esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Related Research on Service Discovery Protocols
### Directory-Less Architecture
#### 4.2.2 Service Discovery in Reactive MANETs  
Most of the service discovery architectures designed for reactively-routed MANETs rely on AODV as a routing protocol. In [22], the authors compare service discovery functionality integrated intoAODV with a pure application-based service discovery protocol, called Nom. In [41, 42], the authors added additional extensions to SD-AODV to support QoS-aware service selection. Service Discovery and Interaction with Routing protocols in Ad hoc Network (SEDIRAN) [43] and the proposal of [PERSON] _et al._[44] are other examples of such architectures. Recently, [PERSON] _et al._[45] extended the service discovery approach used in [22] in order to discover services within the network efficiently and to communicate with the server with the highest trust value. [PERSON] _et al._[46] also proposed a new service discovery protocol called SCAODV, which integrates the route protocol and the process of service discovery.
## 2 Health Condition Monitoring
### Data Acquisition  
A data acquisition system comprises data acquisition devices, signal processing and transmission equipment, and data storage devices [8]. The data acquisition terminal is mainly composed of various sensors to obtain MSAE state parameters through various sensing methods. This data can reflect the failure evolution and performance degradation process of the system effectively. The most widely used signals of measurement sensors on-board include temperature, pressure, voltage and current, speed, and so forth. The obtained parameters are transmitted to the central data processing unit (server) according to specific coding and communication protocol. The data are further classified, processed, and stored for various analyses. With the rapid development of sensing and measurement technology, more and more intelligent condition perception methods have been proposed, designed, and applied to modern ships.
## 4 Case Studies  
The modified IEEE 69-bus test system shown in Figure 8 with two tie lines is used to evaluate the performance of the proposed SR scheme. The test system is a 12.66 kW distribution system with a total load of 3.8 MW and 2.69 MVAR, and the details on the test system, such as the network configuration and line parameters, are provided in [31]. The data of DG used in the simulations are indicated in Table 2. Five DGs with a total capacity of 980 kW are installed so that five islands should be created to avoid the parallel operation of DGs, and all the DGs are modeled by constant active power and voltage (PV) type to enable an island can be operated with a reliable voltage source. The maximum iteration of  
Figure 7: Structure of the SR application using the proposed algorithm in the DAS environment.  
100 in the GA process is applied, and 100 populations are created for each iteration. The probabilities of crossover and mutation are set to 0.8 and 0.2, respectively. The lower and upper limits for bus voltages regarding the constraint shown in Equation (4) are chosen as a value of 0.95 p.u. and 1.05 p.u., respectively. MATLAB R2021b is utilized to implement the proposed SR scheme including data structure generation, GA processes, and load flow calculation, and the case studies are performed in an Intel Core i7-8700 K [EMAIL_ADDRESS] GHz PC.  
Tables 3 and 4 show the data of controllable loads and the importance of each load, respectively. Six controllable loads with the lowest importance coefficient are chosen to be disconnected for restoring more important loads thereby minimizing the objective function in Equation (1). All loads in the test system are grouped into three classes, and different coefficients (\(\alpha_{\text{CL}}\), \(\alpha_{\text{C2}}\), and \(\alpha_{\text{C3}}\)) are imposed according to the importance of each load.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Load Class** & **Coefficient** & **Location (@Bus)** \\ \hline
1 & 100 & 9, 12, 14, 21, 29, 48, 58, 69 \\
2 & 10 & 35, 43, 55, 56, 62, 66 \\
3 & 1 & others \\ \hline \hline \end{tabular}
\end{table}
Table 4: Group of loads according to load importance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**DG No.** & **Bus Connected** & **Capacity [kW]** & **DG Type** \\ \hline
1 & 10 & 240 & PV \\
2 & 15 & 300 & PV \\
3 & 32 & 70 & PV \\
4 & 46 & 250 & PV \\
5 & 67 & 120 & PV \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data of DGs in the test system.  
Figure 8: Modified IEEE 69-bus test system.
# Artificial Intelligence Enabled Routing in Software Defined Networking
###### Abstract  
Software defined networking (SDN) is an emerging networking architecture that separates the control plane from the data plane and moves network management to a central point, called the controller. The controller is responsible for preparing the flow tables of each switch in the data plane. Although dynamic routing can perform rerouting in case of congestion by periodically monitoring the status of each data flow, problems concerning a suitable monitoring period duration and lack of learning ability from past experiences to avoid similar but ineffective route decisions remain unsolved. This paper presents an artificial intelligence enabled routing (AIER) mechanism with congestion avoidance in SDN, which can not only alleviate the impact of monitoring periods with dynamic routing, but also provide learning ability and superior route decisions by introducing artificial intelligence (AI) technology. We evaluate the performance of the proposed AIER mechanism on the Mininet simulator by installing three additional modules, namely, topology discovery, monitoring period, and an artificial neural network, in the control plane. The effectiveness and superiority of our proposed AIER mechanism are demonstrated by performance metrics, including average throughput, packet loss ratio, and packet delay versus data rate for different monitoring periods in the system.  
Keywords:software defined networking; artificial neural network; routing; monitoring period +
Footnote †: journal: Applied Sciences
## 1 Introduction
### Motivation and Contributions  
This paper focuses on the prediction of stiffness degradation in wind turbine blade fatigue testing. Many existing studies are based on physical models, but such methods have limitations when dealing with nonlinear features or complex multidimensional data and rarely explore the great potential of DL in this field. This study clearly demonstrates the superior performance of DL in predicting the fatigue life of blades. Advanced nonlinear modeling based on N-BEATS architecture outperforms established models like LSTM and TCN in prediction accuracy. However, the dataset used in this paper is much smaller, which may hinder the application of deep neural networks. Therefore, the fundamental block structure of N-BEATS was improved to enable more flexible handling of datasets of varying scales. This paper clearly demonstrates that the improved N-BEATS model can be effectively applied to such small-scale prediction tasks and exhibits superior performance. Additionally, this study uses two methods--time series combination and random noise injection--to augment the dataset, aiming to mitigate the overfitting problem and further improve the model's generalizability and prediction accuracy. Finally, most existing prediction algorithms focus primarily on improving accuracy while neglecting bias, which plays a critical role in fatigue prediction. This paper adopts a simple and effective mechanism: the Pinball-MAPE loss function is used to control prediction bias, and its effectiveness is demonstrated. Figure 1 shows the proposed framework for blade stiffness degradation prediction.
## 1 Introduction
### Authors' Motivations and Contributions  
A comprehensive literature review of the existing blind signature schemes was carried out. It was found that these schemes are based on hard problems, that is, elliptic curve, bilinear pairing, and modular exponential, and thus suffer from high computational and communication costs. Hence, the existing schemes are not compatible with small devices, that is, UAVs that have limited computational power. Moreover, these schemes are not validated using formal security validation tools such as automated validation of internet security protocols and applications (AVISPA) or Scyther, among others, which can, somehow, guarantee security. There is a critical need to harness the state-of-the-art certificateless blind signature scheme so as to engineer a viable cryptographic solution for FANET that poses less danger to the battery lifetimes of resource-constrained UAVs.  
The authors, motivated by the aforementioned objectives, to name a few, propose a new scheme, called provably verified certificateless blind signature (CL-BS) scheme for FANET. The proposed scheme is based on hyperelliptic curve, which is an advanced version of the elliptic curve. It provides the same level of security as elliptic curves, bilinear pairing, and modular exponential with smaller key size. Some of the salient features signifying contributions of our research work, in this paper, are as follows:  
* We introduce a novel architecture for flying ad-hoc network (FANET) constituted by UAVs with a multi-access edge computing (MEC) facility that leverages the 5G wireless technology.
* We propose an efficient and provably secure certificateless signature (CL-BS) scheme for the same architecture using the concept of hyperelliptic curve for operating in resource-constrained environments.  
* The proposed scheme is shown to be resistant against various attacks through formal as well as informal security analysis using the widely-accepted automated validation for internet security validation and application (AVISPA) tool.
* The proposed scheme is also compared with existing counterparts and it is shown that our approach provides better efficiency in terms of computational and communication costs.
# A Design for SDN-Based Identifier-Locator Separation Architecture on IoT Networks
## 1 Introduction
Because of these characteristics, DHTs are generally used in peer-to-peer (P2P) and content distribution systems such as a BitTorrent. SDN is defined as a conceptual architecture that encompasses various kinds of network technologies that are designed to make networks more flexible and agile [12; 13]. SDN decouples the control plane and data forwarding plane from the network for enabling the network partitioning and immediate control. Also, it is able to be abstracted the underlying infrastructure for applications and network services. In SDN, it is possible to operate different routing mechanisms on physical and virtual networks, which makes networks more flexible. We focused on this feature to apply a DHT-based routing algorithm for a virtual network and an existing IP routing algorithm for a physical network. We adopted the routing algorithm and structure of a Content Addressable Network (CAN), which is one of the DHT-based routing algorithms, to route the endpoint identifier (EID). In the proposed scheme, mobile or sensor nodes only send packets including an EID as their address to the network and do not participate in the ID-LOC converting procedure. The controllers maintain the EID-LOC mapping information to forward to the destination. Additionally, the controllers send a command to their OpenFlow-enabled switches to configure their flow table entries for the packets. To manage the IDs and LOCs, we made up an overlaid network by borrowing a concept from the CAN in SDN [9; 10; 12; 13]. In the proposed scheme, we used a field replacement function in the OpenFlow-enabled switches (OFSs) to reduce tunneling overheads. This paper is organized as follows. Section 2 presents existing research, schemes, and practical examples. Section 3 introduces the proposed scheme with each operation process. In Section 4, we illustrate the cost model and calculate the packet forwarding and signaling cost. Section 5 provides results and discussions, and, finally, Section 6 illustrates the conclusion and future works.
# Biochar-Improved Growth and Physiology of _Eltretia asperula_ under Water-Deficit Condition
## 3 Results
### Effect of Biochar on Wilted Plant  
The addition of Biochar to the soil enhanced the drought tolerance of _Eltretia asperula_ plants by lowering the wilting point. In the without biochar treatment, the wilting point was reached on the fifth day withholding irrigation, but in the 15 and 20 tons ha\({}^{-1}\) WB treatment groups, the wilting point was reached on the sixth day withholding irrigation. On the other hand, 100% of the plants had wilted by the eighth day withholding irrigation in the without biochar treatment, but in the 15 and 20 tons ha\({}^{-1}\) WB treatment groups, only 80% of the plants had wilted by the tenth day withholding irrigation (Table 5).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Biochar Rates** \\ **(tons ha\({}^{-1}\))** \\ \end{tabular} } & \multicolumn{4}{c}{**Percentage of Wilted Plant after without Irrigation (\%)**} \\ \cline{2-7}  & **Fifth Day** & **Sixth Day** & **Seventh Day** & **Eighth Day** & **Ninth Day** & **Tenth Day** \\ \hline
0 & 54.17 & 66.67 & 75.00 & 100.00 & - & - \\
5 & 41.67 & 54.17 & 66.67 & 100.00 & - & - \\
10 & 20.83 & 33.33 & 58.33 & 75.00 & 91.67 & 100.00 \\
15 & 0.00 & 20.83 & 33.33 & 41.67 & 66.67 & 79.17 \\
20 & 0.00 & 20.83 & 33.33 & 41.67 & 58.33 & 79.17 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Effect of biochar on percentage of wilted plants of _Eltretia asperula_ under WSC.  
Figure 6: Effect of biochar on soil moisture of _Eltretia asperula_ after 10 days without irrigation. Vertical bars represent the mean \(\pm\) SD, \(n\) = 9.
## 3 Materials and Methods
### Improved DETR Model
#### 3.1.1 ResNet Replacement for MLP-Mixer  
The DETR model employs a convolutional neural network as its backbone network to extract features from images, which are subsequently processed by the Transformer's encoder and decoder. However, DETR only utilizes the final layer of features from the backbone network, resulting in potential information loss. This is particularly problematic for compact and densely packed targets where features from lower layers could be more beneficial for detection. To address this issue, we substitute the backbone network from ResNet to MLP-Mixer, and merge the output of the Mixer layer with the transformer encoder layer. This allows the model to integrate features from diverse layers, thereby enhancing the feature representation and multi-scale information.  
Figure 1: The proposed OrthoDETR network structure.  
The MLP-Mixer is an innovative neural network architecture that incorporates a Transformer-inspired approach of reconfiguring the input data into token and embedding formats. It facilitates feature integration across channels and sequences via a multi-layer MLP. This stands in contrast to the conventional ResNet, where feature extraction and fusion processes occur separately. By amalgamating these processes, the MLP-Mixer enhances both the efficiency and accuracy of the network.  
The MLP-Mixer serves as the backbone network for feature extraction within the DETR model, its primary objective being to thoroughly amalgamate inter-channel and inter-sequence features. This is achieved by initially transforming images into token and embedding formats. Such a methodology not only boosts the network's efficiency and accuracy, but it also circumvents the information bottleneck issue inherent in ResNet. As a result, the incorporation of the MLP-Mixer into the feature extraction component of DETR can significantly enhance the network's overall performance and efficiency.  
In addition, adopting MLP-Mixer as the feature extraction part of DETR has the added benefit of better handling the dimensionality requirements of the input data. In a traditional ResNet, the input data needs to meet specific dimensional requirements, which often limits the scope of network application. In contrast, the use of MLP-Mixer allows for more flexible handling of the input data, making it suitable for a wider range of application scenarios.  
Specifically, the output of the mixer layer is summed with the output of the transformer encoder layer and then fed into the next transformer encoder layer. This allows the layer to dynamically assign different weights to different levels of features according to their spatial location and scale.
# Dynamic Cost-Aware Routing of Web Requests
## 6 Evaluation of Parameter Selection Using Simulation  
The performance of the proposed system may be affected by different learning parameters: reward factor \(a\), penalty factor \(b\), memory weight factor \(\alpha\), and random walk probability \(P_{rw}\). In addition, operational factors, such as the number of servers \(N\), total traffic intensity \(\lambda\), electricity rates (price) \(\gamma^{j}\) prevalent at each location \(i\) and the selected revenue loss factor \(\delta\) determine the specific system behavior.  
We use simulation to evaluate the impact of these factors. The advantage of the approach is that it provides a common and repeatable evaluation platform that allows for achieving high statistical accuracy. The simulation is event-driven and models the request generation as an exponential process with parameter \(\lambda\) (i.e., the total traffic intensity arriving at the system). The user requests are initially routed to a server (forward proxy server) that decides the actual origin server to handle each arriving request. The simulation keeps track of the communication dynamics of both requests and replies. Requests have a fixed length of 100 bytes. Reply lengths are exponentially distributed with an average size of 100 Kb.  
The proxy is connected to the origin servers through connections of different delay. To simplify the simulation model, we assumed that a single server was used at each location. Delays from the proxy to the origin servers were distributed linearly from 10 ms to 1 s. The transmission rate of all the links was set to 2 Mbps. As a reference, the response time obtained with two or more servers is approximately 1.5 s. Each server has a single core and requests are served according to the first-come-first-serve model. We further assume that all the servers are of identical characteristics, consuming 100 W while idle and 150 W while transmitting or receiving data. Electricity prices (\(\gamma\)) were also linearly distributed among the different sites from 5 c/kWh to 15 c/kWh. The revenue loss factor was fixed at \(\delta=0.1\) to produce comparable values for energy and delay costs.
# Smart Grid Architecture for Rural Distribution Networks: Application to a Spanish Pilot Network
## 3 Electrical Actuations to Evolve the Rural Grid
### Back-Up Resources
The IDPR functionalities include power quality improvement as a result of the active compensation for current harmonic, reactive power and unbalance on the current demand side. At the same time, active and reactive power can be dispatched because of its 4-quadrant operation. A normal operation mode is conceived for converting the entire system downstream to an aggregated bidirectional load that can be regulated in order to match the upstream requirements of the system operator in terms of stability and energy management. This is how the IDPR is aimed at being a powerful element for integrating DG or renewable sources, domestic or industrial loads, and electric vehicles. Therefore, in this operation mode (so-called slave), the voltage and frequency in the coupling point of the IDPR is provided by the main grid, by an auxiliary generator or by another IDPR. The IDPR, while operating in slave mode, is controlled as a current source for delivering or consuming power, according to exogenous setpoints. It is required that the grid has to be under normal operating conditions in regard to voltage and frequency levels while the IDPR is balancing the circulating currents. Therefore, the local consumption is seen by the grid upstream as aggregated consumption, and this compensates the reactive power while cancelling harmonic content, which in fact minimizes losses in the distribution system. In case of grid failure or a scheduled disconnection, the IDPR is able to restore the LV grid and MV grid through an SS transformer in isolated mode. This grid-disconnected operation provides a voltage reference to the system in order to supply loads, DGs and/or storage systems. Therefore, the developed IDPR is able to work while fixing the voltage and frequency of the local area. At the moment this mode is put into operation (so-called master mode), the main grid has to be decoupled from this area and no other IDPRs can be connected in master mode. The master mode starts from a zero voltage situation with a progressive local grid energization consisting of a voltage ramp. After that, when the grid is stabilized up to its nominal values, consumers, DGs and slave IDPRs allocated inside the same area can be progressively connected and configured in order to assure that the master IDPR is able to guarantee system stability. This mode is disabled if no storage device is installed in the IDPR.
## 4 Experiments and Results
### Experimental Results
#### 4.3.2 Performance Analysis of the Anomaly Detection Model
Using the least squares method, the model fine-tuning training epoch prediction function, with sample size as the independent variable, is fitted as follows:  
\[f(x)= \left[34.94231916+\frac{259.1611542}{1+e^{4-\frac{\ln^{2}}{3}}}\right] \tag{10}\]  
As shown in Table 5, the number of training epochs derived from the proposed function is 78 when the sample size is 1200. By selecting a test step size of 5 and a test window of 20, we aim to find the optimal number of training epochs for this sample size. In experiments where the training sample size was continuously increased from 1200 to 5200, we consistently found a training epoch count within \(\pm 10\) of the function's output that achieved an accuracy of not less than 96%. For a test sample size of 6039, we conducted a set of validation experiments where the number of training epochs was the only variable. We trained models for 18, 38, 58, and 78 epochs, respectively, and used these models for equipment anomaly detection. The detection results are shown in Figure 16. The experiments demonstrate that using this method allows the function to find the optimal number of training epochs within the window range while avoiding overfitting. Therefore, the introduction of this function signifies an improvement from arbitrary trial-and-error in determining the number of training epochs to a function that outputs this number based on the sample size. This provides practitioners with a reference for finding the optimal number of training epochs and significantly reduces the time required to achieve this. Figure 15: Accuracy comparison of different types of DFs.