esa-sceva/satcom-chunk-collection huggingface dataset:
# Green Communication in IoT for Enabling Next-Generation Wireless Systems
###### Abstract  
Recent developments and the widespread use of IoT-enabled technologies has led to the Research and Development (R&D) efforts in green communication. Traditional dynamic-source routing is one of the well-known protocols that was suggested to solve the information dissemination problem in an IoT environment. However, this protocol suffers from a high level of energy consumption in sensor-enabled device-to-device and device-to-base station communications. As a result, new information dissemination protocols should be developed to overcome the challenge of dynamic-source routing, and other similar protocols regarding green communication. In this context, a new energy-efficient routing protocol (EFRP) is proposed using the hybrid adopted heuristic techniques. In the densely deployed sensor-enabled IoT environment, an optimal information dissemination path for device-to-device and device-to-base station communication was identified using a hybrid genetic algorithm (GA) and the antilon optimization (ALO) algorithms. An objective function is formulated focusing on energy consumption-centric cost minimization. The evaluation results demonstrate that the proposed protocol outperforms the Greedy approach and the DSR protocol in terms of a range of green communication metrics. It was noticed that the number of alive sensor nodes in the experimental network increased by more than 26% compared to the other approaches and lessened energy consumption by about 33%. This leads to a prolonged IoT network lifetime, increased by about 25%. It is evident that the proposed scheme greatly improves the information dissemination efficiency of the IoT network, significantly increasing the network's throughput.  
IoT; energy optimization; genetic algorithm; antilon optimization; routing protocol; DSR; energy consumption +
Footnote †: journal: _computers_  
_Article_
## 6 Conclusions  
This study analyzed the potential of RF, SVM, and ELM in LC classification at six different landscapes by integrating Sentinel-1 and Sentinel-2 images. We also used SVM-RFE to select the most informative features from Sentinel-1, Sentinel-2, spectral indices, and textural information. Furthermore, we discussed the impact of G-SMOTE on the classification accuracy of ML algorithms. The result showed that NDVI, VV, and B12 could contribute as main features to improve LC classification accuracy. Our findings also indicated that all three ML algorithms, especially RF and SVM, are robust approaches for LC classification in different landscapes.  
Moreover, the results confirmed that applying G-SMOTE has a significant impact on the accuracy of minority classes. After applying G-SMOTE to ML algorithms, the differences between UA and PA metrics for minority and majority classes have decreased. However, there were significant differences among them without considering the class imbalance problem. Further study could investigate the performance of other algorithms and sample sizes in balancing data.  
Conceptualization, O.G.; Data curation, H.E.; Methodology, H.E. and A.N.; Software, H.E. and A.N.; Supervision, O.G.; Validation, A.N.; Writing--original draft, H.E.; Writing--review & editing, B.F., J.A. and O.G. All authors have read and agreed to the published version of the manuscript.  
The open-access fee of this research was funded by the Institute of Advanced Research in Artificial Intelligence (IARAI) GmbH.  
Not applicable.  
Data available on request.  
The authors thank the Copernicus programs by the European Space Agency (ESA) for Sentinel-1 and Sentinel-2 datasets, and are also grateful to Google Earth Engine (GEE) for their services. The authors thank the three anonymous referees for their helpful and critical comments that helped us improve an earlier version of the manuscript. The open-access fee of this research was funded by the Institute of Advanced Research in Artificial Intelligence (IARAI).  
The authors declare no conflict of interest.
# Tracking the Rhythm: Pansori Rhythm Segmentation and Classification Methods and Datasets
## 4 Experiments and Results
### Classification-Based Rhythm Segmentation  
The modified HR network and the GlocalMuseNet were trained in an end-to-end manner for the classification of Passori rhythms. The entire rhythm classification dataset was split randomly into the train (80%) and test (20%) sets. The performance of the trained classifier on the Passori rhythm test dataset is illustrated in Table 3.  
The GlocalMuseNet outperforms the HR net in this experiment with few training parameters. The GlocalMuseNet with eight initial convolution filters outperforms the other classifiers. The HR network has higher computational complexity than the GlocalMuseNet with 16 initial convolution filters but has relatively more computational complexity. The GlocalMuseNet with eight initial convolution filters perform the best and lightweight for training. The same network was used in classification-based rhythm segmentation. The evaluation metrics used in the classification of rhythmic patterns were accuracy, F1-score, and the area under the receiver operating characteristic curve (ROC-AUC) score. The confusion matrix of our three classifiers is shown in Figure 3a-c. The \"Other\" rhythmic class was found to be more confusing for GlocalMuseNet because it includes a diversified rhythm category, as described on Section 2. \"AniriChangzo\" was found to be confusing with \"Aniri\" for all classifiers, because these two classes have some common characteristics.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network** & **Test Accuracy** & **F1-Score** & \
\begin{tabular}{c} **ROC-AUC** \\ **Score** \\ \end{tabular} &
\begin{tabular}{c} **Training** \\ **Parameters** \\ \end{tabular} \\ \hline HR net & 0.8554 & 0.811 & **0.971** & 9,523,655 \\ GlocalMuseNet (n = 8) & **0.8795** & **0.815** & 0.969 & **1,753,567** \\ GlocalMuseNet (n = 16) & 0.8591 & 0.784 & 0.961 & 6,967,951 \\ \hline \hline \end{tabular}  
* n = number of convolution features in first layer.  
\end{table}
Table 3: Classification score of rhythm classifier.  
The segmentation results of a test sample using GlocalMuseNet are shown in Figure 4. The ground-truth rhythmic boundaries and class categories are described in the caption of the figure. The result shows a strong calibration of segmented rhythm with ground truth. However, some results are biased from actual ground truth due to the lack of training data.
# SC-FDE Layer for Sensor Networks in Remote Areas Using NVIS Communications
## 6 Results
### BER vs. \(E_{b}/N_{o}\)  
On the other hand, Figure 7 presents the relationship between received power (EbNo) and the BER obtained along with the FSK, QAM, and OFDM studied in [20].  
FSK and QAM need higher EbNo than the OFDM and SC-FDE, which show very similar results. For BER of \(10^{-3}\), the SC-FDE needs 1 dB less than the OFDM, but for BER \(10^{-4}\), the OFDM needs 17.5 dB and the SC-FDE needs more than 18 dB.
## 3 Results
### Precision  
Precision at the lowest point on the calibration curve (2.5 \(\upmu\)g/mL) was evaluated based on reported concentration RSD\(\%\). The method was found to be reproducible, with an RSD value of 1.54% for CBDV; 0.45% for CBDA; 0.72% for CBGA; 1.88% for CBG; 1.63% for CBD%; 1.14% for CBN; 3.77% for \(\Delta\)9-THC; 4.25% for \(\Delta\)8-THC; 3.38% for CBC; 2.79% for THCA. These results indicate that the method is precise at the evaluated concentration.
# Metal Release and Cell Viability of 316L Stainless Steel Sputter-Coated with N-Doped a-C:H Coatings
## 3 Results and Discussion
### Cytotoxicity Evaluation
#### 3.3.2 Fibroblasts  
The statistical analysis of the results from the fibroblast monocultures revealed statistically significant differences between the following groups: saliva and SS (\(p<0.001\)), CH_6N (\(p<0.01\)) and CH_9N (\(p<0.001\)); SS and CH_0N (\(p<0.01\)); and CH_0N and CH_9N (\(p<0.05\)), Figure 5b.  
Generically, the results from the fibroblast monocultures show that all medians were below the SL line. All the studied groups showed a decrease in the median cell viability, in the following order: saliva \(>\) CH_0N \(>\) CH_6N \(>\) CH_9N \(>\) SS (Figure 5b). The worst cell viability was obtained for the extracts released from the medical grade 316L SS, whose median is above but quite close to the 70% level. In the frame of the current work, the extracts from SS can still be classified as biocompatible (as expected); however, the carbon -based coatings were found to significantly increase the overall biocompatibility. Moreover, the extracts from the undoped CH_0N coating showed the best cell viability.  
The isolated effect of Ni ions on the viability and number of different types of fibroblasts were studied by other researchers [55]. Accordingly, 50% of cell death and cell viability were found for BALB/3T3 clone A3I fibroblast for Ni ions concentrations of ~0.05 and 0.09 mM/L, respectively [55]. Ni is also capable of actively inducing ROS in intact mammalian cells, which may be the first step of a Ni-mediated ROS mechanism of Ni carcinogenicity [56]. Moreover, Ni was found to reprogram the metabolism of primary human lung fibroblasts and mouse embryonic fibroblasts by strongly repressing mitochondrial fatty acid oxidation, which may contribute to Ni-induced carcinogenicity [57]. Dioxygenases are also inhibited by Ni ions, which can have widespread negative effects on cells, namely concerning DNA repair [58].  
The effect of Fe ions in fibroblasts' viability cannot be disregarded as well [59]. In fact, the cytotoxicity to BALB/3T3 with solutions of iron chloride was found to be concentration-dependent, and a synergistic effect with nickel chloride solutions may occur [60]. Additionally, cell membranes can be damaged by iron ions via Fenton and Haber-Weiss reactions due to the production of ROS [52].  
In the current work, a combined effect between the three metallic ions in the extracts may influence the overall cell viability of fibroblast monocultures.
# Damage Detection in Beam Bridges Using Quasi-static Displacement Influence Lines
## 4 Experimental Example
### Comparison of the Experimental Results Between the Proposed Method and the Traditional Method  
#### 4.3.1 Results with the Same Loading Conditions before and after Damage  
In this section, the data obtained from Part I to Part III are used to compare the performance of the proposed method with that of the traditional approach. As shown in Figure 13(a), compared with the reference state (case 1), it is difficult to differentiate the displacement ILs between the healthy state (case 2 through case 10) and the damaged state (case 11 through case 20). With the traditional method, the difference between the displacement ILs before and after damage (sensor #10 is randomly selected; note that all displacement ILs of the other sensors have variations that are similar to those of sensor #10) are drawn in Figure 13(b). Evidently, the traditional method does not work for this damaged case. The reason for this inefficacy lies in the fact that the quasi-static displacement ILs are no longer  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
\begin{tabular}{c} Healthy bridge; \\ 100 kg moving vehicle \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Description of all cases for the quasi-static load tests on the model bridge.  
Figure 13: Photo of the devices designed to simulate the damage of the transverse connection between the two T-shaped beams.  
smooth curves but volatile curves and thus, the difference in the ILs is no longer sensitive to the damage suffered by the bridge, especially under conditions of small degrees of damage. As discussed in Section 2.2.1, the Hankel matrix of the damage feature contains 22 rows and 24 columns due to the 10 sensors used in this test; the damage feature metrics for the healthy and damaged model bridges are drawn in Figure 14c. The effectiveness of the proposed method is therefore demonstrated by this experimental example.
###### Abstract  
Devastating earthquakes around the world highlight the crucial need to understand the seismic performance of structures. Local soil conditions are among the most significant factors influencing a structure's seismic behavior. Earthquake-soil-structure interactions directly affect seismic damage levels. In performance-based earthquake engineering, accurate target displacements enable a more realistic estimation of the expected performance levels for structures. This depends on obtaining realistic local soil conditions. This study conducted structural analyses on seven different variables, considering four different local soil conditions specified in Eurocode 8. The variables selected were importance class, peak ground acceleration (PGA), damping ratio, ground storey height, frame openings, number of storeys, and storey height, applied to a symmetrical and regular reinforced concrete structure. Period, base shear, stiffness, and target displacements were obtained for each variable through pushover analyses for the four various local soil conditions. All structural results were compared with one another and with other variables. This paper also aimed to reveal the effect of local soil conditions in the context of the 6 February 2023 Kahramanmaras (Turkiye) earthquakes. The study confirms that variations in soil types, as classified in Eurocode 8, have a major impact on the seismic behavior of reinforced-concrete structures. Weaker soils amplify seismic effects, increasing target displacements and structural vulnerability.  
local soil condition; importance class; damping ratio; PGA; storey; RC
## 5 Experimental Evaluation
### Functional Validation
#### 5.2.2 MQTT-Based Communications  
In this test case, both OBUs (1 and 2) use the MQTT protocol to communicate through the Edge MQTT broker. First, the MQTT Client of each OBU is connected to the Edge MQTT broker and both OBUs subscribe to the same topic that is associated with a geographical ROI. Subsequently, OBU 1 triggers the transmission of a DENM message towards the Edge MQTT broker, which subsequently distributes the DENM message to OBU 2. In the opposite direction, OBU 1 is notified by the Edge MQTT broker upon the reception of a DENM transmitted by OBU 2. The warnings are displayed on the web interface of each OBU. Listing 1 shows an extract of a JSON encode DENM message transmitted by one of the OBUs.
## 1 Introduction  
Traffic safety remains a paramount concern that the transportation system must address. The World Health Organization's Global Road Safety Status Report states that road traffic accidents are the eighth leading cause of death, with 1.35 million people dying each year globally due to traffic accidents. Moreover, the vast majority of these accidents are caused by vehicle collisions [1]. Prior to formulating various strategies to prevent vehicular collisions, it is essential to accurately assess the potential risks surrounding vehicles. Thisnot only significantly reduces the likelihood of accidents, but also enhances the efficiency of road traffic. Generally speaking, the assessment of potential road risks is achieved through traffic risk indicators, which are traditionally categorized into three types: time-based risk indicators, distance-based risk indicators, and deceleration-based indicators. In practical applications, time-based risk indicators are the most widely utilized, such as time to collision (TTC) [2], time exposed to collision (TET) [3], and modified time to collision (MTTC) [4]. Distance-based risk indicators, such as headway time (H) [5], are primarily employed to calculate the safe distance required to avert collisions. Deceleration-based indicators assess driving risks in emergency situations based on deceleration rates, such as conflict speed (CS) [6]. Although these safety surrogate indicators are widely used in conflict quantification studies across various traffic scenarios, they also have certain limitations: (1) traditional safety surrogate indicators do not account for the driver's reaction characteristics; (2) constraints of boundary conditions, such as traditional TTC-based indicators failing to quantify rear-end collision risks when \"the following vehicle's speed is less than that of the leading vehicle\"; (3) the uncertainty in the risk level classification thresholds also leads to deviations in the risk quantification results. To address the limitations of single risk quantification indicators, this study comprehensively analyzes the vehicle kinematic mechanisms and the driver's reaction characteristics. It introduces the concept of the driver behavior molecular potential field to systematically describe the distribution of traffic flow risks, the degree of response to the traffic environment, and the dynamic relationship between these factors.  
The structure of the article is as follows: Section 1 presents existing quantification indicators for vehicular driving risks. Section 2 discusses recent research in the field of transportation based on field theory. Section 3 draws an analogy between vehicles and self-driven particles from the perspective of molecular force fields, establishing relevant models for the quantification of driving risks. Section 4 validates the feasibility of the proposed methods by applying a vehicle trajectory prediction model. Section 5 outlines the experimental setup and data processing procedures. Section 6 showcases the assessment results of the driving risks using this method. Section 7 concludes the paper.
## 5 Thermal Analysis and Results Discussion
### Thermal Analysis of a 6U Satellite with Heat Pipes
The maximum operational range of CCU is \(-\)40 \({}^{\circ}\)C to \(+\)85 \({}^{\circ}\)C and the CCU temperature profile shown in Figure 19 indicates 35 \({}^{\circ}\)C of difference from its maximum limit. Although the temperature of CCU is within its operational limit, the overall system interface temperature, HX temperature in this case, remains high at 43 \({}^{\circ}\)C which is still higher for other components. When compared to BMR panel configuration, deployable radiator panel radiates two times higher for the same heat load. Figure 17: 6U Satellite body-mount radiator with heat pipes—Temperature.
# Influence of Cutting Parameters and MQL on Surface Finish and Work Hardening of Inconel 617
## 4 Results and Discussion
### Study C: MQL vs. Conventional coolant
#### 4.3.1 Surface Roughness  
Figure 5 illustrates the measured roughness for the generated surfaces in T1, T4, and T7 cutting conditions, comparing the coolant deposition methods. It can be observed that across all tests, MQL generated a lower average surface roughness compared to the flood coolant regardless of the cutting parameters used. The use of MQL in this process yielded up to a 47% improvement in the average roughness of the machined Inconel 617 workpiece. This aligns with studies performed on Inconel 718 where MQL greatly reduced the surface roughness of the machined surface in comparison to the flood coolant or dry conditions [9; 22].  
To better visualize the effect of the coolant on the generated surface, a surface topography map was generated for T7, which had the largest disparity between its average roughness, shown in Figure 6. It can be seen in the images that machining in the flood coolant creates a uniform peak-and-valley lay pattern, whereas the surface machined with MQL has a much more irregular but smoother finish with lower average roughness.  
Figure 4: Cutting forces observed in the machining of Inconel 617 under T7 conditions (DoC = 0.5 mm, feed = 0.075 mm/rev, cutting speed = 75 m/min). The plots compare MQL and the flood coolant for (**a**) radial force, (**b**) tangential force and (**c**) feed force.
## 4 Simulation Results  
In this section, we evaluate the performance of the proposed OMARA algorithm and compare it with ASCRA [37], PMMRA [38], and Vickrey. We first describe the experimental setup in Section 4.1 and then present the relevant simulation results to demonstrate the advantages of our proposed OMARA algorithm in Section 4.2.
## 7 Results and Discussion
### Mechanical Rotation Issues
The usefulness of these solutions may be questioned, however, considering that they require more complex mechanical systems, the other target-aligned model (TA/TR) operates well, and target-aligned models are seldom used anyway due to their complex implementation.
## 3 Results and Discussions
### Validation Results of Improved Non-Destructive Detection Method for SSC in Bunch-Harvested Grapes
#### 3.3.2 Prediction of SSC for Bunch-Harvested Grapes  
After obtaining the corresponding pixels of each grape, the average reflectance spectrum for each grape was calculated. Using the SSC prediction model, the predicted value of the SSC of each grape was obtained.  
In grape production, it is necessary to obtain the average, maximum, and minimum values of the SSC of the whole bunch of grapes, as an indicator of the maturity classification, to determine whether it is suitable for picking [40]. The results are shown in Table 3.  
It can be seen that among the eight bunches of grapes, the prediction error of the average SSC value of the whole bunch was between 0.09 and 1.05 Brix%, the prediction error of the minimum SSC value of the whole bunch was between 0.04 and 1.34 Brix%, and the prediction error of the maximum SSC value of the whole bunch was between 0.04 and 1.34 Brix%. The test results prove that the SSC non-destructive detection method was also effective and accurate for the whole bunch of grapes imaged.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline SSC & \multicolumn{3}{c}{**1 st Bunch**} & \multicolumn{3}{c}{**2 nd Bunch**} & \multicolumn{3}{c}{**3 rd Bunch**} & \multicolumn{3}{c}{**4 th Bunch**} \\ (Brix\%) & **Avg** & **Min** & **Max** & **Avg** & **Min** & **Max** & **Avg** & **Min** & **Max** & **Avg** & **Min** & **Max** \\ \hline Measured value & 16.78 & 15.5 & 18.8 & 17.18 & 15.4 & 18.4 & 15.33 & 14.2 & 16.3 & 16.47 & 14.6 & 18.2 \\ Predicted value & 17.43 & 16.21 & 18.76 & 17.39 & 15.36 & 18.93 & 15.79 & 13.84 & 17.64 & 16.38 & 14.89 & 17.88 \\ Error & 0.65 & 0.72 & 0.04 & 0.21 & 0.04 & 0.53 & 0.46 & 0.36 & 1.34 & 0.09 & 0.29 & 0.22 \\ \hline SSC & \multicolumn{3}{c}{**5 th Bunch**} & \multicolumn{3}{c}{**6 th Bunch**} & \multicolumn{3}{c}{**7 th Bunch**} & \multicolumn{3}{c}{**8 th Bunch**} \\ (Brix\%) & **Avg** & **Min** & **Max** & **Avg** & **Min** & **Max** & **Avg** & **Min** & **Max** & **Avg** & **Min** & **Max** \\ \hline Measured value & 15.57 & 14.2 & 16.7 & 15.94 & 14.8 & 16.9 & 18.68 & 17.0 & 21.6 & 23.29 & 20.9 & 25.5 \\ Predicted value & 16.62 & 15.51 & 17.30 & 16.88 & 16.14 & 17.72 & 19.42 & 17.54 & 20.49 & 23.04 & 21.07 & 24.83 \\ Error & 1.05 & 1.31 & 0.60 & 0.94 & 1.34 & 0.82 & 0.74 & 0.54 & 1.11 & 0.25 & 0.17 & 0.67 \\ \hline \end{tabular}
\end{table}
Table 3: SSC prediction results for bunch-harvested grapes.  
Figure 13: Obtaining the pixels of a single grape. (**a**) A bunch of grapes. (**b**) Segmentation of single grape by Mask R-CNN. (**c**) Removing the overbright area.  
According to the average coordinates of the grape pixel points, the predicted SSC results of each grape were displayed in the color map to obtain the visualization result (as shown in Figure 14).