esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Maritime Cognitive Radio Network and Channel Modeling  
MCRN can be classified into two categories: (i) ship-to-ship/ship-to-shore networks (close to shore); and (ii) ship-to-ship _ad hoc_ networks in deep sea with the support of a satellite communication link (as shown in Figure 1). Each ship is equipped with devices that are capable of performing cognitive radio functions. They regularly sense the radio environment to access the spectrum, which is not used by the PU. In networks in which ships are far from the land, it becomes impossible to access the fusion center by using the industrial, scientific and medical (ISM) band on the land. Therefore, satellite links can be used as an alternate access means to the fusion center. In addition to the terrestrial CR network's spectrum usage, a ship can switch its operating parameters according to the sea state, geographic location and the density of nodes.  
Recently, spectrum measurements in white spaces have been carried out for CR networks on the land. The most common of those are on the television (TV) and cellular bands. On the contrary, investigations are required for the spectrum allocated for maritime communications, because of the environmental differences. Environmental differences include the obvious maritime radio atmosphere, _i.e._, sea motion and antenna model. There is almost no obstacle in the sea, and the sea surface is also flat. These factors actually cause a huge path loss, due to negative interference between the line of sight (LoS) path and the reflected path [2].  
Figure 1: Architecture of a maritime cognitive radio network.
# Video over DSL with LDGM Codes for Interactive Applications
## 4 Findings
### Comparing LDGM with Error Burst Improved LDGM  
This Section seeks to find the improvement were the algorithm of Section 3.2 to be employed. Settings of \(k=80\), and \(n=100\) packets, as recommended in [23] were chosen, as this very small value of \(k\) minimizes latency, whereas \(n\) is chosen for medium redundancy. As before, the parity check matrix was created by the classic stochastic algorithm of [32]. \(w_{c}\) and \(w_{r}\) were again set to three and four respectively. 2000 blocks of packets were sent to assure data confidence.  
Tables 3 and 4 record the worst, best, and average recovery capabilities across 50 different matrices, for LDGM and improved LDGM respectively. Burst lengths of 5 and 10 packets were simulated with the two given PERs. Average recovery capability is better in all cases for improved LDGM, which implies that implementing this algorithm can improve the mean objective video quality.
## 3 Results and Discussion
### Identification of High-Confidence Allergen Protein Families in Mango
(**A**) Proteins predicted as strong allergens (\(n=63\)). Dot size indicates number of allergen hits; color shows % identity to 3D epitope. Several cysteine protease isoforms showed strong matches with kiwi allergen Act d 1. (**B**) Proteins predicted as weak allergens (\(n=185\)) using the same thresholds. To further demonstrate the similarities among the LTP isoforms in the mango genome, we selected two more LTPs (XP_044473132.1 and XP_044473056.1) and performed multiple sequence alignment in addition to a pairwise comparison of the known LTP allergens listed in the WHO/IUIS Allergen Nomenclature Sub-committee database. Our findings demonstrated that mango LTPs exhibited a conserved epitope binding location among plant LTPs and shared >50% amino acid sequence identity with all well-characterized fruit LTPs (Figure 5B). Cross-reactivity among LTPs from botanically distant fruits is well-documented, with shared IgE-binding epitopes and overlapped T-cell reactivity contributing to the clinical phenomenon of LTP syndrome, where sensitization to one LTP (e.g., Pru p 3 from peach) can trigger allergic responses to multiple plant foods [33; 34; 35].
# An Admission-Control-Based Dynamic Query Tree Protocol for Fast Moving RFID Tag Identification
## 6 Simulation Results  
In this section, we first evaluate the performance of ACDQT in a single reading round and compare it with the three most related benchmark works, i.e., the collision window tree (CwT) [5], M-ary collision tree (MCT) [6] and dual prefix probing (DPPS) [7] algorithms. In the literature, CwT, MCT and DPPS are the most recent works that use bit tracking technology. In what follows, we also give the simulation results in conveyor belt systems. The throughput and tag lost ratio of ACDQT are evaluated and compared with the three most related benchmark works that solve the moving tag identification problem, i.e., efficient bit detecting (EBD) [15], dynamic collision tree (DCT) [29] and basic frame-slotted ALOHA (BFSA) [26]. The simulation is conducted based on the system model introduced in Section 3 where a single reader is mount above the conveyor belt. Tags are assumed equally spaced in the conveyor belt and move at a constant speed. Each tag has a 96-bit unique identifier, and the tag IDs are uniformly distributed. Moreover, similar to most works in the literature [29; 30; 31; 32], we do not consider transmission errors and capture effect during the identification process since they have a similar impact on the comparable protocols. We use MATLAB R2019a to perform the simulation, and each simulation result is averaged from 100 tests. Table 6 lists the values of some parameters used in the simulation. Note that \(L_{cmd}\) is the length of the \(Query\) command, which varies in different algorithms.
# Energy Consumption Framework and Analysis of Post-Quantum Key-Generation on Embedded Devices
## 4 Methodology
### Experimental Procedure  
A structured approach allowed for remote control and automated data collection, enhancing the efficiency and reproducibility of the energy benchmarking experiments. The experiments proceeded through the following stages, initiated and controlled via network communication (see also (Appendix A) for a practical example):  
* **Initialisation and Preparation (Triggered by \"GETREADY\" message):*
* The Raspberry Pi, acting as the control client, sent a \"GETREADY\" message to the Windows data acquisition server. This message included experimental parameters (e.g., test duration, sampling frequency, experiment identifier, etc.). After receiving the \"GETREADY\" message, the Windows machine
* Parsed the experimental parameters.
* Initialised the data logging system, preparing the output file(s) with appropriate headers based on the received parameters.
* Transitioned the TC66C meter to the'ready' state, awaiting the \"START\" command.
* **Data Acquisition (Triggered by \"START\" message):** Once the process on the Raspberry Pi was ready for benchmarking (i.e., after the CPU clock had been hard set and the fans had time to spin up and settle), and immediately prior to starting the experiment, a \"START\" message was sent to the Windows machine. The Windows machine then  
- Initiated data collection from the USB tester at the specified sample rate.  
- Continuously recorded the measured electrical parameters (voltage, current, power, energy, etc.) along with timestamps to the designated output file(s).  
- Simultaneously, a subset of the data were displayed on the Windows screen for real-time monitoring by the operating user (on the Windows machines, so not impacting the DUT).  
* **Termination (Triggered by \"STOP\" message):** At completion of the benchmarking process on the Raspberry Pi, it sent a 'STOP' message to the Windows machine. The Windows machine next  
- Ceesed data acquisition from the USB tester.  
- Closed the output file(s), ensuring all data were saved.  
- Cleanly terminated the data logging application and network server thread.
# YOLO Model-Based Eye Movement Detection During Closed-Eye State
## 3 Experiments and Results
### Model Comparison  
We began by creating the dataset's configuration file (data.yaml), which specifies essential information such as the dataset's path, category names, and the number of categories. This ensures that the model can correctly load and process the data.  
The experiments were conducted using PyTorch 1.8 framework, Python 3.8, an ultrality-ics library, and the Ubuntu 20.04 operating system, with CUDA 11.1 for GPU acceleration. The hardware setup included an Intel(R) Xeon(R) CPU E5-2680 v4 @ 2.40 GHz processor and a 24 GB NVIDIA GeForce RTX 3090 GPU (NVIDIA, Santa Clara, CA, USA). Training and validation images were resized to 640 \(\times\) 640, with a batch size of 32. Models were trained for 300 epochs to ensure comprehensive learning of the dataset features.  
To determine the optimal model, we conducted experiments with multiple versions of YOLO (v5, v8, v9, and v10) and evaluated different sizes of the YOLO v11 model (YOLO11n, YOLO11s, YOLO11m, YOLO11l, and YOLO11x, where the model size follows n \(<\) s \(<\) m \(<\) 1 \(<\) x). By training each model on data from both the training and validation sets, we derived the optimal weights for each model tailored to eye images. These optimized model weights were subsequently utilized to perform experiments on the test set.  
The performance of each trained model on the test set is summarized in Table 2, encompassing key evaluation metrics, including precision, recall, F1 score, average precision, and inference time. Both the confidence score threshold and the IoU threshold were consistently set to 0.5 for all tests. The test results demonstrate that YOLO11 achieves the highest AP0.5 score among different versions with the same model size. Within the YOLO11 series, the 11m attains the best AP0.5 performance. Meanwhile, the model achieves an inference time of 18.7 ms, enabling real-time processing capabilities. This is particularly suitable for our acquisition system, which captures video at a rate of 30 frames per second.
## 3 Results and Discussion
### Antenna Design Overview
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Parameter**} & **Dimension (mm)** & \multirow{2}{*}{**Parameter**} & **Dimension (mm)** & \multirow{2}{*}{**Parameter**} & **Dimension (mm)** \\ \hline a & 4.75 & b & 1.25 & c & 1 \\ \hline d & 1 & e & 1 & f & 0.5 \\ \hline g & 0.7 & h & 2.75 & bw & 1.25 \\ \hline bl & 7 & yw & 15 & yl & 14 \\ \hline dw & 30 & dl & 28 & ykw & 1 \\ \hline ykl & 7 & & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Antenna dimensions. Figure 1: Antenna design: (**a**) dielectric layer and patch; (**b**) ground layer. The graph also illustrates the antenna's usable bandwidth, which is determined by the frequency ranges where S\({}_{11}\) remains below the \(-10\) dB threshold. The 26.6- and 38.6-GHz bands exhibit sufficient bandwidth, confirming that the antenna operates effectively within these ranges with minimal reflection losses. Figure 2 further confirms that the design adjustments, including patch sizing and geometric optimizations, resulted in strong impedance matching and high performance at both target frequencies. This confirms the antenna's dual-band functionality. The extensive parametric analysis demonstrates the successful optimization of the antenna for both gain and impedance matching.
## 3 Methodology
### Experimental Model  
The experimental model consists of 20 wireless nodes represented by circles. These nodes form an IoT-enabled network-based manufacturing system. Node \"0\" is designated as the source node, while node \"12\" serves as the destination node. Node \"4\" is configured as the BHA node, as depicted in Figure 13. All nodes in the system are mobile nodes. The system uses the network animation (NAM) tool of NS2 to visualize the movement of nodes [52]. As shown in Table 4, it uses the AODV protocol to simulate the considered networks [32]. Moreover, the two-ray ground wireless propagation model is used, which considers the line-of-sight as well as the reflected signals during simulation [53]. Considering the random-way point model, a simulation area of 1100 \(\times\) 1100 m\({}^{2}\) is used. Additionally, it employs the NTRUEncrypt technique to address the BHA threat. The system conducts three independent simulation scenarios, including a healthy network simulation, a black  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Description** \\ \hline Platform & Ubuntu \\ \hline Simulator & NS-2.35 \\ \hline Routing Protocol & AODV \\ \hline Channel & Wireless Channel \\ \hline Wireless Propagation Model & Two-Ray Ground \\ \hline Area & 1100 \(\times\) 1100 m\({}^{2}\) \\ \hline Simulation Time & 200 s \\ \hline MAC Type & Mac/802\_11 \\ \hline Number of Mobile Nodes & 20 \\ \hline Node speed & 10 m/s \\ \hline Bandwidth & 3 Mbps \\ \hline Packet size & 512 bytes \\ \hline Number of Malicious Nodes & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation parameters.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Protocol** & **Phase** & **Step** & **Complexity** \\ \hline  & & Initialize the route discovery & \(O(n)\) \\  & & Propagate the route request & \(O(n)\) \\  & & Process the route request & \(O(1)\) \\  & Route Discovery & Construct the route reply & \(O(n)\) \\ AODV & & Propagate the route reply & \(O(n)\) \\  & & Process the route reply & \(O(1)\) \\  & Route maintenance & Propagate route error message & \(O(n)\) \\  & & Process the route error message & \(O(1)\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Computational complexity of the AODV routing protocol.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Protocol** & **Step** & **Complexity** \\ \hline NTRUEncrypt cryptography & Key generation & \(O(nlogy)\) \\ Encryption & Encryption & \(O(nlogy)\) \\  & Decryption & \(O(nlogy)\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Computational complexity of NTRUEncrypt cryptography.  
hole attack network simulation, and an NTRUEncrypt-secured network simulation. It generates the results from the trace file of NS2 via AWK scripts.
###### Abstract  
Sentiment analysis (SA) detects people's opinions from text engaging natural language processing (NLP) techniques. Recent research has shown that deep learning models, i.e., Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Transformer-based provide promising results for recognizing sentiment. Nonetheless, CNN has the advantage of extracting high-level features by using convolutional and max-pooling layers; it cannot efficiently learn a sequence of correlations. At the same time, Bidirectional RNN uses two RNN directions to improve extracting long-term dependencies. However, it cannot extract local features in parallel, and Transformer-based like Bidirectional Encoder Representations from Transformers (BERT) are the computational resources needed to fine-tune, facing an overfitting problem on small datasets. This paper proposes a novel attention-based model that utilizes CNNs with LSTM (named ACL-SA). First, it applies a preprocessor to enhance the data quality and employ term frequency-inverse document frequency (TF-IDF) feature weighting and pre-trained Glove word embedding approaches to extract meaningful information from textual data. In addition, it utilizes CNN's max-pooling to extract contextual features and reduce feature dimensionality. Moreover, it uses an integrated bidirectional LSTM to capture long-term dependencies. Furthermore, it applies the attention mechanism at the CNN's output layer to emphasize each word's attention level. To avoid overfitting, the Guasiannoise and GuasianDropout are adopted as regularization. The model's robustness is evaluated on four English standard datasets, i.e., Sentiment140, US-airline, Sentiment140-MV, SA4A with various performance matrices, and compared efficiency with existing baseline models and approaches. The experiment results show that the proposed method significantly outperforms the state-of-the-art models.  
deep learning; CNN; Bi-LSTM; attention mechanism; social media sentiment analysis; TF-IDF +
Footnote †: 2021: 11, 11255. [[https://doi.org/10.3390/app112311255](https://doi.org/10.3390/app112311255)]([https://doi.org/10.3390/app112311255](https://doi.org/10.3390/app112311255))
## 1 Introduction
### Research Gaps in Previous Research  
Despite extensive research on mmWave antennas, single-element microstrip designs have often been overlooked in favor of array configurations, which are typically preferred for their higher gain and improved return loss. However, this study challenges the prevailing notion that single-element antennas are inherently inferior by demonstrating that a carefully optimized design can achieve performance metrics comparable to and, in some cases, exceeding those of array-based solutions. The proposed antenna delivers superior gain and achieves lower return loss (S\({}_{11}\)), making it a competitive alternative to mmWave antennas.  
A major gap in the existing literature is the scarcity of high-performance single-element microstrip antennas tailored for commercial mmWave frequency bands, such as 28 GHz and 38 GHz, which are crucial for 5G and beyond. Prior studies have predominantly focused on multi-element arrays to meet the stringent performance demands of these bands, often overlooking the potential of well-engineered single-element solutions. This work fills this void by demonstrating that a single-element design can provide high gain and low return loss while maintaining a compact and cost-effective structure when meticulously optimized.  
In addition, this study highlights a frequently neglected factor in mmWave antenna research: the influence of the connectors on the overall performance. The integration of an SMP connector and its impact on key parameters, such as return loss and gain, were systematically investigated. Unlike many studies that either assume negligible connector effects or fail to discuss them altogether, this research provides a detailed analysis, offering valuable insights for practical implementations.  
Furthermore, an extensive parametric analysis was conducted to enhance the robustness of the proposed design against manufacturing variations. By evaluating the sensitivity of each patch edge to the fabrication tolerances, the antenna was fine-tuned to ensure consistent performance. This approach significantly mitigates manufacturing-induced deviations, which is a challenge commonly faced in high-frequency antenna fabrication. The resulting design achieves remarkably low return loss values at the targeted frequencies, reinforcing its superiority over existing single-element antennas in the literature.
# Machine Learning-Based DoS Amplification Attack Detection against Constrained Application Protocol
## 6 Conclusions  
This research defines the amplification attacks that target the CoAP protocol. The main contribution of this work is to extend the state-of-the-art techniques for securing the CoAP against DDoS attacks that are based on a binary classification and focus only on the attacks launched from the CoAP client side to the CoAP server side. Moreover, this research also carries out a multi-classification process for the DoS CoAP amplification attacks. We consider the attacks that happened between a CoAP client against another victim client(s). After categorizing and implementing each of the DoS CoAP amplification attacks, a dataset is created by simulating the attacks to overcome the lack of datasets that contain the DoS CoAP amplification attacks. Then, three machine learning models were tested for detecting and classifying each attack. The proposed model shows an impressive result in detecting and classifying the malware with an accuracy of 99% using the Gradient Boosting Classifier. In the future, implement the man-in-the-middle attack (MITM) spoofing technique is introduced to focus only on the CoAP level packets, while MITM uses the lower layers to initiate the DoS amplification against the CoAP in the application layer level. Moreover, implementing the model in a real environment is recommended to test its efficiency. Extending the dataset and combining it with other DoS attacks that target the CoAP will result in a comprehensive dataset for the research community for practicing and experimenting with different models.  
Conceptualization, S.M.A. and A.A.-M.A.-G.; methodology, M.S.R. and M.R.; software, S.M.A.; validation, S.M.A., A.A.-M.A.-G., and M.S.R.; formal analysis, S.M.A.; investigation, A.A.-M.A.-G., M.S.R., and M.R.; resources, S.M.A.; data curation, S.M.A., A.A.-M.A.-G., and M.R.; writing--original draft preparation, S.M.A.; writing--review and editing, S.M.A. and M.R.; visualization; S.M.A.; funding acquisition, S.M.A.; supervision, A.A.-M.A.-G.; project administration, S.M.A. and M.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The dataset is available upon request.  
The authors gratefully acknowledge the Technical and Vocational Training Corporation (TVTC) for their support in performing this research. Furthermore, we thank the supervisors for all the support and direction provided by them to produce this research. The authors gratefully acknowledge the support provided by the Faculty of Computing and Information Technology (FCIT), King Abdulaziz University (KAU), Jeddah, Saudi Arabia.  
Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the present study. Abbreviations
## 3 Proposed Protocol
### Key Distribution Mechanisms  
In this section, we propose an elliptic curve-based key distribution mechanism for our framework. ECC is well suited for resource-constrained environments, such as UWSNs, due to its ability to provide the same level of security as traditional public key cryptography but with smaller key sizes and faster computations. Our proposed key distribution mechanism involves two steps:  
Key Agreement: In this step, two nodes, say Node A and Node B, agree upon a shared secret key using ECC key agreement protocol. ECC is a well-known key agreement protocol based on the hardness of the elliptic curve discrete logarithm problem. The key agreement protocol consists of the following setup:  
\begin{tabular}{l} Key Agreement Setup \\ \(E_{C}P_{n}CH_{n}\): Public key of Node A \\ \(E_{C}P_{r}CH_{n}\): Public key of Node B \\  Qb: Node B's public key \\  Qa: Node A's public key \\  K: Shared secret key \\  Then, the complex notation for the given scenario can be written as follows: \\  A generates private key a and public key \\ \(E_{C}P_{r}CH_{n}\) \\  a \(\in\) Z, \(E_{C}P_{r}CH_{n}\)= a* G \\  B generates private key b and public key \(E_{C}P_{n}CH_{n}\)n: \\  b \(\in\) Z, \(E_{C}P_{n}CH_{n}\)n = b* G \\  A computes the shared secret key K by multiplying Node B's public key with Node A's private key: \\  Qb = b* G \\  K = a* Qb* G \\  B computes the shared secret key K by multiplying Node A's public key with Node B's private key: \\  Qa = a* G \\  K = b* Qa = a* b* G \\ \hline \hline \end{tabular}  
Key Distribution: Once Node A and Node B have agreed upon a shared secret key K, they use K to encrypt and exchange a session key SK for future communication. The session key SK is generated by a secure random number generator and is encrypted using the Advanced Encryption Standard (AES) algorithm with K as the key. The encrypted session key SK is then sent from Node A to Node B. Node B can decrypt the encrypted session key using K and then use SK for subsequent communication with Node A.  
The key distribution mechanism we propose provides security against various attacks, including eavesdropping and man-in-the-middle attacks, due to the computational hardness of the elliptic curve discrete logarithm problem. where G is the base point on the elliptic curve, * represents point multiplication on the elliptic curve, and AES (K, SK) denotes the encryption of the session key SK using the Advanced Encryption Standard algorithm with K as the key. Our proposed key distribution mechanism using ECC is lightweight and suitable for resource-constrained UWSNs.