esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Fusion-Based Structural Damage Identification Approach Integrating Fractal and RCPN
## 5 Conclusions  
In this paper, a decision-level fusion method for structural damage identification is proposed based on correlation dimension and RCPN. The Benchmark structure of the ASCE is used to verify the potential of correlation dimension as a structural damage identification feature.  
The research results showed that after data fusion, both the feature-level fusion damage identification model and the decision-level fusion damage identification model are significantly superior to the identification results of a single RCPN model. Particularly, the decision-level fusion damage detection model proposed in this paper based on the feature-level fusion results can significantly improve the accuracy of structural damage identification. It has the best identification accuracy, noise resistance and robustness among the three models. However, it is worth noting that the proposed model in this paper is only proved to be feasible and effective in numerical examples, its actual performance in experimental work needs to be further studied.  
In the following research work, low-degree damage will be identified, and it is planned to be combined with decision tree, random forest, deep neural network and other methods to improve the accuracy of damage identification. In future, we will continue to carry out multiple damage identification and prediction and devote ourselves to applying our research results in practical experiments and engineering.  
Conceptualization, C.F. and M.L.; methodology, C.F.; software, C.F.; validation, C.F.; writing--original draft preparation, C.F.; writing--review and editing, C.F.; visualization, C.F. and M.L.; supervision, M.L.; project administration, C.F. and M.L.; funding acquisition, C.F. and M.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Basic Scientific Research Project of Education Department of Liaoning Province (LJKMZ0220750). Liaoning Provincial Department of Education Project (LJKZ056); and Housing and Rural Construction Project (2019-K-080).  
The data presented in this study are available upon request from the corresponding author.  
Thanks to the [PERSON] from Fuzhou University for his help in this research.  
The authors declare no conflict of interest.
## 3 Results
### Model Validation  
For soil moisture content (_theta_) the comparison between measurements and model outcome yielded r\({}^{2}\) values between 0.4 and 0.9 (Figure 2). The average r\({}^{2}\) for each site were 0.82 in 1101, 0.60 in 1202, 0.75 in 1203, 0.77 in 1204, 0.82 in 1205, 0.69 in 1302, and 0.66 in 1303.  
To evaluate interception, we compared the measured net rainfall (rainfall below tree canopy) with its modeled counterpart (rainfall reaching the ground). On all sites measured net rainfall was larger than modeled values. The difference between measured and modeled net rainfall amounted to 0% to 9% from open field rainfall. For temperatures \(\geq\)4 \({}^{\circ}\)C the r\({}^{2}\) of the regression were high, 0.88-0.95. For temperature \(\leq\)4 \({}^{\circ}\)C the r\({}^{2}\) of the regression was 0.80-0.88 lower than for temperatures \(\geq\)4 \({}^{\circ}\)C.  
Figure 2: r\({}^{2}\) values of correlation of measured with modeled soil water content using varying values of the infiltration parameter _infexp_. Only the results for the uppermost (left column), the depths of 0.4 m and 0.6 m and lowest (right column) sensors for each site (rows) are shown.
## 3 Preliminary Concepts Introduction
### Model of Node Placement Optimization Problem  
The node placement optimization problem is a high-dimensional multi-objective optimization problem. Through the optimization of WSN's performance, the best moving position for each node is obtained. It can be simplified to a multi-objective optimization model with constraints.  
The model of the node placement problem has been described in our latest paper [10] and the equations of the model are shown in Equations (2) and (3).  
\[\begin{array}{ll}\max&\mathbf{f}=\left[f_{1}(\Delta\mathbf{x},\Delta\mathbf{ y}),f_{2}(\Delta\mathbf{x},\Delta\mathbf{y})\right]\\ \text{ }s.t.&\left\{\begin{array}{l}\Delta\mathbf{x}\in(-a_{limit}x_{m},a_{ limit}x_{m})\\ \Delta\mathbf{y}\in(-a_{limit}y_{m},a_{limit}y_{m})\\ \mathbf{x_{0}}+\Delta\mathbf{x}\in(0,x_{m})\\ \mathbf{y_{0}}+\Delta\mathbf{y}\in(0,y_{m})\end{array}\right.\\ \end{array} \tag{2}\]  
\[\left\{\begin{array}{l}f_{1}(\Delta\mathbf{x},\Delta\mathbf{y})=Coverage\\ f_{2}(\Delta\mathbf{x},\Delta\mathbf{y})=\min(\mathbf{E_{rest}})\end{array}\right. \tag{3}\]  
where \((\Delta\mathbf{x},\Delta\mathbf{y})\) are two matrices that represent the displacement of all the nodes' coordinates, \(x_{m}\) is the length of the monitoring area, \(y_{m}\) is the width of the monitoring area, \((\mathbf{x_{0}},\mathbf{y_{0}})\) are the matrices of initial node coordinates, \(a_{limit}\) is a coefficient that restricts the movement of nodes, \(\mathbf{E_{rest}}\) is the predicted rest energy set (including the moving energy consumption) for all nodes after operation for a period of time, \(f_{1}\) represents the coverage of the WSN, \(f_{2}\) represents the predicted rest energy of the node with the least energy in the WSN.  
The traditional model of node placement optimization problem in WSN takes node coordinate as the solution. A node will move with the corresponding node number rather than move to the nearest position, resulting in a large distance. In this model, the coordinates of the node are replaced by the displacements of the node coordinates as the solution to the optimization problem. It can simplify the calculation of the moving distance, and help limit the moving distance of each node by restricting the search scope of the solution space.  
Equation (3) shows that coverage and the minimum rest energy (includes node movement energy consumption) after a certain number of rounds are the two main optimization objectives. Since the minimum rest energy in all nodes can represent both the energy consumption and energy balance of the network, it can represent network lifetime.
## 2 Related Work
### Traditional Energy Efficient Routing  
As is shown in the left side of Figure 1, the traditional routing protocols in WSNs can be classified into flat (or data centric [10]), hierarchical and location based routing. Among flat routing protocols, SPIN (Sensor Protocols for Information via Negotiation [11]) can be viewed as the first data-centric routing protocol which utilizes the data negotiation method among sensor nodes to reduce data redundancy and save energy. Direct Diffusion [12] is another representative data-centric routing protocol for WSNs. The data generated by sensor nodes is named by attribute-value pairs. Once a sink node inquires certain type of information (like four-legged animals in a certain area), it will send a query and the observed data can get aggregated and then be transmitted back to the sink node. In addition, the load balancing can be achieved by forwarding the data on different paths based on probability. Rather than always using the lowest energy paths, the authors in [13] use sub-optimal paths occasionally so that the network lifetime is increased by 40% compared to [12].  
Hierarchical routing protocols [6-8,14] are very suitable for WSNs since they can not only provide good scalability for hundreds or thousands of sensors, but also perform data aggregation by cluster head within each cluster. LEACH [6,14] is one of the most famous hierarchical routing protocols for WSNs. It can prolong network lifetime up to 8-fold more than other ordinary routing protocols like direction transmission and minimum transmission energy routing protocols. However, the 5% of cluster head nodes are randomly chosen and the cluster head nodes use direct transmission to the sink node therein. PEGASIS [7] is viewed as an improved version of LEACH. It is a chain based routing protocol which can save more energy compared to LEACH. The message can get aggregated along the chain and finally be sent to sink node via direct transmission by one random node on the chain. The main shortcoming is that PEGASIS requires global knowledge of the whole network. HEED clustering protocol [8] considers the residual energy as the primary parameter and a secondary parameter like node's degree during the selection of cluster head. It can not only minimize the control overhead but also prolong network lifetime than other clustering algorithms like LEACH since the cluster heads are well distributed. Besides, it does not need global knowledge of the whole network and all intelligent decisions are made locally by sensor nodes.  
Location-based routing protocols [15,16] can get location information through global positioning system (GPS) devices or certain estimation algorithms based on received signal strength (RSS). Once the location information is known, the energy consumption can be largely reduced through power control techniques and communication overhead can also be reduced. MECN [15] provides a minimum energy network for WSNs under the support of low power GPS. The authors in [16] make an extension of [15] by considering possible obstacles between any pair of communication nodes.
# A Comprehensive Evaluation Model for Optimizing the Sensor Array of Electronic Nose
## 3 Validation of the Evaluation Model  
### Initial Gas Sensor Array  
In order to verify the effectiveness of the as-proposed sensor array optimization model, we designed an E-nose system, as shown in Figure 1, which consists of three modules: sensor array (including 10 home-made MEMS metal oxide sensors), gas delivery module,and data acquisition module. The characteristics of the gas sensors in the initial sensor array are listed in Table 1.
### Contribution  
The discussed limitations have been considered as an opportunity in the proposed work and efforts have been made to address them. The main contributions are as follows:1. The scheme exploited pre-trained DCNN models, namely Inception-Net, VGG-NET, and Res-Net for feature extraction from the handwritten character-images; due to their excellent performance in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) annual competition [34].
2. The model experimented with the fresh approach of feature-fusion, where the features received from pre-trained DCNN models were fused with the features received from handcrafted methods. In the proposed scheme, Bi-orthogonal Discrete Wavelet Transform (BDWT) was the natural choice for handcrafted features because of its properties like flexibility, separability, scalability, and transformability with the power of multi-resolution analysis. A very limited work has been reported on the use of DWT for the recognition of handwritten Hindi characters [27; 35]. The effective PCA method was implemented in the proposed work for dimensionality reduction of feature-vectors by preserving most of the important details. This helped in achieving a low computational cost.
3. The proposed scheme has thoroughly investigated the performance of the model for individual character-class. The number of performance metrics like precision, recall, and F1-measure was evaluated for the test samples of each character class to determine the amount of correct and incorrect recognitions. A confusion matrix was generated for precise character-wise result analysis.
4. The strength of individual feature-types present in the hybrid-feature-vector and their all-possible combinations were evaluated for recognition accuracy.
5. The proposed features were examined with two popular classifiers, namely MLP and SVM. This was done to examine the performance of proposed features over ANN-based and kernel-based approaches, respectively, for the given multiclass problem.
6. Various timings, related to feature-extraction and character-recognition, were estimated in the proposed work.  
The rest of the paper is organized as follows: Section 2 contains the preliminary; Section 3 contains the methodology (method); Section 4 contains the results and discussions; Section 5 contains the conclusions.
## 3 Close-Proximity Concepts Design and Analysis
### Decision Points  
Decision points are operational points in which the mission timeline is progressed if a set of mission-dependent decision criteria is successfully met.  
For the CS-1 case, the points are inherently placed at the entrance of the Approach Zone, at the entrance of the Keep Out Zone, and at Capture, as illustrated in Figure 5. The entry in these zones is subject to the positive assessment of GO/NO-GO conditions. If the GO conditions are not met, the entry into the zone does not proceed and, if needed, relevant actions are taken to ensure a safe relative trajectory.  
The CS-1 decision criteria are related to the operationality of all critical systems, and to the commissioning of the associated navigation and actuation systems, being defined as follows:  
* GO for AZ--Commanded by Ground
* All critical systems are operational (such as sensors, actuators, battery level, and having enough propellant);
* Positive commissioning of a relative-state estimation;
* Positive commissioning of impulsive maneuvers.
* GO for KOZ--Commanded by Ground
* All critical systems are operational (such as sensors, actuators, battery level, and having enough propellant);
* Positive inspection of the client;
* Successful deployment and validation of the robotic arms;
* Positive initialization and commissioning of 6 DOF pose estimations;
* Positive initialization and commissioning of combined forced motion and 6 DOF navigation.
* GO for Capture--Autonomous
* All critical systems are operational (such as sensors and actuators);
* The 6 DOF estimation and control are operational and reliable;
* Server is compliant with the approach corridor (operational forced motion).  
Before the GO for the KOZ is given, the correct functioning of the 6 DOF navigation and control chain is tested with a station keeping at IPP. Note that, before the GO for KOZ, a forced motion, i.e., 6 DOF control, is already performed to go from the Formation Keeping Point (FKP) to the Initial Proximity Point (IPP), as described in Section 4.3. However, such a transition is performed using 3 DOF navigation (see Section 4.1 for details), and therefore cannot be considered as a reliable proof of the proper functioning of the 6 DOF controller for the sensitive operation of entering the KOZ, as the tuning may differ.  
The criteria to consider the chain to be successfully working are the following:  
* Attitude control error.
* Position control error.
* 6 DOF navigation filter covariance.
* Pose initialization tracking success.  
Figure 5: Illustrative summary of the zones and decision points of the CS-1 mission.  
* Pose tracking success rate. All of these criteria are under pre-defined thresholds and checked by Ground teams. To summarize, the servicer is permitted to enter a designated zone only after meeting a set of conditions that are verified at a decision point, thus validating the requirements associated with decision points.
# Federated Learning for Clients' Data Privacy Assurance in Food Service Industry
## 3 Methodology
### FedML  
Any problem being framed in terms of a federated machine learning scenario will have several aspects in common. This characteristic allows developers to adopt an open-source model to expand the capabilities of machine learning. One of the frameworks that enable deployments and research using FL is FedML [34]. FedML aims to address challenges in the space of algorithmic development by offering APIs for secure aggregation, communication, and benchmarking tools.  
FedML is designed with two layers of high-level and low-level APIs. Low-level APIs handle the establishment of secure communication between servers and clients. High-level APIs enable the developer to handle model type and data manipulations. FedML's main goal is to provide an adequate platform that supports algorithm and development and has standard benchmarking tools. These benchmarking tools can serve as models for setting up benchmarking with additional model types. The FedML platform currently provides FedAvg, FedOpt, FedNova, and FedNAS as FL algorithms. However, the authors have built-in mechanisms to allow outside developers to add their own optimization algorithm and run standard tests on it.  
A federated learning environment has two aspects: infrastructure and training/aggregation. The infrastructure establishes secure communication across the nodes and handles the initiation of the training. The training and aggregation aspect is the part that usually requires more fine-tuning compared to the infrastructure. This is what motivated the development of FedML, where the design principle allows the developers to standardize benchmarking. A key feature of FedML is its support for a diverse set of topologies dictating how nodes can communicate with one another. The current version of FedML supports standalone simulation, distributed computation, topology customization, flexible and customizable message passing, and custom algorithm implementation. In this study, we deployed the standalone simulation capabilities of FedML to study the training capabilities on fluorescence images of 10 clients.
## 3 The Method Description
Thus, they deprive [PERSON] of any information concerning codewords choice. 4. The legitimate users perform privacy amplification with some delay sufficient for [PERSON]'s quantum memory to lose coherence or encrypt all the communication necessary for the privacy amplification stage (in contrast to the previous step, an asymmetrical cipher such as AES is to be utilized). The compression ratio depends on the legitimate users' assumptions concerning [PERSON]. If the decoherence time of her quantum memory is considered to be limited by some finite value, then privacy amplification goes according to Equation (5) up to a minor value of the extra key needed for symmetric encryption. Notably, the method relies on using a pre-distributed secret key for encoding a part of classical communication. However, this does not change the common QKD paradigm, as any quantum key distribution protocols begin with an authenticating classical channel using a relatively short initial key (for this reason, key distribution protocols have an alternative name: \"key expansion protocols\"). Our approach leads to the necessity of a longer initial key for the very first round of key distribution, while no data on the raw key are disclosed during the error correction stage, in contrast with the conventional scenario. The key for encoding classical communication in each subsequent round is to be taken from the secret string distributed in the preceding one. The scheme works well in an asymptotic case, when the size of the distributed key is large enough and post-processing procedures are asymptotically efficient. However, in practice, the difficulties related to the finiteness of the key length lead us to the paradigm of \(\varepsilon\)-secure data exchange [14; 31]. Additional difficulties appear when a part of the generated secret key is utilized in the following round of communication, resulting in the overall security slightly degrading with the number of rounds. It worth noting that within our framework, the security level decreases more quickly than in conventional QKD schemes, since we propose using larger amounts of the previously distributed key for the next round. Thus, an accurate analysis of our method beyond the asymptotic case is a perspective and important area for future research. In summary, the modified scheme involves encrypting classical communication (during error correction and privacy amplification stages) and leaving a part of generated key for the next round. Combined with the assumption concerning the upper bound on the decoherence time of [PERSON]'s quantum memory, this allows the legitimate users to come to classical signals analysis and the equation analogous to the result of [PERSON] and [PERSON] (1), where [PERSON]'s information is bounded according to (5) operating with restricted accessible information (4).
# Phishing Attacks Survey: Types, Vectors, and Technical Approaches
## 3 Phishing Methods and Techniques
### Phishing Vectors
#### 3.2.1 Vishing  
Vishing is the method of phishing that involves the use of voice. Although the use of a telephone to attempt an individual scam is not new, the introduction of voice over IP (VoIP) technology resulted in an increase in this practice. Vishing itself uses the ability to spoof a number so that a call IMappers to originate from a legitimate source. VoIP is used to obscure the actual physical location from which the call originates, and the victim is then manipulated into revealing information. VoIP and modern technologies have facilitated this form of deception because the cost of calls--including international calls--is negligible. Furthermore, the use of automation systems further improves the phishers attacks by making them indistinguishable from legitimate calls [30]. There are many reasons this method of phishing is successful [31], for example:  
* Trust--telephones have a greater record of trust. In a 2007 survey, a phone call was rated the least suspicious form of communication [10].
* Automation--acceptance of automated telephone systems.
* Call centers--the extensive use of call centers means people are accustomed to strangers calling and asking for personal details. This also reduces the suspicion of phishers with foreign accents.
* Victim age--a larger share of the globally aging population is accessible through telephone than by email. This is also a demographic that is easier to manipulate.
## 3 Research Framework of Internet of Emergency Services
### Real-Time Data Collection and Analysis
#### 3.3.1 Data Analytics in Internet of Emergency Services  
Data analytics plays a crucial role in the Internet of Emergency Services (IoES), as it enables the processing and analysis of large volumes of data in real-time, thereby providing insights that can help emergency management agencies make informed decisions during a crisis. IoES generates data from various sources such as IoT devices, social media platforms, and other sources that can be analyzed to identify patterns, trends, and anomalies that can help in situational awareness and decision making. Data analytics can help in various aspects of emergency management, such as identifying high-risk areas, predicting and detecting emergencies, and providing real-time alerts and notifications to stakeholders. For instance, data analytics can be used to identify patterns of criminal activities and accidents in specific areas, enabling emergency management agencies to allocate resources effectively and prioritize their response efforts. In addition, data analytics can be used to analyze real-time data from social media platforms and other sources to detect and predict emergency situations such as natural disasters, disease outbreaks, and public health emergencies. Furthermore, data analytics can be used to improve emergency response times and effectiveness by providing real-time situational awareness and enabling emergency management agencies to coordinate their response efforts more efficiently. For instance, real-time data analytics can help in identifying the most efficient routes for emergency responders, based on traffic congestion, weather conditions, and other factors.