esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion and Future Research Agenda
### Research Opportunity 1: How Can Residual Life Be Predicted in FUS to Improve Systems Efficiency  
Degradation is an unavoidable characteristic, which it requires the utmost attention to pursue. However, a lot of literature is already available to handle the degradation rate at the component level. A limited number of papers ([PERSON] et al. [12]; [PERSON] et al.) [38] have considered system level degradation, especially in the manufacturing systems context. A recent paradigm shift has forced the use of the Internet of Things (IoT) in almost every stage of the product life cycle. In addition, process industries have highly benefitted from the key technologies that emerged from this shift ([PERSON] et al., [70] [PERSON] and [PERSON],) [71]. To make these processes effective and efficient, system-level health monitoring is a new thinking among researchers paying attention to these issues. To improve the health status of the system, an individual system's degradation rate needs to be decreased, which in turn improves the residual life of the machine. Here, the residual life of a machine was characterized as remaining useful time till its level of degradation arrives at a predefined failure threshold. The degradation and residual life follow different distributions depending on the order requirement and system status. Hence, this is a challenging work one can take into consideration to explore further.  
Research Opportunity 2: How to Deal with Heterogeneous Data Obtained from Various Sensory Sources for Predicting the Degradation Rate of FUS?  
Heterogeneous data includes multiple internal and external databases generated from different sources obtained in various dimensions ([PERSON] and [PERSON], 2008 [72], [PERSON] and [PERSON], 2016) [73]. Real-time production data from complex systems produce a huge variety and volume of data. Handling these kinds of data-intensive systems with conventional statistical tools may be insufficient when firms seek to strategically conceal the data [13; 74; 75; 76]; Hence, to handle the heterogeneous data in FUS and predict the degradation rate, improving the residual life advanced analytics is essential. This area opens wider challenges for the researchers to explore.
## 2 Materials and Methods
### Fire Prevention and Fire Detection
#### 2.2.3 Object Detection  
As we mentioned in the previous subsection, the second approach is object detection using the YOLOv5 network model. YOLOv5 is an upgraded network model of the You Only Look Once network model presented in 2016 in [30], YOLOv2 presented in [31],  
Figure 8: Performance metrics of U-Net image segmentation.  
Figure 9: Examples of image segmentation.  
YOLOv3 presented in [32], and YOLOv4 presented in [33]. It is an extremely fast, state-of-the-art network model for object detection. Its main task is to detect instances of objects of a certain class within an image. Object-detection methods can be categorized into two main types: one-stage methods, which prioritize inference speed, and two-stage methods, which prioritize accuracy. YOLOv5 is a one-stage method. It works by dividing images into a grid system; then, each grid is responsible for detecting objects within itself. Outputs are predicted bounding boxes and probabilities for each component. The architecture of the YOLO model consists of three parts. The first part is the backbone. It is used to extract key features from an input image. The neck is the second part. It is used to create pyramid parts. Feature pyramids aid models in generalizing when it comes to object scaling. It also helps in the identification of the same object in various sizes. The last part is the head. It is responsible for the final detection step. It uses anchor boxes to construct final output vectors with class probabilities and bounding boxes. Figure 10 depicts the YOLOv5 loss function as a combination of classification loss and localization loss. If an object is detected, the classification loss at each cell is the squared error of the class conditional probabilities for each class. The localization loss measures the errors in the predicted boundary-box locations and sizes. From the presented loss, the YOLOv5 model achieved excellent results but still leaves room for additional progress. An example of a trained model prediction on our dataset is presented in Figure 11.  
After the image segmentation and object detection, in addition to knowing whether fire is present in an image, we also know the exact location of the fire in that image. This  
Figure 11: Example of object detection with probabilities using YOLOv5.  
Figure 10: Classification loss (**top**) and localization loss (**bottom**) of trained YOLOv5 model.  
is critical for directing the fire-extinguishing nozzle to the proper location for quick and effective fire extinguishment.  
To be able to train an image-segmentation model or object-detection model, a dataset with a ground truth mask of fire and smoke for every image is required. Because of the lack of that kind of dataset, we started building a new one and began manually annotating every image. For that purpose, a new annotation tool was developed that will be further explained in the next subsection
# Path Planning of Intelligent Mobile Robots with an Improved RRT Algorithm
## 4 Testing in Real-World Scenarios  
To verify the feasibility and accuracy of the improved RRT-KF algorithm for mobile robot path planning in real-world scenarios, the robot used in this experiment is installed with the Ubuntu 22.04 LTS operating system. The underlying drive system uses four Mecanum wheels, enabling operations such as in-place turning and rotation. The robot is equipped with sensors such as LiDAR and IMU. The center-to-center distance of the chassis is 0.25 m, and the maximum speed is 0.45 m/s. The motion code is written using Linux and C++ programming languages to control the robot's movement, achieving autonomous control of the robot. Real-time data is transmitted via topics, enabling the validation of the proposed algorithm.  
Figure 13: Experimental results of 3D map environment simulation. In this figure, the green dot represents the starting point, the blue dot represents the target point, the black spheres represent obstacles, the blue lines represent search branches, and the red solid line represents the final path from the starting point to the target point. (**a**) RRT; (**b**) RRT-Greedy algorithm; (**c**) improvement of the RRT-KF algorithm.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & \begin{tabular}{c} **Average** \\ **Path** \\ **Length** \\ \end{tabular} & \begin{tabular}{c} **Average** \\ **Planning** \\ **Time** \\ \end{tabular} & \begin{tabular}{c} **Average** \\ **Number of** \\ **Collision** \\ **Points** \\ \end{tabular} & \
\begin{tabular}{c} **Average** \\ **Number of** \\ **Active** \\ **Points** \\ \end{tabular} &
\begin{tabular}{c} **Efficient** \\ **Folents** \\ \end{tabular} \\ \hline RRT & 1703.350 & 0.469 & 223 & 85 & 38.12\% \\ RRT-Greedy & 1622.174 & 0.273 & 122 & 81 & 66.39\% \\ Improved algorithm & 1614.759 & 0.148 & 97 & 80 & 82.47\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of simulation data for 3D map environment.
# Soft Iterative Decoding Algorithms for Rateless Codes in Satellite Systems
###### Abstract  
The satellite system is one of the most efficient means for broadcasting due to its wide service coverage as well as the fact that it can provide high data rate services by using high frequency bands. However, there are a number of problems in the satellite system, such as a long round trip delay (RTD) and heterogeneity of the channel conditions of the earth stations. Even though utilizing adaptive coding and modulation (ACM) is almost mandatory for the satellite systems using high frequency bands due to the serious rain fading, the long RTD makes it difficult to quickly respond to channel quality information, resulting in a decrease in the efficiency of ACM. A high heterogeneity of earth stations caused by a wide service coverage also makes it difficult to apply a uniform transmission mode, and thus satellite systems require receiver-dependent transmission modes. A rateless code can be an effective means to compensate for these disadvantages of satellite systems compared to terrestrial wireless systems. This paper presents soft iterative decoding algorithms for efficient application of rateless codes in satellite systems and demonstrates that rateless codes can be effectively used for hybrid automatic repeat request schemes.  
rateless codes; soft iterative decoding; Tanner graph; LDPC codes; satellite systems +
Footnote †: journal:  
_Article_  
Soft Iterative Decoding Algorithms for Rateless Codes in Satellite Systems
# Performance Analysis of DF Relay-Assisted D2D Communication in a 5G mmWave Network
## 1 Introduction
### Contribution  
The proposed work is focused on enhancing the coverage probability of the D2D users using the DF relay, as well as finding an optimal method for relay selection; thus, the contributions of the proposed work are given as follows:  
1. The bitwise binary XOR operation was executed to encode the message received at the relay node using a carrier frequency of 28 GHz in an uplink Rician fading channel,which resulted in an enhanced SINR with higher throughput. This scheme is rarely used in 5G mmWave networks; 2. Mode switching was utilized to help reduce user traffic; 3. The proposed dynamic relay selection (DRS) method selects the optimal DF relays based on the higher sum SINR, lower distance, and higher channel gain of the instantaneous SINR of D2D communication; 4. The diffused incoherent scattering power \((P_{S})\) as part of the power consumption was considered at the receiver node for the relay mode operating in the mmWave band for a more realistic and accurate analysis of the EE; 5. The performance metric of the coverage probability of D2D communication was derived to demonstrate the efficacy of the communication system. The EE for the proposed DF relay was also compared with the AF relay scheme. Numerical results also validated the efficiency of the proposed work.
# Development of a System for Cyclic Shear Tests on Full-Scale Walls
## 1 Introduction
The potential of compressed earth blocks (CEBs) reinforced masonry technology to be applied in humanitarian emergencies and multi-hazard resilient construction is high, mainly due to the environmental, economic, and social advantages resulting from the use of local materials with a low environmental impact and the use of the beneficiaries' labor in the construction works [11]. However, there are limits to this technology that need to be better known to build resiliently as, for example, the in-plane and out-of-plane shear behavior, considering they represent a limiting factor for masonry under seismic loads [12]. Nevertheless, the NIKER (New Integrated Knowledge Based Approaches to the Protection of Cultural Heritage from Earthquake-Induced Risk) project [13] focuses its experimental analyses on earthen structures and their strengthening to face earthquake events, validating other ways for their preservation in risk areas. To reach the SHS-Multirisk objectives, an experimental campaign to carry out on cyclic shear tests was performed at the Civil Engineering Department of UA (DECIVIL), involving 15 reinforced soil-cement compressed earth block walls. Within this program, a particular test system for full-scale specimens was developed, conditioned by the guidelines: simplicity, availability of resources (especially components, equipment, and workmanship), rationalization of the available space, and scalability of the tests in the available time. This article aims to present the system developed to conduct full-scale cyclic shear (combined with bending) tests on walls (or other structural elements), discussing its characteristics, the development process, the experiment execution process, and providing a fundamental analysis of the main test outputs.
# Effect of Heat Input on LMHMW Joint of Carbon Steel
## 4 Conclusions  
In this paper, the effects of several important process parameters related to Laser-MIG hybrid arc welding, such as laser power, arc current, the ratio of laser/arc power and heat input, were explored to identify their effects on the quality of welded joints. Accordingly, the following conclusions can be derived from the current results:  
(1) During the laser-MIG hybrid welding of Q235 steel, the heat source is concentrated, and the width of the HAZ is narrow due to the synergic effects of the laser and arc. The microstructure of the weld is mainly eutectoid ferrite precipitated along the grain boundary of the original austenite and a small amount of ferrite and pearlite with needle and plate-like morphology between the grains.  
(2) The microhardness of welded joints of Q235 steel has a direct relationship with heat input. However, the microhardness is mainly related to the ratio of laser and arc power when the difference in heat input is not obvious.  
(3) Within a certain range, the tensile properties of welded joints of Q235 steel increase with the increase of heat input. However, when the heat input is increased to a larger extent, tensile strength begins to decrease. By comparing and analyzing the microstructure and mechanical properties of all samples, it was found that the technological parameters of the No. 4 specimen are the most reasonable, whose tensile strength can obtain 458.5 MPa.  
Conceptualization, S.Z.; data curation, T.Y.; formal analysis, X.Y.; investigation, W.M.; methodology, S.Z.; project administration, X.Z.; resources, Q.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data available on request due to restrictions, e.g., privacy or ethics. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
## 2 Materials and Methods  
### Fish Sampling and Blood Collection  
Black scorpionfish specimens were collected during daylight hours using sets of trammel nets; 1.5 m wide and 32 m long trammel nets with inner layer mesh size of 28 mm and outer layer mesh size of 150 mm. Fish samples were captured in the nearshore coastal waters in the central-eastern Adriatic (northern Mediterranean) at depths ranging between 10 and 40 m. During a one-year study in 2008, a total of 96 black scorpionfish were collected, and they were divided according to the season in which they were caught (Table 1). Sampled fish did not manifest any external signs of trauma, injuries or infestation.  
Once caught, fish were placed in a tank and immediately anaesttised (MS-222 at 0.3 g L\({}^{-1}\)) for 2-3 min until loss of coordination was visible [22]. Afterwards, blood samples were collected by caudal vein puncture using disposable sterile plastic syringes fitted with a needle. A part of each blood sample was transferred into microhaematocrit capillary tubes  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Number** & **TL Range** & **W Range** & **Mean** & **Mean** & **Mean** \\  & & **(cm)** & **(g)** & **TL \(\pm\) SD** & **W \(\pm\) SD** & **CI \(\pm\) SD** \\ \hline Winter & 21 & 14.9–25.9 & 59–446 & 18.0 \(\pm\) 2.83 & 125.5 \(\pm\) 89.14 & 1.91 \(\pm\) 0.23 \\ Spring & 24 & 14.1–26.0 & 49–356 & 18.5 \(\pm\) 3.78 & 131.2 \(\pm\) 87.05 & 1.83 \(\pm\) 0.19 \\ Summer & 34 & 14.4–23.1 & 55–246 & 17.3 \(\pm\) 2.05 & 100.4 \(\pm\) 40.22 & 1.85 \(\pm\) 0.17 \\ Autumn & 17 & 14.0–20.8 & 50–189 & 17.1 \(\pm\) 1.87 & 99.3 \(\pm\) 38.93 & 1.87 \(\pm\) 0.16 \\ \hline \end{tabular}
\end{table}
Table 1: Number, total length (TL) and weight (W) range, mean total length, weight and condition index (CI) with standard deviation (SD) of _Scorpaena porcus_ during different seasons.  
and used for the haematological analysis of the field. The remaining blood was poured into tubes coated with anticoagulant lithium heparin and centrifuged at 12,000\(\times\)\(g\) for 90 s. The resultant plasma was collected and stored at \(-\)20 \({}^{\circ}\)C for further biochemical analysis.  
After blood sampling, the black scorpionfish were euthanised by severing the spinal cord; total length (TL, measured to the nearest 0.1 cm) and weight (W, measured to the nearest 1 g) were recorded, and sex was determined macroscopically by gonad observation. The fish condition index (CI) was calculated as CI = (100 \(\times\) W)/TL\({}^{3}\).
## 2 Materials and Methods
### Parametric Signal Analysis
#### 2.4.1 Auto-Regressive Models  
In order to carry out a parametric analysis, the auto-regressive (AR) models were implemented; these models are mathematically defined by Equation (3) as follows:  
\[x_{t}=\phi_{1}x_{t-1}+\phi_{2}x_{t-2}+\ldots+\phi_{p}x_{t-p}+\varepsilon_{t} \tag{3}\]  
where \(x_{t}\) is the value of the time-series in time \(t\), \(\phi_{1}\), \(\phi_{2}\),\(\ldots\), \(\phi_{p}\) are the parameters or coefficients of the AR model, \(p\) is the order of the AR model indicating how many past values are used to predict the current value, and \(\varepsilon_{t}\) is the error term at time \(t\) which is generally assumed to be white noise with zero mean and constant variance. In this context, the electrical signal of _Aloe vera_ (L.) _Burm. f._ is characterized as non-stationery and time-dependent, with dynamics that include both slow processes and rapid fluctuations. The use of a model of order \(p\) allowed for capturing of the temporal relationships without overfitting the model. The model order (\(p=9\)) was determined experimentally, evaluating different orders and selecting the one that minimized evaluation metrics.  
The objective is to capture the linear dependencies present in the _A. vera_ electrical signal. The AutoReg function of the statsmodels library in Python version 3.12.4 was used to fit the AR model. The model was evaluated using various delays to identify the optimal number of delays that minimizes the mean squared error (MSE). The parameters of the AR model were estimated using the Yule-Walker method. The AR model equation was constructed from these parameters, obtaining a mathematical model.  
In addition, based on MSE, the best model was determined considering a balance between accuracy and computational complexity. This is common in practical applications (as in this study), where a more compact model may be preferred even if it is not optimal in absolute terms of error.
# Compositional Analysis of Chalcopyrite Using Calibration-Free Laser-Induced Breakdown Spectroscopy
## 3 Results
### Quantitative Analysis
#### 3.2.3 Validity of Local Thermodynamic Equilibrium Assumption  
For fulfillment of the conditions of local thermodynamic equilibrium (LTE), the population and depopulation of atomic states must be driven predominantly by electron collisions instead of radiation. To serve this purpose, a sufficient value of \(\rm n_{e}\) is required. This threshold value of \(\rm n_{e}\) can be estimated from the McWhirter criterion, which provides a necessary condition for existence of LTE [17; 37]:  
\[\rm n_{e}\left(cm^{-3}\right)\geq 1.6\times 10^{12}(T_{e}\ (K))^{\frac{1}{2}}( \Delta E(ev))^{3} \tag{3}\]  
In this expression, \(\rm T_{e}\) and \(\Delta E\) stand for electron temperature and energy difference between the involved energy states expected to be in LTE, respectively. For Fe-I line at 438.58 nm and Cu-I line at 521.80 nm, energy difference between states are \(\Delta E\) = 2.83 eV and \(\Delta E\) = 2.37 eV, respectively. For these values of energy difference and plasma temperature of 11,314 \(\pm\) 566 K, Equation (3) gives \(\rm n_{e}\ (cm^{-3})\geq 6.13\times 10^{15}\) and \(2.3\times 10^{15}\). The value of experimentally calculated \(\rm n_{e}\) is much higher than the estimated values from Equation (3), hence it can be assumed that the condition of LTE exists in the plasma under investigation. This criterion has also been checked for many other lines of Fe, Cu, and Ca, identified in Chalcopyrite spectrum, and found to be fulfilled for all the used spectral lines.
###### Abstract  
With the development of the Internet of Things (IoT), most communication systems are difficult to implement on a large scale due to their high complexity. Multiple-input multiple-output (MIMO) precoding is a generally used technique for improving the reliability of free-space optical (PSO) communications, which is a key technology in the 6G era. However, traditional MIMO precoding schemes are typically designed based on the assumption of additive white Gaussian noise (AWGN). In this paper, we present a novel MIMO precoding method based on reinforcement learning (RL) that is specifically designed for the Poisson shot noise model. Unlike traditional MIMO precoding schemes, our proposed scheme takes into account the unique statistical characteristics of Poisson shot noise. Our approach achieves significant performance gains compared to existing MIMO precoding schemes. The proposed scheme can achieve the bit error rate (BER) of \(10^{-5}\) in a strong turbulence channel and exhibits superior robustness against imperfect channel state information (CSI).  
precoding; reinforcement learning; FSO; MIMO; Poisson shot noise +
Footnote †: journal: Applied sciences
# Design of Edge-IoMT Network Architecture with Weight-Based Scheduling
## 2 Related Works
### Data Collection
#### 3.2.2 Data Preprocessing  
For physiological signals, the signal clarity is extremely important. However, most of these physiological signals are non-stationary or non-linear, and during the data acquisition process, the signals are easily contaminated by noise. Therefore, the signal quality needs to be improved by preprocessing to remove noise to ensure the accuracy of medical analysis and diagnosis. With the characteristics of time and frequency domain analysis, in this work, wavelet transform (WT) is applied to decompose a signal into several scales with various frequency bands. At each scale, the signal's instantaneous structures can be determined approximately, which are available for denoising.  
Due to the increased amount of data and transmission power consumption with long-time monitoring, a data compression technique is essential, which achieves effective processing of physiological information, reduces the transmission load, and shortens the communication time. Since most of the physiological signals are non-stationary, the analysis method should not only have good resolution in the frequency domain, but also needs to have to localize signals in time domain. Based on the requirement, Discrete Wavelet Transform (DWT) is a wavelet transformation in which wavelets are discretely sampled, and it captures both frequency and position information of time such that the signal is approximately decomposed with detailed coefficients at each level through a process of low pass filtering, high pass filtering, and soft threshold function, respectively [35; 36; 37; 38; 39; 40]. For the denoised ECG, the wavelet basis function is db5 wavelet and the decomposition architecture is five-layer; db5 wavelet and three-layer decomposition are used to decompose EEG signals; EMG signals use db6 wavelet and three-layer decomposition for signal decomposition. Figure 3 illustrates the compression structure with WT.