esa-sceva/satcom-chunk-collection huggingface dataset:
# Gas Turbine Off-Design Performance Adaption Based on Cluster Sampling
## 3 Results and Discussion
### Discussion of Random Sampling and Cluster Sampling for a Long Period  
To compare and demonstrate the performance of two sampling methods using long-term data, the field data were extended to a one-year period. Figure 9 shows the compressor inlet temperature data in a one-year period. The data were collected every minute and the points with loads below 75% were removed.  
Overall, summer is the peak of electricity consumption, and the gas turbine units run for longer periods, resulting in more data points. In contrast, there were less data in winter. The mean value of the compressor inlet temperature was 301.8 K, with a range of [275.9 K, 316.3 K] in a one-year period. The distribution of the dataset is shown in Figure 10.  
Figure 8: Prediction of the isentropic efficiency of the turbine and system efficiency.  
Figure 9: The compressor inlet temperature data in a one-year period.  
As can be seen in Figure 10, the data have three distribution centers, corresponding to winter, spring/autumn, and summer. The operation conditions of the field data are naturally over-dispersed due to the over-dispersion of environmental conditions. The field data of high (315 K) or lower (275 K) compressor inlet temperature conditions are much less than that under about 307 K. If the random sampling method is used, theoretically, the distribution of the collected data will be consistent with the original data distribution, mainly distributed around 307 K around summer, and the rest of the data distributed around 294 K and 284 K. This will result in the rare operation conditions being probably covered up by the common operation conditions. Furthermore, considering the computation cost of performance adaption, few sampling points are selected from the original data, making it more likely to miss some operating conditions. Cluster sampling clusters the entire dataset based on the minimum distance to the cluster centers, ensuring the diversity and representativeness of the sampling points. Correspondingly, the clustering method was proposed to detect these rare operation conditions. Taking 10 sampling points as an example, the clustering results and the sampling points obtained by the cluster sampling method are shown in Figure 11, together with random sampling points marked by green triangles.  
Compared with the random sampling points, cluster sampling points marked by red circles cover a more diverse range of operating conditions and detect the rare operation  
Figure 11: Clustering results and sampling points obtained by the two sampling methods.  
Figure 10: Data distribution of the compressor inlet temperature.  
conditions both in low and high temperatures. As it can be seen, cluster sampling maintains its advantages of diversity and representativeness even with a longer data collection time and larger data volume.
# Reflective Fiber Surface Plasmon Resonance Sensor for High-Sensitive Mercury Ion Detection
## 2 Simulation and Experiment
### Experimental Device  
The measurement system of the reflective fiber SPR Hg\({}^{2+}\) sensor is shown in Figure 6, which consists of a halogen light source, a Y-type fiber coupler, a marine spectrometer, a computer, and a reflective SPR fiber sensor head. Light is emitted from the light source and transmitted to the sensing area through one port of the Y-type fiber. The sensor probe carries the Hg\({}^{2+}\) concentration information, which enters the spectrometer through another port of the Y-type fiber. Finally, the Hg\({}^{2+}\) concentration can be obtained by demodulating the output spectrum shown on the computer.
# Life Cycle Assessment of Medical Waste Management: Case Study for Istanbul
## 4 Conclusions  
Sustainable medical waste management aims to reduce the amount of waste produced from healthcare facilities, particularly hazardous waste. Medical waste management alternatives were evaluated using life cycle assessment analysis in this research. Scenario 4 (waste segregation at the generation points/waste minimization + incineration + sterilization + landfill) had the lowest total environmental impact in comparison with the baseline and alternative scenarios. In the environmental impact categories of acidification, freshwater aquatic toxicity, and human health toxicity, scenario 4 presented significantly better environmental performance than the current medical waste management system in Istanbul, while the global warming impact of the baseline scenario was lower than that of scenario 4. The main limitations of this study are listed as follows: (i) an economic analysis for each scenario was not conducted, and (ii) recycling and leachate treatment were not considered in LCA analysis. Therefore, it is highly recommended to implement a more comprehensive model for a further decrease in the environmental impacts of medical waste management. Furthermore, decision-makers should consider incorporating the recycling process into the current medical waste management system and building a sterilization facility on the Asian side of Istanbul.  
Conceptualization, E.C.; methodology, E.C.; software, I.A.E.Y.; investigation, I.A.E.Y. and C.O.Y.; resources, I.A.E.Y. and C.O.Y.; writing--original draft preparation, C.O.Y.; writing--review and editing, C.O.Y., E.C. and A.Y.; visualization, C.O.Y. and E.C.; supervision, E.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Environmental Impact Category**} & \multicolumn{5}{c}{**Impact Value**} \\ \cline{2-6}  & **Scenario 0** & **Scenario 1** & **Scenario 2** & **Scenario 3** & **Scenario 4** \\ \hline Global Warming & 20.62 & 14.4 & 22.45 & 19.67 & 22.85 \\ \hline Acidification & 19.28 & 38.64 & 13.46 & 19.19 & 9.42 \\ \hline Eutrophication & 20.1 & 19.25 & 20.24 & 19.99 & 20.42 \\ \hline Ozone Layer Depletion & 22.27 & 12.89 & 24.91 & 17.42 & 22.51 \\ \hline Freshwater Aquatic Toxicity & 18.67 & 44.65 & 11.34 & 19.23 & 6.1 \\ \hline Human Health Toxicity & 18.45 & 48.41 & 10 & 19.14 & 3.99 \\ \hline Total Impact Value & 119.39 & 178.324 & 102.4 & 114.64 & 85.29 \\ \hline Impact Ratio (\%) & 19.89 & 29.79 & 17.06 & 19.10 & 14.21 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Impact values of each scenario for the impact categories.
## 4 Self-Sensing Geosynthetics
### Carbon-Nano-Tube-Based Self-Sensing Geosynthetics
They showed that incorporating 0.5 wt.% MWCNTs into the PVC composite increased the tensile modulus fivefold and considerably reduced the failure strain while leaving the ultimate strength nearly unchanged. They also reported that MWCNT-containing PVC-based self-sensing geosynthetics exhibited higher GFs compared with standard foil strain gauges and conventional metals, indicating that MWCNT-containing PVC-based self-sensing geosynthetics have great potential for application to damage detection and infrastructural performance monitoring. Notably, geosynthetics are typically installed at such depths in civil engineering applications that the geosynthetic temperature does not change appreciably throughout the service lifetime. Hence, the temperature will likely not affect the strain sensitivity of self-sensing geosynthetic devices in typical applications. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Polymer Type** & **Filler Type** & \
\begin{tabular}{c} **Filler Concentration** \\ **(wt.\%)** \\ \end{tabular} &
\begin{tabular}{c} **Maximum** \\ **Strain (\%)** \\ \end{tabular} & **Mean GF** \\ \hline HDPE & CB & 50 & 15 & 20 \\ CNT & 4.38 & 15 & 7 \\  & CB & 33 & 5 & 25 \\  & CNT & 2.8 & 4 & 0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average GFs obtained for CNT- and CB-filled PP and HDPE.
## 3 Experimental Results and Discussions
### Communication-Performance Evaluations
(Inset: the corresponding eye diagrams at the data rates of 500 Mbps, 1.0 Gbps, and 1.2 Gbps). Figure 7: Measurement of real-time BER with OOK format from Hop 1. (Inset: the corresponding eye diagrams at the data rates of 1.0 Gbps and 1.5 Gbps).
## 3 Results
### Experimental Results  
Figure 8 presents a comparison between trajectory tracking accuracies--measured by RMSE--of the control structure for three configurations using real robotic arms between the two studied NN architectures: (A) fast trajectories and (B) intermediate trajectories for a 3-DOF robotic arm and (C) slow trajectories with a 5-DOF serial robotic arm.  
Figure 8: Performance of trajectory tracking with various NNs for (**A**) 3-DOF robotic arm, fast trajectories (**B**) 3-DOF robotic arm, intermediate trajectories; (**C**) 5-DOF robotic arm, slow trajectories.  
Figure 8 also shows that the INN method achieves accuracy close to that obtained with all training data by using solely 100 trajectories, whereas the GNN method requires around 300 trajectories to effectively learn the dynamic response of the PD controller, regardless of the robot arm or trajectory speed.  
Table 4 summarizes the numerical RMSE values among the n-test trajectories and the reduction rates of the errors compared to the reference PD controller. Specifically, the INN method consistently shows at least a 75% reduction in tracking errors compared to the PD controller alone, with an RMSE under 1 degree across all configurations.
# Mobility- and Energy-Aware Cooperative Edge Offloading for Dependent Computation Tasks +
## 4 Dynamic Computation Offloading Problem Formulation
If the decision is made to offload task \(k\) to either the helper node UH\({}_{i}\) (\(h_{k}^{i}=1\)) or MEC server \(S_{m}\) (\(s_{k}^{m}=1\)), the finish time \(FT_{k}\) of task \(k\) should be shorter than the sojourn time \(T_{s,k}\) in the coverage of that computation resource, meaning that any chosen resource should remain in range until the completion of the execution of task \(k\). The offloading to server \(S_{m}\) via relaying by helper node UH\({}_{i}\) requires that the UE is in the coverage area of the helper (with corresponding sojourn time \(T_{s,t}^{h_{i}}\)), that the helper is in the coverage of the server (\(T_{s,t}^{\text{syn},m}\)), and that the UE is still in the coverage of the server (\(T_{s,t}^{\text{syn}}\)) for directly returning the computation results from the server to the UE. We further note that the sojourn time \(T_{s,k}\) in constraint C5 depends on the time \(t\), as explicitly indicated by the \(t\) subscript on the right-hand side of Equation (24) and in Equation (20). In order to avoid notational clutter, we have omitted the \(t\) subscript on the left-hand side of Equation (24) and in the optimization problem in Equation (23). However, we emphasize that the optimization problem in Equation (23) is solved in an online fashion at a particular time \(t\) when a UE request for computing a set of \(\mathcal{K}\) tasks arrives. The start time \(ST_{k}\) of a task (with \(ST_{1}=0\)) is relative to the UE request arrival time \(t\).
###### Abstract  
Vehicular ad hoc networks have played a key role in intelligent transportation systems that considerably improve road safety and management. This new technology allows vehicles to communicate and share road information. However, malicious users may inject false emergency alerts into vehicular ad hoc networks, preventing nodes from accessing accurate road information. In order to assure the reliability and trustworthiness of information through the networks, assessing the credibility of nodes has become a critical task in vehicular ad hoc networks. A new scheme for malicious node detection is proposed in this work. Multiple factors are fed into a fuzzy logic model for evaluating the trust for each node. Vehicles are divided into clusters in our approach, and a road side unit manages each cluster. The road side unit assesses the credibility of nodes before accessing vehicular ad hoc networks. The road side unit evicts a malicious node based on trust value. Simulations are used to validate our technique. We demonstrate that our scheme can detect and evict all malicious nodes in the vehicular ad hoc network over time, lowering the ratio of malicious nodes. Furthermore, it has a positive impact on selfish node participation. The scheme increases the success rate of delivered data to the same level as the ideal cases when no selfish node is present.  
vehicle ad hoc networks; road safety; fuzzy logic +
Footnote †: journal:  
00 2022 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 5 Discussion of the Results
### Identification
#### 5.1.1 Optimization of the Hyperparameters
In this paper, we present an updated version of the comparison of the performance for the different representations, using the optimized hyperparameters from Table 2. The result is shown in Figure 8, where it can be seen that WSST still outperformed the other techniques for high values of SNR, while STFT had a better performance for lower values of SNR. The black line identifies the results from the preliminary paper [2] using the WSST technique. We note that the optimization of the parameters provided a significant improvement for all the values of SNR. \begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Machine Learning Algorithm** & **Optimal Values** & **Identification Accuracy** & **Computing Time Ratio** \\ \hline
**WSST** & == & == & == \\ \hline SVM & \(C=2^{7}\), \(\gamma=2^{8}\) & **0.9236** & 10 \\ \hline KNN & K = 3 & 0.8388 & 8.57 \\ \hline Decision Tree & \(N_{s}=12\) & 0.8225 & 7.28 \\ \hline
**STFT** & == & == & == \\ \hline SVM & \(C=2^{7}\), \(\gamma=2^{9}\) & **0.8503** & 4.64 \\ \hline KNN & K = 17 & 0.706 & 4.285 \\ \hline Decision Tree & \(N_{s}=18\) & 0.753 & 4 \\ \hline
**1D Frequency domain** (magnitude component) & == & == & == \\ \hline SVM & \(C=2^{12}\), \(\gamma=2^{8}\) & 0.753 & 4.35 \\ \hline KNN & K = 3 & **0.7558** & 1.785 \\ \hline Decision Tree & \(N_{s}=18\) & 0.7352 & 1.428 \\ \hline
**1D Time domain** (magnitude component) & == & == & == \\ \hline SVM & \(C=2^{12}\), \(\gamma=2^{8}\) & 0.7558 & 3.64 \\ \hline KNN & K = 9 & **0.7910** & 1.1 \\ \hline Decision Tree & \(N_{s}=17\) & 0.7265 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Optimal values for the hyperparameters of the machine learning algorithms with the related identification accuracy and computing time ratio. Finally, the confusion matrices were calculated. Two figures are presented: The original confusion matrix from [2] is presented in Figure 9, where KNN with K = 1 was used. A new confusion matrix is provided in Figure 10, which is based on the optimal values reported in Table 2: SVM with \(C=2^{7}\), \(\gamma=2^{8}\). Both confusion matrices have been calculated at SNR = 50. The lower accuracy for the last three phones (10 to 12) was due to the strong similarity of the RF emissions of the Apple (i.e., iPhone) models. Figure 8: Accuracy for different values of SNR (expressed in dB) for 1D TD, 1D FD, 2D STFT and 2D WSST using the optimal selection of hyperparameters defined in Table 2. The black line is inserted to show the comparison with the application of the WSST technique with KNN (K = 1). Figure 9: Confusion matrix for WSST calculated with the KNN machine learning algorithm with K = 1 at SNR = 50 dB.
## 2 Materials and Methods
### Our Proposed Framework  
#### 2.3.1 Feature Extraction  
The traditional force estimation methods used the statistics of the extracted features from the biological signal [27]. However, all those methods require manual parameter tuning for feature extraction, which leads to poor accuracy and higher sensitivity to the noise. It is difficult to extract the valid features of sEMG using a manual method due to the non-stationary characteristic and the nonlinearity of sEMG. Deep learning methods have been widely used in feature extraction. Studies have proved that the deep neural network is more effective in the feature extracting of one-dimensional time series [28]. CNN is a kind of feed-forward neural network which includes a convolution operation and deep structure. The essential characteristics of this network are local perception and parameter sharing. It can realize the high-dimensional representation of the original data and extract valid spatial features from the original input data. In 2016, [PERSON] et al. [29] proposed the ResNet based on the cross-layer connection principle of the VGGNet to effectively solve the degradation phenomenon, and the input \(x\) is mapped and added to the output feature. The framework of ResNet is shown in Figure 4. In this section, we used ResNet to extract the different features of electromyography. It is a one-dimensional operation on two-dimensional input data, and it was used to extract the spatial features of the time series from the input. The network consists of the convolution layer, pooling layer, and the residual block, which showed as follows:  
1. Convolutional Layer  
The convolution layer is mainly used to extract the features of local regions by the convolution operation of sEMG time series segment \(x_{i}\). Different convolution kernels extract different time series features, and the convolution operation can be expressed as:  
\[x_{j}=f\left(\sum_{i}k^{i,j}*x^{i}+b^{i}\right) \tag{3}\]  
where, \(x^{i}\) represents the input time series of \(i\)th layer, \(x_{j}\) represents the feature of \(j\)th layer, \(k^{i,j}\) represents the convolution kernel, \(f(.)\) represents the ReLU activation function.  
Figure 4: ResNet framework.  
2. Pooling Layer The pooling layer mainly conducts feature selection by subsampling the output of the convolutional layer, which can reduce the feature size. We adopt the average pool method, and the expressions are as follows: \[y_{ij}=\frac{1}{c^{2}}\left(\sum_{i=1}^{c}\sum_{j=1}^{c}F_{ij}\right)+b\] (4) \[y_{ij}=\genfrac{}{}{0.0 pt}{}{c}{i=1,j=1}{max\left(F_{ij}\right)}+b\] (5) \(F\) represents the input of the feature matrix, and \(c\) is the moving step. (3) Residual Learning Residual learning is the core of ResNet, whose main feature is the introduction of the concept of \"shortcut connection\" by establishing residual blocks. The schematic diagram of the residual block is shown in Figure 5. The output feature \(H(x)\) is expressed as: \[H(x)=F(x)+x\] (6) where, \(F(x)\) is a residual error, \(x\) is input.
## 1 Introduction  
The global municipal solid waste (MSW) generation forecast for 2025 is about 2200 million tons. Consequent to that, the organic fraction of municipal solid waste (OFMSW) is estimated to be 46% [1]. In 2015, 241 million tons of MSW with a share of 40-60% of organics were generated in the EU [2]. If OFMSW is not collected and treated separately and instead disposed of in landfalls with other MSW components; degradation processes such as anaerobic digestion (AD) occur underground, producing several environmental damages such as greenhouse gas (GHG) emissions or hazardous substances causing health hazards. Therefore, reduction targets for landfilling of MSW and OFMSW in the EU have been defined and further developed since the late 1990s. In addition, the separate collection and treatment of OFMSW via AD or composting have been promoted. EU-wide, landfilling is declining but still relevant, while the depositing of untreated MSW and OFMSW is already forbidden in Germany [3, 4, 5].  
Composting of OFMSW leads to GHG emissions in the form of CO\({}_{2}\) released directly into the atmosphere and can cause unpleasant odors. Due to the generation of renewable energy, AD of OFMSW is indicated with a better GHG balance, compared to composting, and odor problems are easier to control [1, 2, 6, 7, 8, 9, 10, 11]. CH\({}_{4}\) generated in the AD of OFMSWcan be used in various applications (e.g., combined heat and power or fuel) and represents a storable energy carrier [1]. AD and composing can be combined to exploit the material and energetic utilization potentials of OFMSW fully. The utilization of OFMSW via AD is in the sense of the circular economy according to the EU agenda, which envisages keeping products and materials in the market for as long as possible. However, the improvement of AD as a major component of future energy systems is the focus of politics and research facilities [1; 12; 13]. AD helps to achieve several of the UN sustainable development goals [14] by producing clean and affordable energy, contributing to sustainable municipalities, and supporting climate protection [15].  
OFMSW is not the only waste material with untapped potentials for the generation of renewable energy. Worldwide, the CH\({}_{4}\) potentials for residues from crops (3080-3920 TWh), manure (2600-3800 TWh), food waste (880-1100 TWh), or sewage sludge (210-300 TWh) can be exploited on a larger scale via AD [2; 15].
## 3 Interference Mitigation Schemes
### MAC Approach
#### 3.2.3 Lightweight and Robust Interference Mitigation Scheme  
A new interference detection method based on the estimation of beacon delivery ratio (BDR) is lightweight and robust interference mitigation scheme (LRIM) which is introduced in [27]. The transmission efficiency (TE) is a function of successfully received packets, the number of backoffs, and the number of retransmissions. The threshold values of TE and BDR are used to indicate the severity of interference in WBASNs. The coordinator makes decisions on adaptive channel hopping based on TE and the channel hopping requests from its sensors. If more than half the sensors send the request, the coordinator decides to hop channels by selecting a watcher. The watcher, a node with high RSSI and low traffic load in a WBASN, senses the channel status and sends the result to the coordinator. When the coordinator discovers an idle channel, it will hop the operating WBASN to that channel.