esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Research Methodology
### Damage Localization  
Damage detection using the OC-SVM is followed by localization estimation, achieved by comparing the differences between the measured and reconstructed transmissibilities for adjacent sensor locations \((i,j)\) distributed throughout the structure. This comparison is performed at each frequency increment (\(f_{k}\)) where the local transmissibilities are calculated, identifying the sensor pair with the largest discrepancy \((i^{*},j^{*})\):  
\[\left(\ i^{*},j^{*}\right)_{f_{k}}=\underset{(i,j)}{argmax}\ \triangle\ T_{ij}(f_{k}) \tag{9}\]Once the individual maximums are identified across all frequency lines, the sensor pair with the maximum number of occurrences over the entire frequency range \(\left(i^{*},j^{*}\right)_{max}\), can be determined as the potential damage location:  
\[\left(i^{*},j^{*}\right)_{max}=\underset{\left(i,j\right)}{argmax}\ N_{ij} \tag{10}\]  
where \(N_{ij}\) is the number of times each sensor pair \(\left(i,j\right)\) appears as \(\left(i^{*},j^{*}\right)_{f_{k}}\) over the entire frequency range \(\left[f_{1},\ f_{2},\ldots,f_{n}\right]\). Based on the number of counts of maximum occurrences, a relative damage index indicating the sensor pair most likely associated with the damage location, and serving as a clear and comparable metric is expressed as follows:  
\[DI_{i,j}=\frac{N_{ij}}{N_{max}} \tag{11}\]  
where \(N_{max}\) is the largest occurrence count across all sensor pairs.
## 5 LoRa Architecture for Waste Management  
After the completion of the design and development of the sensor node and gateway, we now have to implement in the real-time set up for analyzing the performance of the nodes during the transmission of the data. In order of implementing the customized node for sensing and transmitting the filling level of the bins, we are proposing architecture for waste management that is based on basic LoRa architecture and as shown in Figure 4. Physical Layer, gateway, network server, and cloud server are the key layers that exist in this architecture. The sensor nodes are part of the physical layer, the function of the sensor node is to sense the filling level of the bins and transmits the sensory data to the gateway via LoRa (Long-range).  
Figure 4: LoRa architecture for solid waste management.  
Figure 3: LoRa-based gateway.  
The gateway in Figure 4 receives the messages from the sensor node to the network server. Since a transmission from a sensor node may be processed by more than single gateway, they establish a star-of-stars topology as standardised by the LoRaWAN architecture. Gateway utilizes the internet connection at their location to transmit data between the nodes and the network server. In proposed system, thhe gateways can communicate with one another via IEEE 802.11-based wireless mesh network in order of reaching the network server. This part of the system is independent of the LoRaWAN architecture. To optimize the system, we presume that the gateways have a good and direct connection to the network server.
## 5 Result Discussion
### Comparison of Acoustic Emission Signal and Prestress Value  
An analysis of the prestress variation during the loading process of the P3 test beam was conducted. The load-prestness relationship is plotted in Figure 13. Figure 13 illustrates that the prestress in the P3 beam gradually increases at a relatively constant rate until the load reaches 80 kN. Although there are no significant variations in prestress, the analysis of the acoustic emission (AE) signal characteristics indicates that the P3 beam has already undergone the compaction stage, crack stable propagation stage, and crack breakthrough stage. This occurs when the beam is loaded to 80 kN. After reaching 80 kN, the test beam enters the stage of complete failure, indicating an imminent failure. At this stage, the rate of prestress growth significantly increases, and the numerical value rises noticeably. In conclusion, relying solely on the variation pattern of prestress with load is insufficient to accurately predict the various failure stages of the test beam.  
The analysis results from Sections 5.1-5.4 indicate that the critical load values for the P1, P2, and P3 beams, when through-cracks appear, are approximately 20 kN, 60 kN, and 70 kN, respectively. On the contrary, the load-deflection curve of the P1 beam lacks a significant inflection point, whereas the load-deflection curve of the P3 beam displays noticeable inflections during the transitions between different failure stages. Similarly, the variation of prestress with load in different failure stages of the P3 beam shows no significant changes. Therefore, it can be concluded that relying solely on the collected data of mid-span strain, deflection, and prestress during the experimental process is insufficient to determine the critical load values for the various failure stages of the test beams. Therefore, it is necessary to utilize other methods such as acoustic emission to mutually validate the results and obtain more accurate timing of the occurrence of each failure stage in the beam.
## 4 Case Study
### Numerical Application of the Test Plan  
Steps 1, 2, and 3. The test plan includes six essential elements, including the required reliability index \(R(t)=0.97\) and confidence level \(CL=0.75\), as specified by the GMW3172 standard. The Weibull family derived in Section 4.1, are \(\beta=4.8031\) and \(\eta=1445.7\) cycles. The sample size associated with \(\eta\), calculated using Equation (14) is \(n=32.8\), while the required testing time, determined using Equation (15) is \(t=698.8\) cycles.  
Step 4. Using Equation (16), the required sample size \(n_{2}\) corresponding to \(R(t)\)=0.97 and \(CL=0.75\) is \(n_{2}=45.5\).  
Step 5. By substituting \(n_{2}=45.5\) into Equation (17), the upper bound of the Weibull scale parameter is \(\eta_{U}=1547.4\) cycles.  
Step 6. Using Equation (18), the lower bound of the Weibull scale parameter is determined as \(\eta_{L}=1350.6\) cycles.  
Step 7. Using the test time calculated in step 3, the reliability index corresponding to \(\eta_{U}=1547.4\) cycles is determined as \(R(t)=0.9783\). Note that, because no failures are allowed in this testing plan, the \(R(t)\) index for the lower value was not determined.  
Step 8. For the selected sigma levels (one sigma, two sigma, and three sigma) and Cp and Cp indices, using the standard deviation \(\sigma_{\eta}=72.6\) cycles in Equations (17) and (18) the corresponding normal percentiles, Weibull upper (\(\eta_{U}\)) and lower (\(\eta_{L}\)) scale parameter limits, confidence level (\(CL\)), and reliability \(R(t)\) indices were determined. The results are presented in Table 9. Additionally, Table 9 includes the corresponding data for the designed test plan.  
From the last row of Table 9, we observe that by performing the designed test plan, with require \(R(t)=0.97\) and \(CL=0.75\), \(46\) pieces must be tested for a testing time of \(t=698.8\) cycles each. If none of them fail, the analyzed element achieves a minimum reliability of \(R(t)=0.9783\). Note that this occurs because \(\eta_{U}\) is greater than \(\eta\) (\(1547.4>1545.7\)). The general conclusions are as follows.
# Application of Reflectance Indices for Remote Sensing of Plants and Revealing Actions of Stressors
## 3 Main Reflectance Indices
### Pigment Indices
Car/Chl can also be used for the estimation of the nitrogen status of plants because nitrogen restrictions lead to the loss of chlorophyll and increase the concentration of carotenoids in comparison with the concentration of chlorophylls [6; 127; 128; 129]. For example, the normalized pigment chlorophyll index (NPCI) [6], which shows the ratio of the total amount of photosynthetic pigments to chlorophyll, can be used for the estimation of the nitrogen status, which plays a key role in the productivity of plants. The other prospective pigments for the detection of stress changes are anthocyanins. It is known that anthocyanins protect plants from the destructive influence of excess light; they can be \"optical filters\" and absorb high-energy quanta [130]. Anthocyanins stimulate the plant antioxidant system, neutralizing reactive oxygen species and radicals [13]. Anthocyanins mainly absorb light in green and yellow spectral regions [130]; the recent meta-analysis [124] shows that RIs that are calculated on the basis of measuring the reflectance at 550-560 nm (the green) and references at 700-710 nm (the red edge) or 780-790 nm (NIR) are effective for the estimation of the anthocyanin content. [PERSON] and [PERSON] [93] proposed the index based on broadbands in the red and green spectral regions; it is effective for the estimation of the ratio of concentration of chlorophylls to concentrations of anthocyanins. The other indices for the estimation of the anthocyanin concentration are anthocyanin reflectance indices 1 and 2 (ARI\({}_{1}\) and AR\({}_{2}\)), which are based on the green and NIR reflected light [94]. Thus, the indices based on the concentration of photosynthetic pigments seem to be multipotential. These pigment indices can be effective tools for the estimation of the concentration of chlorophylls, anthocyanin, and carotenoids in plants [124]; however, efficiencies of many pigment indices are lower on canopy or landscape levels. In contrast, these efficiencies for RIs that are sensitive to concentrations of total chlorophyll and chlorophyll a remain high on these spatial levels [124]. Besides, these pigment indices can also be used for revealing stress changes in plants and for the morphological analysis, including the estimation of LAI, which is similar to the estimation of this parameter on the basis of VIs.
## 4 Case Studies  
### Wind Power, Photovoltaic Power and Load Power Forecasting  
#### 4.1.1 Data Description  
Three real datasets are used to conduct wind power forecasting, photovoltaic power forecasting and load power forecasting [41]. The wind power data comes from a certain wind power station in Inner Mongolia, China. The photovoltaic power prediction data comes from a photovoltaic power station in the same area. The load data comes from a substation connected to a distribution network and transmission network. There are a total of 7788 data in the wind power dataset. The first 5500 data are used as the training set, and the remaining data are used as the test set. There are a total of 8688 data in the photovoltaic power dataset. The first 6500 data are used as the training set, and the remaining data are used as the test set. There are a total of 9432 data in the load power dataset. The first 7000 data are used as the training set, and the remaining data are used as the test set. Feature engineering is performed on the basis of the original dataset, as shown in Table 2. The original features refer to the features contained in the original dataset, and the added features refer to the features constructed from the original features. The number of features after the feature engineering are 14, 44 and 44, respectively, for wind power forecasting, photovoltaic power forecasting and load power forecasting.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Features** & **Wind Power Forecasting** & **Photovoltaic Power Forecasting** & **Load Power Forecasting** \\ \hline \multirow{4}{*}{Original features} & Forecasting wind speed & Forecasting illuminance & Forecasting temperature \\ \cline{2-4}  & Forecasting wind direction & Forecasting temperature & Forecasting humidity \\ \cline{2-4}  & Forecasting humidity & Forecasting air pressure & Forecasting wind speed \\ \cline{2-4}  & Forecasting temperature & Forecasting humidity & Forecasting precipitation \\ \cline{2-4}  & Forecasting air pressure & Historical power at same moment. & Historical load at same moment. \\ \hline \multirow{4}{*}{Added features} & Square of forecasting wind speed & Average power at the same moment in previous three days & Average load at the same moment in previous three days \\ \cline{2-4}  & Cube of forecasting wind speed & Power at the same moment in previous three days & Load at the same moment in previous three days \\ \cline{2-4}  & Product of wind speed & Power of previous 24 time & Load of previous 24 time \\ \cline{2-4}  & and humidity & points in one day & points in one day \\ \cline{2-4}  & Historical wind speed for previous four hours & Difference between predicted and previous meteorological data & Difference between predicted and previous meteorological data \\ \hline \multicolumn{4}{c}{...} & \multicolumn{1}{c}{...} & \multicolumn{1}{c}{...} \\ \end{tabular}
\end{table}
Table 2: Feature engineering in different prediction models.
###### Abstract  
Despite the significant advances noted in semantic segmentation of aerial imagery, a considerable limitation is blocking its adoption in real cases. If we test a segmentation model on a new area that is not included in its initial training set, accuracy will decrease remarkably. This is caused by the domain shift between the new targeted domain and the source domain used to train the model. In this paper, we addressed this challenge and proposed a new algorithm that uses Generative Adversarial Networks (GAN) architecture to minimize the domain shift and increase the ability of the model to work on new targeted domains. The proposed GAN architecture contains two GAN networks. The first GAN network converts the chosen image from the target domain into a semantic label. The second GAN network converts this generated semantic label into an image that belongs to the source domain but conserves the semantic map of the target image. This resulting image will be used by the semantic segmentation model to generate a better semantic label of the first chosen image. Our algorithm is tested on the ISPRS semantic segmentation dataset and improved the global accuracy by a margin up to 24% when passing from Potsdam domain to Vaihingen domain. This margin can be increased by addition of other labeled data from the target domain. To minimize the cost of supervision in the translation process, we proposed a methodology to use these labeled data efficiently.  
deep learning; domain adaptation; semantic segmentation; generative adversarial networks; convolutional neural networks; aerial imagery +
Footnote †: journal: Applied Sciences
## 3 Distributed Target Tracking Systems
### Phase II: Choosing the Sub-Cluster Members  
Based on the claimed message sent from the leader and the cluster ID of the leader, the target will send a message to inform the active sensors about the corresponding cluster for the tracking task, which also  
Figure 2: The cluster-based network topology and target movement with 25 time steps (**left**); an example of leader and sub-cluster member selection (**right**).  
notifies the active sensors with the same cluster ID to be the candidate sub-cluster members associated with the leader. To choose the members associated with a leader, instead of directly selecting the active sensors from the leader, the sensor selection may be determined based on the reporting order of target position estimates from the neighboring active sensors of the leader. Accordingly, a candidate sub-cluster member, say sensor \(m\), may decrease its LWT along with an extra backoff time \(BT_{m}\), which is inversely proportional to the estimation accuracy, for reporting the estimate of target position. When the timer \(LWT_{m}\) expires, sensor \(m\) will deliver the tracking information to leader \(i\). That is, based on the time stamps of the received estimates, the target tracking group is then automatically formed with the leader.  
For those active sensors without receiving a \(Leader\) message, they transmit the estimated target position directly to the clusterhead and become supplementary sub-cluster members. This is attributed to the fact that the active sensors may not have direct communication with the leader. Hence, they may send their tracking estimates to the clusterhead for providing supplementary information on the tracking task. Therefore, when the number of sub-cluster members meets the desired number \(n\), the leader will perform the CI model (as detailed in Section 3.3.3) to obtain a global target position estimate and send a \(Position\) message to the clusterhead, which also serves to specify the final sub-cluster members for cooperative target tracking. If the leader does not collect sufficient number of estimates (_i.e._, \(|M|<n\), M is the index set of the sub-cluster), then the leader may send its fused estimate to the clusterhead and request the clusterhead to incorporate the supplementary estimates if possible. Afterwards, the clusterhead will perform the CI algorithm based on the received fused estimate and the supplementary estimates. Note that in order to spread the energy burden in the network, the cluster is responsible for informing the base station about the target tracking and positioning. Figure 2(right) shows an example of leader and sub-cluster member selection.
## 5 Discussion  
For CH selection, researchers [41] developed an optimized genetic algorithm (OptiGeA). The communication distance between the sink and CH during network execution has been shortened using a variety of mobility sink techniques in order to address hotspot challenges and provide a secure and energy-efficient IoMT-equipped WSN. They are complex algorithms that are challenging to effectively construct and may be vulnerable to numerical noise. They have trouble tackling discrete optimization issues. The rank-based energy-efficient key management (RBE-EKM) method aims to find an energy-efficient solution for sending data from the sensor to the base station [42]. To boost the efficacy and longevity  
Figure 10: Implementation cost of the proposed and existing methods [41; 42; 43; 44; 45].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Implementation Cost (\%)** \\ \hline OptiGeA [41] & 95 \\ RBE-EKM [42] & 82 \\ RPL-RPMA [43] & 71 \\ ACI-GSO [44] & 63 \\ PSL-RH [45] & 52 \\ ECC-EERP (Proposed) & 41 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Implementation cost of the proposed and existing methods.  
of secured communication, researchers [43] designed a routing protocol with low power and random-phase multiple access (RPL-RPMA). Growing a network inherently causes complexity to increase. Other authors [44] aimed to offer a novel hybrid approach that combines glowworm swarm optimization with ant colony optimization (ACI-GSO). When working with a lot of data, this method has several drawbacks in terms of convergence speed and solution correctness. The CH selection goal is to minimize the distance between the chosen CH nodes. It improves the system's energy efficiency and transmission security. None of the existing methods meet the security and energy efficiency needs of WSN-based IoMT healthcare services. Probabilistic super learning-random hashing (PSL-RH) enhances the security of medical data kept in IoT clouds. Costs for IoT sensors can be reduced by employing the PSL-RH learning strategy. However, the problems faced by PSL-RH include a high mistake rate, ineffective forecast outcomes, and procedure latency, which reduce the effectiveness of the complete security system [45].
# The Next Generation Platform as A Service: Composition and Deployment of Platforms and Services
## 4 Virtual Network Function as A Service, Overview
### Supported Virtual Network Functions (VNFs) and Value Added Services (VAS)  
#### 4.4.1 Core Services: Virtual Firewalls and Virtual Routers  
This section gives a brief overview of the VNFs and VASs supported by the Telco PaaS prototype. As part of the experimental set-up, we use a commercially available router and firewall VNFs. The Router VNF is an enterprise-class router designed to run on x86 standard servers but comprising all the same features as equivalent hardware routers [31]. Similarly, the Firewall VNF is a virtual appliance aimed at enterprise-level security deployments [32]. The implementation of both VNFs is based on virtual machines and in the presented prototype their running instances are hosted by OpenStack.
## 1 Introduction
Therefore, the peak shaving capacities of EV and ES are particularly important, concerning that they will be connected to the grid in large scale, the reliability of EV and ES resources cannot be ignored as well [19; 20]. Flexible loads resources refer to flexible loads that have a power demand response. Demand response technology is one of the core technologies in the smart grid. The application of demand response technology can fully exploit the load-side resources and realize the comprehensive optimization of resources configuration [21]. Different users' response willingness and responsive device capacities are different, such as supermarkets, shopping malls, and hotels and other commercial users with less response willingness and larger response capacity, the residents are the opposite, with greater willingness to respond and smaller response capacity. The response capacities of various users are very different. Therefore, as for flexible loads, features related to response are evaluation-focused. It can be seen that the current evaluation systems focus on the evaluation of single DSER, and the selected indexes are often individuality indexes of DSER. So, a set of general evaluation system should be set up to evaluate the commonality indexes and individuality indexes of all kinds of DSER. At the same time, these DSER are widespread distributed in the urban power grid, their distribution has natural geographical features and is inseparable from the way of urban planning and partition. According to the above situation, this paper proposes an evaluation model of DSER based on geographic information. Based on the functional partition of the city, the commonality indexes system and the individuality system are established by analyzing the commonality and individuality of various DSER, and the commonality indexes and individuality indexes are selected separately, that is, each index is defined. Then AHP is used to determine the weight of the commonality indexes. The entropy weight method is used to determine the weight of the individuality indexes. Finally, the comprehensive scores of the commonality indexes and individuality indexes are obtained respectively. The specific process is shown in Figure 1. The comprehensive scores of various DSERs in various regions are favorable for urban and power grid planning, guiding DSER investors and government planners, and providing a strong support for the long-term optimization planning and medium-term optimization aggregation of DSER.