esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Defective Features in Cerasus Humilis Fruit Based on Hyperspectral Imaging Technology
## 3 Results and Discussion
### Image Information Detection
#### 3.4.3 Verification of The Defect Feature Identification Algorithm  
To verify the effectiveness of the defect feature identification algorithm seen in Figure 9, image recognition of defective and intact samples is carried out according to the detection algorithm. The typical sample identification diagram of three types of external defects are shown in Figure 10.  
A prediction set of 105 samples with different defect feature types was used to evaluate the above defect feature detection algorithm. Among them, 23 _Cerasus humilis_ fruits with trust spots, 21 _Cerasus humilis_ fruits with cracks, 21 _Cerasus humilis_ fruits with insect damage, and 40 undamaged samples were used. The identification results are shown in Table 5.  
As can be seen in Table 5, a total of 12 samples were undetected; the correct identification rate for all prediction set samples was 88.57%. It is shown that the method was feasible for detecting all types of defective samples of _Cerasus humilis_ fruits. The undetected samples include four samples with rust spots, four samples with cracks, two samples with insect damage, and two undamaged samples. For the four rust spot samples, the possible reason was that the rust spot defects on the samples were too small to be detected. The four undetected crack samples were not found because the cracked regions of the samples were rotten, rather than because the sample cracks were too small to be used in this discriminant algorithm. For insect damage samples, the insect damage area was too small and reflective, so it was not completely identified. Two undamaged samples were mistakenly detected as a rust spot defects; the possible reason was the reflective and uneven colors of the skin of the fruit. The classification accuracy of cracked _Cerasus humilis_ fruits was the lowest. This was because the cracked region in _Cerasus humilis_ fruits was rotten.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multirow{2}{*}{**Defect Types**} & **Sample** & **Detected** & **Accuracy (\%)** \\  & & **Number** & **(Undetected)** & **Accuracy (\%)** \\ \hline \multirow{3}{*}{Defective (\(n=65\))} & Rust Spot & 23 & 19 (4) & 82.61 \\  & Crack & 21 & 17 (4) & 80.95 \\ \cline{1-1}  & Insect Damage & 21 & 19 (2) & 90.48 \\ \cline{1-1}  & Intact & 40 & 38 (2) & 95.00 \\ \cline{1-1} Total & 4 & 105 & 93 (12) & 88.57 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Test results by applying the developed algorithm to 105 independent samples with different defect types.  
Figure 8: The first eight PCs grayscale image of defect feature of _Cerasus humilis_ fruit samples: (**a**) Crack sample; (**b**) Insect damage sample; (**c**) Rust spot sample.
# Digital Signal Processing of the Inharmonic Complex Tone
## 2 DSP Algorithms Used for the Analysis of String Instruments' Recordings
### Digital Filter Bank
However, the same structure of the filter bank can be used for different analysis scenarios, including octave bank design or uniform bank design. The overall computational complexity depends on the order of the prototype filter and the number of channels. In the current form of the application, for each additional channel, an additional filter pair is required, which is obtained from the prototype filter by applying transformations (7) and (8). The number of channels used in the testing phase of the design was up to 90, with stop-band attenuations of up to 80 dB and \(\omega_{p0}\) in the range [0.45 0.495]. Due to the inherent properties of the Butterworth filter, its applicability is limited to stop-band attenuation of 60 dB and \(\omega_{p0}\) up to 0.45. The proposed design of the filter bank is suitable for analyzing a single tone of a harmonic musical signal, with or without noticeable inharmonicity, and for different numbers of prominent partials and fundamental frequencies within a wide range.
# VEPO-S2S: A VESsel Portrait Oriented Trajectory Prediction Model Based on S2S Framework
## 5 Experiments
### Experiment Settings  
#### 5.1.1 Dataset  
We adopted AIS data ([[https://marinecadastre.gov/accessais/](https://marinecadastre.gov/accessais/)]([https://marinecadastre.gov/accessais/](https://marinecadastre.gov/accessais/))) (accessed on 1 June 2024) from the coastal waters of the southeastern and southwestern United States for training, validation, and testing [13]. As shown in Table 1, there are 68 types of vessels and 28,645 vessels in this dataset. At the same time, due to the particularity of the portrait construction (as mentioned in Section 4.2.2), we selected the vessel attributes (including the length, width, draft, and type). After processing, we obtained 45 types with a total of 6194 vessels. The detailed distribution of the vessel types is shown in Figure 8. In our dataset,  
Figure 7: The Multi-head Decoder Component consists of two GRU blocks and a fusion unit. It can flexibly adjust the weighting between the trajectory information and the vessel characteristics during the prediction process.  
the maximum number of ship types was 45, consisting of passenger ships, pleasure crafts, sailing ships, etc. The distribution of ship types was uneven, and they were mainly classified into five categories: passenger ships, pleasure craft, sailing ships, tug tow, and fishing. Passenger ships and pleasure craft together accounted for over 23% of the total. The remaining ships, mainly consisting of cargo ships, container ships, and tankers, collectively constitute 21% of the dataset. To enhance the richness and comprehensiveness of the data, we employed a sliding window approach, dividing the normalized data into groups of 15 trajectory points with a sliding step of 1 (see Figure 9). Subsequently, each vessel was allocated to training, validation, and testing sets in a ratio of 8:1:1. This approach increased the diversity of the data and ensured that each vessel received sufficient validation.
# Joint Location-Allocation Model for Multi-Level Maintenance Service Network in Agriculture
## 5 Case Study  
In this section, a real case in East China is conducted to demonstrate the efficiency of the proposed mathematical model and solution algorithm. The real maintenance data in three provinces, e.g., Shandong, Anhui, and Jiangsu, is shown first and the optimal location-allocation plan is then obtained in the next part. Finally, several analyses are conducted to verify the application and performance of the developed mathematical model and solution approach.
# A Novel Connectivity-Based LEACH-MEEC Routing Protocol for Mobile Wireless Sensor Network
## 4 Simulation Results and Discussion
### Results Discussion  
The results of LEACH-MEEC were compared with LEACH-MAE, Mobile-LEACH and LEACHDistance-M using the parameters mentioned above.
# Machine Learning Techniques to Detect a DDoS Attack in SDN: A Systematic Review
## 8 Conclusions and Future Directions
* **ML/DL models rely on dynamically and frequent updating:** models that can be dynamically and routinely updated in accordance with new types of attacks are a necessity due to constant and rapid changes in attack patterns, and is a crucial element in today's world of quickly developing new technologies that carry with them more sophisticated threats. However, no such DL models are available in the literature. * **Requirement for lightweight ML/DL models:** lightweight models are necessary for networks such as the Internet of Things, MANETS, and wireless sensor networks, as these have limited computational power and memory and are highly susceptible to security threats. In the future, it is expected to become increasingly necessary to develop effective and portable DL models for these contexts. * **Need for appropriate datasets:** the current datasets lack diversity in terms of the types of attacks and quality of the data recordings they contain, leading to biased detection systems that are unable to identify all types of attacks. It is essential to have sufficient datasets in order to ensure accurate and effective detection models. To close, addressing these areas of research is important in order to realize significant advances in this field and bridge the gaps that currently exist in the literature. Conceptualization, T.E.A. and Y.-W.C.; Methodology, T.E.A. and Y.-W.C.; Software, T.E.A. and Y.-W.C.; Validation, T.E.A. and Y.-W.C.; Resources, T.E.A. and Y.-W.C.; Writing--original draft, T.E.A. ; Supervision, Y.-W.C. and S.M. All authors have read and agreed to the published version of the manuscript. This research was supported by Publication Fund under Research Creativity and Management Office, Universiti Sains Malaysia and Universiti Sains Malaysia (USM) external grant (Grant Number: 304/PNAV/650958/U154). This article does not contain any studies involving human participants or animals performed by any of the authors. Informed Consent Statement: Informed consent was obtained from all individual participants included in the study. The authors declare that they have no conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} AE & Auto Encoder \\ ANN & Artificial Neural Network \\ BOW & Bag of Word \\ CIC & Canadian Institute of Cyber security \\ CL & Convolutional Layer \\ CNN & Convolutional Neural Network \\ DDoS & Distributed Denial of Service \\ DL & Deep Learning \\ DNN & Deep Neural Network \\ DoS & Denial of Service \\ DT & Decision Tree \\ FNR & False Negative Rate \\ FPR & False Positive Rate \\ GPU & Graphics Processing Unit \\ IDS & Intrusion Detection System \\ IoT & Internet of Things \\ IP & Internet Protocol \\ KNN & k-Nearest Neighbours \\ LR & Logistic Regression \\ LSTM & Long short-term memory \\ \end{tabular}
# Entity Relationship Extraction Based on a Multi-Neural Network Cooperation Model
## 3 Proposed Method
### Learning Rate Optimization Based on Cosine Annealing Algorithm  
In deep learning technology, the gradient descent algorithm is generally used to optimize the objective function during the operation of the model. When the loss function is at the minimum value, the learning rate should be adjusted to minimize it, to prevent excessive parameter update and overfitting. Especially under the condition of a small balanced sample, the effect is better and more obvious.  
The current mainstream methods include the Genetic Algorithm, Particle Swarm Optimization, the Differential Evolution algorithm, the Exponential Decay algorithm, and the Cosine Annealing algorithm. The Genetic Algorithm, Particle Swarm Optimization, Differential Evolution algorithm, and Exponential Decay algorithm are prone to premature convergence and poor convergence ability for high-dimensional complex problems of multi-neural network cooperation [34; 35], especially the Particle Swarm Optimization algorithm, which is very prone to premature convergence [36]. Therefore, this paper selected the Cosine Annealing algorithm [37] to optimize the learning rate based on the fusion optimization of the feature layer, and the effectiveness of the method for the results was verified by experiments.  
Based on this, the cosine annealing algorithm was adopted to make the learning rate change according to the law of the cosine cycle, In a cosine cycle, the learning rate slows down first and accelerates the decline. In each cycle, the cosine annealing of the learning rate is carried out according to the following formula, where lrmax is the highest learning rate initially set, lrmin is the lowest learning rate initially set, Tcur is the number of iterations epochs since the last restart, and T is the total number of iterations epochs contained in a cycle.  
\[\mathrm{lr}=\mathrm{lr}_{\mathrm{min}}+\frac{1}{2}(\mathrm{lr}_{\mathrm{max}}+ \mathrm{lr}_{\mathrm{min}})\bigg{(}1+\cos(\frac{\mathrm{T}_{\mathrm{cur}}}{ \mathrm{T}})\pi\bigg{)} \tag{12}\]  
This model is the optimal multi-neural network cooperation model found through a large number of experimental processes for small sample conditions. The main differences from existing models are reflected in two aspects: firstly, the various layers in the multi-neural network are different, similar to the existing BBF (BERT-BiGRU-FC) model, but the experimental effect of RBF is stronger than the BBF model. Secondly, there are differences in the optimization methods used. This paper innovatively uses the cosine annealingalgorithm to optimize the multi-neural network cooperation model, and the experimental results show that the optimization effect is effective.
# Machine Learning-Based Model for Emergency Department Disposition at a Public Hospital
## 3 Materials and Methods
### Machine Learning Models
#### 3.4.2 Adaptive Boosting (AdaBoost)  
AdaBoost is an ensemble learning algorithm that combines multiple weak learners to create a strong learner. It iteratively trains weak models, adjusting misclassification weights to focus on challenging examples by assigning higher weights to misclassifications. Final predictions are made by a weighted majority vote [30].
# Cooperative Three-View Imaging Optical Coherence Tomography for Intraoperative Vascular Evaluation
## Materials and Methods
### Mechanical Design and Fabrication  
The mechanical design of the holder to accommodate the designed optical setup is shown in Figure 5A. It contains four parts: a lens tube, an adjustable ring, a scanning head and a micro stage. Figure 5B is the cross-sectional view of the holder. Achromatic doublets, customized waveplates, and the corresponding retaining fixtures were encapsulated inside the tube. A pair of 5 mm \(\times\) 5 mm mirrors were mounted symmetrically on a 45\({}^{\circ}\) inner wall of the scanning head. The lens tube was fixed with the galvanometer scanner through a cage system and connected with the scanning head through a groove on its bottom. The outside of the lens tube was equipped with an adjustable ring with an internal thread, which can be used to control the working distance.  
To avoid the imaging instability caused by the actual operation tremble, we designed a micro stage with a diameter of 31 mm coupled with the scanning head. As shown in Figure 5D, the middle protruding part of the micro stage has a tilt angle of 3\({}^{\circ}\) with a groove to accommodate vessel. That 3\({}^{\circ}\) tilt angle can serve as Doppler angle for future velocity measurement. The assembled sample arm is shown in Figure 5C mounted on a cage system. All the structure components and the enclosure shown in Figure 5A were made of aluminum, which had a weight of 210 g and a size of 131 mm \(\times\) 38 mm \(\times\) 38 mm.  
Figure 4: Spot diagrams of the sample arm on the image plane spanning a 12.8\({}^{\circ}\) X-FOV and a 2.4\({}^{\circ}\) Y-FOV for 1260 nm (blue), 1310 nm (green) and 1360 nm (red) light.
# Blind Recognition of Forward Error Correction Codes Based on a Depth Distribution Algorithm
## 7 Simulation Results  
In this section, the effectiveness of the error correction coding type recognition analysis algorithm based on the depth distribution characteristics of the codeword matrix is verified through simulation experiments. In the simulation results, it is assumed that the communication system adopts a QPSK debugging mode, the signal transmission channel is the AWGN channel, and the signal-to-noise ratio is expressed by the bit error rate BER.  
The simulation results listed in this section mainly include two aspects. Firstly, the code length estimation method based on the Gaussian elimination method and the code word starting point moment estimation method are simulated and verified. Secondly, the depth distribution characteristics of the codeword matrix of linear block codes, convolutional codes and Turbo codes are simulated and verified.
# Error Recovery in the Time-Triggered Paradigm with FTT-CAN
## 2 Background and Related Work
The paper includes a test case where the mechanism is applied to a critical message with 8 bytes payload, a period of 100 ms and an intended transmission reliability of \(10^{-9}\) errors per hour and using an Aggressive environment with BER equal to 2.6 \(\times\)\(10^{-7}\), showing that using the windowed method the intended reliability level is obtained with a window 26.4% shorter than using multiple copies, which in this case, implies the transmission of four copies. The implementation of this mechanism implies building specific hardware (FPGA based) for the nodes and is still inflexible in what concerns modifying the message set dynamically, as the schedule is built offline. In the scope of FTT-CAN, slack time can be placed in the synchronous window to allow the automatic retransmission of messages affected by errors [19]. This approach is more bandwidth efficient than the previous ones, since the extra-time allocated in the synchronous window is shared by all the TT messages sent in the current EC. The amount of slack time is dependent on the maximum number of errors considered in each EC and is always wasted when there are no errors, thus limiting the efficiency of the approach. Nevertheless, this approach allows for the recovery in the same cycle where errors occurred. In this paper, we use a novel approach, which is based on a server that allocates dynamically bandwidth for retransmissions. The server parameterization uses a Poisson-based fault-model, allowing to guarantee a timely recovery of errors up to a given desired probability target. As opposed to previous works, in our proposal bandwidth is consumed only when errors do actually happen and the interference in the remaining TT traffic is strictly bounded by the server parameters, thus boosting the bandwidth efficiency.
## 5 Data Analyses and Results  
The data analyses consisted of two main phases: image pre-processing, which was performed to prepare the image pairs for processing; and the DIC data analyses, which were performed to infer information on the ground deformation and for statistical analyses of the accuracy of the measurement method.
## 4 Experiment
### Experiment Setup  
In the experiment, the F1-score used by most relational classification networks was used as the evaluation index of the model. The F1-score can evaluate the model more comprehensively by considering the two indicators of precision and recall. The calculation formulas of the precision rate (precision), recall rate (recall), and F1-score are shown in Equations (21)-(23).  
\[precision=\frac{TP}{TP+FP} \tag{21}\]  
\[recall=\frac{TP}{TP+FN} \tag{22}\]  
\[F1=\frac{2*precision*recall}{precision+recall} \tag{23}\]  
where _TP_ is the number of correct samples predicted to be correct, _FP_ is the number of wrong samples that are predicted as correct samples, _FN_ is the number of correct samples predicted as wrong samples, and _TN_ is the number of wrong samples that are predicted to be wrong. Table 2 introduces the relevant environmental information and related parameter settings used in the experiment.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameter Name**} & \multicolumn{3}{c}{**Parameter Value**} \\ \cline{2-4}  & **Standard Text Dataset** & **SemEval-2010 Task 8** & **KBP37** \\ \hline GPU & NVIDIA-A100 & NVIDIA-A100 & NVIDIA-A100 \\ Programming language & Python3.9 & Python3.9 & Python3.9 \\ Deep learning framework & PyTorch1.9 & PyTorch1.9 & PyTorch1.9 \\ BERT Version & Bert-large & Bert-large & Bert-large \\ Max sentence length & 384 & 384 & 384 \\ Learning rate & \(1\times 10^{-5}\) & \(7\times 10^{-6}\) & \(2\times 10^{-5}\) \\ Number of epochs & 30 & 30 & 20 \\ Dropout rate & 0.2 & 0.1 & 0.1 \\ Size of mini batch (train) & 16 & 16 & 23 \\ Size of mini batch (test) & 32 & 32 & 46 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Relevant environmental information and related parameter settings used in the experiment.  
Figure 6: KBP37 dataset relationship category and quantity introduction.
## 3 Results
### Throughput for Fixed Modulation--With Interference  
Implementing the same fixed QPSK modulation without channel coding in a hypothetical interference scenario, where the INR couples with the elevation angle of the receiving antenna, we obtain the following results presented in Table 4.  
Note that the throughput 30.988 Gbits is below the minimum for successful image transmission (36.922 Gbits). In this case, we would observe failures in image processing.
## 4 Recognition Infrastructure
### Training and Recognition  
Object recognition systems usually consist of a training stage and a recognition stage. Training is performed prior to recognition in order to learn appropriate object models. The right side of Listing 1 shows that each recognition setting is configured in its own paragraph. The _name_, the used _input features_ and _descriptor types_, the _recognition strategy_, and the used _distance measures_ are specified. Furthermore, it is possible to specify certain post-processing steps, as shown for object regions in Line 19 where the convex hull is used to cluster individual feature matches to a combined region, as described in Section 3.2.  
During training and recognition, all modules of a visual feature extraction graph are executed in the calculated order. Thereby, the required inputs are selected for each module independently from the IO-set with respect to the specified input filters. After a module was executed with the generate-descriptors method, its output is again stored in this IO-set. Finally, the specified visual features of each recognition approach are selected from the IO-set and returned under the given name. These visual features can be further used to learn new object models, for example for machine learning strategies. As mentioned before, this model learning step is not necessary for feature matching strategies. Instead, post-processing modules might be used to transform the generated feature matches to an object model, for instance with a geometric verification as proposed in [67].  
**Recognition Strategies:** CORI supports three different types of recognition strategies: feature matching, combined matching, and machine learning approaches. Simple feature matching strategies are nearest neighbor, k-nearest neighbor, or thresholding for one input feature type using one distance measure. The base class of all recognition strategies is shown in the left side of Figure 8. Matching strategies are implemented independently of the used distance measures and the same code is used to compute feature matches with different distance measures for different feature types. Furthermore, it is possible to extend CORI by both new strategies and distance measures, without the need to change existing code. The clue behind this functionality is the distance class that contains functions to compute the distance between two features of the same type. New distance measures have to be derived from this base class and it is necessary to overwrite the distance functions of those feature types that they  
Figure 8: Class diagram of recognition strategies.  
support. Matching strategies contain one or more distances that are used with the current descriptor type. Combined matching strategies contain several matching instances that are executed one after another in the recognition process and combined afterwards. In contrast to feature matching, machine learning approaches are similarly implemented as modules for visual feature extraction and, in fact, they are also derived from the module class of Figure 7.