esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 USV System Structure
### Locomotion Module  
The locomotion module was the execution module of our USV. After receiving the control instructions, it drove the USV to perform various actions. Diesel engine, diesel generator, water-jet propeller, fuel tank, and battery constituted the locomotion module. It is worth mentioning that traditional propeller propulsion was replaced by a water-jet propulsion. Better working ability and maneuverability in the shallow water, higher thrust, and lower noise were the obvious advantages of water-jet propulsion technology over propeller propulsion [19; 20]. The locomotion module ensured that the maximum speed of the USV was no less than 13 kn(Knot), the cruising speed was about 10 kn, and the ranges of working speed were 4 to 8 kn. The USV could run for about 5 h at cruising speed and 12 h at working speed.
## 5 Discussion
### Comparison between the Models  
In this study, we hypothesized that an RRI-based deep learning model would offer similar or superior performance in age prediction compared to a conventional HRV feature-based model. We showed that the RRI model outperformed the HRV and combined models in BAC, precision, and F1-score when using hybrid loss with ADASYN, while achieving the highest BAC and F1. Although the prediction of minority classes was not successful, we expected that the performance of minority groups would be improved if the RRI model was trained with balanced data. Our assumption is based on the promising results of the aforementioned previous studies. [PERSON] et al. achieved 70% accuracy in classifying three age groups by using a balanced HRV dataset obtained from the supine rest position [23]. We also calculated BAC, QWK, and F1-score for the model proposed by [PERSON] et al. using the confusion matrices provided in their article. Balanced accuracy was 70%, QWK was 0.55, and F1-score was 0.70, indicating that the prediction was equally successful for all age groups. In addition, we expect that the prediction of numeric ages would result in better performance than the classification of age groups because misclassification caused by a lack of age information would be prevented. Therefore, we believe that the RRI-based deep learning model has the potential to be used as an age prediction tool, assuming that its training is conducted with a well-balanced dataset without the loss of numeric age data. However, more studies are required in the future to test the effect of adding more minority samples and using numeric age data on the performance of the prediction models.
## 3 Results and Discussion
#### 3.1.3 Effect of Scan Speed on Nanoparticle Yield, Colloidal Density and Mean Diameter  
A scan speed that is too slow leads to shielding effects whereby the current cavitation bubble absorbs many secondary photons, disenabling further cavitation bubbles (more primary nanoparticles) to be produced. A laser scan speed that is too fast disenables the powders to absorb enough energy for ablation to occur (to overcome the threshold fluence). Initially, various scan speeds below 2000 mm/s (and below 100 mm/s) were studied. None of them yielded a significant number of nanoparticles and the colour of the colloid barely  
Figure 6: Effect of laser fluence on nanoparticle mean diameter (nm).  
changed (remained clear) even after 60 min of ablation time. Similarly, scan speeds over 4000 mm/s did not yield a significant number of nanoparticles. The colloids synthesised at above 4000 mm/s were all clear (or almost clear) in colour, signifying a low nanoparticle yield. A scan speed of 99,000 mm/s (maximum scan speed for this laser) was examined and no significant number of nanoparticles was synthesised regardless of ablation time, laser fluence, pulse width and repetition rate (kHz).  
A general increase in absorbance concurrent with increase in scan speed is observed regardless laser fluence/ablation time. By examining Figure 2, it can be observed that a general pattern exists whereby the colloidal density increases and then decreases (and vice versa) as one goes from low to middle to high laser scan speeds (3000-3250-3500 mm/s) generating a sort of zig-zag pattern. Hence, we can conclude that the scan speed has a non-linear relationship with the colloidal density.  
The laser scan speed influenced the nanoparticle mean diameter at all combinations of processing parameters as shown in Figure 7. A linear decrease in nanoparticle mean diameter with increasing scan speed is observed at low fluences (1.83 J/cm\({}^{2}\)) and short ablation times (2 min) (Figure 7, top left corner). On the other hand, a linear increase in mean diameter with increasing scan speed is observed at high fluences (1.91 J/cm\({}^{2}\)) and 25 min ablation time (Figure 7, bottom right corner). Additionally, an 'n' shaped pattern is observed in five of nine sample sets where there is an increase in nanoparticle mean diameter with increase scan speed from 3000 to 3250 mm/s followed by a decrease from 3250 to 3500 mm/s. Conversely, a 'u' shaped pattern is observed once (Figure 7, top right corner).  
The absence of a similar trend across all sample sets in Figure 7 proves that the nanoparticle mean diameter is controlled by a combination of processing parameters which is why a model of the process enables the researcher to get some insights into the PLAL technology while simultaneously saving experimental time and costs. A change in the liquid medium from IPA (to ethanol for example) will yield significantly different results to those presented herein, again showing the importance of modelling work in this field.  
Figure 7: Effect of laser scan speed on nanoparticle mean diameter (nm).
# Artificial Neural Network-Based Mechanism to Detect Security Threats in Wireless Sensor Networks
## 6 Conclusions  
In this work, we proposed a feed-forward neural network for detection of the most important routing attacks in the field of WSNs, with training and testing conducted using the CICIDS2017 dataset. A confusion matrix was used for analysis of the proposed system.  
Figure 14: Detection rate Comparison.  
Figure 15: Comparative analysis of average detection rate and accuracy.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**S.No** & **Technique** & \(DR\) (\%) & _Accuracy (\%)_ \\ \hline
1. & SVM & 64.33 & 90.51 \\
2. & RF & 68.02 & 92.42 \\
3. & DT & 75.16 & 94.11 \\
4. & [32] & 97.83 & 98.84 \\
5 & **Proposed System** & 99.21 & 99.49 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance comparison.  
The system's detection rate of 99.21%, with a 99.45% accuracy rate, as shown by the confusion matrix, demonstrates its capacity to correctly detect and categorize the great majority of routing attacks. The system's dependability in reducing misclassifications and guaranteeing that legitimate network activity is not tagged as harmful is further highlighted by the low false-positive rate. Simulation and analysis results proved that proposed system detected these attacks efficiently; as a result, quality of service and overall network performance improved.  
The results of this study not only confirm the efficacy of our approach, but also make a valuable contribution to the development of secure communication in WSNs. In order to further improve the system's performance, we plan in the future to investigate additional features and routing attacks by using advanced ANNs to extend our approach to real-world deployments, aiming for increased performance and reduced computation time.  
Conceptualization, S.K.; Methodology, S.K. and M.A.K.; Software, M.A.K.; Formal analysis, S.K.; Investigation, M.A.K.; Data curation, N.A.; Writing--original draft, S.K.; Writing--review & editing, N.A.; Visualization, N.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors extend their appreciation to the Research Support Program at Abdullah Al Salem University, Kuwait.  
The authors declare no conflict of interest.
## 3 Experiments for Transfer Efficiency  
As an input of the model, transfer efficiency is an important parameter in ensuring the accuracy of the simulation result. Different types of materials and the magnitude of contact force may lead to varying transfer efficiencies. Furthermore, other influencing factors, such as contact time and effective contact area, should be considered. Most of the workbench surfaces in the workshop are made of iron sheets. Here, the transfer efficiencies of oil contamination from the iron piece to the glove and from the glove to the iron piece were determined through experiments.  
The measurements of transfer efficiencies from the iron pieces to the gloves were used to describe the transfer process. Different masses of weights were selected for the rubber gloves to replace the different contact forces. First, an analytical balance (ME104/02, METTLER TOLEDO, Shanghai, China) was used to weigh the clean iron piece, denoted as \(m_{1}\). Second, a certain amount of MWFs was uniformly applied to the iron piece, and then the piece was weighed again with an analytical balance, with the reading denoted as \(m_{2}\). Third, a pair of tweezers was used to put a glove with a certain mass of weight on the iron piece for 10 s without initial velocity, and then the piece was weighed with the reading and denoted as \(m_{3}\). After collecting the data, the transfer efficiency was calculated as \(\frac{m_{2}-m_{1}}{m_{2}-m_{1}}\)[28]. The measurement method of the transfer efficiency from the gloves to the iron pieces was the same. Each contact time was guaranteed to be 10 s, and other influencing factors were the same in the experiment.  
The contact force was reflected by controlling the mass of the weight from 50 to 500 g at an equal interval of 50 g. The transfer efficiency experiment was repeated three times for each contact force, and then the corresponding transfer efficiency was calculated. The results showed that the transfer efficiency of oil contamination from the rubber glove to the iron piece fluctuates between 0.3 and 0.4, and the transfer efficiency from the iron piece to the rubber glove is generally stable at 0.5. Therefore, in this model, the transfer efficiency of oil contamination from the glove to the workbench (or workpiece) and from the workbench (or workpiece) to the glove was set to 0.35 and 0.5, respectively.
# Feasibility Test and Analysis for Heavy Haul Freight Using WDPS Based on MIMO-OFDM
## 3 Measurement and Test  
The test was carried out at an average speed of 100 km/h in the approximately 21 km section from Jillye station to Busansinhang station in Korea. The formation was as Figure 4. Instead of the existing FSK method, MIMO-OFDM, which is strong in multi-path, was applied to the WDPS, and a repeater was installed on the 25 th freight train to secure communication coverage even with signal attenuation according to the terrain (curve, gradient) of the driving section.
###### Abstract  
Anticipating the ornamental quality of plants is of significant importance for genetic breeding programs. This study investigated the potential of predicting and classifying whether ornamental pepper plants will exhibit desirable ornamental traits based on RGB images, comparing these results with an approach relying on morphological measurements. To achieve this, pepper plants from fifteen accessions were cultivated, and photographs were taken weekly throughout their growth cycle until fruit maturation. A Vision Transformer (ViT)-based model was employed to predict the suitability of the plants for ornamental purposes, and its predictions were validated against assessments conducted by eight experts. An XGBoost-based classifier was employed as well for estimations based on morphologicalmeasurements with an accuracy over 92%. The results showed that the ornamental suitability of plants can be accurately estimated and predicted up to seven weeks in advance from photos, with accuracy over 80%. Interestingly, higher-resolution RGB images did not significantly improve the accuracy of the ViT model. Furthermore, the estimation of ornamental potential using morphological measurements and RGB images yielded similar accuracy, indicating that a single photograph can effectively replace costly and time-consuming morphological measurements. As far as the authors are aware, this work is the first to forecast the ornamental potential of pepper plants (_Capsicum_ spp.) multiple weeks ahead of time using image-based deep learning models.  
ornamental plants; RGB image analysis; Vision Transformer (ViT); XGBoost; morphological measurements; plant breeding; phenotypic prediction
## 2 Methods
### Variable Control  
(1) Exercise consumption control  
In exercise intervention studies, controlling exercise duration is a key factor in ensuring the validity and consistency of the results. It is essential to ensure that the total intervention duration, the duration of each exercise session, and the exercise frequency are consistent. To minimize the impact of potential time factors on the outcomes, participants should engage in exercise during similar time periods each day, preferably in the evening. For interventions such as HIIT, it is also necessary to strictly control the intervals between high-intensity and recovery phases to ensure that all participants have consistent rest and training times. By recording and monitoring each participant's actual exercise duration and frequency, the implementation of the exercise intervention can be ensured to meet the research requirements, thus making the study results more reliable.  
(2) Diet control  
Based on existing exercise intervention studies, participants were instructed to maintain their usual diet throughout the intervention period [31]. Validated three-day food diaries (covering two weekdays and one weekend) were used to assess dietary intake before and after the intervention to control for dietary effects and ensure that all observed changes post-intervention were due to the exercise intervention. Additionally, dietary advice was provided, but no intervention was made in the participants' diets, and the total intake of calories and macronutrients (i.e., carbohydrates, fats, and proteins) was evaluated.  
(3) Lifestyle control  
To effectively control participants' lifestyle habits, we first assessed their sleep quality, work stress, and daily activity levels through interviews before the study began, in order to select individuals who meet the research criteria. Next, we established standardized lifestyle guidelines for participants, including recommended sleep duration, reasonable work hours, and stress management advice. We regularly collected and monitored data on participants' sleep quality, work stress, and daily activity levels to ensure these lifestyle habits were effectively controlled throughout the study. We provided education and guidance on healthy lifestyle habits to help participants improve their sleep, manage stress, and maintain healthy daily activities. Additionally, if lifestyle habits were found to potentially impact the study results, we offered additional support and intervention measures, such as psychological counseling or stress-relief activities.
## 2 Downlink Beamforming Design and Uplink Power Allocation
### Methodology: Convex Optimization-Based Downlink Beamforming Design and Uplink Power Allocation
#### 2.1.1 Downlink Scenario  
Since the denominator of the \(\text{SINR}_{\text{d},k}[n]\) in (4) is strictly positive, the constraints of the problem (P1) can be rewritten as follows:  
\[\frac{1}{\gamma_{\text{d},k}\sigma^{2}}|\mathbf{h}_{\text{d},k}^{H}[n]\mathbf{ w}_{k}[n]|^{2}\geq\sum_{i\
eq k}\frac{1}{\sigma^{2}}|\mathbf{h}_{\text{d},k}^{H}[n] \mathbf{w}_{i}[n]|^{2}+1. \tag{9}\]  
It was shown in [23] that the above inequality can be rewritten as follows:  
\[\frac{1}{\sqrt{\gamma_{\text{d},k}\sigma^{2}}}\operatorname{Re} \Bigl{\{}\mathbf{h}_{\text{d},k}^{H}[n]\mathbf{w}_{k}[n]\Bigr{\}} \geq\sqrt{\sum_{i\
eq k}\frac{1}{\sigma^{2}}\Bigl{|}\mathbf{h}_{ \text{d},k}^{H}[n]\mathbf{w}_{i}[n]\Bigr{|}^{2}+1} \tag{10}\] \[=\Bigl{\|}\tilde{\varepsilon}\mathbf{h}_{\text{d},1}^{H}[n] \mathbf{w}_{1}[n],\ \ \ \cdots,\ \ \ \frac{1}{\sigma}\mathbf{h}_{\text{d},k}^{H}[n] \mathbf{w}_{k}[n]\Bigr{\|}_{2}.\]  
Thus, using (10), the problem (P1) can be reformulated as the following SOCP:  
\[\begin{split}\text{(P1^{\prime})}:\min_{\{\mathbf{w}_{k}[n]\}_{k =1}^{K}}&\sum_{k=1}^{K}\|\mathbf{w}_{k}[n]\|^{2}\\ \text{s.t.}&||\mathbf{h}_{\text{d},k}^{T}[n]\mathbf{ W}[n],\sigma||\leq\sqrt{1+\frac{1}{\gamma_{\text{d},k}}}\mathbf{h}_{\text{d},k}^{T}[n] \mathbf{w}_{k}[n],\ \forall k\end{split} \tag{11}\]  
where \(\mathbf{W}[n]=[\mathbf{w}_{1}[n],\cdots,\mathbf{w}_{K}[n]]\). It is worth noting that both the objective function and constraints are convex. Thus, the nonconvex problem (P1) is recast into a convex form in (P1\({}^{\prime}\)), which can be solved numerically via convex optimization methods, such as the interior point method [7].
# Three-Dimensional Wind Measurements with the Fibered Airborne Coherent Doppler Wind Lidar LIVE
## 2 Experiments
### Lidar Design  
LIVE Lidar has been designed to be compact, robust and suited to ATR42 aircraft integration. It was composed on an aircraft adapted bay that includes (see Figure 2):  
* A rack for various Lidar power supplies.
* A control rack for the scanning system.  
Figure 1: **Left:** picture of the power amplifier. **Right:** temporal shape of laser pulse.  
A rack for the emission of the Lidar beam including the Onera laser source.
* A rack for the Lidar signal reception and its coherent detection.
* A rackable computer for real time processing.  
The total power consumption is about 900 VA (Volt Ampere) and the weights of the scanning head and the lidar pay are 18 kg and 58 kg, respectively. The lidar bay is sufficiently compact to occupy a volume of \(55\times 65\times 130\) cm\({}^{3}\).  
The telescope is in a carbon tube positioned above the hatch of the aircraft on a metal interface plate that also supports the scanning system. Scanning for the analysis of the wind fields involves a system consisting of two mirrors to allow conical addressing. The beam emitted by the telescope is sent back to a fixed mirror and then to a mirror mounted on a gimbal-type scan turret as represented in Figure 2.  
For a flight altitude of 5000 m, and an aircraft speed of 100 m.s\({}^{-1}\), the 3 km horizontal resolution is obtained with conical scanning with a total opening angle of 30\({}^{\circ}\) and one round in 17 s. After each lap, the lidar points in the nadir direction in 2 s in order to achieve good accuracy on vertical wind measurements.  
The lidar is installed inside the SAFIRE ATR42 looking through a trapdoor in the floor of the plane in while aiming towards the nadir (Figure 3).  
Figure 3: ATR42 aircraft, with trapdoor localization and lidar scanning representation.  
Figure 2: Diagram of the LIVE lidar installation inside the airplane. V\({}_{1}\), V\({}_{2}\)... represent radial wind speed measured by lidar, and V\({}_{x}\), V\({}_{y}\) and V\({}_{z}\) are the components of the retrieved wind vector.
## 1 Introduction
### Motivation and Contribution  
The above literature review and the research content of this paper are shown in Figure 1. Motivated by Refs. [19; 20], we focus on the use of RL to solve the relay selection problem in FSO systems. We utilize the RL approach to select the relay, which aims at maximizing the channel capacity.  
Figure 1: Literature review and the research content of this paper.  
As a result, we have to choose the most appropriate algorithm from the RL family. There are three main algorithms in the RL family, which are \(Q\)-learning, DQN, and deep deterministic policy gradient (DDPG) [21]. \(Q\)-learning deals with the discrete states and actions, DDPG has an actor-critic architecture that handles the continuous action and state spaces, whereas in the DQN algorithm, the action space is discrete, while the state space is continuous.  
In relation to our relay selection problem, the agent chooses the best action after observing the current environment state. The action consists in selecting the appropriate relay, while the state includes both the previous selected relay and the current CSI. DQN appeared as the most appropriate method. As a result, this study employed the dueling-DQN approach to select the relay in order to maximize the channel capacity, where the relay nodes acted as the decode-and-forward (DF) mode, i.e., a cooperative DF-FSO. The main contributions of this study are as follows.  
* A Markov Decision Process (MDP) model was developed to describe the relay selection problem.
* Different from the abovementioned literature, the switching loss between relay nodes was considered. The corresponding expression of channel capacity was also derived.
* We propose a DQN-RS algorithm based on the dueling DQN method, which deals with the relay selection issue in a cooperative DF-FSO system in order to achieve higher channel capacity.
* In our proposed DQN-RS algorithm, a state contained both the previous selected relay and the current CSI. An action presented the current selected relay, where the corresponding reward was equal to the derived channel capacity.
## 5 Development of an Auto-Response Early-Warning System
### Core of the System  
The core forecasting frame is based on the ECMWF global NWP product, the same model applied in Sections 3 and 4. CFAN has a long-term collaboration with ECMWF [22]. To reduce the communication cost via INMARSAT satellite service, most computational jobs are set in the servers. The request might be initialized at any time by vessel officers or users on land, who could send out a formatted email with latitudinal and longitudinal information to a particular email address (e.g., [EMAIL_ADDRESS]). The successful arrival of this email will trigger a set of jobs that will read the near-to-date high-volume ECMWF product from the US server, generate the future weather element change trend near the target location, compress them, and then deliver the package to the server located in Dalian Maritime University, China. The Chinese server will apply a bias-correction algorithm to adjust the raw data according to water depth, the season, wind direction, and distance to the nearest land. Finally, the future CP/LSP trend will be stated in a conciseway and sent back to the ship's satellite receiver. Figure 10 briefly states the system process, which is all performed automatically.