esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment and Results
### Experimental Setup  
In this study, three experiments were conducted to verify the validity, superiority, and generalizability of the proposed combinatorial optimization model. Experiment I was designed to compare the forecasting performances of combined models with different numbers of sub-models for wind speed and wind power forecasting in the first season. Experiment II contrasted the forecasting performances of the combined models. The models were optimized by different optimization algorithms for wind speed and wind power forecasting in the second season. Experiment III verified the performance of the combined model for wind speed and wind energy forecasting in the third and fourth seasons. The details of the experiments are as follows:  
Experiment I was designed to compare the combined model with different numbers of forecasting sub-models to determine the optimal number of sub models required by the combined model. Wind speed and wind power data collected from four sites in the first season were used to determine the forecasting capacity of the proposed model. Furthermore, the forecasting step was classified as day-ahead and used to assess the conducted models.
## 4 Results and Discussion
### Test Systems
#### 4.2.4 Test System 4  
For testing, the second-order linear system is used, and formula 23 illustrates the model transfer function. The activation function's mapping range is set to 0 to 30. The performance characteristics and step response data are displayed in Table 7 and Figure 11.  
\[G(s)=\frac{133}{s^{2}+25\text{s}} \tag{23}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Algorithm** & **Overshoot (\%)** & **Rising Time (s)** & **Settling Time (s)** & **Fitness** \\ \hline GWO and \(g(x)\) & 0.62 & 3 & 3 & 1.0651 \\ GWO and \(t(x)\) & 0 & 2 & 4 & 1.0234 \\ GWO and \(h(x)\) & 0 & 2 & 2 & 1.0978 \\ GWO and PID & 10 & 2 & 3 & 11.9876 \\ ALGWO and \(g(x)\) & 0.02 & 2 & 3 & 1.0360 \\ ALGWO and \(t(x)\) & 0 & 2 & 4 & 1.0238 \\ ALGWO and \(h(x)\) & 0 & 2 & 2 & 1.0804 \\ ALGWO and PID & 6 & 2 & 4 & 11.8913 \\ \hline \end{tabular}
\end{table}
Table 7: Performance characteristics on test system 4.  
Figure 10: The comparison results of algorithm optimization search on test system 3.  
Table 7 demonstrates how the ALGWO method has lower fitness than the GWO algorithm in some control schemes. As an illustration, the average precision is decreased in a BPNN PID control system applied to \(g(x)\) from 1.0651 to 1.0360, and the overshoot index is also decreased from 0.62% to 0.02%. The average accuracy is therefore increased by the ALWO technique. In terms of whether to use BPNN technology or not, the average accuracy decreased from 11.8913 to 1.0238, which significantly indicates that the ALGWO-BPNN-PID control scheme is more effective in second-order linear systems. Furthermore, the \(g(x)\) activation function has better results, as shown in Table 7.  
As can be clearly seen from Figure 11, the maximum value of the tracking values decreased from 1.10 to 1.007. As a result, ALGWO-BPNN-PID controllers have a lot of benefits over traditional PID controllers in terms of reducing overshoot.
## 5 Conclusions  
This research introduces a scalable ship detection dataset--VHRShips--and proposes a deep learning-based HieD approach to perform an end-to-end DLRI process with optical satellite images. HieD is a dynamic and iterative approach formed with the classical CNN and YOLO networks, in which each stage can be independently reformed and tuned to maximize performance. This flexibility is not the only feature that HieD proposes; the PFMO method also helps to boost the performance of the recognition and identification stages. Additionally, the evaluation method used in the study is slightly different from the conventional methods in that we considered the effects of the error cycle and represented them in the outputs. Even though the stages of DLRI are connected with the strict error cycle phenomenon, the performance stays above a satisfactory level. Additionally, a comparison with the one-stage YOLOv4 method provides positive feedback. Another important outcome of this research is the VHRShips dataset, which was prepared from optical satellite images. We generated a complex dataset containing a large variety of ship classes with different ship sizes at various inshore and offshore background distributions to objectively represent real-life conditions. Moreover, images from this dataset reflect  
Figure 5: Comparative visuals of HieD (left column) and YOLOv4 (right column) for offshore regions (blue labels are the ground truth and yellow labels with a circle at the center of bBox are the results of HieD or YOLOv4).  
different acquisition conditions, such as viewing geometry and seasonal variations, so that it can be used as a base dataset for further studies related to maritime mapping and ship detection. The outputs of HiED are very promising for further studies and could be used as a benchmark.  
In conclusion, the VHRShips dataset and proposed approach could be used for the transfer learning and generalization evaluation of similar tasks. The VHRShips dataset, HieD method, and end-to-end evaluation metrics adequately represent real-world applications.  
Conceptualization, [PERSON] and [PERSON]; methodology, [PERSON], [PERSON] and [PERSON]; validation, [PERSON] and [PERSON]; formal analysis, [PERSON]; writing--original draft preparation, [PERSON], [PERSON] and [PERSON]; supervision, [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The codes and VHRShips test data to evaluate the HieD approach is available at: [[https://github.com/radres333/VHRShips](https://github.com/radres333/VHRShips)]([https://github.com/radres333/VHRShips](https://github.com/radres333/VHRShips)) (accessed on 7 June 2022).  
We would like to thank anonymous reviewers for their supportive comments to improve our manuscript.  
The authors declare no conflict of interest.
# A MAC Protocol to Support Monitoring of Underwater Spaces +
###### Abstract  
Underwater sensor networks are becoming an important field of research, because of their everyday increasing application scope. Examples of their application areas are environmental and pollution monitoring (mainly oil spills), oceanographic data collection, support for submarine geolocalization, ocean sampling and early tsunamis alert. The challenge of performing underwater communications is well known, provided that radio signals are useless in this medium, and a wired solution is too expensive. Therefore, the sensors in these networks transmit their information using acoustic signals that propagate well under water. This data transmission type not only brings an opportunity, but also several challenges to the implementation of these networks, e.g., in terms of energy consumption, data transmission and signal interference. In order to help advance the knowledge in the design and implementation of these networks for monitoring underwater spaces, this paper proposes a MAC protocol for acoustic communications between the nodes, based on a self-organized time division multiple access mechanism. The proposal was evaluated using simulations of a real monitoring scenario, and the obtained results are highly encouraging.  
underwater sensor networks; underwater monitoring; MAC protocol; acoustic transmission
# Assistance of a Person with Muscular Weakness Using a Joint-Torque-Assisting Exoskeletal Robot
###### Abstract
While wheelchairs can improve the mobility of patients with complete paralyses, they cannot provide standing postures and bipedal walking, which are important for healthcare. Many studies have shown that bipedal walking training can increase quality of life and life satisfaction [6,8,9,10]. ReWalk, a commercial robot by ReWalk Robotics [11], is currently being distributed to rehabilitation hospitals around the world. Other commercially available robots for disabled and paralyzed people are Ekso [12] from Ekso Bionics, Indego [13] from Parker Hannifin corporation, and Rex [14] from Rex Bionics. Many other robotic exoskeletons are being developed and commercialized [15,16]. In such cases of a wearable robot for complete paraplegics, i.e., people with complete loss of walking ability, it is common to use a position tracking control scheme. There are cases where position tracking control is used in robots for people with partial impairment in walking ability, such as hemiplegic or stroke patients [17]. This allows the user to imitate the trajectory of a normal gait or the trajectory of a healthy side leg. However, for incomplete paraplegics with remaining sensory and motor functions, the position control strategy constrains the human's voluntary motion, and thus, the wearers of such robots have to learn to suppress voluntary motion and to fully rely on the robot. This may not be appropriate in the viewpoint of rehabilitation medicine, and at present, it is difficult to find a wearable robot that can compensate only for the lack of the weakness of muscular power without disturbing a human's voluntary motions. There are soft wearable suits, but such soft a mechanism is not suitable to help self-balancing of people with muscular weakness [6,18]. For gait rehabilitation and assistance of people with partial impairment of walking ability, a torque control method may be more effective. The torque control is a method that allows the user to control the movement while allowing the appropriate assistance at the required moment, such as pushing the swing from behind. Therefore, there is a clear need for a control method that captures the exact moment when assistance is needed and provides as much assistive torque as needed. The main aim of this paper is to improve the walking of patients with paretic gait by applying the torque assist algorithm using a wearable robot. In order to achieve this goal, the following objectives are formulated:  
* integration of torque-mode actuators and a wearable robot,development of a real-time gait detection algorithm with simplified gait phases, generating a patient-customized assistive torque and control algorithm, and validation of the effect of walking assistance using the proposed wearable robot through a case study.
# A GTA Welding Cooling Rate Analysis on Stainless Steel and Aluminum Using Inverse Problems
###### Abstract  
This work presents an analysis of the thermal influence of the heat transfer by convection and radiation during GTA (gas tungsten arc) welding process. The authors' in-house C++ previously-developed code was modified to calculate the amount of heat transfer by convection and radiation. In this software, an iterative Broydon-Fletcher-Goldfarb-Shanno (BFGS) inverse method was applied to estimate the amount of heat delivered to the plate when the appropriate sensitivity criteria were defined. The methodology was validated by accomplishing lab-controlled experiments on stainless steel AISI 304L and aluminum 6065 T5 plates. Due to some experimental singularities, the forced thermal convection induced by the electromagnetic field and thermal-capillary force were disregarded. Significant examples of these singularities are the relatively small weld bead when compared to the sample size and the reduced time of the welding process. In order to evaluate the local Nusselt number, empirical correlations for flat plates were used. The thermal emission was a dominant cooling effect on the aluminum cooling. However, it did not present the same behavior as the stainless steel samples. The study found that the heat losses by convection and radiation of the weld pool do not affect the cooling process significantly.  
Keywords:inverse problems; heat flux estimation; thermal analysis; cooling and heating rate; radiation +
Footnote †: journal: Applied Sciences
## 4 Performance Evaluation
### Numerical Results  
The numerical results can be divided in two parts. First, the effectiveness of the proposed algorithm is verified using a case study. Second, the detection performance of the proposed algorithm is then evaluated using Monte Carlo simulations.  
To effectively quantify the performance, we, respectively, specify the SNR of the RC and the SNR of the SC at the \(m\)-th frequency band as:  
\[SNR_{RC,m} =\frac{|b_{m}|^{2}}{\sigma_{r,m}^{2}}, \tag{31}\] \[SNR_{SC,m} =\frac{|\alpha_{m}|^{2}}{\sigma_{s,m}^{2}}, \tag{32}\]  
where the definitions of \(b_{m}\), \(\alpha_{m}\), \(\sigma_{r,m}^{2}\), and \(\sigma_{s,m}^{2}\) can be found in (1) and (2).
## 3 Results and Discussion
To measure the electrochemically active surface area (ECSA), the capacitance of an electrical double layer (\(C_{\rm dl}\)) at a solid/electrolyte interface was evaluated from the CV curves of Co\({}_{3}\)Se\({}_{4}\) on GC and FTO at various scan rates, as shown in Figure 5. The Cd\({}_{\rm dl}\) is estimated from the slope of the plot of J\({}_{\rm c}\), which is the current at the constant potential with increasing scan rate from 20 to 100 mV/s. The capacitance is 0.19 mF/cm\({}^{2}\) and 0.66 mF/cm\({}^{2}\) for Co\({}_{3}\)Se\({}_{4}\) on GC and FTO, respectively. These values are much higher than that of a typical compact flat electrode reported to date (approximately 10-20 \(\mu\)F/cm\({}^{2}\)) [39]. The slope is also proportional to the exchange current density, which is directly related to the electrocatalystally active surface area [25; 40]. The ECSA is calculated from the Cd\({}_{\rm dl}\) divided by the capacitance of a smooth planar surface of the catalyst (C\({}_{\rm s}\)). Using the general C\({}_{\rm s}\) value of 13-17 \(\mu\)F/cm\({}^{2}\) in pH 0 solution (1M HClO\({}_{4}\)) [41], the average ECSA of Co\({}_{3}\)Se\({}_{4}\) on GC and FTO are (2.67 and 4.45) \(\times\) 10\({}^{-5}\) cm\({}^{2}\), which corresponds to roughness factors of (13.67 and 22.25) \(\times\) 10\({}^{-4}\) cm\({}^{2}\), respectively. Therefore, Co\({}_{3}\)Se\({}_{4}\) on GC has a large electrochemical catalytic surface area, which may also provide effective active sites for the HER. Figure 5: The current vs. potential result at diverse scan rates and The capacitance current (J\({}_{\rm c}\)). (**a**) The CV curve of Co\({}_{3}\)Se\({}_{4}\) on GC with different scan rate. (**b**) J\({}_{\rm c}\) of Co\({}_{3}\)Se\({}_{4}\) on GC at J\({}_{\rm net}\) = 0 mA cm\({}^{-2}\) depending on scan rates. (**c**) The CV curve of Co3 Se4 on FTO with different scan rate. (**d**) J\({}_{\rm c}\) of Co\({}_{3}\)Se\({}_{4}\) on FTO at J\({}_{\rm net}\) = 0 mA cm\({}^{-2}\) depending on scan rates.
# Analysis and Experimental Validation of Efficient Coded OFDM for an Impulsive Noise Environment
## 5 Performance Analysis
### Bit Error Rate Computation
#### 5.3.1 Case 1: Emitter and Receiver Both Placed at 1 m from the Source  
The results for this first experimental scenario are depicted in Figures 8 and 9 for the LOS and NLOS situations, respectively. The first remark is that we note some irregularities in the curves. This can be explained by the critical aspect of the error performance of bounded distance for the RC decoder. Indeed, considering a received word, a bounded distance either procures a codeword or affirms a failure within an agreed radius of the received word. In the first case, an error of decoding appears when the codeword produced does not correspond to the sent codeword. In addition, as can be seen in Figures 8 and 9, we have a degradation of performance when the impulsive noise is present whatever the voltage used. This degradation is more important in the LOS situation than the NLOS case. In Figure 8, the voltage increase does not affect the BERs much. We have approximately the same  
Figure 7: Average delay performance versus the voltage for all configurations.  
performances both on 12 kV and 18 kV. A target BER of \(10^{-4}\) is achieved at an EbNoof 3.2 dB when the channel is not affected by impulsive noise and approximately 6 dB in the presence of impulsive noise for all voltages. By comparing the results in Figures 8 and 9, we can notice that the first case, i.e., the LOS case achieves the best performance. However, in Figure 9, the performance degradation is not as remarkable without impulsive noise compared to the case of the presence of impulse samples. A target BER of \(10^{-4}\) is achieved at an EbNo of 5.2 dB when the impulses are not affecting the channel. The worst performances are obtained by the presence of 12 kV noise. This is expected because the channel is more affected in this case, as confirmed by the PDR, which is 0.25.
# IoT-Enabled Vehicle Speed Monitoring System
## 3 Proposed IoT-Enabled Speed Monitoring System
### License Plate Detection  
First, a system is developed to detect the license plate from a still image or a single video frame containing a vehicle. The system is developed by using contours with the help of the Open-Source Computer Vision (OpenCV) library and Python. The captured vehicle image is converted to grayscale, and then, by using bilateral filters and edge detection algorithms, the useful information (i.e., the license plate) is extracted. After getting the plate from the image, all other information is excluded, and the area with the license is marked for character segmentation.
# Salespeople Performance Evaluation with Predictive Analytics in B2B
## 2 Literature Review
### Predictive Analytics for Sales  
Predictive analytics is an area increasingly entering the business and academic fields [11]. Companies more and more have been using DM to improve their internal processes and automate not only repetitive, but complex tasks nowadays completed by humans [12; 13].  
Authors in the academic area refer that PA has been used for several years by companies to get a competitive advantage, [14; 15]. At first, by companies acting in the B2C with a large customer base and capacity to collect and store transactional data from customers, and only then by companies acting in the B2B area [14].  
B2B selling companies are hiring cloud-based PA providers to draw on both inside and outside data sources to identify new leads so that they can take advantage of PA [16].  
[PERSON] and [PERSON] did a comprehensive study on the application of PA over CRM data in 2014. The results show 57 articles found in 4 databases, where the studies focused on dimensions like Customer Acquisition, Attraction, Retention, Development, and Equity Growth [17]. Another factthe results show is that PA techniques between 2003 and 2013 gained a lot of popularity in areas like casinos, retailers, telecommunications, manufacturing, insurance and healthcare [17].  
To understand what has been studied in the academic area in terms of predictive analytics, the authors hereunder describes some success cases of PA applied in sales forecasting.  
2.1. Sales Forecasting of Computer Products Based on Variable Selection Scheme and Support Vector Regression (SVR)  
Like many other industries, sales forecasting is also a challenge for computer product retailers. Wrong forecasts can cause product backlog or inventory shortages, incorrect customer demands and decrease customer satisfaction [18].  
[PERSON] et al. combined Multi Variable Adaptive Regression Spines (MARS) with SVR to make a sales forecasting model for computer products. The main idea over the scheme was first to use MARS to select the essential forecasting variables and then use the identified key forecasting variables as the input variables for SVR. The data used was a compilation of the weekly sales data of five computer products from a computer retailer in Taiwan. The sales in the dataset referred to products like Notebooks, LCDs, Main Board, Hardrives, and Display cards [18].
# Sensor Network Environments: A Review of the Attacks and Trust Management Models for Securing Them
## 6 Trust Model and Calculation Techniques
### Probabilistic Models  
Trust is not a hard measured metric; it can be quite subjective. Therefore, it makes sense that probabilistic approaches work well when classifying trust. Bayesian probability uses the frequency of some event (a trust measurement parameter) to determine the probability of some random variable (trust). A Bayesian formulation for determining a trust or reputation value is used in [12, 20, 25, 27]. A probability distribution of the variable being calculated is needed for Bayesian methods. There are many types of distributions (beta, Gaussian, binomial, poison) that can be used depending on the circumstances of the model. Beta distribution is used in [20, 25] and binomial distribution is used in [12].  
Dempster-Shafer belief theory [15, 20, 24] is a similar probabilistic approach. This method is a framework for reasoning with uncertainty. DS theory can be used for calculating the recommendation trust of nodes from their neighbors. In [24], using DS theory, a belief function is used to represent the data presented by the recommenders, and the functions are combined using Dempster's combination rule. DS theory is also used to update recommendation trust in [20]. DS theory is used in [15] to combine all pieces of trust evidence obtained from the machine learning models and to define the upper and lower limits of trustworthiness for the data.  
Markov chains constitute a system that changes from one state to another, according to a set of probabilistic rules. The probability of transitioning to any state in a Markov chain is only dependent on the current state and the time elapsed, i.e., it never depends on previous states or events [52]. In [38], trust is modeled as a sequence of states, with the lowest state being state 0 and the highest being state I. The events that will change the state are defined, and each event has an impact score. An impact of \(X\) will increase or decrease the state by \(X\). The model uses this to determine the trust values of nodes based on their behavior and, if they drop below state 0, they are blacklisted. The Markov chain aspect in this case means that the current state of the node is never affected by any of its past states or actions.
## 4 Experiment and Result Analysis
### Implementation Details  
The lithium-ion battery RUL prediction model established in Section 2 is verified with the two datasets preprocessed in Section 3. Specifically, transfer experiments of different working conditions and different types of batteries are designed. The former is to realize the RUL migration prediction capability between different working conditions on the same type of lithium-ion battery. The latter aims to realize the ability to migrate battery data from LFP to LCO. This subsection will introduce the dataset division and training way of these two experiments in detail.