esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Approach
### Theoretical Analysis
#### 2.3.2 Incident and Reflected Elastic Waves
The reflection coefficient of the fundamental frequency is given by [19]:  
\[R=\left|\frac{A_{1}}{A}\right|=\frac{1}{\sqrt{1+(2K_{1}/\rho c\omega)^{2}}} \tag{36}\]  
where \(A_{1}\) is the amplitude of the fundamental frequency of reflected pulse from the contact interface. In practice, \(A_{1}\) can be found from the experimental pulses from the amplitude of Fast Fourier Transform (FFT) of the fundamental frequency. Rearranging Equation (36) gives the linear interfacial stiffness \(K_{1}\) in terms of reflection coefficient:  
\[K_{1}=\frac{\rho c\omega}{2}\ \frac{\sqrt{1-R^{2}}}{R} \tag{37}\]  
The second order nonlinear parameter for the reflected ultrasound from the interface \(\gamma\) (derived from Equation (35)) is defined by [39]:  
\[\gamma=\left|\frac{A_{2}}{A_{1}^{2}}\right|=\frac{K_{2}}{\rho c\omega\sqrt{1+ \left(K_{1}/\rho c\omega\right)^{2}}} \tag{38}\]  
where \(A_{2}\) is the amplitude of the second harmonic of the reflected wave. In the analytical approach (Equation (38)), \(A_{2}\) is the coefficient of the \(2\omega t\) term. Again, experimentally, \(A_{2}\) can be obtained from the amplitude of the second order harmonics obtained using a Fast Fourier Transform (FFT). It should be noted that the amplitude of the third harmonic is small compared to the amplitude of the second harmonic, therefore only the second harmonic is considered here. \(K_{2}\) is the second order nonlinear interfacial stiffness. The second order nonlinear parameter for reflected ultrasound \(\gamma\) can be measured by the experiment with the values of \(A_{1}\) and \(A_{2}\). In order to derive the nonlinear stiffness per unit area of the interface \(K_{2}\), Equation (38) is rearranged:  
\[K_{2}=\gamma\ \rho c\omega\ \sqrt{1+\left(K_{1}/\rho c\omega\right)^{2}} \tag{39}\]The nonlinear nominal contact pressure relationship (Equation (26)) can then be defined at any nominal contact pressure \(p_{0}\) with the corresponding linear and nonlinear interfacial stiffness, \(K_{1}\) and \(K_{2}\) from Equations (37) and (39).
# Communication Performance Assessment for Advanced Metering Infrastructure
## 4 Simulation and Experimental Results
### Field Measurements and Communication Performance Assessment
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Day**} & **Number of** & **Reading** & \multirow{2}{*}{**Response**} & \multirow{2}{*}{**Day**} & \multicolumn{2}{c}{**Number of**} & **Reading** & \multirow{2}{*}{**Response**} \\  & **Reading** & **Success** & & & & **Success** & **Rate (\%)** & \\  & **Success** & & & & & & **Success** & **Time (ms)** \\ \hline
1 & 96 & 100.00 & 381.2 & 17 & 96 & 100.00 & 215 \\
2 & 96 & 100.00 & 620 & 18 & 84 & 87.50 & 148.6 \\
3 & 82 & 85.42 & 421.4 & 19 & 65 & 67.71 & 401.7 \\
4 & 96 & 100.00 & 358.7 & 20 & 77 & 80.21 & 261.7 \\
5 & 96 & 100.00 & 418.8 & 21 & 84 & 87.50 & 320 \\
6 & 96 & 100.00 & 293.8 & 22 & 77 & 80.21 & 510 \\
7 & 96 & 100.00 & 218.8 & 23 & 84 & 87.50 & 364.3 \\
8 & 96 & 100.00 & 260 & 24 & 77 & 80.21 & 361.7 \\
9 & 96 & 100.00 & 341.3 & 25 & 73 & 76.04 & 178.6 \\
10 & 96 & 100.00 & 366.3 & 26 & 96 & 100.00 & 240 \\
11 & 84 & 87.50 & 400 & 27 & 96 & 100.00 & 547.5 \\
12 & 77 & 80.21 & 178.3 & 28 & 96 & 100.00 & 386.3 \\
13 & 96 & 100.00 & 493.8 & 29 & 96 & 100.00 & 205 \\
14 & 84 & 87.50 & 205.7 & 30 & 96 & 100.00 & 258.7 \\
15 & 27 & 28.13 & 368.6 & 31 & 96 & 100.00 & 297.5 \\
16 & 46 & 47.92 & 260 & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Field Measurements of \(M_{1}\) for the Actual Small-Scale PLC-based AMI. Figure 17: Calculated CPIs of Day 1 for Actual Small-Scale PLC-based AMI. Figure 18: Calculated Average CPIs from Days 1 to 31 for Actual Small-Scale PLC-based AMI.
## 4 Conclusions  
By controlling the simulated temperature and the content of graphene in the composites, the influence of two factors on the interface properties was studied, and the mechanism was analyzed. The main conclusions were drawn as follows:  
1. During the simulation process, the pulling force increased first and then decreased. The normal strength and pulling force decreased with the increase in temperature. The influence of graphene content on the normal strength was obvious, and the normal strength of 6.71 at% decreased by 6.8% compared with that of the composite containing intact single graphene;
2. During the pull-out process, the pull-out force first increased rapidly, then fluctuated continuously, and finally decreased to zero. The pull-out force and interfacial shear strength decreased with the increase in temperature and had a large decreasing trend between 700 K and 1100 K. The graphene content had little effect on the pull-out force but a significant effect on the interfacial shear strength;
3. The interface cohesion increased rapidly with the simulation and then decreased to 0. The maximum cohesion and cohesion strength decreased with the increase in temperature, and the cohesion strength was almost not affected by the graphene content;
4. The analysis of the effect of temperature and graphene content shows that temperature will affect atomic activity. The increase in temperature leads to a decrease in the attraction between carbon atoms and copper atoms and the softening of the copper matrix, which reduces the interface strength. The presence of graphene sheets will hinder the dislocation expansion of the copper matrix, so the normal strength increases with the increase in graphene content, and the pull-out force is only affected by the number of carbon atoms at the critical pull-out surface, so the maximum pull-out force remains basically unchanged.  
Figure 23: Load transfer diagram.  
**Author Contributions:** [PERSON] analyzed the data and wrote this manuscript. [PERSON] and [PERSON] provided valuable suggestions on the experiments and manuscript. [PERSON], [PERSON] and [PERSON] modified the manuscript in detail. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Science Foundation of China (grant no. 51878547, 51641809), the Chinese Postdoctoral Science (grant no. 2018M633478) and Special scientific research project of Shaanxi Provincial Education Department (grant no. 21)K0711).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# An Enhanced Energy Balanced Data Transmission Protocol for Underwater Acoustic Sensor Networks
## 5 Performance Evaluation  
In this section, we evaluate the performance of our proposed protocols via simulations, and compare these with two existing protocols; BTM and UWDAR. The sensor nodes are deployed in a random uniform manner in a circular field with varying radius and sink at the center of the field. Except EEBET, each Ring Sector of the network field has equal number of sensor nodes such that these nodes are randomly deployed within the given ring sector. The network radius is denoted by R. The initial energy of each node is 300 J. Each sensor node generates a packet of 200 bits; 50 bits of control field and 150 bits of data field. For transmitting packets (channel access), the sensor nodes use Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) under the non-beacon enabled mode of IEEE 802.15.4. We have used _Linprog_ linear programming solver in MATLAB to solve the optimization problem of throughput maximization. We have used three metrics for evaluating the performance of our proposed routing protocols; network lifetime, throughput, and path loss. The simulation parameters are shown in Table 3.
## 5 Discussion and Summary
The results from this study were confirmed by the satellite engineers through the use of a single blind experimental setup. Figure 11 shows a summary of the steps that were followed. This approach could be applied to other systems in which abnormal behaviours need to be recognised. As would be seen, domain knowledge is necessary throughout the process. Furthermore, it should be noted that because the approaches we used in this paper were  
Figure 11: A summary of the process used to identify terminal states in a large satellite network. data-driven, the models could potentially be updated continuously. **This creates a system that learns continuously and that able to self-adapt based on new data from the network. ** The machine learning approaches we have used in this work, particularly the decision tree, offers an engineer ability to sense-check that the generated classification models are correct. In this situation, the engineer would use his experience of the satellite network to ensure that the attributes used by the model makes sense. **In other words, this offers readability and interpretability to the engineer. ** As stated in the case study section, it was discovered that unlike the single variable that the engineers were using, there were a multiple of variables that contributed to the state of terminals in the network. The visualisation and notification approach provided in Figure 9 also offers a condensed view of the network to the engineer. It highlights the states of the terminals in the network and any potential issues. These results show the feasibility of using an autonomous system to differentiate a normal state, cloud cover, and disruption. In summary, our architecture offers (1) flexibility to add new or extend existing machine learning algorithms to meet the user needs, (2) an enhanced pattern recognition and prediction through the support of machine learning algorithms and the expert knowledge on satellite infrastructure, (3) the ability to adapt to changing conditions of the satellite network, and (4) the ability to augment satellite engineers through interpretable results.
# Deep Learning at the Mobile Edge: Opportunities for 5G Networks
## 4 Challenges of DL for 5G Operations at the Mobile Edge
### URLLC and eMBB through Quality of Service Constraints  
Quality of Service (QoS) measures the performance experienced by the end users of the network. Common QoS metrics include bandwidth, latency, and error rate, among other parameters, changes that can severely alter the network's ability to provide critical services. Every user connects to the network with a set of QoS requirements, which may be more stringent for latency-sensitive applications, such as on-demand video streaming, and voice over IP (VoIP). Meeting QoS and SLA agreements can be integrated as a goal in the DL systems for automated resource allocation in 5G mobile edge computing [59] by requiring chosen allocation schemes to maintain network operations within QoS ranges [73]. For mobile edge computing applications, such as robotic systems, resource allocation systems based on DL and QoS metrics must capture the ultra-low-latency requirements for feedback signals between end devices [74]. This ensures both safety and Quality of Experience (QoE). Furthermore, QoE, as it relates to QoS, can be optimized based on user similarity within groups or geographical regions to dynamically allocate resources according to group needs [75].  
Achieving ultra-reliable and low-latency communication (URLLC) is one of the major challenges in 5G networks. This type of service offers a wide variety of challenges, such as QoS requirements [77], strict handovers for uninterrupted service [78; 79], power consumption in UEs battery [80], etc. In addition, the coexistence of eMBB and URLLC with different service requirements is also a challenge [76].  
The article [69] suggests a novel network architecture using a resource cognitive engine and data engine to address the problem of achieving ultra-low end-to-end delay for the ever-growing number of cognitive applications. The resource cognitive intelligence, based on network context learning, aims to attain a global view of the network's computing, caching, and communication resources.  
It is quite a challenge for 5G networks to meet URLLC specifications and this will entail major changes to the system architecture of the existing telecom infrastructure. While current user requirements are initially based on high bandwidth, it is also expected that latency and reliability will play a vital role in real-time applications and mission-critical networks [81].  
Figure 6: These two images show the difference between traditional caching and predictive caching using machine learning in mobile edge computing. In step 1 of predictive caching, the most popular contents that match user’s predicted preferences according to their profile are downloaded from the cloud to the edge node. In the second step, when the user requests a specific content, there is a higher probability that the desired content has already been downloaded to the edge node previously, increasing QoE.
# Propagation Characteristics of Modulated EHF Signal in the Wake Region of Plasma Sheath
## 4 Discussion
When \(50^{\circ}\)\(<\alpha\leq 60^{\circ}\), both of the maximum electron density and the channel length are relatively small, which may make it easier to obtain better BER performance.
###### Abstract
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Variable** & **Definition** \\ \hline \(R_{i}\) & The i_th_ routing path selected for key distribution \\ \hline \(K\) & Global key reconstructed at the destination \\ \hline \(p_{j}\) & Security probability of relay node \(j\) \\ \hline \(S_{i}\) & Security state of the path \(i\), \(S_{i}\in\{0,1\}\) (1: secure, 0: insecure) \\ \hline \(\delta_{j}(t)\) & Temporal penalty imposed on node \(j\) at time \(t\) \\ \hline \(W_{j}\) & Penalty weight assigned to relay node \(j\) \\ \hline \(P(S_{i})\) & Adjusted security probability of path \(R_{i}\) \\ \hline \(PT(A,B)\) & Combined security fraction between source node A and destination node B \\ \hline \(P_{\min}\) & Minimum acceptable security threshold \\ \hline \(\epsilon\) & Multiplicative security probability metric for path evaluation \\ \hline \(\text{Cor}_{S(P)}\) & Path correlation metric for evaluating path diversity \\ \hline \(\text{TH}_{\text{Corr}}\) & Dynamically adjusted correlation threshold for path security evaluation \\ \hline \(\lambda_{\text{Cor}}\) & Scaling factor used in path correlation threshold calculation \\ \hline \(W_{j}\) & Penalty weight assigned to node \(j\) based on historical compromise data \\ \hline \(\Delta W\) & Incremental penalty adjustment for nodes detected as compromised \\ \hline \(R(t)\) & Reward function for network adjustments considering node security states \\ \hline \(\alpha,\beta\) & Weights assigned in the reward function to compromised and secure nodes, respectively \\ \hline \(d_{i}\) & Transmission delay associated with path \(R_{i}\) \\ \hline \(E(A,B)\) & Performance metrics balancing security and efficiency between nodes A and B \\ \hline \(\gamma,\eta\) & Scaling coefficients balance security and efficiency in the metric \(E(A,B)\) \\ \hline \(d_{\max}\) & Maximum radius for penalizing nodes near compromised links \\ \hline \(\delta_{j}(t)\) & Temporal penalty imposed on node \(j\) at time \(t\) based on proximity to compromised links \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Notations table.
# Land Suitability Mapping Using Geochemical and Spatial Analysis Methods
## 3 Materials and Methods
### Data Processing and Spatial Analysis  
In the present study, data were inserted in a GIS environment, and a spatial database was created for processing, analyzing and presenting spatial information. The software code ArcGIS was used for the spatial analysis of the area studied. The geological formations and the land uses were added to the database as polygon layers. The land uses were categorized into different classes according to their characteristics. Additionally, the Cd and Co contents in soil were visualized as a graduated symbol. Further, they were grouped using the natural breaks classification method into three categories. The numerical values of Cd and Co were interpolated using the inverse distance weighted (IDW) method. This spatial interpolation method has been used in several works for mapping of natural hazards [17; 18], land-use suitability [21; 22; 23; 25] and soil properties [26; 28; 29], indicating reliable results and high accuracy of the produced map. The produced maps demonstrated the spatial distribution of Cd and Co with continuous numerical values. Values were grouped into three classes according to the guidelines provided from the literature. Finally, they were correlated with the spatial distribution of each land use to estimate the suitability of the land uses.
# A Long Time Span-Specific Emitter Identification Method Based on Unsupervised Domain Adaptation
## 5 Experimental Results and Discussion
### Training Accuracy and Loss of LTS-SEI  
Figures 10 and 11 show the training accuracy curves and loss curves on A1\(\mathtt{\text{-}}\)\(\mathtt{>}\)A2, A1\(\mathtt{>}\)A3, B1\(\mathtt{>}\)B2, B1\(\mathtt{>}\)B6, B1\(\mathtt{>}\)B9, and B1\(\mathtt{>}\)B14, respectively, to more effectively understand the samples training process of LTS-SEI. From Figure 10, it can be seen that the classification accuracy of source domain samples and target domain samples increases with increase of iterations. For A2 and A3, which have a shorter time span, improvement in their classification accuracy is almost synchronous with A1. For Dataset B, which has a long time span, when the data distribution of the source domain samples and the target domain samples is similar (B1\(\mathtt{>}\)B6), their training accuracy quickly converges. The source domain samples with available labels train faster than the target domain samples without labels when there is a considerable difference between the source domain and target domain (B1\(\mathtt{>}\)B2, B1\(\mathtt{>}\)B9, and B1\(\mathtt{>}\)B14). The improvement in target domain samples classification accuracy cannot  
Figure 9: Confusion matrix for identification of different specific emitters: (**a**) A1\(\mathtt{>}\)A2 (I/Q), (**b**) A1\(\mathtt{>}\)A2 (Shallow layer: CMD), (**c**) A1\(\mathtt{>}\)A2 (LTS-SEI), (**d**) A1\(\mathtt{>}\)A3 (I/Q), (**e**) A1\(\mathtt{>}\)A3 (Deep layer: HOMOM), (**f**) A1\(\mathtt{>}\)A3 (LTS-SEI), (**g**) B1\(\mathtt{>}\)B2 (I/Q), (**h**) B1\(\mathtt{>}\)B2 (Shallow layer and deep layer: MMD), (**i**) B1\(\mathtt{>}\)B2 (LTS-SEI), (**j**) B1\(\mathtt{>}\)B3 (I/Q), (**k**) B1\(\mathtt{>}\)B3 (Shallow layer and deep layer: JDA), and (**l**) B1\(\mathtt{>}\)B3 (LTS-SEI).  
be achieved without the joint efforts of various components of the LTS-SEI framework, including the confrontation between the feature extractor and domain discriminator, the alignment of deep features, and the correct recognition of edge samples. From Figure 11, it can be seen that the training loss of the source domain samples continuously decreases with the increasing of iterations and eventually converges to approximately 0. The adversarial loss between the feature extractor and domain discriminator can ultimately achieve the Nash equilibrium, which aligns the shallow and deep features of the source domain samples and target domain samples in the feature space. The training accuracy curves and training loss curves indicate that LTS-SEI has the ability to learn RFFs of target domain samples that remain unchanged over an extended time span.
# Construction Progress and Aviation Flight Test of BDSBAS
## 3 BDSBAS Service Performance Evaluation
### Availability  
Availability is defined as the percentage of the number of samples whose PL is less than the alarm limit (AL) within a period of time to the total number of samples, as shown in Figure 5. For the horizontal and vertical directions, the above conditions must be met (that is, HPL < HAL, VPL < VAL).  
Figure 5: Concept of service availability.  
When judging whether the service of each epoch is available, there may be several different situations:  
* The positioning error (PE) is less than the protection level, and the PL is less than the AL. Therefore the service is available;
* The PE is less than the AL, and the AL is less than the PL. The service is unavailable;
* The PL is less than the AL, and the AL is less than the PE. In this case, the service will be misjudged as available, causing system miss alarms and result in serious integrity risk events;
* The PE is less than the AL, and the AL is less than the PL. In this case, the service will be misjudged as unavailable, causing system false alarms and affecting the availability and continuity of the service.
## 4 Performance Evaluation and Discussion
### Comparison with Other Positioning Systems for AR applications  
In this subsection, the attributes of the proposed audio marker system are compared with those of the positioning systems for AR applications such as global navigation satellite system (GNSS) based dense reference networks [24] and Bluetooth beacons [25]. Table 3 shows the compared attributes, including the operating range and resolution, sensor type, and possible application field.  
According to the comparison, a drawback of the proposed system is that it has a somewhat lower resolution than the other systems, making it difficult to apply to a user-specific marker transmission. In other words, if multiple users are grouped in front of the audio marker, it could provide the same information to all the users in the group instead of providing it to a specific user in the group. This is because it operates by broadcasting the audio marker information to the region of interest. Moreover, the operation range of the proposed system is substantially shorter than those of the other systems; thus, its application could be limited to close-range service scenarios. Despite these drawbacks, the proposed system has the advantage of being able to construct a service environment at no additional cost in various indoor applications because a speaker and a microphone are the most common sensors equipped with personal computers and hand-held devices. In addition, it should be noted here that even though the proposed audio marker system uses a COTS microphone and a speaker as sensors, it is free from privacy intrusion problems such as eavesdropping, because the LFU frequency band used in the proposed system does not contain any information on the user's speech or audio.