from its source nodes to the sink.
* Energy Imbalance Factor (EIF) [15]. This is the standard deviation of the network's residual energy. It is a measure of the routing protocol's efficacy in terms of energy-saving balance.  
\[EIF=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}\left(RE_{i}-RE_{avg}\right)^{2}}\]  
where \(n\), \(RE_{i}\), and \(RE_{avg}\) are the number of nodes, the node's \(i\) residual energy, and all nodes' average residual energy, respectively
# Eco-Friendly Metadome-Antenna Innovations for Wearable Millimeter Wave Radar Sensing
## 3 Results
### Fabrication  
Adhesive backed Al-tape with 33 \(\upmu\)m thickness was used for the metallic parts of the GAA and for the cross metalization geometry of the metaradome. Both the GAA and the metaradome prototypes on aluminum-cladded PP were fabricated using conventional milling machining with LPKF ProtoMat H100 machine (LPKF, Garbsen, Germany) (see Figure 8) instead of laser micromachining (proven very suitable for simple geometry patch type array [32]) to avoid adhesive residue getting stuck on the many slots when the laser heats the aluminum before removal.  
Figure 8: Milling machine LPKF ProtoMat H100 and fabricated prototypes: GAA top and bottom view and MTR.  
Figure 7: Arrangement of (a) GAA+radome and (b) MTR-GAA. (c) Radiation pattern cuts for Phi = 0\({}^{\circ}\) and Phi = 90\({}^{\circ}\) at 24.15 GHz.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**GAA+Radome**} & \multicolumn{4}{c}{**MTR-GAA**} \\ \hline
**h\({}_{\text{r}}\) (mm)** & **G (dBi)** & **\(\eta\) (\%)** & **FTBR (dB)** & **G (dBi)** & **\(\eta\) (\%)** & **FTBR (dB)** \\ \hline
0.3 & 12.0 & 69 & 23.2 & 12.3 & 82 & 21 \\
0.5 & 12.8 & 77 & 24.4 & 13.1 & 87 & 21.5 \\
0.7 & 12.3 & 81 & 23.5 & 13.4 & 88 & 22.7 \\
0.9 & 13.4 & 83 & 23.8 & 13.6 & 88 & 24.7 \\
1.2 & 13.7 & 86 & 23.9 & 13.8 & 88 & 25.1 \\
1.4 & 13.8 & 88 & 24.5 & 13.6 & 87 & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Radiation properties vs. height from GAA.  
The fabrication of each prototype using the LPKF ProtoMat H100 involves several key steps. The designs created using FEM simulation software are exported in Gerber format along with the drilling files in Excellon format. These files are then imported into CircuitCAM software from LPKF, which converts the design data into milling paths. Any necessary adjustments to the design, such as modifying line widths or adding aluminum areas, are made at this stage. Subsequently, the BoardMaster software from LPKF is used to control the ProtoMat H100, where the processed files are loaded, and the milling tools and parameters are configured according to the design specifications. The PCB material (Al-Cladded-PP) is then secured in the ProtoMat H100, and the milling process is initiated via BoardMaster, allowing the machine to execute the milling paths and drill the holes as per the design. Upon completion of the milling process, the PCB is inspected to ensure there are no errors.  
Silver loaded epoxy adhesive and hardener are used to fix by hand an SMA connector operating up to 26 GHz, to feed the GAA. The fabricated prototypes are shown in Figure 8.