esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Principle of Vortex Bladeless Wind Turbines
### The Need for System Resonance Tuning  
The wind velocity must fall inside the vortex frequency lock-in range in order to yield the most energy from the structural resonance. However, depending on external circumstances, the wind velocity can vary widely and easily go beyond the lock-in range. The overall power conversion efficiency is drastically reduced or even stopped if the wind speed is outside the lock-in range for the BWT.  
For example, when the wind speed is at the level of \(v_{1}\), it closely matches the resonant frequency of the BWT structure (\(f_{BWT,0}\)) and forces the BWT to vibrate at its highest level, as described in Figure 2. When the wind speed increases to \(v_{2}\), it is out of the initial lock-in range. Thus, BWT vibration decreases, consequently reducing energy generation, as shown by the red-colored area of the curve in Figure 2.  
If the BWT can adaptively adjust the resonant frequency of the structure (\(f_{BWT,0}+\Delta f_{BWT}\)) to match the increased wind speed \(v_{2}\), the BWT can be in the resonance state again. For such a case, the vibration level would be gained through resonance tuning to obtain higher energy generation efficiency. If the structural resonance can be shifted according to the input wind speed with the different stiffness modes, the vibration gain due to the resonance would be highly maintained, as shown by the dotted curve in Figure 2.  
Several solutions have been developed to improve the overall working efficiency of BWTs by incorporating a resonance-tuning system capable of expanding the lock-in range. Accordingly, a permanent magnet-based tuning device is applied in a BWT by [PERSON] et al. [4]. As the wind speed increases, the vibration amplitude of the BWT also increases, which draws the two magnets of the same poles closer together. This acts as a compression spring, which exerts a strong repulsive force on the two magnets. [PERSON] et al. [26] proposed another similar resonance-tuning system using magnetic repulsion force, which varies the relative position of the two magnets for resonance tuning. A different approach is a self-tuning piezoelectric system [27] that uses centrifugal force and drag to adjust the effective beam length in order to tune the resonance of the device.  
Previous studies have adopted resonance-tuning systems based on magnetic repulsion or piezoelectric systems. On the other hand, this work proposes a resonance-tuning system with a different approach that utilizes a smart elastomer that can vary its stiffness in response to a magnetic field.
## 4 Case Study A: LED Array MIMO Multiplexing
### Receiver  
**Stage 1. Preprocess received transmission:** After detecting and localizing the ARUCO markers, each of the four LEDs is cropped into separate frames to be used as input to the DNN model for classifying the LED state. Each of the classifications are stored separately, and  
Figure 5: A \(2\times 2\) LED—Camera Communication MIMO Setup.  
Figure 6: Packet Structure.  
noise and outliers are removed. Then, we need to find the first occurrence of a bit equal to one in the predicted list for each LED, and all the previous bits in the predicted list are removed.  
**Stage 2. Process packet metadata for each LED:** From the pre-processed data, the Barker index needs discovered and parsed with classified packets in each LED List. Then, Packet Number bits are checked along with CRC remainder calculation to verify transmission correctness. The parsed data bytes from the packet are stored in memory.  
**Stage 3: Data demodulation:** In this final demodulation step, data bytes are combined from each LED list in order and compared with every 6 bytes of ground truth. Every byte error in every packet is counted and reported as packets with the Barker code Error, Packet Number Error, Data Error and CRC Errors. Finally, the starting and ending markers are removed to convert every 6 bytes of data to ASCII representation.
## 2 Results and Discussion
### Headspace SPME-GC/MS Analysis of VOCs
The increasing tendency of 1-pentanol, 1-hexanol, 1-octen-3-ol, 1-heptanol and 1-octanol was observed in tuna samples during storage, which showed a similar result, and its increase was  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**VOCs** & \multicolumn{4}{c}{**Relative Concentration (Area 10\({}^{-6}\))**} \\ \hline  & **Day 0** & **Day 3–0** & **Day 6–0** & **Day 3–4** & **Day 6–4** \\ \hline Aldehydes & & & & & \\ Hexanal & 8.61 \(\pm\) 0.12  & 10.63 \(\pm\) 0.64  & 31.39 \(\pm\) 0.69  & 23.72 \(\pm\) 0.02  & 35.56 \(\pm\) 2.5  \\ Heptanal & 5.74 \(\pm\) 0.12  & 1.12 \(\pm\) 0.05  & 0.51 \(\pm\) 0.14  & 28.87 \(\pm\) 0.38  \\ Octanal & 1.35 \(\pm\) 0.01  & 3.83 \(\pm\) 0.01  & 23.36 \(\pm\) 0.06  & 0.02 \(\pm\) 0.00  & 18.04 \(\pm\) 0.27  \\ Nonanal & 1.21 \(\pm\) 0.04  & 9.98 \(\pm\) 0.10  & 1.08 \(\pm\) 0.03  & 0.02 \(\pm\) 0.00  & 17.62 \(\pm\) 0.31  \\
2,6-Nonadien/(_E,Z_)- & - & 0.01 \(\pm\) 0.00  & 0.02 \(\pm\) 0.00  & 6.54 \(\pm\) 0.04  \\ Tetadecan & - & 0.01 \(\pm\) 0.00  & 0.01 \(\pm\) 0.00  & 0.02 \(\pm\) 0.00  & 2.95 \(\pm\) 0.05  \\ Propanal & 0.79 \(\pm\) 0.01  & 10.05 \(\pm\) 0.12  & 4.96 \(\pm\) 0.14  & 2.83 \(\pm\) 0.02  & 20.90 \(\pm\) 0.98  \\
4-Heptension, (_Z_)- & - & 0.29 \(\pm\) 0.01  & 0.28 \(\pm\) 0.01  & 0.02 \(\pm\) 0.00  & 5.67 \(\pm\) 0.05  \\ Pentadecanl- & - & 0.01 \(\pm\) 0.00  & 0.50 \(\pm\) 0.02  & 0.02 \(\pm\) 0.00  & 16.18 \(\pm\) 0.20  \\ \hline
**ketones** & & & & & \\ Acetone & 0.04 \(\pm\) 0.01  & 0.29 \(\pm\) 0.01  & 0.58 \(\pm\) 0.01  & 5.23 \(\pm\) 0.36  & 0.13 \(\pm\) 0.00  \\
2-Heptension & - & 0.05 \(\pm\) 0.00  & - & 0.08 \(\pm\) 0.06  & 0.14 \(\pm\) 0.06  \\
2-Nonanone & - & 0.04 \(\pm\) 0.01  & 0.06 \(\pm\) 0.01  & 0.09 \(\pm\) 0.03  & 0.76 \(\pm\) 0.04  \\
2-Undecanone & - & - & 0.93 \(\pm\) 0.26  & - & 0.56 \(\pm\) 0.06  \\
2,3-Pentanedione & 0.05 \(\pm\) 0.00  & 0.04 \(\pm\) 0.00  & 0.08 \(\pm\) 0.01  & 0.09 \(\pm\) 0.00  & 0.02 \(\pm\) 0.00  \\ \hline
**Alcohols** & & & & & \\
1-Pentanol & 0.96 \(\pm\) 0.18  & 11.53 \(\pm\) 0.03  & 10.30 \(\pm\) 0.09  & 23.37 \(\pm\) 0.00  & 69.16 \(\pm\) 2.70  \\
1-Hexanol & 4.03 \(\pm\) 1.51 [FOOTNOTE:5]Footnote 5: footnotemark: [ENDFOOTNOTEpreviously related to derivation from auto-oxidation of unsaturated fatty acids and arachidonic acid by 12-lipoxygenase [29; 30]. Therefore, alcohols are important contributors to off-flavours due to their low odour score [31]. Among aromatic compounds, benzaldehyde were detected because it arose from the degradation of amino acids and because of its pleasant stone fruit and almond nutty aroma [32]. Finally, nonanoic acid, ethyl ester and hexanoic acid increased from the middle stages of storage (day 3) until the end of shelf life (day 6). During seafood spoilage, the accumulation of nonanoic acid, ethyl ester and hexanoic acid have been detected in seafood due to aerobic conditions and high water content accelerating growth and metabolic activity of pseudomonads [29]. The other VOCs detected in bigeye tuna were acids, aromatic compounds and others, which were present at low concentrations.
# Linear Computation Coding: A Framework for Joint Quantization and Computing
## 5 Performance Analysis
### Exponential Aspect Ratio  
The key point to the good performance of the multiplicative matrix decomposition in (6) is the exponential aspect ratio. The number of columns of the codebook matrix, i.e., \(K\), scales exponentially with the number of its rows \(N\). For a linear computation code, we define the code rate as  
\[R=\frac{1}{N}\log_{2}K. \tag{21}\]  
The code rate is a design parameter that, as we will see later on, has some impact on the trade-off between distortion and computational cost.  
The exponential scaling of the aspect ratio is fundamental. This is a consequence of extreme-value statistics of large-dimensional random vectors: Consider the correlation coefficients (inner products normalized by their Euclidean norms) of \(N\)-dimensional real random vectors with IID entries in the limit \(N\rightarrow\infty\). For any set of those vectors whose size is polynomial in \(N\), the squared maximum of all correlation coefficients converges to zero, as \(N\rightarrow\infty\)[46]. Thus, the angle \(\alpha\) in Figure 1 becomes a right angle and the norm of the angle error is lower bounded by the norm of the target vector. However, for an exponentially large set of size \(2^{RN}\) with rate \(R>0\), the limit for \(N\rightarrow\infty\) is strictly positive and given by rate-distortion theory as \(1-4^{-R}\)[47]. The asymptotic squared relative error of approximating a target vector by an optimal real scaling of the best codeword is therefore \(4^{-R}\). The residual error vector can be approximated by another vector of the exponentially large set to get the total squared error down to \(4^{-2R}\). Applying that procedure for \(s\) times, the (squared) error decays exponentially in \(s\). In practice, the scale factor cannot be a real number, but must be quantized. This additional error is illustrated in Figure 1 and labeled distance error as opposed to the previously discussed angle error.
## 3 Methodology  
This work focuses on optimizing bandwidth allocation for 5G networks by employing machine learning models, specifically LSGB and the BRFFNN. Simulations are conducted to study data exchange between UAVs and ground stations, with movement patterns categorized into circular and random. The evaluation is performed using QoS parameters, including the PDR, delay, and throughput, to identify the most effective approach for improving network performance. Figure 1 shows the overall flow of the methodology.  
In this research, we developed a machine learning prediction to simulate and optimize bandwidth allocation in a UAV-GS communication system under 5G constraints. The data used in this study is taken from Kaggle, which provides a dataset of 5G network traffic [22]. It comprises 15 key features, including Signal_Strength, Latency, Required_Bandwidth, Resource_Allocation, and various application-specific traffic types such as Video_Call, Streaming, VoIP_Call, Web_Browsing, and IoT_Temperature. From these, 13 features are used as the state inputs to the two machine learning models deployed to predict the required bandwidth: a LSGB model and a BRFFNN. The GB model uses the LS Boost method, while the FFNN model employs Bayesian regularization. A simulation for data communication between GS and UAV is conducted to assess the network QoS performance. It begins by generating 5G communication channels between the GS and UAVs, followed by simulating the network traffic flow for a specified time using bandwidth ranges defined by 5G standard specifications. Throughout the simulation, three QoS parameters, which are the PDR, throughput, and delay, are calculated for analysis. The simulation continues until the maximum simulation time of 150 s is reached.
## 4 Proposed QL-Driven ABC-Based Solving Approaches
### QL-Based Employed Bees Phase  
RL studies the sequential decision process of interaction between the agent and the environment [75]. Q-learning (QL) is one of the major RL algorithms that allows agents to learn an optimal policy through trial and error in an unknown environment. It does not rely on prior knowledge of the environment's dynamics [76]. The algorithm uses a value function called the \(Q\)-\(function\) to estimate the expected cumulative reward for taking specific actions in certain states and following the optimal policy thereafter.  
During the learning process, the agent explores the environment, taking actions and observing the resulting rewards and next states. These experiences are used to iteratively update the Q-values, which represent the estimated quality of actions in different states. The update process is guided by the Bellman equation, which relates the current Q-value to the expected future Q-values.  
Motivated by the above consideration, this paper introduces the QL principle to the selection of neighborhood structures in order to guide the search process in the employed bees phase of the proposed IQABC algorithms. Employed bees are in charge of searching for new and hopefully better food sources. To this end, in the original ABC algorithm, each employed bee generates one new candidate solution in the neighborhood of the current solution in one food source. The new candidate replaces the old solution if it is better. In this paper, each employed bee is placed on a solution from the population and applies one of the neighborhood structures on it, to generate a new solution. The choice of the neighborhood structure is performed by the application of a QL algorithm. The new solution replaces the current one if its quality is better, otherwise, a non-optimization trials counter, nb_trials, is incremented.
# A Context-Aware Neural Embedding for Function-Level Vulnerability Detection
## 3 Experiments and Evaluation
### Experiment Settings
#### 3.2.3 Settings for Evaluating on Real-World Open Source Projects  
The third group of experiments are to compare our framework with four function-level vulnerability detection systems in terms of their performance on real-world software projects. For comparison, we choose one open-source static code analysis tool which is Flawfinder (version 2.0.7) [37] and three DL-based approaches which are Cross-VD [14], Multi-VD [27], and DeepBalance [38] as the baselines.  
The choice of the baseline systems is due to the fact that Flawfinder was used in many previous studies as a recognized benchmark, such as [5; 6]. It takes a source code file/function as input and outputs a line number, indicating the location of possible security flaws with the level of severity. It also generates warnings, suggestions and flaw types to facilitate code fixes. Flawfinder is a pattern-based code scanner with well-known vulnerable patterns built-in. By comparing the code pattern with the pre-defined vulnerable patterns, the tool raises warnings when the code pattern matches the security flaw pattern.  
Cross-VD [14] builds on a two-layer Bi-LSTM network with Word2 Vec embedding. Their idea is to learn representations from historical real-world vulnerability data sets and used the generated representations from the Bi-LSTM as feature sets for training a random forest classifier. Instead of using source code functions, their work takes abstract syntax trees (ASTs) extracted from source code functions as inputs and uses the generated representations from the Bi-LSTM as feature sets for training a random forest classifier for vulnerable function detection.  
Multi-VD [27] utilizes two Bi-LSTM networks to extract representations from both the artificially constructed vulnerability data source and real-world open-source projects. Therefore, one network is fed with the ASTs extracted from source code functions derived from real-world open-source projects. The other network is fed with the source code of the artificially constructed function samples. Then, the extracted representations from two data sources, which are generated by the two networks, are concatenated and fed to a random forest classifier for further training and testing.  
DeepBalance [38] aims to deal with the data imbalance issue in the scenario of vulnerability detection. The authors also utilize the high-level representations extracted from a Bi-LSTM neural network as features for training and apply a fuzzy oversampling technique to rebalance the training data by generating synthetic samples for the vulnerable class. DeepBalance also takes ASTs as inputs and performs fuzzy-based class rebalancing on AST-based feature representations.  
In this group of experiments, we use the embeddings generated from the proposed framework as feature sets and feed them to conventional ML algorithms: a random forest (RF) algorithm and a support vector machine (SVM) with radial basis function kernel for the classification of potential vulnerabilities.
# Defect R-CNN: A Novel High-Precision Method for CT Image Defect Detection
## 1 Introduction
[29] developed a track defect recognition method based on an enhanced Real-Time Detection Transformer (RT-DETR) model, which improves small target detection by reducing background noise interference; the refined model reaches a mAP of 98.5%, marking a 1.8% improvement over the baseline RT-DETR. Although the aforementioned algorithms perform well in various detection tasks, their performance declines when applied to specialized applications. Existing target detection algorithms for CT images often struggle to accurately detect small targets [30]. Conversely, methods designed for small target detection are ineffective in handling the complex backgrounds and noise interference typical of CT images, often resulting in false positives or missed detections [31]. Compared to one-stage models, two-stage models are more effective in recognizing small defects in complex backgrounds, improving accuracy in high-noise and low-contrast images [32]. Addressing the industrial demand for graphite components' defect detection, this study introduces Defect R-CNN, an improved Mask R-CNN model designed for CT-based detection of diverse defect types. By integrating advanced edge feature extraction and multi-scale fusion, the model enhances the accuracy and robustness of small defect recognition in noisy environments. To objectively evaluate detection performance in such complex scenarios, this study adopts commonly used metrics, including mean average precision (mAP), precision-recall curves (PR curves), and frames per second (FPS). The mAP is calculated separately for bounding box detection and segmentation masks across different defect types, with detailed descriptions provided in the subsequent sections. The structure of this article is organized as follows: First, the baseline Mask R-CNN model and the rationale for its enhancement are briefly introduced. Next, the network architecture of the improved Defect R-CNN and its key modifications are described in detail. A comparison is then made between Defect R-CNN and five other classical defect detection networks, followed by an analysis of the results. Ablation experiments are conducted to further validate the effectiveness of the proposed improvements compared to the baseline Mask R-CNN model. The article concludes with a discussion and summary.
# Initial Estimation of Kinematic Structure of a Robotic Manipulator as an Input for Its Synthesis
## 2 Materials and Methods
### Type C--Achieving a Pose with Common Perpendicular  
This algorithm finds a common perpendicular between the Z axis of the base and the Z axis of the pose. The joints are placed on this perpendicular line, and the last joint is oriented in the direction of the Z axis. Both the position and orientation can be achieved using this approach, as Figure 6 shows.  
At first, a common perpendicular and intersection points \(P\) and \(Q\) are determined between the \(Z_{b}\) and \(Z_{p}\) axes using the script made by [PERSON] [32]. However, his algorithm was not providing good results if the 2 lines were parallel, so we enhanced it and added some functionality to mitigate this issue.  
The next step is to calculate the angle \(\alpha_{sum}\) between those two axes:  
\[\alpha_{sum}=\tan^{-1}\left(\frac{||\vec{a}_{b}\times\vec{a}_{p}||}{\vec{a}_{ b}\cdot\vec{a}_{p}}\right) \tag{31}\]Again, when using a right-handed coordinate system, it is necessary to determine whether the angle is positive or negative. We check the pose \(Z_{i+1}\) axis as a projection in the base \(Y_{i}\) axis:  
\[\alpha_{sum}=\begin{cases}-\alpha_{sum},&\textbf{if }\vec{o_{b}}\cdot\vec{a_{p}} <0\\ \alpha_{sum},&\textbf{otherwise}\end{cases} \tag{32}\]  
\(\alpha_{1..n}\) is the angle between the \(Z\) axes of particular joints:  
\[\alpha_{1..n-1}=\frac{\alpha_{sum}}{n-1} \tag{33}\]  
\[\alpha_{n}=0 \tag{34}\]  
Now, we can calculate the rest of the DH parameters. \(a_{1..n}\) is the distance between the joints. \(l\) is the length of a common perpendicular, and \(n\) is the number of joints:  
\[a_{1..n-1}=\frac{l}{n-1} \tag{35}\]  
\[a_{n}=0 \tag{36}\]  
\(d_{0}\) is the distance from the base coordinate frame to the P-intersection point of the \(\vec{a_{b}}\) and common perpendicular. \(d_{n}\) is the distance from the \(Q\), the intersection point of the \(\vec{a_{p}}\) and a common perpendicular, to the pose coordinate frame. \(d_{n}\) is also the translation of the end-effector from the last joint:  
\[d_{0}=(P-\vec{t_{b}})\cdot\vec{a_{b}} \tag{37}\]  
\[d_{1..n-1}=0 \tag{38}\]  
\[d_{n}=(\vec{t_{p}}-Q)\cdot\vec{a_{p}} \tag{39}\]
## 4 Conclusions  
This work addresses the scheduling and the service policy in relayed multi-hop underwater acoustic networks, so that maximum throughput, minimum delay, and network fairness are achieved. We propose a graphic procedure to determine the optimal case in a STDMA network with very long propagation delays. The network topology is linear and its purpose is sending packets from the network nodes to a sink node, or Gateway, located at one of the line edges.  
The method is based on a 2D space-time lattice and exploits the characteristic long propagation delay of the underwater acoustic channel and gives the optimal scheduling in terms of throughput. Analytic expressions are given to calculate the throughput and the results were verified by simulation. Two service policies, FIFO and Round Robin, have also been considered to analyze network delays and queue lengths. When fairness in terms of delay was taken into account, we observed that, in general, Round Robin is fairer than FIFO policy, while maintaining a similar number of packets that are delivered to the Gateway. An interesting point that came up while assessing the service policies is that fairness in terms of delay is sensitive to the initial condition of packet generation.  
This work has been supported by University of Malaga (Campus de Excelencia Internacional Andalucia Tech).  
[PERSON] and [PERSON] conceived the idea of analyzing a linear network, and to search for the optimal scheduling. [PERSON] found the geometrical method, coded all the Matlab scripts, and performed all the simulations. [PERSON] and [PERSON] jointly wrote the manuscript; [PERSON] and [PERSON] supervised the rigor of the terms used and the mathematical development. [PERSON] and [PERSON] jointly reviewed the final version of the manuscript.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# 3 GPP 5G V2X Error Correction Coding for Various Propagation Environments: A QoS Approach
## 5 Simulation Results
### Impact of Traffic Model on V2X System Model  
From scenarios S1 and S3 for the LOS channel state and scenarios S2 and S5 for the NLOSv channel state, we can extract a quantitative measure of the impact of the traffic model change on the system performance. This measure is illustrated in Figure 3d. From the simulations, we observe that when the model changes from highway to urban, the FER worsens by 120.9% in the case of the LOS channel state and by 12.4% in the case of the NLOSv channel state, concluding that changing the traffic model from highway to urban results in a 66.6% deterioration in system performance on average. We also observe that there is a huge difference between the traffic model change that occurs in the LOS channel state versus the NLOSv channel state. This is because NLOSv performs poorly and cannot be greatly improved by the traffic model, whereas, in the LOS state, the traffic model and the number of scatterers play a decisive role in the performance of the system.