esa-sceva/satcom-chunk-collection huggingface dataset:
# Blockchain Based Secure Routing and Trust Management in Wireless Sensor Networks +
## 3 Problem Statement  
Recently, WSNs have contributed immensely in the development of many domains such as industrial surveillance, the military and healthcare. However, the networks encounter different challenging issues. Therefore, the authors in [19] used a blockchain based model to detect malicious nodes. However, the model has a high computational cost because the PoW consensus mechanism is used. Moreover, the nodes' authentication is not performed, which allows unauthorized nodes to access and utilize the network resources. A blockchain based trust model is proposed in [10] on the basis of data and behavioral based trust. However, in data based trust, indirect trust is evaluated through the recommender nodes. When the recommender nodes are malicious, wrong information is provided about the legitimate nodes in the network. A routing algorithm for the WSN to find the secure route is proposed. However, the authors do not consider the malicious nodes' detection and authentication [29]. The malicious nodes forge the actual identities of the benign nodes and drop the packets that result in a low Packet Delivery Ratio (PDR). Additionally, lifetime of the network is badly affected due to the high consumption of energy in forwarding the data packets to neighbors. Moreover, a symmetric key is used for packet encryption and decryption. However, using the symmetric key, a third party can easily gain access to the encryption key and use it for the decryption of the packets to obtain the original data. An authentication protocol for the IoT network is proposed to authenticate the nodes. The BS assigns a sequence number to every SN when it receives acknowledgment of the message. However, the BS does not verify the credentials of SNs during the assignment of  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Problems Addressed** & **Already Proposed** & **Solutions Proposed** & **Already Validations Already Done** & **Problems to be Addressed** & **C1** & **C2** & **C3** \\ \hline No authentication, presence of malicious nodes, low PDR, high delay, usage of symmetric keys & A blockchain based authentication and trust evaluation mechanism is proposed for secure routing. RSA encryption scheme is used [Proposed Model] & Network lifetime, energy consumption, throughput, gas consumption, transaction latency, processing time of RSA encryption and processing time of trust evaluation & High time consumption in generating the RSA keys & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) \\ \hline \hline \end{tabular} Note: C1, C2 and C3 denote authentication, trust evaluation and security, respectively.  
\end{table}
Table 1: _Cont._sequence numbers [24]. As a result, this increases the chances of the malicious nodes to become part of the network.
## 2 Optimization Design Method
### Central Composite Design
#### 2.3.3 Design of Damping  
The harmonic response analysis of the prototype simulation model shows that the driving rod 1, connecting rod 1 and end g are the most sensitive to damping. Therefore, the optimization design is carried out based on the three factors of the driving rod 1, connecting rod 1 and the end g. The test design table adopts three factors and two levels (the three factors are represented by driving rod 1, connecting rod 1 and end g respectively), as shown in Table 4. A CCD test with a total of 20 tests was performed.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Order** & **Driving Rod 1** & **Connecting Rod 1** & **End g** & **Order** & **Driving Rod 1** & **Connecting Rod 1** & **End g** \\ \hline
1 & 0.0020 & 0.0260 & 0.0260 & 11 & 0.0113 & 0.0407 & 0.0407 \\
2 & 0.0260 & 0.0020 & 0.0260 & 12 & 0.0113 & 0.0113 & 0.0113 \\
3 & 0.0260 & 0.0260 & 0.0260 & 13 & 0.0407 & 0.0407 & 0.0113 \\
4 & 0.0260 & 0.0260 & 0.0500 & 14 & 0.0113 & 0.0407 & 0.0113 \\
5 & 0.0260 & 0.0260 & 0.0260 & 15 & 0.0407 & 0.0113 & 0.0407 \\
6 & 0.0260 & 0.0500 & 0.0260 & 16 & 0.0260 & 0.0260 & 0.0260 \\
7 & 0.0500 & 0.0260 & 0.0260 & 17 & 0.0407 & 0.0113 & 0.0113 \\
8 & 0.0260 & 0.0260 & 0.0020 & 18 & 0.0260 & 0.0260 & 0.0260 \\
9 & 0.0113 & 0.0113 & 0.0407 & 19 & 0.0260 & 0.0260 & 0.0260 \\
10 & 0.0407 & 0.0407 & 0.0407 & 20 & 0.0260 & 0.0260 & 0.0260 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Center composite design experimental data design table of damping values.  
Figure 10: Series-parallel controllable mechanism welding robot component names.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Order** & \begin{tabular}{c} **Connecting** \\ **Rod 2 (mm)** \\ \end{tabular} & \
\begin{tabular}{c} **Swing Arm** \\ **g (mm)** \\ \end{tabular} & **End g (mm)** & **Order** &
\begin{tabular}{c} **Connecting** \\ **Rod 2 (mm)** \\ \end{tabular} & **Swing Arm** & **End g (mm)** \\ \hline
1 & 14 & 9 & 10.633 & 11 & 12 & 8 & 8 \\
2 & 14 & 9 & 9 & 12 & 12 & 8 & 10 \\
3 & 14 & 7.367 & 9 & 13 & 12 & 9 & 9 \\
4 & 14 & 9 & 7.367 & 14 & 14 & 9 & 9 \\
5 & 14 & 9 & 9 & 15 & 16 & 10 & 10 \\
6 & 17.266 & 9 & 9 & 16 & 12 & 10 & 10 \\
7 & 14 & 10.633 & 9 & 17 & 14 & 9 & 9 \\
8 & 10.735 & 9 & 9 & 18 & 14 & 8 & 10 \\
9 & 12 & 10 & 8 & 19 & 16 & 8 & 8 \\
10 & 16 & 10 & 8 & 20 & 14 & 9 & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Center composite design experimental data design table of bar thickness.  
The damping value of each member of the original model of the prototype is treated as undamped. According to the 20 groups of data in Table 4, the damping values of the corresponding driving rod 1, connecting rod 1 and end g in the original model are set. Under the condition that other steps remain unchanged, the amplitude frequency curve of the end displacement value of the controllable mechanism welding robot can be obtained by the simulation calculation.
# Dynamic Spectrum Access for Internet of Things Service in Cognitive Radio-Enabled LPWANs
## 2 Related Works
### Spectrum Access Strategies
In order to model complex selfish SUs' behaviors, the joint spectrum sensing and access problem are formulated as an evolutionary game and derive the evolutionarily stable strategy (ESS). Furthermore, this study has also designed a distributed learning algorithm for SUs to converge to the ESS, where each SU senses and accesses the primary channel with the probabilities learned purely from its own past utility history, and finally achieves the desired ESS. [PERSON] et al. [37] propose a novel queueing model suitable for the optimization of access, in order to effectively solve the problems of the optimization of joint access of PUs and SUs. In this study, there are several types of PUs with different requirements for the service time and preemptive priority over SUs. SUs can share a server, while PUs occupy the whole server. The arrival flow is described by the marked Markovian arrival process. The service time distribution is of phase-type. The effect of retrials of SUs is taken into account. [PERSON] et al. [38] propose two queueing schemes for SUs, which are based on the delay tolerance of interrupted elastic services. In multi-channel cognitive radio networks, SUs can achieve maximal capacity using dynamic channel assembling (CA) strategies. However, the channel allocation schemes suffer from high blocking and forced termination when primary users become active. In a multi-channel network with heterogeneous traffic, two queues are separately allocated to real-time and elastic users, and channel access opportunities are distributed between these two queues in a way that real-time services receive higher priority. Furthermore, continuous time Markov chain models are deployed to evaluate the performance of the proposed CA strategy with queues. [PERSON] et al. [39] introduce several approaches for asynchronous spectrum sensing and asynchronous spectrum access, respectively, in a cognitive radio network, with which SUs can access the primary channels dynamically even without achieving synchronization with the PUs. This study discusses the key techniques, corresponding solutions, and potential application scenarios of the asynchronous spectrum sensing and access schemes. Specifically, this study presents two asynchronous spectrum sensing schemes under non-cooperative and cooperative scenarios, respectively. [PERSON] et al. [40] propose an analytical framework to quantify the queue dynamics of multi-SU multichannel cognitive radio networks (CRNs). The framework includes the important lower-layer mechanisms and settings, including spectrum sensing errors, medium-access control (MAC) protocols, link adaptation technologies such as adaptive modulation and coding (AMC) and automatic repeat request (ARQ), and limited buffer size. In order to analyze impacts on the quality-of-service (QoS) performance for SUs, the queue dynamics are modelled as a discrete-time finite-state Markov chain (FSMC). The performance metrics include average queueing delay, packet-loss rate, and effective throughput.
# 5G Physical Layer-Based Procedure to Support Time-Sensitive Networking
## 4 End-to-End Synchronization Using TESC
### Synchronization of the Internal Clock of the UE: Physical Layer-Based Synchronization  
The SC determines a timing adjustment based on the timing information, including the packet reference time and residence time, as they are transmitted from a GM clock to the SC. The residence time is further described above in connection with Figure 5. Also, the timing information includes a Timing Advance (TA), which is the time for the Reference Signal to travel from the SC to the UE's receiver. TA may be determined using the Random-Access process. Thus, from the timing information, a virtual clock domain time is determined at which the UE will receive the radio frame transmitted from the SC. The Synchronization Time represents the time--in the virtual clock domain--when the radio frame will arrive at the UE. The Synchronization Time is further described in Section 5.  
The SC adjusts a timing signal of the radio frame to adjust the timing of the UE clock. This timing signal may be a physical layer signal as, for example, a slot indication signal or a symbol boundary signal. The SC transmits the slot indication signal such that it arrives at the UE at an even timing interval of the reference time. This enables the UE to maintain precise synchronization with the reference clock between slot indication signalsby using its accurate internal clock. The timing signal received by the UE is combined with a timing feedback circuit to adjust the internal clock flank of the UE. The feedback circuit is configured to adjust the timing of the internal clock flank such that the flank is coincident with subsequent timing signals. The synchronization of the UE clock to the virtual clock domain using a physical layer signal of the wireless network is detailed in the next section.  
This paper presents an innovative approach that harnesses the slot indicator signal transmitted at the start of each frame to achieve precise synchronization, in line with our research findings. This slot indicator signal plays a crucial role as a cornerstone of the timing system. By strategically addressing issues related to path-delay estimation and time-of-arrival estimation errors, our method optimizes the slot indicator signal's utility. Furthermore, our research underscores the significance of enhancing the granularity of reference time, a factor proven to reduce synchronization errors effectively. Additionally, we emphasize the importance of optimizing synchronization frequency to ensure the slot indicator signal's periodic transmission is leveraged to the fullest extent. This comprehensive approach not only aligns with our research outcomes but also reinforces the feasibility of meeting synchronization requirements of less than \(1\)\(\upmu\)s [6; 13; 18] or better in integrated 5G and TSN systems.
# Path Planning and Collision Avoidance in Unknown Environments for USVs Based on an Improved D* Lite
## 3 Improved D* Lite  
To solve the disadvantages of D* Lite for USVs, we propose several approaches to improve the D* Lite. First, we set a safe distance between the path and obstacles to avoid collisions when a USV autonomously navigates along with the planned path. Second, we increase the number of adjacent nodes and add turning constraints to reduce the turning angle of the planned path. Third, we use a minimum binary heap to optimize the priority queue \(U\) to accelerate the planning process and improve the planning efficiency. Finally, we expand the D* Lite to be a multi-goal path planning algorithm.
# A Jumping Robot Driven by a Dielectric Elastomer Actuator
## 3 Prototype Implementation and Experiment  
### Fabrication of Multilayer DE  
The fabrication process of multilayer DE includes two processes: the fabrication of single-layer DE and the stacking of multilayer DE, as shown in Figure 8. The DE material is VHB4910 produced by the company 3M, and the main material is acrylate. In the single-layer process, a DE film with a thickness of 2 mm was stretched to 12.25 times (3.5*3.5) its original area by a biaxial stretching machine to obtain a pre-stretched DE film with a thickness of 100 \(\upmu\)m. The pre-stretched DE film was taken out with a large retaining ring and a small retaining ring with inner diameters of 160 mm and 140 mm (PMMA, cut with a laser cutter), respectively. The upper and lower surfaces of the pre-stretched DE film were covered with a mask (Release film, cut with a laser cutter) which had been vacated for an electrode. The flexible electrode was applied by spin coating, and the electrode pad was attached. In the multilayer DE stacking process, the prepared DE with a small retaining ring was sleeved inside another DE with a large retaining ring, and the air bubbles between the two layers of DE were removed by rolling. The large retaining ring was cut off along the edge to obtain two-layer DE with a small retaining ring. The two-layer DE was then nested into a new DE layer with a large retaining ring.  
Figure 7: **(a)** The jumping heights of the robot for different k values. **(b)** The key positions of the jumping robot in the simulation.  
We repeated the above operation and ensured that the electrode pads were aligned in the alternating layers. Variety in the multiple-layer DE could be obtained according to the requirements of different actuators by this way.
## 3 Simulation Experiments and Statistical Analyses of Results
### Comparison of IGGIII-BFGS Algorithm with PSO Algorithm  
We added a comparative experiment between the PSO algorithm and the IGGIII-BFGS algorithm, randomly selecting one observation point, respectively, at the maximum settlement point and the inflection point. We added a gross error of 200 mm to the settlement value of each point to generate the perturbation dataset and then selected the more classic and widely applied intelligent optimization algorithm--the PSO algorithm--comparing it with the IGGIII-BFGS algorithm. The two algorithms, respectively, carried out 20 inversions of probability integral parameters. The mean value and relative error of the inversion parameters were calculated. The comparison of the mean values and errors of the inversion parameters of the two algorithms is shown in Table 2.  
Table 2 shows that for most parameters, the absolute error and relative error of the IGGIII-BFGS algorithm are significantly lower than those of the PSO algorithm. The  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Inversion**} & \multicolumn{2}{c}{**PSO Algorithm**} & \multicolumn{3}{c}{**IGGIII-BFGS Algorithm**} \\  & & **Absolute** & **Value of Error** & **Relative Error** & **Mean** & **Absolute** & **Value of Error** & **Relative Error** \\ \hline q & 0.812 & 0.038 & 4.53\% & 0.850 & 0.000 & 0.00\% \\ b & 0.259 & 0.009 & 3.54\% & 0.250 & 0.000 & 0.02\% \\ tan \(\beta\) & 2.224 & 0.074 & 3.42\% & 2.150 & 0.000 & 0.00\% \\ \(\theta_{0}\) & 86.413 & 0.413 & 0.48\% & 85.953 & 0.047 & 0.05\% \\ s\({}_{1}\) & 42.599 & 7.401 & 14.80\% & 50.045 & 0.045 & 0.09\% \\ s\({}_{2}\) & 53.110 & 3.110 & 6.22\% & 49.000 & 1.000 & 2.00\% \\ s\({}_{3}\) & 33.857 & 16.143 & 32.29\% & 49.138 & 0.862 & 1.72\% \\ s\({}_{4}\) & 67.206 & 17.206 & 34.41\% & 50.408 & 0.408 & 0.82\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of IGGIII-BFGS algorithm with PSO algorithm.  
Figure 6: Comparison of subsidence and horizontal movement.  
IGGIII-BFGS algorithm is less affected by gross errors and can predict parameters more stably and accurately. In contrast, the PSO algorithm has large error fluctuations in some parameters and is less stable when dealing with gross error interference. Overall, the IGGIII-BFGS algorithm performs better in parameter prediction.
## 5 Engine Model Calibration  
In order to validate the engine model, a MATLAB (2015b, MathWorks, Natick, MA, USA, 2015) code was employed to produce a Brake Specific Fuel Consumption (BSFC) map which was identical to a chosen theoretical one. This theoretical BSFC map corresponded to the selected engine profile of a Ford 2000 cc Zetec-SE DOHC engine (Ford Motors Company, London, UK), which is a four-cylinder engine with a 16-valve design and has a maximum Brake Mean Effective Pressure (BMEP) value of approximately 10 bar and a maximum engine speed at 5000 rpm. The BSFC map is shown in the figures below.  
Following the selection of the theoretical engine configuration and BSFC map, the MATLAB code needed to be tested to determine the level of its accuracy. To accomplish this, the map was discretized at 29 different points that represent respective engine speed values and BMEP values. Particularly, the BSFC map was divided into five columns representing engine speeds from 1000 to 5000 rpm, and six rows representing BMEP values from 2 bar until reaching the maximum curve. This discretization is shown in Figure 2a with red dots.  
All of these 29 points correspond to different BSFC values, which were estimated visually and gathered into a table that was later used as an input in the MATLAB code. The MATLAB code can produce different performance maps, depending on the input values given from the user and for this current simulation the BSFC values were used, along with the engine speed (\(x\)-axis) and BMEP values (\(y\)-axis). After the optimization a new BSFC map was obtained as shown in Figure 2b, pointing out to the satisfactory accuracy of the code. For validation of the code results the Ford engine was modelled in GT-Power and the results correlated against the MATLAB code data. The GT-Power models of both the engine and the dynamic HEV sub-models are shown in Figure 3a,b, respectively.  
The complete model included a throttle controller that was adjusted accordingly in the simulations, while the values of the inlet and exhaust ports were estimated after benchmarking and suggestions of the software. The table that included these BSFC values was given as an input to the MATLAB code and the BSFC that was generated matched the BSFC values of the theoretical model accurately. The next step after validating that the engine model can produce accurate values was to modify the model so that the new engine displacement was implemented. When the changes were implemented, the simulations were run for thirty discrete engine/load cases. It is important to point out that this work did not aim to evaluate the effect of a dynamic ORC bottoming cycle on the overall driving cycle of the vehicle, but to provide a discrete-point assessment of a conventional ORC layout for implementation in a hybrid vehicle.  
Figure 2: BSFC map of Ford 2.0 L Zetec-SE DOHC engine: (**a**) predicted and discretized BSFC map of 29 points; and (**b**) the BSFC map given from optimized MATLAB code.
## 3 System Model
### Threat Model and Security Assumptions  
As shown in Figure 2, we establish a threat model that encompasses both security and privacy dimensions, with particular attention to the unique challenges presented by edge nodes.
# Applications of Probabilistic Forecasting in Demand Response
## 2 Materials and Methods
### Dataset  
The proposed method is illustrated using real data from a small city in Spain, with a population of around 4500 inhabitants. For the last decade, Spanish national databases (2011) account for around 1700 main and 650 secondary residences in this city and its surroundings. This allows us to estimate 2-3 inhabitants per household, which enables us to assume, to some extent, customer requirements (for instance, daily hot water demand or tank size according to inhabitants). Power ratings of commonly used household appliances such as electric heaters (EHs) and Water Heaters (WHs) are considered around 1.5-2.0 kW and around 1.0-2.0 kW respectively. This work has considered also that more than 50% of customers could engage a DR pilot with EH and WH appliances in this geographical area.  
The raw dataset for this residential area contains the hourly electricity demand of the whole city, as well as some climate variables we describe below. Data range from 1 October 2008 to 31 March 2011; the first two years were used as the training sample and the last six months as the test dataset. The climate variables considered in the dataset include the hourly forecasts of the variables shown in Table 2.  
These forecasts were obtained from Meteogalicia, the regional weather service for the region of Galicia, Spain [27]. Meteogalicia uses the numerical prediction model Weather Research and Forecast (WRF) [28], enabling it to provide hourly forecasts of a set of meteorological variables for an analysis grid covering the entire Iberian Peninsula. The points in the analysis grid are spaced approximately 12 km apart. For each day in the training and testing dataset, the forecasts of the variables listed in Table 2 correspond to the four nearest points of the analysis grid to the city's location, with forecasting horizons from 1 to 48 h downloaded. The final hourly forecasts of the weather variables for the city were obtained through a weighted average of the forecasts for the four nearest points, using the inverse of the distance from the city center to each point as the weighting factor.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Meaning** \\ \hline temp & Temperature at 2 m (K) \\ swflux & Surface downwelling shortwave flux (W/m\({}^{2}\)) \\ mod & Wind module at 10 m (m/s) \\ rh & Relative humidity at 2 m (0 to 1) \\ cft & Total cloud cover (0 to 1) \\ prec & Accumulated rainfall (kg/m\({}^{2}\)) \\ snow & Accumulated large scale snowfall (kg/m\({}^{2}\)) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Forecasted weather variables.  
According to [29], around 42.9% of the residential consumption corresponds to space heating and 17.9% to water heating. In this work, it is supposed that all the users are of the residential type with homogeneous characteristics in their controlled loads (the total of Electric Heating and Water Heaters). In addition, the comfort conditions established are as follows: from 17 \({}^{\circ}\)C to 22 \({}^{\circ}\)C for the internal room temperature and at least 35 \({}^{\circ}\)C for the hot water supply.