esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Proposed Scheme
### Privacy-Preserved Security Energy Trading
#### 4.2.3 Open Channel  
In this phase, [PERSON] will choose \(t\) strings \(\chi_{i}\) at random, after which she will lock the \(x\) bitcoin on HTLC. Algorithm 3 allows for the calculation of the \((\chi_{i},\omega_{i})\) forwarded to other nodes in a subsequent transaction procedure.  
Before [PERSON] makes payment to [PERSON], both parties need to create a third-party common account. [PERSON] and [PERSON] use the Paillier homomorphic encryption public key \(P_{Pub-P}\) to encrypt the amount deposited in the common account to obtain the ciphertext \(M_{1}=Enc(\beta_{0}^{[PERSON]})\) and \(M_{2}=Enc(\beta_{0}^{[PERSON]})\), where \(Enc(m)\) means that \(m\) is encrypted with the Paillier encryption public key \(P_{Pub-P}\).  
[PERSON] and [PERSON] send \(M_{1}\) and \(M_{2}\) to KGC, respectively, and then KGC obtains the public account balance through calculation:  
\[\beta=M_{1}\times M_{2}=Enc(\beta_{0}^{[PERSON]})\times Enc(\beta_{0}^{[PERSON]})=Enc( \beta_{0}^{[PERSON]}+\beta_{0}^{[PERSON]}). \tag{18}\]  
Common account \(ACC\) is expressed as:  
\[ACC=(Address_{[PERSON]},Address_{[PERSON]},Pid_{[PERSON]},Pid_{[PERSON]},\beta,\zeta,\lambda). \tag{19}\]  
\(\beta\) represents the common account balance and \(\lambda\) represents the capacity available on the path. \(Address_{[PERSON]}\) and \(Address_{[PERSON]}\) represent the account addresses of [PERSON] and [PERSON], respectively. KGC calculates the balance of the common account \(\beta\) according to the Paillier homomorphic property.  
```
1:Input: \(\chi\), \(\xi_{i}\)
2:Output: \((\chi_{i},\omega_{i})\)
3:for\(i=1\to t\)do
4:if\(\forall i\in[t]\)then
5:\(\chi_{i}\in 0,1^{*}\)
6:\(\omega_{i}=H(\oplus_{j=1}^{t}\chi_{j})\), \(j\geq i\)
7:endif
8:return\(((\chi_{1},\omega_{1})\ldots(\chi_{t},\omega_{t}))\)
9:endfor
```  
**Algorithm 3** HTLC.  
Before the first off-chain transaction, Alice encrypts the transfer amount \(\alpha\) and the account balance \(\beta_{A}\) after the transfer to obtain the ciphertext \(M_{3}=Enc(\alpha)\), \(M_{4}=Enc(\beta_{A})\). [PERSON] sends \(M_{3}\) and \(M_{4}\) to KGC to verify the validity of its initial transfer--that is, to verify the formula  
\[M_{1}=Enc(\alpha)\times Enc(\beta_{A})=Enc(\alpha+\beta_{A}). \tag{20}\]  
If the equation holds, it means that [PERSON] meets the balance requirements for the initial account. KGC outputs a channel identifier \(\sigma_{(U_{0},U_{1})}\) after passing the verification.
## 3 Detection Techniques
### Detection Technique II  
Detection Technique I simplifies the process but comes with a slight increase in bit error rates. To tackle this challenge, we use Detection Technique II, also known as the Low Complexity Maximum Likelihood (LC-ML) decoder. This method strikes a balance between reducing complexity and managing bit error rates effectively.  
Therefore, this technique serves as an interim solution that successfully reduces both the bit error rate and the complexity of detection. Continuing with this, the Equation (15) is shown as  
\[\bar{y}=\vec{R}_{1}x+\bar{n} \tag{24}\]  
The LC-ML decoder [14] then selects a maximum likelihood (ML) result. Four components make up the vector \(\hat{x}\): \(\hat{x}_{1},\hat{x}_{2},\hat{x}_{3}\), along with \(\hat{x}_{4}\). The value of \(\xi\) from the sample space satisfies the following criterion:  
\[\hat{x}=\arg\min\|\bar{y}-\vec{R}_{1}x\|^{2}=\arg\min_{x\in\xi}\sum_{k=1}^{4} \vec{d}_{k} \tag{25}\]  
In the same \(d_{k}\) for \(k=1,\ldots,4\) are given by  
\[d_{1}=|\dot{y}_{1}-kx_{3}-ax_{1}|^{2}\quad d_{2}=|\dot{y}_{2}-bx_{4}-ax_{2}|^{2} \tag{26}\]  
\[d_{3}=|\dot{y}_{3}-cx_{3}|^{2}\quad\ \ \dot{d}_{4}=|\dot{y}_{4}-cx_{4}|^{2} \tag{27}\]  
Hence, (25) can be expressed as  
\[(\hat{x}_{1}\hat{x}_{3})=\min_{x_{1}x_{3}\in\xi}(d_{1}+d_{3})(x_{2}.x_{4})= \min_{x_{2},x_{4}\in\xi}(d_{2}+d_{4}) \tag{28}\]The \(\hat{x_{1}}\), \(\hat{x_{2}}\), \(\hat{x_{3}}\) and \(\hat{x_{4}}\) are used to recognize the GQSM symbol.
# An Overview on Push-Based Communication Models for Information-Centric Networking
## 3 Push-Based Approaches in ICN
### Push Communication via Special Data Packets  
Push-communication can also be implemented based on special Data packets. Such approach follows the natural communication model of ICN. An example for such an approach considers _pData_ packets [16]. pData packets are sent by producers and forwarded by routers without checking the PIT, given that there is no prior Interest packet. pData packets are forwarded based on the implemented forwarding strategy, via the FIB stored information. Therefore, if no entry for a specific pData Name prefix is available, then pData packets are broadcasted.  
The trade-off associated with a push model based on Data packets is a higher network overload, which can be controlled via, for instance, TTLs.  
In large-scale, highly dense and with high mobility scenarios, such a push-based approach can be coupled with name-based routing protocols such as DABBER [17], thus avoiding broadcast strategies. Following this approach, _pData_ packets (e.g., _/homenumber123/room/temperature/pData/ valuerroom = A/valutemp = 22_) are forwarded only towards consumers that have subscribed _pData_ name prefixes (e.g., _/homenumber123/room/temperature/pData_).  
Models under this category can be used in time-critical scenarios. An example is the need to send alerts in Industrial IoT, concerning, for instance, malfunctioning. Another example is the need to push data to surrounding devices around [16; 18].
# Remote and Proximal Sensing Techniques for Site-Specific Irrigation Management in the Olive Orchard
## 2 Materials and Methods
### Irrigation Management and Measurements  
The trees were irrigated using a subsurface drip irrigation system (2.3 L h\({}^{-1}\) pressure-compensated drippers spaced at 0.6 m). Irrigation volumes were calculated on the basis of the effective evapotranspiration (ET\({}_{\text{c}}\)), using a crop coefficient (Kc) of 0.55, 0.60 and 0.65 in July, August and September, respectively. The irrigation period lasted from DOY 188 (7 July) to DOY 274 (1 October), during which fully-irrigated (FI) and deficit-irrigated (DI) trees received a total volume of 2160 and 970 m\({}^{3}\) ha\({}^{-1}\), respectively, whereas the rainfed (RF) ones received only precipitation. Fertigation was used to supply mineral nutrients in spring before irrigation treatments started.  
Tree water status was monitored at three dates (DOY 187, DOY 218 and DOY 245) by measuring the stem water potential (SWP) on six trees per irrigation treatment (three trees per each cluster-irrigation combination, as explained in Section 2.5). SWP was measured after blocking transpiration of leaves inserted near the main scaffolds of the tree [47].  
Vegetative growth was evaluated as the annual increment in the trunk cross-sectional area (TCSA). The TCSA was calculated from the circumference of the trunk at 0.40 m from the ground, measured on DOY 11 and 356. Immediately before harvest, 100 fruits were randomly sampled from around the canopy of three trees per each cluster-irrigation regime combination to measure mesocarp and endocarp fresh and dry weight. At harvest (DOY 285), each tree was harvested by hand and the final fruit yield was weighed. The oil content of the fruit mesocarp of 20 fruits per tree previously sampled for fresh weight determinations was measured at harvest by nuclear magnetic resonance Oxford MQC-23 analyzer (Oxford Analytical Instruments Ltd., Oxford, UK). The mesocarp was cut in small pieces (2-5 mm) and oven-dried at 70 \({}^{\circ}\)C for 48 h. The oil yield of individual trees was calculated as previously reported [48]. Water use efficiency for fruit (WUE\({}_{\text{f}}\)) and oil (WUE\({}_{\text{o}}\)) was calculated as the ratio between fruit yield (dry weight) or oil yield, respectively, and annual ET\({}_{\text{c}}\) within each irrigation treatment.
# Effect of Phase Noise on the Optical Millimeter-Wave Signal in the DWDM-RoF System
## 5 Conclusions  
In this study, we examined the effect of phase noise on the mm-wave signal. The architecture used a single MZM to generate the mm-wave signal in order to reduce the system complexity and insertion losses resulting from the signal's phase imbalance. The DWDM-RoF architecture was evaluated under various phases to show the performance of the mm-wave signal. Our analysis investigated the impact of phase noise resulting from phase imbalance. We found that the phase noise could be decreased by controlling the phase imbalance of the mm-wave signal at the PS. The best controlled phase imbalance value was obtained at the phase of \(5\pi/12\) as 0.1 degrees, and reduced the phase noise of the mm-wave signal to 0.02 degrees.  
**Author Contributions:** [PERSON] studied and simulated all the data; [PERSON] consulted and reviewed the paper; [PERSON] and [PERSON] reviewed and edited the paper; [PERSON] and [PERSON] reviewed the paper. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Fundamental Research Grant (FRGS), Ministry of Higher Education, Malaysia, No. FRGS/1/2019/TK04/UPM/02/07, and Inisiatif Putra Muda Grant by Universiti Putra Malaysia, No. GP-IPM/2017/9524100.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Configuration of Defect Models  
#### 2.5.1 2-D Plane Strain Model  
Figure 3a illustrates the simulation setup for defects in a plane strain model, while Figure 3b-d depict the profiles of the investigated defects. The model's length, \(l\), is defined as the ratio of length \(L\) to thickness \(h\). The defects are modeled as either a smoothly shaped symmetric thickness reduction (Hann-shaped), a one-sided smooth thickness reduction (Hann-shaped), or a rectangular notch (blind hole with a flat bottom). The depth of the defect is denoted as \(h_{0}\) and its length as \(a\). The excitation point, \(P\), is positioned at 0.30 \(l\) from the defect center.  
Out-of-plane displacement components are measured at 0.25 \(l\) from the defect center, labeled \(LL\) and \(RR\) in Figure 3a. The plate material is steel, with properties listed in Table 1. Time-domain scattered signals from various defects are windowed to isolate the \(A_{0}\) mode, which is then converted to the frequency domain. Signal amplitudes at 50 kHz are normalized by the incident signal amplitude to calculate the relative reflection coefficient \(Refl\) and the transmission coefficient \(Trans\), given as follows:  
\[Refl=\frac{|R(\omega)|}{|I(\omega)|},Trans=\frac{|T(\omega)|}{|I(\omega)|} \tag{5}\]  
where \(R(\omega)\), \(T(\omega)\), and \(I(\omega)\) are the frequency spectra of the displacement obtained from the reflected, transmitted, and incident signals, respectively. The study examines the effects of relative depth \(H_{0}=h_{0}/h=[0.1\ldots 0.5]\) and relative length \(A=a/h=[2\ldots 6]\) on these coefficients, and calculates the percentage error between the Mindlin and FEM models. The defect length is in the range of 0.5 \(\ldots\)1.5 wavelengths of the probing wave, representing the cases that are near the imaging limit of GWT.
# Handover Management in 5G Vehicular Networks
## 3 Preliminaries
### HO Execution
#### 4.4.2 Proposed Reactive HO Scheme  
In the case of reactive inter-MAG HO execution (Figure 11), the vehicle sends a \(1.MIH\_Li\_nk\_up\_React\) to the selected network, which includes information about the MN-ID and the MN-LLID and enables the communication channel. Subsequently, the vehicle sends a \(2.UNA\) message to the selected network, to complete the establishment of both the Link and Network layers, as well as a \(3.MIH\_N2N\_HO\_Commit\_request\) message to reserve the required resources. By receiving this message, the selected network reserves the required resources and sends back a \(4.MIH\_N2N\_HO\_Commit\_response\) to the vehicle.  
Thereafter, the MAG of the selected network identifies that the vehicle was not previously connected to any of the networks that it manipulates. Thus, the selected network sends a \(5.Handover\_Initiate\) message to the previous one through the LMA. In this message, the Proxy (P) and the Buffering (B) flags of the message are set equal to 1. By receiving this message, the previous network responds with a \(6.Handover\_Ack\) message, and a bi-directional tunnel is established between the two networks. Thereafter, the buffered packets from the previous network are transmitted through the tunnel to the selected network to be delivered to the vehicle when its HO completes. Furthermore, the MAG of the selected network exchanges the \(7.PBU\) and \(8.PBA\) messages with the LMA to update the Binding Cache Entry (BCE) of the LMA. Consequently, the vehicle becomes ready to transmit or receive packets through the selected network.  
Figure 10: The intra-MAG predictive VHO signaling.  
For the intra-MAG reactive VHO execution (Figure 12), the messages after the \(4.MIM\_N2N\_HO\_Commit\_response\) of the above case are not exchanged between the vehicle and the selected network. Specifically, in this case, the MAG of the selected network identifies that the vehicle was previously connected to a network that it manipulates. Thus, the exchange of signaling messages between the MAG and the LMA is not required, and the vehicle is ready to transmit or receive packets through the selected network.  
Finally, it has to be noted that after the successful execution of each of the above HO schemes, the number of handovers for the vehicle \(N_{CR_{u}}\) is increased for the estimation of the vehicle's velocity.
# Actuation Mechanisms and Applications for Soft Robots: A Comprehensive Review
## 2 Actuation Materials and Mechanisms for Soft Robots
### Electroactive Polymer (EAP)
#### 2.3.1 Dielectric Elastomer  
Owing to inherent flexibility, large strain, high efficiency, high energy density, and fast response, dielectric elastomer actuators (DEA) have been widely used in designing soft robots [114; 115]. DEA is a kind of motion-generating material, which is similar to human muscle in terms of force, strain (displacement per unit length or area), and driving pressure/density [116; 117]. This material mainly includes silicone and acrylic. The former has a faster response speed, whereas the latter can produce a greater deformation [118]. In addition to the material preparation produced by the planar method, DEA can also be realised by 3D printing, and its mechanical properties are adjustable [119].  
The development of worm-like soft robots is taken as an example for illustrating DEA. [PERSON] et al. leveraged the characteristics of DEA to develop a wall-climbing soft robot (Figure 6a) [120]. In Figure 6b, the elastomeric body of the robot can be expanded and bent at a high voltage (6 kV). When the voltage is lost, the robotic body will contract to achieve a forward motion. The robot can obtain a fast climbing speed owing to the rapid periodic deformation of the elastomeric body. It can be seen from Figure 6c that, when DEA is at a high state, the rear foot is anchored but the front foot moves forward. The two feet exchange the motion state when DEA is at a low state. In a movement cycle, at the first stage (from 2 nd to 4 th time interval), the front foot is driven by DEA deformation, and at the second stage (from the 4 th to 6 th time interval), the rear foot is pulled by the restored DEA.  
There are various applications of EDA to actuate soft robots. Examples as illustrated in [121; 122] are bionic swimming robots that can conduct underwater exploration. At actuation voltages of above 3 kV, their swimming speed can reach more than 1.5 mm/s. The research in [123] shows a micro robot that mimics the annelid. The robot has a small size (20 mm in diameter, and 45 mm in length) and a light weight of 4.7 g. The maximum moving speed of the robot is 2.5 mm/s, and the load capacity is more than twice its own weight. Multi-legged crawling robots in [124; 125] are attractive because of their high load capacity (more than 900 g) and fast speed (about 9 mm/s). The rolling robot presented in [126] possesses the advantages of not only a higher rolling speed (36.27 mm/s on average) but also a larger speed-mass ratio (about 41.22 mm/s-g). Therefore, it has good environmental adaptability. Additionally, the bionic caterpillar robot in [127] was designed with an integrated artificial nervous system.
# Design and Implementation of a RFID Reader/Router in RFID-WSN Hybrid System
## 1 Introduction
The main objective of this research is to develop a portable wireless RFID reader/router for RFID-WSN hybrid system, which has long identification distance, low cost, high flexibility, scalability, and a good interactive interface. The RFID reader/router also offers the hybrid system with the  
Figure 1: The integration structure of Radio Frequency Identification (RFID) and Wireless Sensor Network (WSN) (**a**) Integration of RFID tags with sensor nodes; (**b**) Integration of RFID readers with sensor nodes; (**c**) Mixed integration. capability of both identification/tracking and capture/transmitting. This design can be used in a wide range of applications. The remainder of this paper is organized as follows. Firstly, the System architecture is introduced in Section 2. Hardware design of the RFID reader/router is described in Section 3. Section 4 presents the software design of RFID reader/router. Section 5 provides results of field test in real-world scenarios. Finally, some conclusions of this paper follow in Section 6.
# A Game Theoretic Approach for Balancing Energy Consumption in Clustered Wireless Sensor Networks
## 7 Conclusions and Future Work  
In this paper, we present a game theoretic approach for balancing energy consumption in clustered WSNs, which is called GTAB. The CH election process is modeled as a clustering game, and sensor nodes in a close proximity are considered as players which will participate in the game for CH election. We construct a novel convex payoff function for each sensor node in the game, where a penalty mechanism is introduced to well capture this node's practical behaviors. Then the sensor nodes which hold more energy are compelled to compete for the CH more passionately. Through convex optimization, we get the NE strategy of this clustering game. Each sensor node decides whether to act as the CH based on this NE strategy for a tradeoff between providing required services and energy saving. Specifically, its own payoff can be maximized. Extensive simulation results have verified that our protocol can effectively improve the performance of energy balancing among nodes, and the network lifetime is significantly enhanced.  
As future work, we will continue to use game theory to study clustering in WSNs from three aspects: Firstly, we will adopt game theory to solve the cluster area allocation problem in multi-hop clustered WSNs, where CHs in the network cooperatively transmit data to BS by a multi-hop communication method so that unequal clustering is required to avoid energy hole problem; Secondly, we will use game theory to solve the CH election problem in energy harvesting WSNs, where sensor nodes are equipped with energy harvesting devices that can harvest energy from the ambient environment; At last, we will utilize game theory to address the security issue in cluster-based WSNs, and both authentication and trust based mechanisms will be studied.  
We acknowledge the financial support of the Scientific and Technological Project of Chongqing (Grant No.: cstc2017 syj-syjB0014).  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON] wrote the paper; [PERSON], [PERSON], [PERSON] and [PERSON] participated in paper revision and made many suggestions.  
The authors declare no conflict of interest.  
Figure 10: Network lifetime versus different node densities.
## 4 Discussion
The differences in the torque values and time were minimal, both in terms of the time and force, and probably impossible to be clinically detected. In a cyclic fatigue test, the fracture occurs at the maximum-stress concentration point, which is usually located in the middle of the curvature. In a torsional test, the instrument usually breaks at the point where it is locked. In tapered instruments, the resistance to the bending stress decreases when the diameter increases (more coronally), while the resistance to the torsional stress decreases when the diameter decreases (more apically). For both tests, the stress was applied to a similar portion of the rotary file, in order to minimize the differences that could invalidate the results. In terms of the clinical relevance, and without considering the different stressful conditions related to the specific canal anatomies, the NiTi rotary files have a safer range, in terms of the resistance to bending, when compared to the resistance to the torsional stress. The torsional failure may happen more rapidly and unexpectedly. The SEM images showed no differences between the heat-treated and non-heat treated files for both manufacturers. All the instruments tested for torsional resistance exhibited the same fractographic patterns, showing shear failure, with concentric abrasion marks and microscopic dimples at the center of the rotation. Similarly, all the four different types of NiTi rotary files tested for cyclic fatigue showed the presence of crack initiation areas and overload. These findings are in accordance with many previously published studies [31]. Comparing the non-heat-treated NiTi rotary files, the ET and PT showed similar torsional resistance, while the ET showed significantly higher resistance to cyclic fatigue. Since the two tested instruments are very similar, in terms of design and dimensions, such differences may be related to the different alloys or the manufacturing processes (grinding). Comparing the heat-treated NiTi rotary files, the ETP and PTG showed similar torsional resistance, while the ETP showed significantly higher resistance to cyclic fatigue. Since the two tested instruments are very similar, in terms of the design and dimensions, such differences may be related to the different manufacturing processes (the heat treatment and alloy).