esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, we revealed the relationship among n-level polybinary transformation, the time-packing factor and the performance of the MLSE algorithm. Within a certain range of the time-packing factor, we find that the method of combining appropriate n-level polybinary transformation and MLSE can effectively eliminate serious ISI in the received FTN signals. With the addition of converting the two-dimensional constellation diagram into the one-dimensional constellation projection PDF, we identify the reason that a different n-level polybinary transformation is required for different time-packing factors. For a time-packing factor within the large range of 1.00-0.45, we identify the three ranges of values within which the best n-level polybinary transformation should be adopted. Based on the above analysis, we propose a modified-BPS algorithm to compensate for PN in the FTN system with an extremely small time-packing factor. The proposed modified-BPS algorithm can effectively cope with the PN with the \(\Delta\)f\(\cdot\)Ts at 1.07 \(\times\) 10\({}^{-5}\), 1.79 \(\times\) 10\({}^{-5}\), 2.86 \(\times\) 10\({}^{-5}\) and 3.57 \(\times\) 10\({}^{-5}\) under time-packing factors of 0.45, 0.50 and 0.55, respectively. In the case of the above conditions, the SE can be enhanced up to approximately 4.88 bit/s/Hz, 4 bit/s/Hz and 3.27 bit/s/Hz, respectively. It is noteworthy that, to verify the performance of our proposed scheme in the FTN system with an extremely small time-packing factor, we assume that FO, CD, PMD, RSOP and other impairments do not exist or have been equalized. However, in a large accumulated CD system, such as a long-haul transoceanic optical fiber communication system, we will encounter the problem of enhanced phase noise (EEPN), which is caused by the conflict between the long CD response time and short time along with the laser phase noise variation. Unlike CD or PMD impairments, there is no effective solution to mitigate this [18]. We will pay attention to the EEPN problem in our next work. In addition, in a real system, we must consider the hardware implementation efficiency, among which we should pay attention to the effective number of bits (ENOB) of the analog digital converter (ADC) or digital analog converter (DAC). In this paper, our proposed scheme is verified only at the level of software calculation with floating point numbers, to be capable of enhancing the performance of the decoding algorithm by using appropriate polybinary transformation, and the influence of ENOB is not considered. Therefore, we also need to explore the hardware implementation in the future.  
The authors declare no conflict of interest.
# A New Individual-Based Model to Simulate Malware Propagation in Wireless Sensor Networks
## SEIRS-D Agent-Based Model
### Transitions Rules  
The transition rules of the SEIRS-D model define the conditions that a sensor \(x_{i}\) must be satisfied to change from one state to another in a step of time \(t\), where the state of \(x_{i}\in\{S,E,I,R,D\}\). The conditions of these rules are based on previously defined coefficients.  
The coefficients that can be applied to the sensor agents are infection, transmission, detection, recovery, maintenance and energy; the malware coefficient can be used to the malware agents. Before using these coefficients in the transition rules, it is necessary to define their boolean values from the probabilities given above, as seen below:  
* Infection variable \[\sigma[x_{i}]=\left\{\begin{array}{ll}0,&\text{if the node has not been infected by malware, with probability }1-a[i,t]\\ 1,&\text{if the node has been infected by malware, with probability }a[i,t]\end{array}\right.\]
* Transmission variable \[\beta[x_{i}]=\left\{\begin{array}{ll}0,&\text{if the node cannot transmit the malware to its neighbours, with probability }1-b[i,t]\\ 1,&\text{if the node can transmit the malware to its neighbours, with probability }b[i,t]\end{array}\right.\]
* Detection variable \[\delta[x_{i}]=\left\{\begin{array}{ll}0,&\text{if the infection by malware in the node has not been detected, with probability }1-d[i,t]\\ 1,&\text{if the infection by malware in the node has been detected, with probability }d[i,t]\end{array}\right.\]
* Recovery variable \[\rho[x_{i}]=\left\{\begin{array}{ll}0,&\text{if the node has not been recovered from the infection by malware, with probability }1-r[i,t]\\ 1,&\text{if the node has been recovered from the infection by malware, with probability }r[i,t]\end{array}\right.\]
* Maintenance variable \[\gamma[x_{i}]=\left\{\begin{array}{ll}0,&\text{if the node has not been received the maintenance, with probability }1-c[i,t]\\ 1,&\text{if the node has been received the maintenance, with probability }c[i,t]\end{array}\right.\]
* Energy variable \[\epsilon[x_{i}]=\left\{\begin{array}{ll}0,&\text{if the node has not an optimal energy level, with probability }1-e[i,t]\\ 1,&\text{if the node has an optimal energy level, with probability
## 2 Materials and Methods
### Feature Extraction  
#### 2.5.1 A-Scan Features  
As discussed in Section 1.1 previously, there are several signal features enabling the measurement of water with GPR. Before presenting the ones chosen in this work, it is important to understand the typical signal shapes that occur on layered floor constructions. Figure 7 gives an exemplary A-scan showing three prominent amplitude peaks and their respective origin in the setup. Since the direct wave (\(A_{DW}\)) partly travels through the superficial ground, it is influenced by the underlying nearest materials, here by the floor cover and the screed. The first reflection arises from the border between screed and insulation and is mostly recognized in the second dominant amplitude peak \(A_{RW1}\). After that,  
Figure 5: NMR measurements showing the depth-resolved moisture distribution during the saturation process of the 5 cm CT (**left**) and CA (**right**) screed.  
Figure 6: Measurement procedure with 40 cm long radar survey lines 1 and 2. The neutron probe is placed in the center of the construction.  
\(A_{RW2}\) shows the second reflection's amplitude, originating from the insulation-concrete interface below.  
All described reflection waves can interfere, especially for dry or thin layers, where resulting higher velocities or short traveling paths impede a clear separation in time. This is also why quantitative statements about actual water content cannot be reliable for such layered floor constructions. However, classifying the investigated scenarios is still possible and will be performed with the following signal features:  
* Amplitude of direct wave [6; 7; 8]
* Amplitude of second reflection [6; 7; 8]
* Dominant frequency of second reflection (STFT) [26]
* Ratio between the amplitudes of first reflection and direct wave [9]
* Ratio between the dominant frequencies of first reflection and direct wave  
The presented signal features cover all relevant signal parts, with insulation damage mostly influencing the second and first reflection and screwed damage causing variations in the first reflection and the direct wave. However, the same features are also influenced by underlying layer thicknesses and different material types. Therefore, another preprocessing step is needed to overcome these construction-specific dependencies, which will be achieved by the B-scan features presented in the following section.
# Network Performance Optimization for Low-Voltage Power Line Communications
## 4 Network Performance Optimization Based on Dynamic Game Theory
### Performance Optimization Model Based on Game Theory  
The competitive channel process for the stations is modeled as an incomplete information dynamic game model. Station dynamics are used to estimate the numbers of active stations using the HMM algorithm. Nash equilibrium (NE) solutions are obtained; bandwidth utilization is achieved; and access delays are optimized. The NE solutions are the optimal channel access probabilities of the stations \(\tau_{1}\) and \(\tau_{2}\).  
Figure 5: Relationship between bandwidth utilization and probability \(p_{0}\).
## 2 Proposed DR Framework  
A DR framework is proposed with \(N\) community operators and one DR purchaser, which is shown in Figure 1. DR purchaser in real power system can be grid dispatching department or energy selling company. Community operator, being as an intermediary institution, plays an important role in load aggregation for the flexible DR resource of community consumers, and then sells the resource to DR purchaser for profit. In each community, except for DR resource, some community operators have equipped auxiliary equipment to promote the quality of DR resource, such as gas boiler or energy storage.  
In our proposed scenario, before conducting DR project, community operator will sign contract with users in the community to obtain the control of flexible load. According to the contract volume, community operator will compete the market share with other operators in DR bidding market. However, since energy consumption of users has a certain randomness, some users may break the contract and control the load by self. Once there exists DR resource's breach, community operator will not complete the bidding amount and will be punished by DR purchaser. Therefore, in the bidding market, community operator must take DR resource's breach into the consideration. As for bidding mechanism in the market, DR purchaser will broadcast market price information to all community operators. In our scenario, DR purchaser has divided DR resource into different grades according to the degree of DR resource's breach. The price will be higher for DR resource with lower breach. Consequently, some community operators will compensate the breach amount with gas boiler or energy storage to promote the grade of DR resource. Hence, community operators can also be divided into different types according to their auxiliary equipment. After broadcasting price information, community operators tell DR purchaser if they participate in the bidding market. Furthermore, DR purchaser will gather participants' information and broadcast to all participants. However, considering the protection of private information of participants, the broadcast information only contains the number of participants, probability distribution on community operators' types. According to the received information, community operators will make the optimal strategy to maximize the self-profit with Bayesian game theory and then summit the bidding strategy to DR purchaser. Additionally, the above referred DR framework needs to be supported by grid's physical platform. Therefore, we assume that: (a) Community operators and DR purchaser can communicate each other with bi-directional information network. (b) Advanced metering infrastructure is equipped in consumer side to control flexible loads and transfer data, including smart meter, bi-directional information network, and measuring-data management system.
# Mdpj _sciences_
## 3 Review of the DMRN Protocol
### Protocol Description
#### 3.4.2 Phase B: The Identification Phase from UE at HN  
(3)-(5) Upon receiving {SUCI, \(ID_{HN}\)} from the UE, the SN generates a 256-bit random number \(R_{SN}\) and subsequently forwards {SUCI, \(R_{SN}\)} to the HN. Once the HN receives the SUCI, the HN parses it into its constituent components: \(c_{1}\), \(c_{2}\), and \(c_{3}\). Next, the HN proceeds to decapsulate \(c_{1}\) with \(sk_{HN}\) to derive the secret key \(K_{S1}\). Using the derived secret key \(K_{S1}\), the second component \(c_{2}\) is decrypted to retrieve the SUPI, the public key \(pk_{U}\), and the identifier \(ID_{SN}\). In turn, the \(MAC^{\prime}_{U}\) is computed using the HMAC algorithm with the derived secret key \(K_{S1}\) and the ciphertext \(c_{2}\). The result is then compared with the received \(c_{3}\) while the \(ID_{SN}\) is verified; the process is aborted if either this comparison fails or if the \(ID_{SN}\) is invalid.
# A Survey of Routing Protocols in WBAN for Healthcare Applications
## 3 Classification of Routing Protocols for WBAN
### Qos-Based Routing
#### 3.5.2 Hybrid Data-Centric Routing Protocol (HDPR)  
[PERSON] et al. [33] proposed a data-aware hybrid routing protocol HDPR, which adopts a modular approach. As shown in Figure 13, the design of Qos varies across the network layer and the MAC layer. Data are entered by the receiver into the MAC layer, and then sent to the data classification module, which divides the data into delay sensitive data (DSD), normal data (ND), critical data (CD), and reliability sensitive data (RSD). Data priority is CD, DSD, RSD, and ND, from high to low. Data are classified into the Qos perception module, through their respective data modules. The task of this module is to send them to the corresponding module, according to the needs of each module. For example, DSD data is sent to the delay estimation module, and then the routing module finds the best path for them. This protocol mainly considers the Qos indexes, such as path loss, link reliability, delay, and temperature. Its greatest advantage is that it takes the above Qos into account and carries out the overall optimization design. Moreover, the relay node used in this protocol, only has the function of receiving and sending, which reduces the energy consumption of the acquisition node and prolongs the network life.  
Figure 12: Modular diagram of the LRPD protocol.
# A Fully Integrated Low-Power Multi-Mode RF Receiver for BDS-3/GPS
## 3 Systematical Design of the Receiver  
To match the overall requirements, the key indicators should be properly allocated to each sub-circuit. By making compromises in NF, linearity, filter order, etc., power and area (costs) can be reduced [13]. Next, the allocation of some indicators of the proposed receiver is discussed, and the allocation results are summarized in Table 1, where GMT is the mixing stage, including the Gm, passive mixer, and transimpedance (Tia) amplifier.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sub-Circuit** & **Input** & **LNA** & **ACTBA** & **GMT** & **BPF** & **PGA** \\ \hline Max Gain (dB) & & 23 & 18 & 10 & 0 & 63 \\ Min Gain (dB) & & 23 & 18 & 10 & 0 & 0 \\ NF (dB) & & 2.3 & 8 & 13.5 & 35 & 35 \\ IBP (dBm) & \(-75\) & \(-52\) & \(-34\) & \(-24\) & \(-24\) & \(-24\) \\ OBIP (dBm) & \(-56\) & \(-33\) & \(-15\) & \(-25\) & \(-100\) & \(-100\) \\ \(IP_{\text{1 dB}}\) (dBm) & & \(-23\) & \(-18\) & \(-10\) & 0 & 0 \\ IIP3 (dBm) & & \(-13\) & \(-8\) & 0 & 10 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Requirement breakdown of each sub-circuit.
# Comparative Study of Sound Transmission Losses of Sandwich Composite Double Panel Walls
###### Abstract  
The increasing motivation behind the recently wide industrial applications of sandwich and composite double panel structures stems from their ability to absorb sounds more effectively. Meticulous selection of the geometrical and material constituents of both the core and panels of these structures can produce highly desirable properties. A good understanding of their vibro-acoustic response and emission index such as the sound transmission loss (STL) is, therefore, a requisite to producing optimal design. In this study, an overview of recent advances in STL of sandwich and composites double panels is presented. At first, some salient explanation of the various frequency and controlled regions are given. It then critically examines a number of parameter effects on the STL of sandwich and composite structures. Literatures on the numerical, analytical and experimental solutions of STL are systematically presented. Efficient and more reliable optimization problems that maximize the STL and minimize the objective functions capable of degrading the effectiveness of the structure to absorb sounds are also provided.  
sound transmission loss; sandwich/composite structures; double panel walls; controlled regions; vibro-acoustic response +
Footnote †: journal: Applied Sciences
# Comparison of the Heights of Sporadic E Layers and Vertical Ion Convergence Parameters
## 4 Conclusions  
This study compared the heights between Es layers and IVN and VICmax, using the WACCM-X model and COSMIC-1 RO observations for the period from January 2014 to December 2014. The main findings can be summarized as follows:  
1. IVN proves to be a more suitable parameter for accurately representing the height of Es layers;
2. The descents of ion vertical velocity null agree well with that of Es layers at different latitudes.  
Figure 6: Estimated descent rates of IVN and Es in the low- and mid-latitudes for (**a**) March–May, (**b**) June–August, (**c**) September–November, and (**d**) December–February.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs15245674/s1](https://www.mdpi.com/article/10.3390/rs15245674/s1)]([https://www.mdpi.com/article/10.3390/rs15245674/s1](https://www.mdpi.com/article/10.3390/rs15245674/s1)), Figure S1: Local time-height distributions of EsOR with equatorial regions in different seasons and latitudes of 2014; Figure S2: Local time-height distributions of the occurrence rate of IVN with equatorial regions in different seasons and latitudes of 2014; Figure S3: Local time-height distributions of EsOR of the southern hemisphere in different seasons and latitudes of 2014; Figure S4: Local time-height distributions of the occurrence rate of IVN of the southern hemisphere in different seasons and latitudes of 2014.  
**Author Contributions:** Conceptualization, Y.Y. and L.Q.; methodology, Y.Q. and Y.Y.; validation, L.Q.; data curation, Y.Q.; visualization, Y.Y. and Y.L.; writing--original draft preparation, Y.Y.; writing--review and editing, L.Q., S.L. and T.Y.; supervision, J.W. and T.Y.; project administration, X.Y. and T.Y.; funding acquisition, T.Y. and X.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (grant no: 42230207, 42074191).  
**Data Availability Statement:** Data are contained within the article and Supplementary Materials.  
**Acknowledgments:** The WACCM-X model source code can be found at ([[https://www2.hao.ucar.edu/modeling/waccm-x](https://www2.hao.ucar.edu/modeling/waccm-x)]([https://www2.hao.ucar.edu/modeling/waccm-x](https://www2.hao.ucar.edu/modeling/waccm-x)), accessed on 10 May 2021). The F3/C data were provided by the COSMIC Data Analysis and Archival Center and the Taiwan Analysis Center for COSMIC ([[https://cdaac-www.cosmic.ucar.edu/](https://cdaac-www.cosmic.ucar.edu/)]([https://cdaac-www.cosmic.ucar.edu/](https://cdaac-www.cosmic.ucar.edu/)), accessed on 20 August 2018).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Lane Detection Algorithm Using LRF for Autonomous Navigation of Mobile Robot
## 4 Lane Recognition and Prediction
### Lane Tendency and Lane Prediction Using a Curvature Algorithm  
In general, as explained above, curvature is used for determining the robot's path or time-dependent location in cases where the start and end points as well as the robot's navigating and angular velocities are clearly known. The curvature radius is calculated using the start and target points as well as the robot's direction angle, and the robot's navigating path is figured out using the curvature radius and the robot's velocity.  
However, the method proposed in this paper was used for predicting the navigation path of the mobile robot by using a curvature algorithm, in cases where the lanes became recognizable while driving, by applying the feature points recognized up to the start point of the discontinuity and using the recognized tendency. Thus, this method has no preset target point. Equation (15) is used for figuring out the lane tendency up to the point of discontinuity in order to predict the future course of the lanes. It expresses the point-to-point distances at the \(n-1\)th point and the change rate of the yaw-axis angle.  
\[(D_{ave},\theta_{ave})=\left(\frac{1}{n-1}\sum_{a=2}^{n}\left(\sqrt{x_{a}-x_{0 }}\right)^{2}+\left(\sqrt{y_{a}-y_{0}}\right)^{2},\frac{1}{n-1}\sum_{a=2}^{n} \theta_{a}\right) \tag{15}\]  
where \(D_{ave}\) denotes the average distance; \(\theta_{ave}\), the average change rate of the yaw-axis angle; \(x_{0}\), the \(n-1\)th x-axis coordinates; \(y_{0}\), the \(n-1\)th y-axis coordinates; and \(\theta_{a}\), the amount of angular change between the \(n-1\)th point and the \(a\)th point. The lane tendency thus obtained can be calculated to yield the curvature radius by using Equation (16).  
\[\rho=\frac{D_{ave}}{2\cos(\theta_{ave}+180)} \tag{16}\]  
The navigation path can be predicted using the calculated curvature radius and the point-to-point distance.  
\[x_{est}=\rho\cos(\theta_{ave}) \tag{17a}\] \[y_{est}=\rho\sin(\theta_{ave}) \tag{17b}\]  
Equations (17a) and (17b) is used for calculating the x and y coordinates at the point where the lanes are not visible; the robot can move along these coordinates.
# On the Performance of Underlay Device-to-Device Communications
## 4 Performance Analysis
### OP of the D2D User  
Outage probability is referred to the probability that the end-to-end SINR is below a predefined threshold [55]. More precisely, let us denote \(\text{OP}^{s}_{k}(\gamma)\) as the outage probability of the \(k\)-th D2D receiver under the \(s\in\{equ,ran,pl\}\) scheme, it can be computed as [56]  
\[\overline{\mathcal{R}}_{k}^{s}=\mathbb{E}\{\log_{2}(1+\Xi_{k}^{s})\}= \int\limits_{0}^{\infty}\log_{2}(1+x)f_{\Xi_{k}^{s}}(x)dx=\frac{(a)}{\ln(2)} \int\limits_{0}^{\infty}\frac{1}{1+x}\overline{F}_{\Xi_{k}^{s}}(x)dx\] \[\stackrel{{(b)}}{{=}}\frac{1}{\ln(2)}\Bigg{(}A_{k,c }^{s}\Bigg{(}\prod\limits_{i=1,i\
eq k}^{N}B_{k,i}^{s}\Bigg{)}\Bigg{)}\int \limits_{0}^{\infty}\frac{1}{1+x}\exp\Bigg{(}-\frac{x\sigma_{k}^{2}}{P_{D_{k} }^{s}\Omega_{dr_{k}d_{k}}}\Bigg{)}\] \[\times\Bigg{(}\frac{C_{k,c}^{s}}{A_{k,c}^{s}+x}+\sum\limits_{i=1, i\
eq k}^{N}\frac{D_{k,i}^{s}}{B_{k,i}^{s}+x}\Bigg{)}dx \tag{13}\] \[=\frac{1}{\ln(2)}\Bigg{(}A_{k,c}^{s}\Bigg{(}\prod\limits_{i=1,i \
eq k}^{N}B_{k,i}^{s}\Bigg{)}\Bigg{)}\Bigg{(}C_{k,c}^{s}H_{1}\Bigg{(}\frac{ \sigma_{k}^{2}}{P_{D_{k}}^{s}\Omega_{dr_{k}d_{k}}},A_{k,c}^{s},1\Bigg{)}\] \[+\sum\limits_{i=1,i\
eq k}^{N}D_{k,i}^{s}H_{1}\Bigg{(}\frac{ \sigma_{k}^{2}}{P_{D_{k}}^{s}\Omega_{dr_{k}d_{k}}},B_{k,i}^{s},1\Bigg{)}\Bigg{)},\]  
where \((a)\) is obtained by applying the integral by parts and \(\overline{F}_{\Xi_{k}^{s}}(x)\) is the complementary cumulative distribution function (CCDF) of random variable \(\Xi_{k}^{s}\); and \((b)\) is held by using following identity  
\[\prod\limits_{i=1,}^{N}\frac{1}{x+\varphi_{i}}=\sum\limits_{i=1}^{N}\frac{a_ {i}}{x+\varphi_{i}};a_{i}=\prod\limits_{k=1,k\
eq i}^{N}\frac{1}{x+\varphi_{k }}\Bigg{|}_{x=-\varphi_{i}} \tag{14}\]  
\[H_{1}(a,b,c)= \int\limits_{0}^{\infty}\frac{1}{c+x}\frac{1}{b+x}\exp(-ax)dx= \frac{1}{c-b}\int\limits_{0}^{\infty}\frac{\exp(-ax)}{b+x}dx+\frac{1}{b-c} \int\limits_{0}^{\infty}\frac{\exp(-ax)}{c+x}dx\] \[= \frac{1}{c-b}[H_{2}(a,b)-H_{2}(a,c)]\] \[H_{2}(a,b)= \int\limits_{0}^{\infty}\frac{\exp(-ax)}{b+x}dx\stackrel{{ (a)}}{{=}}\exp(ab)E_{1}(ab), \tag{15}\]  
where \(C_{k,c}^{s}=\prod\limits_{i=1,i\
eq k}^{N}\frac{1}{\mathbb{R}_{k,i}^{s+1}} \Bigg{|}_{\gamma=-A_{k,c}^{s}}\), \(D_{k,i}^{s}=\left.\frac{1}{A_{k,c}^{s+1}}\right.\prod\limits_{u=1,u\
eq k,i}^{ N}\frac{1}{\mathbb{R}_{k,u}^{s+1}}\Bigg{|}_{\gamma=-B_{k,i}^{s}}\); \(E_{1}(.)\) is the exponential integral function ([58], 8.211.1); and \((a)\) is obtained with the help of ([58], 3.352.4).