esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Improved Multi-Sequence Frequency-Hopping Communication Anti-Jamming System
## 5 Simulation Results and Analysis
### Network Training Parameters  
The training parameters of the model utilized in this paper are illustrated in Table 2. To evaluate the effectiveness of the frequency recognition network in communication scenarios, the jamming-to-signal ratio (JSR) and signal-to-noise ratio (SNR) were both set to 0. The network's training process is illustrated in Figure 6. The results demonstrate that, despite operating in a complex jamming environment and using a small-scale training sample size, the network achieves recognition accuracy that satisfies the requirements for reliable communication.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Image Size & 150 \(\times\) 150 \(\times\) 3 \\ Total Number of Samples & 2560 \\ Test Sample Number & 1280 \\ Training Sample Number & 1280 \\ Loss Function & Categorical \\ Initial Learning Rate & 0.03 \\ Optimizer & Stochastic Gradient Descent \\ Batch Number & 32 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model training parameters.  
Figure 6: Training accuracy function curve.
# Path Planning of Obstacle-Crossing Robot Based on Golden Sine Grey Wolf Optimizer
## 5 Simulation Experiment and Analysis
### Path-Planning Simulation Experiment
#### 5.3.4 Stability Test  
In order to verify the stability of the algorithm for obstacle-crossing robot path planning, the GWO, TGWO, IGWO and GSGWO were tested 30 times on different maps. The average and standard deviation of the test results are shown in Table 6. The data in the table are organized as shown in Figure 8. The comparison shows that the stability of the GSGWO in three map environments is better than the GWO and TGWO and is similar to the IGWO. As the environment becomes more and more complex, the stability of the four algorithms decreases, but the decrease in the GSGWO is the smallest.  
Figure 7: Stepped simulation experimental result.
## Results and Discussion
### Microbiological Analysis  
Microorganisms associated with aquatic species reflect the bacterial population of the surrounding environment and the conditions during manipulation or processing. In newly processed seafood, SSO exist in low numbers and constitute a small fraction of fish microflora. During storage under particular conditions (e.g., specific temperature), SSO grow faster than the remaining microflora, produce metabolites responsible for off-flavors, and finally cause sensory rejection of a product [9; 22]. The most common SSO associated with marine species from temperate waters, stored on ice (0 \({}^{\circ}\)C) under aerobic conditions, are _Shewanella spp_ and _Pseudomonas spp_. They are associated with ammoniacal spoilt and hydrogen sulfide-type odors [13]. The TVCc after the catch varies between 10\({}^{1}\) and 10\({}^{4}\) colony-forming units (cfu)/g or cm\({}^{2}\), while at times of rejection, the values vary between 10\({}^{7}\) and 10\({}^{9}\) cfu/g, according to fish species or fish product [13; 14].  
The microbiological analysis results are presented in Figure 4a. All data shown represent the mean of two different samplings from all individuals tested (\(p<0.01\)). Microbiological analysis of the skin showed an initial TVCc of 10\({}^{3}\) cfu/cm\({}^{2}\), which increased gradually until day 7 (10\({}^{6}\) cfu/cm\({}^{2}\)). The H\({}_{2}\)S-producing microorganisms (that appear as black colonies owing to the precipitation of iron sulfide) presented lower TVCc than in nutrient agar (Nt) and iron agar solid medium (IR) during the first 9 days (10\({}^{3}\)-10\({}^{5}\) cfu/cm\({}^{2}\)). However, after day 9, the plate counts achieved values close to the TVCc in Nt and IR. Using the fitted regressions, the recommended limit values for bacterial counts (10\({}^{7}\)-10\({}^{8}\) cfu/cm\({}^{2}\)) was surpassed after 10 to 11 days, with values of 10\({}^{8}\) cfu/cm\({}^{2}\) for TVCc on Nt and IR, while for H\({}_{2}\)S producers, the plate count was 10\({}^{7}\) cfu/cm\({}^{2}\). Counts of _Enterobacteriaceae_ on MacConKey agar (McA) were very low, never exceeding 10\({}^{2}\) cfu/cm\({}^{2}\) or being negligible. The general hygiene condition of fish samples depends on water quality and handling, which starts on board, and seemed to be acceptable for the samples studied, taking into account the results obtained in this work.  
Figure 3: (**a**) Principal component analysis (PCA) correlation plot describing QI values and individual sensory parameters: skin appearance (S), Odor (O), firmness (F), gill color (GC), gill odor (GO), gill mucus (GM), eye color (EC), eye shape (ES), anus (A) of all GrA samples; (**b**) partial least-squares (PLS) regression model of the QI scores of GrA samples versus the predicted values. Trace lines represent the confidence limits of regression (95%).
## 5 Features and Data Preparation
### Data Processing and Environment Parameters  
The raw data comes from samples taken by Dewesoft's real-time inertial measurement unit (IMU) and subsequently exported at a frequency of 100 (Hz) to be processed through Python. The signals that were extracted for processing were: linear speed in (m/s), linear acceleration (m/s\({}^{2}\)), lateral acceleration (m/s\({}^{2}\)), vertical acceleration (m/s\({}^{2}\)), time (s), distance (m), electric power (w), roll and pitch.  
An example of linear acceleration and linear velocity data over time is shown in Figure 8.  
To synthesize these signals into values that serve as test characteristics, the mean and standard deviation has been calculated. Another compounded variable calculated is the relative positive acceleration (RPA) that is defined as a fundamental measure for the characterization of routes [38]. RPA is a parameter introduced in 1997 by [PERSON] [39] for characterizing vehicles in emission testing cycles of Diesel Internal Combustion Engines.  
In this study, RPA is defined as the sum for a given time delta of the product of the instantaneous speed times the instantaneous positive instant acceleration divided by the traveled distance during the given time interval. This definition is mathematically defined in Equation (1).  
\[\text{RPA}=\frac{1}{\mathcal{X}}\int_{0}^{\text{T}}\text{v}(t)*a^{+}(t)\cdot \text{dt} \tag{1}\]  
where v(t) is the instantaneous velocity at time t, \(a^{+}(t)\) is the instantaneous acceleration of positive magnitude, \(\mathcal{X}\) is the total distance traveled, and T is the total duration. RPA can also be interpreted as a measurement of the total energy required for the instantaneous positive longitudinal accelerations performed by the powertrain while traveling a given distance.  
The jerk is defined as the coefficient of variation between the standard deviation and the mean of the derivative of the linear acceleration in time [40]. The calculation method for the jerk is presented in Equation (2).  
Figure 8: Values of velocity and acceleration over time.  
where \(SD_{j}\) is the standard deviation of the jerk, and \(\overline{J}\) is the mean value.
# An Image Hashing-Based Authentication and Secure Group Communication Scheme for IoT-Enabled MANETs
## 5 Results and Analysis for Performance Evaluation
### Computational Cost  
In the authentication scenario, the \(Ni\) forwards the security credentials to the \(\mathrm{GH}\) in the request message to join the specific group. Moreover, the image hashing technique is utilized for node authentication. As shown in Figure 5(a), we computed the computational cost in terms of the number of nodes at the \(\mathrm{GH}\). The results show that the computational cost varies with the number of nodes. We consider the communication cost when the number of nodes is 200. Then, the computational cost of the IHA, ESIH, and SRIH is 36.79 ms, 98.23 ms, and 104.51 ms, respectively. As shown in Figure 5(b), we computed the computational cost in terms of the number of nodes at the NA.  
Figure 5: Cost of failure is shown in (a), and the reliability per group is presented in (b).  
The results show that the computational cost varies with the number of nodes. We consider the communication cost when the number of nodes is 200. Then, the computational cost of the IHA, ESIH, and SRIH is 33.29 ms, 112.04 ms, and 119.61 ms, respectively. The results show that the computational cost of our proposed scheme is lower than that for the SRIH and ESIH, as these schemes also consider the image processing along with image hashing in the node authentication phases. The network administrator forwards the encrypted message with the required security credentials to the GH as per the request. The authentication of the node depends upon the information of the NA.
# Resource Allocation of UAV-Assisted IoT Node Secure Communication System
## 5 Conclusions  
In this paper, we have considered a wireless communication system consisting of a UAV and three IoT nodes equipped with SWIPT technology. The UAV transmits confidential information to the destination node. All nodes could collect energy. The secure  
Figure 5: Optimized power splitting ratio versus the time slot.  
Figure 6: Instantaneous secrecy rate versus the time slot. \(H\) = 5.0 m.  
Figure 7: Average secrecy rate versus iteration.  
transmission of confidential information to the destination node is also considered. Therefore, we apply the PLS method with limitation on the harvested energy and information threshold of the energy nodes to prevent it from decoding information and help it to collect energy.  
The objective function is achieved by the joint optimization of the UAV's trajectory, transmit power, and PSR. Then it is solved by an iterative algorithm based on the BCD and SCA. Numerical simulation results are provided to show the effectiveness of our method. Compared with the baseline mode, the PS mode is more meaningful in an in vivo case, which not only meets the security rate but also optimizes the effective energy distribution of the node.  
The current study focuses on a fixed number of IoT nodes equipped with SWIPT technology and a relatively low height range of UAVs. However, in practical scenarios, the number of IoT nodes and the flight altitude may vary dynamically. Therefore, our future work will further explore the scalability of our system. A variable number of nodes and a wider range of flight altitudes will be taken into consideration. More developing algorithms and protocols that can adapt to the dynamic network topology and optimize resource allocation accordingly will be investigated.  
Conceptualization, methodology, and software, B.M. and D.X.; formal analysis, X.R.; validation, J.L.; data curation, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Guangdong Province under Grant Nos. 2023A1515011420, 2022A1515011830, 2021A1515011842.  
The authors declare no conflicts of interest.
# Underwater Image Mosaic Algorithm Based on Improved Image Registration
## 2 Materials and Methods
### Method of Image Enhancement
#### 2.1.3 Quality Evaluation Index of Image Enhancement Effect  
After mixed method and sigma filtering, to prevent artificial image distortion, the three indexes of mean square error \(\alpha\), peak signal to noise ratio \(\beta\), and one-dimensional image entropy \(\gamma\) are used to test and evaluate the image quality after the process of image enhancement [31; 32; 33]. The calculation formulas are given below.  
\[\alpha=\frac{1}{m\times n}\sum\limits_{0\leq x_{0}^{\prime}<m}\sum\limits_{0 \leq y_{0}^{\prime}<n}\left(e_{x_{0}^{\prime}y_{0}^{\prime}}\ -\ e_{x_{0}^{\prime}y_{0}^{\prime}} ^{\prime}\right)^{2} \tag{5}\]  
\[\beta=10\times\lg\frac{l\times l}{\alpha} \tag{6}\]  
\[\gamma=-\sum\limits_{v=0}^{255}\xi_{v}\log\xi_{v}. \tag{7}\]  
In Equations (5)-(7), \(m\) and \(n\) represent the length and width of the image, respectively, \(e_{x_{0}^{\prime}y_{0}^{\prime}}\) is the pixel value of the point \((x_{0},y_{0})\) in the original image, \(e_{x_{0}^{\prime}y_{0}^{\prime}}^{\prime}\) is the pixel value of the point \((x_{0}^{\prime},y_{0}^{\prime})\) in the enhanced image, \(\xi_{v}\) is the probability of the occurrence for the gray level \(v\) in the image, \(l\) is the maximum gray value of the pixel in the image, and the value of \(l\) is 255.  
Firstly, the mean square error \(\alpha\) is a measure of the difference between the original image and the enhanced image, which can judge the filtering effect. Secondly, the peak signal to noise ratio \(\beta\) is a statistic of the pure error between the original image and the enhanced image, which reflects the denoising effect. Finally, the one-dimensional image entropy \(\gamma\) is a measure of the average uncertainty of the image, which reflects the information of image gray distribution. Thus, the three indexes are appropriate for image quality evaluation after mixed method and sigma filtering. Three indexes will be discussed in Section 3, which can detect the differences between the original image and enhancement image, and the degree of difference determines whether image distortion occurs.
## 4 Experiments and Results
### Datasets  
The experiments were performed using two datasets: the Automated Cardiac Disease Diagnosis Challenge (ACDC) and the Multi-Centre, Multi-Vendor, and Multi-Disease Cardiac Image Segmentation Challenge (M&Ms).  
The ACDC dataset [24] is composed of 150 exams divided into five groups evenly distributed: (1) healthy patients; (2) patients with previous myocardial infarction; (3) dilated cardiomyopathy; (4) hypertrophic cardiomyopathy; and (5) abnormal right ventricle.  
The 150 exams in the ACDC dataset are divided into two sets: training and testing. The first contains 100 exams captured during the end-systole (ES) and end-diastole (ED) phases. This set also includes the ground truth, which delimits the MYO, LVC, and RV  
Figure 6: IRAX-Net: the network proposed for RV segmentation.  
structures in all basal, medial, and apical slices. The training set was used in local tests during the implementation of the proposed method. Finally, the testing set consists of 50 exams without ground truth. Therefore, the generated masks had to be submitted to the challenge's online platform to evaluate the proposed method using this set. All exams contain images of the cardiac cycle in the short-axis view.  
The M&Ms dataset [25] consists of short-axis cine MRI exams of 345 patients who were healthy or affected by the following pathologies: hypertrophic, dilated, and ischemic cardiomyopathies; coronary disease; right ventricular anomalies and myocarditis. All were scanned in clinical centers in three different countries using MRI scanners from four distinct vendors, providing greater image acquisition diversity.  
As in the case of the ACDC dataset, M&Ms contains exams extracted in the ES and ED cardiac phases. Of the total available, only 320 contain the annotations of the LVC, MYO, and RV structures. The dataset with annotations is divided into three sets: 150 patients for training, 34 for validation, and 136 for testing. The slices have width and height dimensions varying between \(196\times 240\) and \(512\times 512\).
## 3 Hierarchical Clause Annotation
### Annotation Framework
#### 3.1.2 Clause Combination  
The main ways in which clauses combine to form sentences are by joining clauses of equal syntactic status (coordination) and subordinate relation (subordination):  
(1) Coordination and coordinator:  
Coordination is an interrelation between clauses that share the same syntactic status and are typically connected by a coordinator such as _and_, _or_, _but_, etc. In addition, coordinators can be correlative structures (e.g., _either...or..._ and _not only...but also..._) or just substituted by comma punctuation.  
Figure 2: Modified version of [PERSON]’s clause hierarchy.  
(2) Subordination, subordinator, and antecedent:  
Subordination occurs in a subordinate clause and a matrix clause that is superordinate to the subordinate clause. Subordination can be cataloged as follows:  
* _Nominative:_ Function as clausal arguments or noun phrases in the matrix clause and can be subdivided into _Subjective_, _Objective_, _Predicative_, and _Appositive_.
* _Relative:_ Define or describe a preceding noun head in the matrix clause.
* _Adverbial:_ Function as a _Condition_, _Concession_, _Reason_, and such for the matrix clause.  
Subordinators are the words that introduce a subordinate clause and indicate a semantic relation between the subordinate clause and its matrix clause, including subordinate conjunctions, relative pronouns, and relative adverbs. Simple subordinators contain a single word, e.g., _that_, _wh_-words, _if_, etc., while complex ones consist of more than one word, e.g., _as if_, _so that_, _even though_, etc. Antecedents are nouns or pronouns modified before relative clauses and nouns explained before appositive clauses.  
To better explain these HCA definitions, we demonstrate some example sentences, which are segmented into multiple clauses in Table 4.
## 3 Stationary Localization Algorithms
### Centralized Localization Algorithms
#### 3.1.1 Area Localization Scheme (ALS)  
For large scale UWSNs, identifying the exact location of every unknown node may not be feasible. Therefore, in [12], an efficient Area Localization Scheme (ALS) is proposed for UWSNs. ALS was firstly proposed for terrestrial WSNs in [13]. This scheme estimates the position of every unknown node within a certain area rather than its exact location. The main responsibility of anchor nodes is to send out signals with different levels of power to localize unknown nodes. Unknown nodes simply listen to the signals and record the anchor nodes' IDs and their corresponding power levels. Together with collected data, the recorded information is sent to sink node. Sink node is assumed to know the positions of all anchor nodes and their respective transmitted power levels. Therefore, with proper signal propagation algorithms, sink node is able to draw out the map of areas divided by all the anchor nodes' transmitting signals. Then, sink node can localize unknown nodes.  
The advantages of ALS are its being range-free and having no synchronization requirement. Moreover, all complex calculations are handled by the powerful sink node instead of unknown nodes. This reduces energy consumption of unknown nodes and extends the lifetime of network. Localization coverage can be appropriately broadened by adjusting the transmitting power of anchor nodes. However, as a coarse localization scheme, ALS is not convenient for applications that require accurate and instant location information. In addition, ALS incurs high communication overhead and energy consumption. ALS handles localization by assuming that sink node knows the positions of all anchor nodes and their respective transmitted power levels. This assumption reduces flexibility of the network. After deploying the network, it is not possible to change positions of anchor nodes. If the positions of anchor nodes are changed by water currents, the performance of ALS decreased greatly.
# Neural Network-Based Analysis and Its Application to Spectroscopy for Mango
## 2 Equipment and Method
### Experimental Materials and Instrument
#### 2.1.3 Spectral Data Acquisition  
* Spectrometer Parameter Setting  
The spectrometer and light source are first preheated and allowed to stabilize before performing a standard zero correction based on daily light intensity levels to minimize the impact of experimental system instability on results. To mitigate the effects of field-of-view constraints and unclear phase characteristics on spectral data acquisition, an intensity calibration algorithm is employed after analyzing the subject's phase properties to enhance data accuracy [14]. Three detection points are chosen on the smooth equatorial region of the mango sample to ensure precise alignment with the probe at each measurement point [15]. Employing the OceanView software integral to the system, the parameters are configured as follows: integration time is set to 4 milliseconds, average frequency to 120, smoothing frequency to 4, and the spectrometer is equipped with electronic dark noise correction (EDC).(OceanView v2.0.16. [[https://www.oceaninsight.com/products/software/acquisition-and-analysis/oceanview-70e79976/](https://www.oceaninsight.com/products/software/acquisition-and-analysis/oceanview-70e79976/)]([https://www.oceaninsight.com/products/software/acquisition-and-analysis/oceanview-70e79976/](https://www.oceaninsight.com/products/software/acquisition-and-analysis/oceanview-70e79976/)) (accessed on 2 February 2024)) Three specific points, namely the equator, base, and apex, are targeted for three successive scans using the spectrometer, with the resultant average of the three scans deemed the preliminary spectrum for the experiment.
* Selection of spectral data acquisition point  
Following equipment preheating, the reflectance spectral curves of 95 mango samples are obtained in a controlled environment. The wavelength is plotted on the horizontal axis while spectral absorbance is depicted on the vertical axis. Uniform labeling is applied to mark the equatorial, basal, and apex regions of the mangoes for near-infrared (NIR) spectrum acquisition, as illustrated in Figure 1, with the red box indicating the measurement location.
## 5 Experiments and Results
### Drone Path Planning for PFBs Inspection
#### 5.1.2 Traveling Salesman-Based Exploration  
As implemented in the authors' previous work, the inspection uses a traveling salesman approach where the drone visits all targeted locations and returns to its starting point in an efficient, planned path [1]. The optimized route is calculated through simulated annealing, whose path is given for the drone to follow. Due to constraints of the drone's maximum flight time, a maximum number of five fans would be visited in a single mission for final experimental results. Although there have been successfully planned flights with six or all seven fans, emergency low-voltage landing caused by insufficient battery levels would occur on many of those flights, leading to inconsistencies. For the optimized path, the drone will travel to the vicinity of an expected targeted fan location, search and detect it via Haar-cascade detection, approach to scan its QR code for target verification and calibration, rise to match the elevation of the PFBs, take images of the front of the PFBs, and fly around the fan to take pictures of the back of the PFBs.  
The experiments conducted aimed to assess the performance of drones with varying numbers of turbines, including a damaged turbine (ID 4). Key findings are summarized: With a single turbine (ID 4), the average battery usage per mission around the turbine ranged between 4 and 5%. For two turbines, the highest battery usage was 58%, with an average mission time of 37 s. When three turbines were included, battery usage increased up to 64%. Experiments with four turbines showed a significant battery usage up to 82%. The highest battery usage was recorded with five turbines, reaching 90%. Figure 14 illustrates the battery usage across different experiments. As the number of turbines increases, the battery usage also increases, demonstrating the higher energy demand when multiple turbines are involved in the mission. Figure 15 shows the correlated flight times across these different experiments. It is evident that, as the number of fans increases, the flight time also increases, reflecting the longer duration required to complete missions involving more fans.  
Figure 13: Illustration of the relationship between actual and calculated distance based on object-detection bounding boxes [1].