esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Measurement of Coal Moisture Based on Microwave Spectrum via Distance-Weighted kNN
## 4 Conclusions  
In this paper, a rapid and non-destructive measurement system for coal MC is designed, and the verification experiments are carried out in the routine range of MC by combining with machine learning techniques. The designed measurement system can efficiently generate and measure broader continuous-frequency microwave signals and visualize the spectra. To improve the cleanliness of modeling spectrum data, an iterative clipping method based on Mahalanobis distance (MD-ICM) is used to detect and eliminate outliers. Comparative evaluation of typical machine learning regression models is performed. The results show that proper weight distribution is able to effectively overcome  
Figure 7: The results of the verification experiments: (**a**) The scatter results of the true and the predicted moisture contents using DW-kNN model; (**b**) Verification experiments based on different methods; (**c**) Verification experiments based on different frequency bands using DW-kNN model; \(R^{2}\) represents the coefficient of determination.  
the influence of microwave oscillation in high frequency band. Moreover, the DW-kNN algorithm can process imbalanced data well and show higher generalization performance in MC determination of coal samples from unknown regions. Based on multiple microwave frequency bands, different regression models are evaluated. The comparison results show that the proposed method based on the DW-kNN algorithm using broader bandwidth microwave signals for coal MC measurement performs best, achieving an \(R^{2}\) of 0.9106, MAE of 0.2374 and RMSE of 0.2940. Finally, on-site testing was carried out using the designed measurement system, which proves its availability and reliability.  
Although the proposed method has not been widely applied in real applications, the measurement accuracy demonstrates that it is of great potential in on-line measurement and control of coal MC. It can provide technical guidance for the development of rapid and non-destructive determination devices of coal MC in the coal washing and processing industries.  
Conceptualization, M.L. ([PERSON]) and H.Z.; methodology, M.L. ([PERSON]) and D.Y.; software, M.L. ([PERSON]); validation, J.T. and Y.W.; formal analysis, J.T.; investigation, J.T. and Y.W.; resources, Y.W. and H.Z.; data curation, Y.W.; writing--original draft preparation, J.T.; writing--review and editing, D.Y. and M.L. ([PERSON]); funding acquisition, M.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Natural Science Foundation of China with grant number 51904297.  
Not applicable.  
The authors would like to thank [PERSON] and [PERSON] for their inestimable advice and help for this article, respectively.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Construction of Quality Score Prediction Model
#### 3.3.3 Construction of the Artificial Neural Network Prediction Model  
A near-infrared spectral prediction model for YCXY tea quality scores was constructed using the Jordan-Elman neural network method, with the first three principal components as inputs and the tea quality scores as outputs. Different transfer functions were tested between layers during model development, yielding varying prediction results. In this study, the learning rate was set to 0.1, and three transfer functions were evaluated: the linear function (range: \([-1,1]\)), the logistic function, and the tanh function. The prediction results of the model are summarized in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**PCs** & **PC1** & **PC2** & **PC3** & **PC4** & **PC5** & **PC6** & **PC7** \\ \hline Contribution rate (\%) & 92.45 & 4.85 & 1.75 & 0.70 & 0.14 & 0.05 & 0.03 \\ \hline \end{tabular}
\end{table}
Table 3: Contribution rate of the first seven PCs.  
As shown in Table 4, among the Jordan-Elman neural network models for predicting YCXY tea quality scores using the three transfer functions, the model with the linear function exhibited the weakest prediction performance (Rp\({}^{2}\) = 0.901, RMSEP = 0.541), while the model employing the hyperbolic tangent (tanh) function achieved the best results (Rp\({}^{2}\) = 0.980, RMSEP = 0.341). This is because the spectral information of YCXY tea is inherently complex, comprising multiple components rather than a single substance. As a result, the linear transfer function yields suboptimal performance. The logistic function, with its S-shaped curve, accounts for some nonlinear aspects of the spectral data and performs moderately better than the linear function. However, the tanh function, a hyperbolic tangent, demonstrates faster convergence, requires fewer iterations, and delivers the most accurate predictions among the three transfer functions, ensuring the highest robustness of the prediction model.
## 2 Control Algorithm
### Parameter Tuning  
In the proposed algorithm, there are four parameters to tune in total: \(\lambda\) and \(\alpha\) to construct \(s\)-surface, \(\eta\) to construct \(v_{su}\), and \(\Phi\) to determine the saturation function. We have tried to analytically understand the effect of each parameter. However, this is too difficult due to the complexity of nonlinear systems and mathematics. Instead, approximated properties in a 2 nd order system are analyzed, and numerical simulations are presented to demonstrate the potential effect of each parameter, so that readers will have some ideas on our tuning procedures. Notably, numerical examples are not solid proofs. An effect may vary in different cases.  
The example from [19] is employed to study the effect of each parameter, and how to tune these parameters. The example, given as follows, was originally from [27] and employed to demonstrate the validity of the our proposed algorithm:  
\[\ddot{x}+a(t)\dot{x}^{2}\cos 3\tilde{x} = \tilde{u}. \tag{31}\]  
The function \(a(t)\) is unknown but varies within the range \(1\leq a(t)\leq 2\). Let  
\[\hat{f} = -1.5\dot{x}^{2}\cos 3\tilde{x},\]  
The control input of SMC is  
\[\tilde{u} = 1.5\dot{x}^{2}\cos 3\tilde{x}+\ddot{\tilde{x}}_{d}-\lambda\dot{e}\] \[-(0.5\dot{x}^{2}|\cos 3\tilde{x}|+\eta)\text{sat}\left(\frac{s}{ \Phi}\right).\]  
with \(s=\dot{e}+\lambda e\). The control input of PISMC is  
\[\tilde{u} = 1.5\dot{x}^{2}\cos 3\tilde{x}+\ddot{\tilde{x}}_{d}-\alpha^{2}e-( \lambda+2\alpha)\dot{e}-\ddot{e}\] \[-(0.5\dot{x}^{2}|\cos 3\tilde{x}|+\eta)\text{sat}\left(\frac{s}{ \Phi}\right)\]  
with  
\[s = s_{1}+s_{2} \tag{32}\] \[= \left(\frac{d}{dt}+\lambda\right)e+\left(\frac{d}{dt}+\alpha \right)^{2}\tilde{\zeta}_{e}\] \[= 2\dot{e}+(\lambda+2\alpha)e+\alpha^{2}\int_{0}^{t}ed\tau.\]  
Notably, we employed this example in our work in [19], and showed the validity of our work. In this paper, however, we employ this example to show the properties and influences of parameters. As a result, despite the dynamical equation and parameters of the equation being directly employed from [27], other settings are changed. For example, we used state errors and their derivatives, instead of the output rate in the reference. The parameters of the controller are different from those in the reference. The desired signal \(\tilde{x}_{d}\) is set as unit step for investigating transient properties, which is different from the original \(\tilde{x}_{d}=\sin(\pi t/2)\). Therefore, the comparison occurs between our PISMC and SMC to know the parameters better. It did not occur between our PISMC and the SMC of the reference.
# Internet of Nano-Things, Things and Everything: Future Growth Trends
## 6 Challenges and Impediments to IoT
### Deployment of IPv6  
In February 2011 [66] the supply of IPv4 addresses held by the Internet Assigned Numbers Authority (IANA) was exhausted. The ushering in of IPv6 (Internet Protocol version 6) was critical to cover this IP address shortage, as billions of sensors will each require a unique IP address. The deployment of IPv6 will further make network management less complex, with its enhanced security features and network auto-configuration capabilities. However, the deployment of IPv6 has its own challenges, the following are the major probable ones:  
* In its infancy, intruders, man-in-the-middle attacks or any general attacker may demonstrate a greater level of knowledge and expertise in IPv6 compared to the IT professionals, including the network administrators of any organizations. During the nascent period of deployment, it may initially be very strenuous to manage and discern unauthorized or even unidentified IPv6 assets within the legacy operational IPv4 networks.
* Operating both the protocols simultaneously during the transition period may also add to the overall complexity and cost in terms of time, human resources and monetary value.
* A prolonged period for IPv6 to mature, especially in terms of implementing it in security protocols and devices, poses additional risks.
* An increasing myriad of IPv6 tunnels along with the existing IPv4 ones, may add extra layers of complexity to the existing defense mechanisms.
* Another major challenge will be finding an optimized approach of dealing with the existing legacy systems, assets and devices.  
To address these overall challenges, along with plans for a phased development, programs of education and training for IT staff to widen their knowledge and expand their expertise in IPv6 need to be seriously considered.
## 4 Discussion  
The results obtained from the proposed damage assessment method indicate that the target bridge currently exhibits satisfactory stiffness without significant damage that could affect its structural behavior. However, the bridge's performance is approximately 63% of the original design performance. Visual inspection results, detailed in the actual inspection report, indicate minor damage, and the deck is graded as C. This study illustrates the correlation between the poor behavior of the bridge's deck due to cracks, specifically deflection. If cracks due to the aging of the bridge continue to occur or propagate, it is anticipated that the measured deflection response ratio will increase sharply, leading to larger negative deviation values. Therefore, although the stiffness reduction of the target bridge due to aging is currently satisfactory, continuous monitoring is necessary.  
This study aimed to develop a damage assessment method for the efficient maintenance of aging small- to medium-sized RC slab bridges. Structural analysis results for an RC slab bridge based on the proposed damage assessment method indicated a very high correlation between deflection and stiffness reduction. Accordingly, the damage assessment results for an actual in-service bridge indicated that the target bridge did not exhibit damage that would affect its structural response. These results were similar to those obtained in the visual inspection report of the actual inspection, validating the effectiveness of the proposed damage assessment method.  
This study was conducted to develop a damage assessment method for small- to medium-sized RC slab bridges. Therefore, it does not consider temperature variations of the target bridge in the fragmentary load test. However, it is expected that more reliable results can be achieved through future research by considering the behavior variations of the bridge according to temperature changes and expanding the research to include other types of bridges, such as truss bridges.  
The current damage assessment method was studied for a single target bridge. In future work, correlation data for deflection response ratios and stiffness reduction rates  
Figure 16: Estimation of the deflection response ratio considering the stiffness reduction rate for (a) LC1, LC3; (b) LC2; (c) LC4, LC6; (d) LC5.  
for multiple bridges must be obtained. By continuously collecting data on the correlation between the deflection response ratio and stiffness reduction rate, damage assessment can be conducted via regression analysis of the collected data without requiring additional structural analysis. Therefore, structural analysis on multiple RC slab bridges will be considered in future work in addition to the target bridge to derive correlation results between the deflection response ratio and stiffness degradation rate and to use the continuously accumulated correlation data for the regression analysis. Additional field load test measurements will be taken and used in the regression analysis to further verify the reliability of the method.
## 4 High SNR Analysis and Secrecy Throughput
### Secrecy Throughput  
On the basis of SOP, the ST more comprehensively measures the reliability and security of the system in a whole way, which is the product of the complementary probability of \(P_{out}\left(R_{S}\right)\) and the predetermined secrecy rate \(R_{S}\) (Equation (24), [43]), [44]. Thus, the throughput expression of the two schemes can be written as  
\[T_{out}^{\varphi}\left(R_{S}\right)=\left(1-P_{out}^{\varphi}\left(R_{S}\right) \right)R_{S}, \tag{59}\]  
where \(\varphi\in\left(\text{MRC}/\text{MRT-SC},\text{MRC-ZFB}/\text{MRT-SC-ZFB}\right)\).  
Remark 3.: _Given the Equation (59), we numerically find that \(T_{out}^{\varphi}\left(R_{S}\right)\) exist an optimal value in a given range. When \(R_{S}\) is small, \(T_{out}^{\varphi}\left(R_{S}\right)\) of the two scenarios is relatively small. However, when \(R_{S}\) exceeds an optimal value, the \(T_{out}^{\varphi}\left(R_{S}\right)\) would decrease until zero. Therefore, determining the optimal value, \(R_{S}^{\varphi}\), is practically significant to system designers, which can achieve the local optimal ST of system._
## 3 Methodology
### Initialization and Termination  
The single object tracker heavily relies on the quality of the initialization bounding box in the first frame, which is usually hand picked in a SOT scenario. However, object candidates provided by an external detector under the current MOT framework are the only inputs to count on, though a high percentage of detections are imperfect in location and scale. We follow the argument in [6], that trackability of an object is not only dependent on its visibility, but also its size. After analyzing visibility and size of each external detector, we set the confidence score threshold for initialization as \(\delta=-1,0.6,0.7\) corresponding to Deformable part model (DPM), Faster Region-Convolutional Nueral Network (FRCNN) and Scale-Dependent pooling (SDP). The single object tracker is initialized using the detection results in set \(D_{t}\) left after pre-processing by the detection purification and filtering by \(\delta\). As for track termination, since occluded objects will be saved in memory at every frame and trying to be re-established by the above procedures, the ones that can not be relinked for \(t>55\) frames are considered lost and removed from the memory to save computational resources.
# Effect of Micropiles on Clean Sand Liquefaction Risk Based on CPT and SPT
## 3 Discussion and Results  
In this section, the effect of jet grouting using micropiles to enhance soil properties, based on the results of CPT and SPT tools, is analyzed. Linear equations are presented for simplicity purposes, along with practical considerations by geotechnical engineers to assess the effect of soil remediation to enhance soil properties. Furthermore, improvement of the factor of safety against soil liquefaction, before and after soil remediation, is investigated following three methods, and specifications for each of the CPT- and SPT-based analyses are discussed.
## 4 Conclusions  
The main advantage of the proposed hybrid charging scheme is the division of the entire charging process into two stages: charging by the mobile charger and energy trading for balancing. The mobile charger is only concerned with the cluster heads, and thus moves over a shorter path, consequently spending less time for charging. During energy trading, since there are more seller nodes than buyer nodes, energy transfer is performed through many-to-one correspondences. The many-to-one correspondences enable all the sensor nodes in the wireless sensor network to be charged over the target level in a relatively short time. The impact of higher antenna gain is beneficial for reducing the time needed to charge the cluster heads, but subsequently increases the proportion of time needed for energy trading. Nevertheless, the proportion of time needed for energy trading is considerably smaller than the time required for charging by the mobile charger, providing a significant reduction with higher antenna gain in total charging time as well as total charging energy.  
This research was supported by the MSIP (Ministry of Science, ICT, and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2016-R2718-16-0011) supervised by the IITP (Institute for Information & communications Technology Promotion).  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] analyzed the data; [PERSON] contributed reagents/materials/analysis tools; [PERSON] wrote the paper.  
The authors declare no conflict of interest.  
Figure 13: Number of overcharged sensor nodes as a function of time with 12 dB antenna gain for \(T_{f}\)= 0.01 and \(T_{f}\)= 0.05.
## 3 Application Community  
### Application Community Definition  
Traditionally, a community is defined as a set of nodes that are closely related to each other and sparsely connected to the outside based on connectivity between nodes. While in this paper, we need to consider both communication relationships and traffic behavior characteristics to distinguish multiple services carried by the same IP nodes and the normal and malicious connections that are accessing a service port on the same host. The nodes in each application community satisfy the following conditions.  
1. There is a group of service nodes and other member nodes have communication relations to these service nodes;
2. When the member nodes communicate with service nodes, the characteristics of data transmission modes are similar.  
When the set of nodes that meet these two conditions, we call it an application community (here in after referred to \"the community\"). In Figure 1, Servers A and B provide different services. Clients 1-7 and Servers A and B communicate based on the connection. The line type is used to indicate different data transmission behaviors. According to the above description of communities, the nodes in the graph can be divided into three communities, namely {A, 1, 2}, {A, 3, 4, 5}, and {B, 4, 5, 6, 7}. Among these, Servers A and B are leader nodes determining the community type, and Clients 1-7 are member nodes that communicate with the leader nodes to obtain a certain service. Although {1, 2, 3, 4, 5} communicates with the A node, {A, 1, 2} and {A, 3, 4, 5} do not belong to the same community because {1, 2} and {3, 4, 5} differ in data transmission behavior. The difference in data transmission behavior may because that (1) Server A provides multiple services at the same time or (2) Client {1, 2} or {3, 4, 5} access the server in an unconventional manner. Using the method proposed in this paper, one can identify different communities when (1) the same node participates in different applications, and (2) the same service node provides normal services or is attacked.
# SDN-Based Secure Common Emergency Service for Railway and Road Co-Existence Scenarios
## 4 Implementation of the Proposed SDN-Based Service for Rail and Road Coexistence
### Packet Differentiation and Emergency Service Delivery  
Figure 5 shows the logical flow diagram of the SDN application that has been implemented to differentiate the data traffic from railways and roads, which is mainly based on VLAN tagging, as described in [27]. It also aims to differentiate emergency data packets/messages and non-emergency data packets/messages and forwarding of emergency messages to the RailEmer and CarEmer servers.  
When a User Datagram Protocol (UDP) data packet arrives at any access point/switch, this network device looks into its forwarding rules/table, and if there are no forwarding rules installed at the current access point/switch for the arrived source and destination IP pairs, the data packet is sent to the SDN controller for treatment as an OpenFlow PacketIn message. The developed SDN application checks the destination port assigned in the UDP data packet and if the destination port is equal to 135 (the port defined as the emergency port in this proof of concept), the controller marks this data packet as an emergency data packet and tags this packet with VLAN ID 5 (VLAN associated with emergency here). For  
Figure 4: Considered scenario: Mininet-Wifi perspective.  
Figure 3: Considered scenario: ONOS perspective.  
the implementation of the application, any port and any VLAN ID can be used. Here, VLAN ID 5 is used to differentiate the emergency data packet and port 135 is set to receive this data packet via UDP.  
If the developed SDN application is operating in case 1, the SDN application duplicates the emergency UDP data packet and changes the destination IP address of the duplicated emergency data packet. If the emergency message is initiated by the car, the original emergency data packet is sent to the car emergency server and the duplicated emergency data packet is sent to the rail emergency server. On the other hand, if the emergency message is initiated by a train, the original emergency data packet is sent to the rail emergency server and the duplicated emergency data packet is sent to the car emergency server. If the developed SDN application is operating in case 2, the SDN application distributes the emergency data packet to trains and cars on the secondary wireless interface which is reserved for emergency data communication.  
If the incoming UDP data packets have a destination port other than 135, they are designated as non-emergency data packets. VLAN ID 3 is assigned to the data packet to/from the rail, RailServer, and RailElmer server, while VLAN ID 4 is assigned to the data packet to/from the car, CarServer, and CarEmer server. It is worth noting that any VLAN ID can be used to tag the packets for traffic differentiation.  
Figure 5: Flowchart.