esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation and Performance Analysis
### Discussion  
We can observe from Figure 12 that, except for the LR method, the overall identification accuracy of other methods was over 90%. This shows that there is indeed a clear connection between the channel state information and the identity of the terminal. It is feasible to authenticate the identity of terminals in the wireless communication system based on the physical layer. The authentication method based on deep reinforcement learning proposed in this paper showed superior authentication accuracy performance compared with other methods. Furthermore, considering that the identification model of this method can converge quickly, which indicates that the training complexity of the method is low and it can adapt to wireless channel environments with changing characteristics, we can conclude that method DQN-PIA is suitable for the scenario and has better identification ability for wireless terminals compared to commonly used methods.  
For future work, from the perspective of method application, we believe that there are three steps that need to be carried out: Firstly, we need to verify the authentication efficiency and accuracy of the proposed method for a large number of terminals. Secondly, we need to verify the method's ability to authenticate and recognize different types of terminals. Finally, we need to design authentication protocols tailored to the considered  
Figure 11: Convergence of the proposed DQN-PIA method under different training rounds. (**a**) Total of 30 trainings per round. (**b**) Total of 50 trainings per round. (**c**) Total of 100 trainings per round. (**d**) Total of 200 trainings per round. (**e**) Total of 300 trainings per round. (**f**) Total of 400 trainings per round.  
scenario in order to avoid affecting authentication efficiency and network availability when authentication errors occur in the proposed method.  
Figure 12: Identity authentication confusion matrix of each comparison method. The authentication accuracy of DQN-PIA, LSTM, SVM, LR, KNN, MLP, RF, CNN is 98.9%, 97.7%, 96.4%, 88.5%, 94.7%, 93.4%, 92.05%, and 97.3%, respectively.
# MAC/PHY Comprehensive Visible Light Communication Networks Simulation
## 3 Mac Layer in IEEE 802.15.7 Standard
### Back-Off Algorithm  
The access method during CAP is random. Thus, it requires a set of steps called back-off mechanisms to alleviate collision likelihood and decrease latency. This standard uses CSMA/CA, where the transmitter node needs to sense the channel before beginning its transmission to be sure that it is idle. The random access mechanism is based on the following steps. An initial random waiting time _backOffPeriod_ is chosen randomly following a uniform distribution (Equation (4)):  
\[backOffPeriod=aUnitBackoffPeriod\cdot U(2^{BE}) \tag{4}\]  
Figure 1: The superframe structure of IEEE 802.15.7 standard.  
_aUnitBackoffPeriod_ is the minimum back-off period and the Back-Off Exponent (BE) determines the width of uniform distribution \(U(\cdot)\), which ranges from 1 to \(2^{BE}\). After waiting _backOffPeriod_ optical clocks, the user node senses the channel before transmitting as CSMA/CA suggests. In case of being unable to transmit, a failure is assumed, and a re-transmission is scheduled. If the channel is free, it transmits and waits for an ACK response to assess that the packet has been correctly delivered. When an unsuccessful transmission happens (ACK not received, or channel occupied, e.g.), a counter named Number of Backoffs (NB) is increased. NB is used for limiting the number of transmission attempts. Furthermore, the BE also increases (up to an upper boundary named _macMaxBE_), rising the maximum possible waiting time. The block diagram of this procedure is shown in Figure 2.
# Patch-Font: Enhancing Few-Shot Font Generation with Patch-Based Attention and Multitask Encoding
## 6 Ablation Study
### Ablation Results
#### 6.2.2 Patch Attention Mechanism  
Implementation Details: Building upon Variant B, we added a patch-based attention mechanism in Variant C. This mechanism was designed to weigh the encoded patches based on their relevance to the final font style, allowing the model to focus on the most critical aspects of the local style.  
Results: The addition of patch attention further elevated Acc (S) to 42.43% and Acc (C) to 78.18%, showing the effectiveness of selective focus on stylistic features. The LPIPS score was reduced to 0.211, and SSIM improved to 0.523. Notably, the FID score continued to drop to 117, suggesting an overall improvement in the quality of the generated fonts.
# High-Speed Dynamic Projection Mapping onto Human Arm with Realistic Skin Deformation +
## 3 Proposed Projection Mapping System
### High-Speed Projection  
To perform dynamic projection mapping within milliseconds [20], it is necessary to reduce the latency between image transmission and projection. Commercial off-the-shelf projectors usually have a latency above 10 ms after the projected image is delivered by the computer, being unsuitable for the proposed system. Alternatively, projectors based on the Digital Light Processing (DLP) chipsets have been proposed to achieve extremely high projection speeds. For instance, [PERSON] et al. [50] developed a single-chip DLP high-speed monochrome projector with a rate of 1000 fps and a latency of 3 ms. Such high performance is achieved by the synchronized operation of a digital micromirror device and an LED (light emitting diode) by using a specialized image transmission module. To project  
Figure 3: Shape reconstruction and compensation executed in parallel using joint tracking results.  
RGB images using DLP projectors, three-chip DLP architectures have been combined to project the red, green, and blue image channels [8,51]. For instance, a customized 24-bit RGB projector at 480 fps has been devised using a three-chip DLP configuration [8]. However, this configuration requires complicated optical systems that are costly and bulky. [PERSON] et al. [52] then developed a single-chip DLP 24-bit RGB projector with fast response and high brightness.  
In the proposed system, considering the system configuration and latency, we use a state-of-the-art high-speed single-chip projector that displays 24-bit XGA (Extended Graphics Array) images at a maximum rate of 947 fps [52]. As a result, the latency in image transmission from computer generation until projection can be reduced to less than 3 ms.
# The Detection of Fake News in Arabic Tweets Using Deep Learning
## 4 Experiments and Results
### Discussion  
Based on the experimental results, it seems that two types of context-based word embeddings (ARBERT and MARBERT) can improve upon other classic word embeddings. Context-based word embedding has the advantage of learning word representation based on context. It generates the embedding for the word after acquiring the contextual meaning of the word in the sentence. Therefore, it will give a different embedding for the same word based on the context within which the word appears, whereas classic word embeddings represent each word by a unique and distinct vector without considering the word's context. However, the ARBERT and MARBERT models failed to improve the model's performance when used in sentence_level embedding generation. The experimental findings for the classic word embeddings indicated that the models performed well when using Keras-generated word embeddings. The Keras embedding layer was configured to learn word embeddings during the process of training the model on task-specific corpora. Although FastText and word2 vec were pre-trained on huge general corpora, the Keras embedding showed a better performance with CNN and BiLSTM. Each word embedding model is fed to the CNN- and BiLSTM-based models. In most situations, CNN exhibited a slight improvement over BiLSTM. Moreover, it is computationally less expensive compared to BiLSTM. ARBERT and word2 vec acquired a better performance when BiLSTM was applied to learn the embedded vectors.  
Lastly, to assess the effectiveness of the best performing model, MARBERT-CNN, we further tested it on the dataset introduced by [40]. It is important to mention that the original dataset comprises 2708 Tweets, but we were only able to retrieve 1642 Tweets. This may be because the Tweet's author has protected or deleted their Tweets, and thus could not be reached via the Twitter API. It is also possible that the Twitter API has restricted access to those Tweets due to the privacy policy. The model obtained 0.7467, 0.7490, 0.7467, and 0.7400 for accuracy, precision, recall, and F1-score, respectively. Despite the dataset containing Tweets related to a specific topic, i.e., the Syrian crisis, the proposed model achieved an acceptable accuracy. This result proves that the proposed model accurately detects fake news related to various topics.
## 5 Experiments and Analysis
### Sensitivity Analysis
#### 5.3.3 Sensitivity Analysis for Weights of Latency and Energy Consumption  
As illustrated in Figure 4, the weighted cost (combining latency and energy consumption) exhibited distinct trends across different algorithms as the latency weight factor \(w_{0}\) increased from 0.1 to 0.9. The Local-only strategy demonstrated a linear growth pattern (slope = 0.82), which is expected since it exclusively depends on local processing latency and remains unaffected by UAV energy consumption. Conversely, the Edge-only approach showed a decreasing trend (slope = \(-\)0.76) due to its complete reliance on UAV resources, making it more sensitive to energy consumption factors. The proposed MATD3 algorithm and baseline MADDPG approach both exhibited decreasing weighted costs, with MATD3 achieving superior performance. This improvement stems from MATD3's optimized task offloading ratio that simultaneously considers both latency and energy factors. Specifically, while both algorithms are influenced by processing latency, MATD3 demonstrates better adaptability to the changing weight factors through its twin-delayed policy updates and more accurate value function estimation.
## 4 Case Study
### Operations
#### 4.2.1 Learning MAC Addresses  
Both access QuadBoxes and trunk QuadBoxes learn MAC addresses. Access QuadBoxes learn the MAC addresses of nodes connected to its DANH ring, whereas trunk QuadBoxes learn the MAC addresses of all nodes in the network.
# An Improved Heteroscedastic Modeling Method for Chest X-ray Image Classification with Noisy Labels
## 3 Methodology  
### Revisiting Heteroscedastic Modeling  
Heteroscedastic-based methods have been explored to model label noise, offering an objective method for image classification with data-dependent label noise. [PERSON] et al. [3] propose to model such noise based on probabilistic modeling [30; 31]. Suppose there are some latent vectors of utility \(u(x)\in R^{C}\), where \(C\) is the number of classes. The utility \(u(x)\) is the sum of a deterministic reference utility \(v(x)\) and a stochastic component \(\epsilon\), \(u(x)=v(x)+\epsilon\). The label of input image \(x\) is generated by sampling from the utility and taking the argmax, i.e., an image belongs to class \(c\) if its associated utility is greater than the utility for all other classes,  
\[p_{c}=P(y=c|x)=P(\arg\max_{j\in[C]}u_{j}(x)=c), \tag{1}\]  
where \(C\) is the number of the classes. Conventionally, the popularly used cross-entropy loss function in training a neural network is precisely the closed-form solution of predictive probabilities \(p_{c}\) under the assumption that the stochastic component \(\epsilon\) is the Gumbel distribution independently,  
\[p_{c}=\frac{exp(\mu_{c})}{\sum_{j=1}^{C}exp\left(u_{j}\right)}\iff\epsilon_{j} \sim i.i.d.G(0,1)\forall j. \tag{2}\]  
Meanwhile, the Gumbel noise distribution is always too restrictive for the data-dependent label noise. [PERSON] et al. [3] propose to break the above assumption by assuming that the noise term \(\epsilon\) is a multivariate normal distribution, \(\epsilon\sim\mathcal{N}(0,\Sigma(x))\). To solve the problem of no closed-form solution, the Monte Carlo estimation is used to approximate the expectation of \(\arg\max_{j\in[C]}u_{j}(x)\). Notice that the gradient-based optimization would be infeasible; thus, the \(\arg\max\) is approximated with a temperature-parameterized \(\arg\max_{\tau}\),  
\[p_{c}\approx\frac{1}{S}\sum_{l=1}^{S}(\text{softmax}_{\tau}u^{(l)}(x))_{c},u^{ (l)}(x)\sim\mathcal{N}(\mu(x),\Sigma(x)), \tag{3}\]  
where \(S\) is the number of Monte Carlo samplings.
## 1 Introduction
[15] noted that the observation of 40 m dish antenna from the National Time Service Center of the Chinese Academy of Sciences provides a way to analyze these types of systematic code variations since the MP combination observations got by it accurately detected the satellite-induced code variation without considering MP errors resulting from the station surroundings. Meantime, they used the 40-m dish antenna to gain more insight into the code-carrier divergence characteristics of BDS-3 experimental satellites, identifying the existence of code bias variations in a range of 0.1 m. There are differences between the BDS-3 experimental satellites and the BDS-3 satellites, and the existing research lacks the more accurate research on the satellite-induced code bias variation BDS-3 satellite. In this study, we therefore analyzed MP combination value from the 40 m dish antenna to provide accurate code bias characteristics for BDS-3 satellites. This paper first provides an overview on the BeiDou system and data sources, then introduces the MP combination formula used to analyze the code bias, and finally analyzes the elevation-dependent code bias, B1C_data and B1C_pilot code bias, and compares the code bias of observations between the normal geodetic antenna and the 40-m dish antenna.
## 2 Study Area and Data
### Data  
#### 2.2.1. Gpp NIRv Data  
The GPP NIRv dataset, developed by [PERSON] et al. [34], marks a significant improvement in estimating GPP using satellite-based near-infrared reflectance (NIRv). Covering the period from 1982 to 2018 with a spatial resolution of 0.05\({}^{\circ}\), this dataset provides extensive long-term data, effectively capturing monthly GPP variations, crucial in understanding global carbon dynamics. The dataset was established through empirical relationships between NIRv and GPP, based on data from 104 eddy covariance flux sites across various climate zones and vegetation types, ensuring its reliability and broad applicability. Validation showed strong correlations between the GPP NIRv and observed GPP, with an R\({}^{2}\) of 0.71 for monthly variations, confirming its effectiveness in tracking temporal GPP changes. Furthermore, comparisons with other global GPP products, such as the FLUXCOM GPP and TRENDY GPP, demonstrated that the GPP NIRv not only aligns closely with these datasets but also offers a more consistent depiction of the long-term GPP trends, especially when compared to process-based models.  
The global GPP estimated using NIRv during the study period averaged 128.3 \(\pm\) 4.0 Pg C year\({}^{-1}\), consistent with other GPP estimates, further highlighting the value of the GPP NIRv dataset in enhancing the understanding of terrestrial carbon fluxes. This dataset is a critical tool for researchers and policymakers, offering a reliable, long-term  
Figure 1: Location of the Tibetan Plateau.  
resource that supports climate change mitigation efforts and advances the understanding of the global carbon dynamics. This dataset can be downloaded from the TPDC \"[[https://data.tpdc.ac.cn/zh-hans/data/d6](https://data.tpdc.ac.cn/zh-hans/data/d6) diff40f-5 dbd-4f2d-ac96-55827 ab93 cc5]([https://data.tpdc.ac.cn/zh-hans/data/d6](https://data.tpdc.ac.cn/zh-hans/data/d6) diff40f-5 dbd-4f2d-ac96-55827 ab93 cc5) (accessed on 30 September 2024)\". In this study, the GPP NIRv dataset is treated as observational data and utilized as the target GPP in an ML approach to optimize the parameters of the CNN model.
# A Low-Power WLAN Communication Scheme for IoT WLAN Devices Using Wake-Up Receivers
## 2 Background
### Multiuser Transmission Procedures Using Wake-Up Radio  
In the MU case, the conventional transmission procedure causes a large delay and the delay increases as the number of users increases. To reduce the delay in this scenario, optional schemes that meet the different delay and power requirements for multiple wake-up STAs were proposed in [17].  
Because the average transmission delay is caused by the repetition of the entire transmission procedure, the options to reduce the average delay are focused on waking up multiple STAs in order to apply MU transmission, as defined in IEEE 802.11 ax.  
The transmission procedure for the MU scenario consists of sending multiple unicast wake-up frames and waking up all STAs first by broadcast wake-up frame, and then returning them to the sleep state if no frame intended for the STA is received after a predefined time.
## 3 Polyunsaturated Fatty Acids (PUFA) Exploitation from Autotrophic Microalgae
### Pufa-Synthesis by Microalgae  
LC-PUFA biosynthesis pathways by microalgae are initiated by \(\Delta\)12 desaturation of C18:1\(u\)9, producing LA, which might be further desaturated by a \(\omega\)3-desaturase generating ALA (see Figure 5) [58]. The \(\omega\)3-pathway is initiated with the \(\Delta\)6 desaturation of LA and leads to the synthesis of \(\omega\)3-LC-PUFA eicosapentaenoic acid (EPA, C20:5\(u\)3) and DHA, whereas the \(\omega\)6-pathway initiates with the \(\Delta\)6 desaturation of ALA and produces the \(\omega\)6-LC-PUFA, AA [58]. However, some EPA-producing eugtigmatophytes, such as _Nannochloropsis_ sp. and _Monodus subterraneus_, are thought to preferentially synthesize EPA via the \(\omega\)6-pathway by the action of a \(\omega\)3-desaturase, which catalyzes the conversion of AA to EPA [58; 59].  
Figure 4: Concept’s network obtained with VOSviewer software [54] for the research on “microalgae AND food AND health” in Web of Science database [10].  
As with other organisms, the microalgal fatty acid composition is known to vary among the different phylogenetic groups [58]. In Figure 5, is possible to visualize an alternative route for LC-PUFA biosynthesis: \(\Delta\)8 desaturase pathway. According to [PERSON] [55], this pathway is known to exist in some microalgae, namely in haptophytes _Isochrysis galbana_, _Pavlova salina_, and _Emiliana huxleyi_, and the Euglenophyte _Euglena gracilis_. For the DHA-producing haptophyta _Isochrysis galbana_, a gene encoding a C22-\(\Delta\)4 polyunsaturated fatty acid specific desaturase has been isolated and characterized [61]. The Trebouxiophyceae _Lobosphaera incisa_ is a rare case in which AA is the major product of LC-PUFA biosynthesis in microalgae [55].
## 4 Measurement and Discussion
### Axial Ratio  
The simulated and measured AR results of the antenna in free-space and on-phantom scenarios are shown in Figure 11. They are evaluated on the normal axis (\(\theta\) = 0\({}^{\circ}\)) of the antenna. For the free-space scenario (see Figure 11a), the simulated flat antenna has a  
Figure 8: SAR simulation result at 2.4 GHz.  
Figure 9: Measurement setups. (**a**,**b**) Free space. (**c**,**d**) On-body. (**e**,**f**) On-phantom.  
CP bandwidth (AR \(<\) 3 dB) of 2.29-2.53 GHz, and, accordingly, the measurement result is 2.2-2.54 GHz. The measurement bandwidth is wider than that of the simulation. This may be caused by fabrication errors during the manual fabrication process. The measured results when the antenna is bent show a frequency shift (depression) compared to the flat scenario. This is due to the symmetry of the structure that is broken. In detail, the modal currents of the dominant modes at high frequencies, i.e., modes 3-6 in Figure 6c-f, mainly flow on the rotationally symmetric parasitic elements. So, when the symmetric is broken, the CP waves cannot be effectively generated at high frequencies, and a frequency shift (depression) is observed. As for the on-phantom scenarios (see Figure 11b), a similar phenomenon can be observed.
## 2 Overview of Blockchain Technology
### Blockchain Architecture
#### 2.1.3 Transaction Process  
Every transaction inside a given block in the blockchain should be verified so that only legitimate users perform the transactions. This is achieved with the help of digital signatures such as elliptic curve digital signatures, which use the concept of asymmetric key cryptography [79]. The digital signature signing and verification process is graphically illustrated in Figure 4.  
Figure 4 makes it clear that a certain user will sign a blockchain transaction using his private key so that other users may access it using the public key provided to the person who executed the transaction. While verification entails decrypting the signed content employing the public key and comparing it to the hash digest of the operation/transaction, signing entails computing the hash value and encrypting it using the private key [80]. A digital signature ensures that only legitimate (valid) users are performing transactions by verifying that the calculated hash of the recipient and the decrypted hash match. Figure 5 depicts the generic transaction process.  
The technique of consensus is implemented to insert operations/transactions onto an existing blockchain. As evident from Figure 5, a transaction is initiated by a transaction request, which has been encrypted with the private key belonging to the sender and contains the sender's and recipient's addresses within the transaction itself. Then, the transaction is published to every network member, such that each user saves the signed transaction locally. The transaction is then verified by every network node by means of the public key assigned to the sender. The transactions are then gathered by a miner who is chosen based on consensus, and they are combined into a block that is put on the blockchain. The fresh block will then be broadcast throughout the network, and, before it is added to the blockchain locally by every single node, it is checked for legitimacy [5].  
Figure 4: The signing and verification process of digital signature.  
Figure 3: The structure of the Merkle tree in a block of a linear blockchain.  
In the existing literature, two types of transactions can be identified based on the blockchain platform: Bitcoin transactions and Ethereum transactions.  
In Bitcoin transactions, the main transaction element is the transaction output that is not spent (UTXO), while the possession of some bitcoin amount is transferred from one address to another. A miner can obtain transaction fees and rewards for block creation using a Coinbase transaction [2].  
Ethereum transactions, on the other hand, have the main transaction element as an account, where transactions directly update the account balance. An Ethereum transaction transfers ether and may also trigger a smart contract. Ethereum has incentives to be provided to block creators in the form of gas points to be provided for transaction fees [81].