esa-sceva/satcom-chunk-collection huggingface dataset:
# A Key Management Scheme for Mobile Wireless Sensor Networks
## 4 Evaluation and Comparison
### Overall Comparison  
The best scheme to protect a WSN depends on the characteristics of the network. In order to select which scheme represents the best solution the following methodology is adopted:  
* the eligible schemes must be compliant with the requirements of the networks: the size of the network, the density of the nodes, the mobility of the nodes, the possibility to compromise a node during the initialization phase, the node adding, the deployment knowledge;
* the eligible schemes must be secure against injection of false messages and replay attacks;
* if an eligible scheme provides the best resilience independently from the quantity of compromised nodes, it is the best scheme;
* if a scheme provides the best level of resilience only for a specific quantity of compromised nodes (i.e., not independently of the quantity of compromised nodes), it is the best scheme in concurrence with other schemes;
* if two schemes have the same level of resilience, the scheme that requires less computational and communication overheads is the best scheme.  
Figure 8 shows the diagram of the best schemes for mobile networks and for static WSN with the assumption that a node can be compromised during the initialization phase. FPWK represents always  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & \multicolumn{3}{c}{**Assumptions**} \\ \cline{2-4}
**Protocols** & **Static** & **Deployment** & **Network** \\  & **Network** & **Knowledge** & **Size Limit** \\ \hline SSL, 2C, 1–5C, EG, PCK and SKKE & NO & NO & NO \\ LEAP+, FLEAP and RSDTMK & YES & NO & NO \\ FPWK and PCAT & NO & NO & YES \\ \hline \hline \end{tabular}
\end{table}
Table 7: Assumptions.  
the best solution for small networks. Otherwise, PCAT represents the best scheme for medium size networks without a high density. For the other types of network, SSL is the best solution.
## 2 Materials and Methods
### Global Digital Controller
#### 2.2.1 Generating Light-Dependent Data  
This system employs near-field communication to transmit data and clock signals over a wireless data telemetry system, ensuring all the devices use the same data transfer protocol. On the receiver side, these data are received and processed to form various pulse patterns, as detailed in this section.  
Data should be light-dependent, and each sent data packet should reflect a specific intensity of light; this is a crucial factor when designing a transmitter. A prototype FPGA (CMOD A7) was used to generate the data for the proposed system. The data were transmitted in binary form, consisting of 0 and 1. The FPGA transmitter generated a clock at a rate of 100 MHz. On the transmitter side, the clock could be separated according to the requirements of the pulse generator. Because the entire stimulation duration may be adjusted at the transmitter side, the system is more dependable, owing to variable clock rates. For the current system, a single clock with a frequency of 2 MHz was selected.  
Clock dividers were used to generate the required frequency of 2 MHz from the built-in clock of the FGPA at 100 MHz. This 2 MHz clock was output from the transmitter to the receiver through a wireless transmitter system to create pulse profiles and synchronize operations.  
The FPGA receiver received 7-bit external data. The data were input as a series of 0 and 1, yielding 128 combinations. The form and amplitude of the biphasic pulse were determined based on each combination. The start bit, 23-bit header, and stop bit were added to the input data. The start bit marked the beginning of a new data packet. Each data pattern had its own header that contained the data address and validated the data packet. The stop bit served as parity and signified that the data packet had ended.
# Optimal Dispatch Strategy of Virtual Power Plant for Day-Ahead Market Framework
## 6 Simulation Results
### Scenario 1
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & \begin{tabular}{c} **Best Solution** \\ **(Euro Cent)** \\ \end{tabular} & \
\begin{tabular}{c} **Worst Solution** \\ **(Euro Cent)** \\ \end{tabular} &
\begin{tabular}{c} **Mean** \\ **(Euro Cent)** \\ \end{tabular} \\ \hline PSO [44] & 277.3237 & 303.3791 & 288.8761 \\ GA [44] & 277.7444 & 304.5889 & 290.4321 \\ CPSO-T [44] & 275.0455 & 286.5409 & 277.4045 \\ CPSO-L [44] & 274.7438 & 281.1187 & 276.3327 \\ AMPSO-L [44] & 274.4317 & 274.7318 & 274.5643 \\ E-GA & 226.1452 & 238.2145 & 231.1451 \\
**Proposed algorithm BAS** & **201.6348** & **208.7854** & **205.2101** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the cost of generation for various algorithms corresponding to Scenario 1. Figure 9: Power generation for Scenario 1.  
optimal DA scheduling strategy is developed for the VPP in this scenario by considering all the available resources. The energy transaction with the grid is developed based on the power purchasing price from the grid. There are few literature studies available with this scheduling strategy that considers both the market strategy and uncertainties of renewable generation.
# Multiple Mobile Sinks for Quality of Service Improvement in Large-Scale Wireless Sensor Networks
## 4 Results and Discussion
### Reliability Evaluation  
In this simulation scenario, we compared the network's reliability by varying the number of mobile and static sinks monitoring the network to study the impact of using multiple mobile sinks on reliability in LS-WSN.  
The simulation results illustrated in Figure 5a show that the use of a single mobile sink, whatever the model, increases the average reliability compared to a fixed sink from +17% for RWP up to + 20% for RD, and the best result is obtained with the RD model and the EA-CRP routing protocol with a gain of +22%.  
The simulation results illustrated in the other figures show that the use of multiple mobile sinks increases the average reliability of the network compared to that of a single static sink (Figure 5a):  
Figure 4: Total data collected by multiple statics and mobiles sinks with different routing protocols, (**a**) Single Sink for the entire network, (**b**) two Sinks for two sub-networks, (**c**) four Sinks for four sub-networks, (**d**) eight Sinks for eight sub-networks.  
By using two mobile sinks (Figure 5b) from +43% for RWP up to +52% for RD, the best result is obtained with the RD model and the EA-CRP routing protocol + 65%.
* By using four mobile sinks (Figure 5c) from +73% for RUK up to +88% for RD, the best result is obtained with the RD model and the EA-CRP routing protocol + 105%.
* By using eight mobile sinks (Figure 5d) from 91.5% for RUK up to 110% for RD, the best result is obtained with the RD model and the EA-CRP routing protocol + 140%.  
It can be deduced from the previous results that the best mobility model is RD, which offers more than twice more throughput compared to a single fixed sink for the whole network, using eight mobile sinks. More precisely, the RD moves in the network by approaching in a realistic way towards the SN clusters to reduce the distance between the sink and the CH and consequently reduce the phenomena of collisions and packet retransmission, consequently increasing the network reliability, especially with the EA-CRP routing protocol which uses the combination of two routing techniques, clustering, and multi-hop.  
On the other hand, by comparing the flow scenarios, we note that the use of four mobile sinks gives results very close to that of eight mobile sinks. So, we can conclude that  
Figure 5: Network Reliability with different routing protocols by using multiple statics and mobiles sinks, (**a**) Single Sink for the entire network, (**b**) two Sinks for two sub-networks, (**c**) four Sinks for four sub-networks, (**d**) eight Sinks for eight sub-networks.  
in terms of profitability, the use of four mobile sinks establishes a good tradeoff between the network reliability and the cost of investment, and we can obtain better reliability of +105% more with only four mobile sinks by using the RD mobility model and the EA-CRP routing protocol.
# Changes in Soil Moisture Improved by High-Performance Ester Materials under Dry-Wet Cycles
## 4 Conclusions  
1. This article studied the changes in soil moisture brought about by high-performance ester materials under dry-wet cycling conditions. The materials can effectively enhance soil moisture and EC and improve soil's ecological performance. Even after various dry-wet cycles, they can still exert excellent improvement effects on soil.  
2. High-performance ester materials can weaken the degradation effect of dry-wet cycles on soil structure, while polymer water-retaining materials can maintain soil's water holding capacity and EC after multiple dry-wet cycles. Adhesive materials enhance the structural stability of the soil, and weaken the impact of dry-wet cycles on the soil aggregate structure. Adhesive materials also enhance soil's water holding capacity and EC to a certain extent, but their excessive inclusion will limit the effectiveness of water-retaining materials. An optimum proportion for the inclusion of high-performance ester materials was discovered. The optimum material proportion obtained from our research on improving soil's ecological performance under dry-wet cycling conditions is 0.18% of water-retaining material and 0.06% of adhesive material.  
3. The response of soil improvement with different ratios of high-performance ester materials to dry-wet cycles were revealed through our study, providing new and valuable theory and data contributions for the use of high-performance ester materials in soil's ecological restoration.  
H.L.: investigation, visualization, writing-original draft, writing-review, and editing. C.Z.: conceptualization, methodology, formal analysis, supervision, resources, and validation. Z.L.: conceptualization, methodology, formal analysis, supervision, resources, and validation. All authors have read and agreed to the published version of the manuscript.  
National Natural Science Foundation of China (42293354); National Natural Science Foundation of China (42293351); National Natural Science Foundation of China (42293355); National Natural Science Foundation of China (4229335); and National Natural Science Foundation of China (42293350).  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors would like to thank all the editors and reviewers of this manuscript.  
The authors declare no conflicts of interest.
## 4 The Proposed Selective Retransmission Mechanism
### Energy Consumption
The results of this experiment are presented in Figure 10.  
As previously attested, we note in Figure 10 that the proposed selective retransmission mechanism performs better than traditional fully-reliable transmission approaches, saving energy of intermediate nodes. Moreover, such saved energy is higher when a 2-level 2D DWT is applied over the original image, especially when DR = 15 for HL(2), LH(2) and HH(2) subbands. An interesting consideration is that the selective retransmission mechanism based on 1-level 2D DWT consumes more energy for higher mean PER at the middle of the path, even though fewer packets with DR = 255 will need to be transmitted on average over half the path. This happens because packets with DR = 0 will require on average more retransmissions and transmission of ACK messages, impacting the resulting energy consumption.
# Modeling Population Dynamics and Assessing Ecological Impacts of Lampreys via Sex Ratio Regulation
## 5 Sex Ratio Impact on Ecosystem Stability Model (SRIES Model)
### Host-Parasite Sex Ratio Dynamics Model (HPSRDM)  
The purpose of this model is to help us understand how changes in sex ratios within host populations can affect the growth and spread of host populations, and how this interaction can, in turn, affect the health and number of host populations [18].  
Modeling the growth of host populations:  
\[\frac{dH}{dt}=r_{H}H\bigg{(}1-\frac{H}{K_{H}}\bigg{)}-\alpha PH \tag{32}\]  
where \(H\) is the number of host populations, \(r_{H}\) is the natural growth rate of the host, \(K_{H}\) is the environmental carrying capacity of the host, \(\alpha\) is the parasitism rate coefficient, and \(P\) is the number of parasitized populations.  
Growth modeling of parasitized populations:  
\[\frac{dP}{dt}=\beta PH-\mu P \tag{33}\]  
where \(\beta\) is the transformation efficiency of the parasitized population to acquire new parasites from the host population and \(\mu\) is the mortality rate of the parasitized population.
## 5 Conclusions  
This article presents a comprehensive analysis of the variation patterns of ET in the YRB from 2001 to 2020 at annual and monthly scales and the mechanisms influenced by climate change at different spatial and temporal scales. The main conclusions found in this study are as follows:  
1. The annual average ET was 592.58 mm, the interannual ET variation fluctuated significantly, and the average value of the interannual ET statistic Z was 0.97, with an overall increasing trend. Among them, the ET of 6.90% of the areas showed a significant decrease trend, while the ET of 34.99% of the areas showed a significant increase trend. Monthly ET increased significantly in January to April, June, and December. Monthly ET decreased significantly in July and November, mainly distributed in the middle and lower reaches of the YRB.
2. The spatiotemporal distribution of the regions with significant correlation between ET and T, SR, and P in the YRB showed obvious evolution patterns, and the spatial change pattern was strongly related to the elevation. Nevertheless, the regions with significant correlations between monthly ET and SH and U in the YRB did not show obvious cyclical changes in months.
3. At the annual scale, the area proportion of the dominant climatic factors affecting ET in the study area was SR (35.16%) > SH (34.51) > P (16.84%) > T (7.96%) > U (5.52%). However, monthly ET in most areas of the YRB was driven by SR and T, especially in summer and autumn, while ET in spring and winter was mainly driven by SR, T, and SH.  
Conceptualization, M.L. and M.W.; methodology, M.L.; software, Z.Z.; investigation, M.L. and M.W.; data curation, Q.A.; writing--original draft preparation, M.L.; writing--review and editing, M.W., Z.Z., Q.A. and J.L.; supervision, M.W., Z.Z., Q.A. and J.L.; funding acquisition, M.W. and Z.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the funded the Key Laboratory of Land Satellite Remote Sensing Application, Ministry of Natural Resources of the People's Republic of China, which is an open fund without a funding number. It is also supported by the National Natural Science Foundation of China under grants 61801443 and 41801348. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: No new data were created or analyzed in this study. Data sharing is not applicable to this article. Conflicts of Interest: The authors declare no conflict of interest.
# A New Method of Applying Data Engine Technology to Realize Neural Network Control
## 4 Motor Experimental Setup and Results
### Tracking Performance  
The performance of the DRNN hybrid controller based on DE technology is investigated at different operating conditions, the control system parameter settings of the DRNN and the initial conditions were the same with them. The dynamic behaviors of the PI control and with neural network control are shown in Figure 13 at no load conditions.  
Compared with the simulation experiment in the ideal state, there are many uncertainties in the practical application research, such as multi-variable strong coupling, strong interference, feedback hysteresis, and unknown parameter information of the nonlinear controlled device. Therefore, the corresponding difference equation cannot be obtained. This paper assumes that the controlled motor is an unknown dynamic system, and the DRNI is used to estimate the sensitivity information of the unknown equipment for the DRNC. Figure 13a shows the reference signal \(y_{r}(k)\) with parameter uncertainties and its estimations given by the DRNN based on the DE technology. It can be seen that despite these uncertainties leading to the failure to achieve online learning speed and tracking characteristics under the pure simulation conditions shown in Figure 8, the proposed DRNN controller estimates the reference signal very well and the error can gradually converge within a reasonable range after 58 s.  
Figure 13b,c present the performance comparison during steady state operation respectively, and it is observed that the reference tracking performances are nearly same with PI and neural based drive system. From the partial magnification diagram can be found when the weights are converging, the control signal \(y(k)\) calculated by DRNN controller tends to be smooth and gradually fit the reference model \(y_{r}(k)\), but in the PID control curve, it still fluctuates. The steady-state error signal by variance was analyzed with the oscilloscope real-time sampling in this paper to determine the degree of deviation from the volatility. The results are shown in Table 1 below. Although, in the results, it is observed that the PI control is tuned to give an optimum response at steady state, the neural network control has faster response time, smaller steady-state error, and better tracking performance.  
Figure 12: Collaborative control configuration diagram of different hardware devices.
## 5 Formal Modeling and Analysis of SAPS
### SAPS Analysis and Verification
);Y\({}_{\text{B}}\)) \(\rightarrow\) H\({}_{\text{YB}}\)  
\(\bullet\)Encrypt((QB, HYB)) \(\rightarrow\) P\({}_{\text{B}}\)  
\(\bullet\)Encrypt([Concat(X\({}_{\text{B}}\), P\({}_{\text{B}}\),T3), K(B-TS)]) \(\rightarrow\) C\({}_{\text{B}}\)  
\(\bullet\)Send((IDB-T, C\({}_{\text{B}}\))) to TS and Update (IDB-T)  
For verification and checking of message integrity of party (B), the TS performs the same steps as Party (A). The TS securely generates secret credentials based on the information gathered from both ends, and distributes a common key which is partially completed. Secure parameter distribution steps are shown as below, which are transmitted securely to the participants. \(\bullet\)Encrypt([Concat (X\({}_{\text{B}}\), ID\({}_{\text{A}}\),ID\({}_{\text{B}}\),Y\({}^{\prime}\)\({}_{\text{A}}\), N1), H\({}_{\text{Y}^{\prime}A}\)]) \(\rightarrow\) CP\({}_{\text{A}}\)  
\(\bullet\)Send((IDB-T, CP\({}_{\text{A}}\))) to A  
\(\bullet\)Encrypt([Concat (X\({}_{\text{A}}\), ID\({}_{\text{A}}\),ID\({}_{\text{B}}\), Y\({}^{\prime}\)\({}_{\text{B}}\), N2), H\({}_{\text{Y}^{\prime}\text{B}}\)]) \(\rightarrow\) CP\({}_{\text{B}}\)  
\(\bullet\)Send([IDB-T, CP\({}_{\text{B}}\)]) to B  
Although data/credentials transmission is performed using strong encryption techniques by the TS to increase security and un-traceability of participants, the proposed scheme has a novel approach in its partial session key mechanism. After successful authentication and session key establishment, a \"Forget Operation\" performed at each participating entity will result in removing temporary values like nonce value, time stamp, temporary encryption key H*, and MAC calculating parameters. These operations ensure security against forwarding secrecy and user traceability issues.
# Data Structure and Management Protocol to Enhance Name Resolving in Named Data Networking
## 3 Proposed Solution
### Proposed PNP Protocol (PNPP)  
This section discusses the proposed protocol used by the PNP Client (PNPC) to communicate with the PNP Server (PNPS), which is named PNP Protocol (PNPP). PNPP  
Figure 3: BCM lookup operation process.  
client-to-server messages (add, remove, get, getbcm) are encapsulated within the ApplicationParameters element of an NDN Interest packet. On the other hand, server-to-client message (reply) is encapsulated within the Content element of an NDN Data packet. PNPP client-to-server messages are mapped one-to-one to ECHT messages, and the ECHT reply is mapped to a PNPP server-to-client message.  
The client-to-server messages are usually generated from a producer or normal consumer side either to modify and update the mapping table or to obtain the delivery location(s) of a requested domain name.  
The process starts with the PNPS receiving one of the following messages from the client/caller:  
* _ADD:_ A two-component message that contains the producer's domain name and its corresponding delivery locations. Their data types are of NDN name and a list of NDN names, respectively. This message is encapsulated in an ApplicationParameters element of an NDN Interest packet. The Name element of this Interest is set to the NDN name of the PNPS. Upon receiving this message, the PNPS triggers the ECHT insert operation, which will decide if a new node will be added or an entry_value will be modified.
* _REMOVE:_ On the structure level, REMOVE is similar to ADD, with one difference being the second component is an optional one. If the delivery location component is present, PNPS will modify the associated ECHT entry_value, and when it is absent, the whole node along with its entry_value will be removed from the ECHT.
* _GET:_ A one-component message that contains the producer's domain name in NDN name format. This message is encapsulated in an ApplicationParameters element of an NDN Interest packet. The Name element of this Interest is set to the NDN name of the PNPS. Upon receiving this message, the PNPS triggers the ECHT exact matching lookup operation.
* _GETBCM:_ This is the same as GET, but it triggers a BCM lookup operation instead, and therefore, it receives a two-component reply instead of one from ECHT. A sample of a GET message is shown in Figure 4.  
The process ends by encapsulating the ECHT reply in a Content element of an NDN Data packet and sending it back to the caller.
# Bias Effects on g- and s-Factors in Westcott Convention
## 4 Conclusions  
Bias effects have been investigated on the g- and s-factors in the Westcott convention. As origins of biases, a joining function shape, neutron temperature, and sample temperature, have been investigated. The quantitative results are given for 8 isotopes, i.e., \({}^{241}\)Am, \({}^{151}\)Eu, \({}^{197}\)Au, \({}^{59}\)Co, \({}^{103}\)Rh, \({}^{115}\)In, \({}^{177}\)Hf, and \({}^{226}\)Ra. The results show the bias induced by sample temperature is small, in the order of 0.1% for the g-factor and in the order of 1% for the s-factor. Biases induced by a joining function shape for the s-factor are shown to be in the order of 10% for many non-1/v isotopes. The bias size for the reaction rate in the case of an epithermal neutron index r = 0.1 is also shown. The largest bias is obtained for \({}^{226}\)Ra among the eight isotopes. The order of bias size for the s-factor or reaction rate can be grasped from the results given for the eight isotopes in the figures. For non-1/v isotopes, the bias effect originating in a joining function shape needs to be carefully investigated. The formulas to calculate g- and s-factors in this paper enable bias calculation without a special code. If the size of the calculated bias is close to the required accuracy of NAA, a more careful study on neutron energy spectrum at the irradiation position and energy-dependent nuclear data is recommended.