esa-sceva/satcom-chunk-collection huggingface dataset:
# Shear Resistance Assessment of the Y-Type Perfobond Rib Shear Connector under Repeated Loadings
## 3 Monotonic and Repeated Loading Tests
### Monotonic Loading Test
#### 3.1.2 Shear Resistance and Ductility  
The shear resistance and ductility of Y-type perfobond rib shear connectors were evaluated from the monotonic loading test. Figure 5 defines the characteristic values related to the shear resistance and ductility of Y-type perfobond rib shear connectors [19], which are based on Eurocode-4 [24]. The characteristic load (\(P_{RK}\)) is defined as the value of 90% of the ultimate shear strength (\(P_{u}\)), which estimates the ultimate slip (\(\delta_{u}\)) and characteristic slip (\(\delta_{90}\)). The characteristic slip (\(\delta_{uk}\)) is defined as the value of 90% of the ultimate slip (\(\delta_{u}\)), which determines the ductile behavior in Eurocode-4 [24]. This study evaluates the ductility with the ratio (\(\delta_{u}/\delta_{90}\)) based on the characteristic slip (\(\delta_{90}\)) and ultimate slip (\(\delta_{u}\)) suggested by [PERSON] et al. [19].  
Table 5 summarizes the results of the shear resistance and ductility for all of the specimens, and Figure 6 shows the load-slip curves. It is found that the higher rebar grade does not increase the shear resistance and does not contribute to the ductile behavior. The  
Figure 4: Test set-up of the push-out test.  
Figure 5: Evaluation method of shear resistance and ductility.  
specimens with D19 rebars show much higher shear resistances as well as ductility-related slips compared to the specimens with D16 rebars. It is quite important to select the proper size rebars, which may keep balances with the stiffness of Y-ribs. It needs to be noticed that the increasing rate of the shear resistance from D19 specimens to D22 specimens is not much and the ductility-related values are even reduced (\(\delta_{u}\), \(\delta_{uk}\), and \(\delta_{u}/\delta_{90}\)). In Figure 6\(f\), the average load-slip curve for D22 rebars shows a faster drop after the peaks of \(P_{u}\) than that for D19 rebars.  
Figure 6: Load–slip curves for monotonic loading tests. (**a**) SD400-D16, (**b**) SD400-D19, (**c**) SD500-D16, (**d**) SD500-D19, (**e**) SD500-D22 and (**f**) averaged load–slip curves.
# A Carbon Benefits-Based Signal Control Method in a Connected Environment
## 6 Discussion
### Prospects for Application  
The issue of carbon emission reduction is a global concern. The proposed method addresses the need for vehicles at intersections to follow speed guidance to achieve carbon reduction goals by proposing a framework that integrates CIM, carbon trading markets, and intelligent transportation systems. On the one hand, the method achieves eco-driving guidance at intersections by providing financial incentives to drivers. On the other hand, it optimizes both signal control and vehicle guidance in a coordinated manner, raising the upper limit of emission reduction efficiency at intersections. The proposed method's implementation requires only a connected environment, which significantly enhances its potential for widespread application.  
The application of the proposed method involves several key areas. First, it is essential to establish an accurate carbon emission accounting mechanism that can be applied to various traffic control demands. This effort requires government involvement in setting relevant policies. Second, the development of a system or application to transmit intersection speed guidance instructions to vehicles is necessary. This system should be widely accessible with navigation apps on smartphones being a potential solution. Third, the influence of the financial reward amount on driver motivation warrants further exploration, potentially involving research in economics and behavioral science.
## 3 Results and Discussions
### Shale Inhibition Properties of Water-Based Drilling Fluid with SDCap
#### 3.3.3. Shale Inhibition Mechanism of SDCap with Low-Molecular-Weight  
Adsorption of SDCap on sodium bentonite:  
The adsorption isotherm of SDCap is illustrated in Figure 7. It was found that SDCap could effective get adsorbed onto the clay surface. As the concentration of SDCap increased, the adsorbance increased significantly. The absorption capacity is the primary reason for the inhibition performance of SDCap. During drilling, when the drilling fluid containing SDCap interact with clay particles on wellbore or in drilling cuttings, through both the hydrogen bonding between the amide group and clay, and the electrostatic interaction between cationic group and clay, SDCap can be adsorbed on the surface of clay particles and does not fall off easily. Therefore, it inhibits the clay hydration dispersion, thus maintaining the wellbore stability and keeping the integrity of drill cuttings.  
Analysis on clay particle size distribution:  
Figure 8 shows the variations of distributions of the bentonite particle size in the presence of different concentration of SDCap. After adding SDCap, the size of bentonite particles obviously increased. D\({}_{50}\) of bentonite suspension without SDCap was 11.1 \(\upmu\)m, while that of suspension with  
Figure 6: Swelling rates of samples in different WBDFs.  
Figure 7: Variations of adsorbance of SDCap on clay particles as a function of concentration.  
0.5% SDCap was 131.3 \(\upmu\)m, indicating the addition of 0.5% SDCap increased the D\({}_{50}\) of bentonite suspensions by more than 10 times. This demonstrated that the dispersed bentonite particles were wrapped together by SDCap, and were not easily dispersed in the drilling fluid. The results also reflected the strong adsorption capacity of SDCap.  
Zeta potential analysis:  
The results of zeta potential tests in Figure 9 showed that the addition of SDCap did not significantly impact the surface charges of clay for it was a zwitterionic polymer with both cationic and anionic groups (sulfonic acid groups). The cationic groups helped SDCap get adsorbed onto the surface of clay particles by electrostatic adsorption, while the effect of electrical neutralization was not apparent, thus avoiding the excessive electrical neutralization or even electrical reversal caused by cationic polymer which resulted in serious flocculation of drilling fluid (shown in Figure 10).  
Figure 8: Clay particle size distribution in the presence of different concentration of SDCap.  
Figure 10: Flocculation of 4% bentonite suspension caused by addition of cationic polymer encapsulator.  
Figure 9: Variations of zeta potential as a function of concentration of SDCap.
## 3 Results and Discussion
### Phytochemical Characterization
#### 3.2.1. Bitter Acids Quantification  
Hops \(\alpha\)- and \(\beta\)-acids were analyzed and quantified using HPLC UV, revealing interesting results. In Table 4, the content of bitter acids is shown. The \(\alpha\)-Acids values ranged from 0% in L14 to 4.125 \(\pm\) 0.969% in the L21 genotype, which represents the significant highest value, together with the L7 genotype (Table 4). In the studied population, genotypes with high \(\alpha\)-acids contents are not present; these data are in accord with prior studies on European wild hop germplasm, where bitter accessions were not found [21, 36], and with a recent study on Italian wild hop germplasm, where \(\alpha\)-acids do not exceed the value of 4.10% [33]. In Table 4, it is also possible to observe an important variability, with significant differences noticeable among the studied accessions. \(\beta\)-Acids, instead, ranged from 1.064 \(\pm\) 0.009 in the L54 genotype to 6.462 \(\pm\) 0.355 in the L53 genotype, which possess the significantly higher content of beta acid. In the studied accessions, \(\beta\)-acid values were tested on values higher than those reported by [PERSON] [36] and [PERSON] et al. [21] in European wild hops. The cohumulone content is an important qualitative value for brewers and hop users, as it confers bitterness, and its value is expressed as a percentage on the total content of \(\alpha\)-acids. L37 is the accession with the highest cohumulone value (32.046 \(\pm\) 1.66%); instead, L14 possesses the lowest, since it does not contain alpha acids. The data are partially in accord with a study on wild hop in Banja Luka Area [37], in which hop samples reached the value 28.8 \(\pm\) 1.2% of cohumulone. Nevertheless, the cohumulone content in the analyzed accessions showed a lower level if compared to US or Canadian wild hops; in fact, in a  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Locus**} & \multicolumn{2}{c}{**L
###### Abstract  
The trap states and defects near the active region in deep-ultraviolet (DUV) light-emitting diodes (LED) were investigated through wavelength-dependent photocurrent spectroscopy. We observed anomalous photocurrent reversal and its temporal recovery in AlGaN-based DUV LEDs as the wavelength of illuminating light varied from DUV to visible. The wavelength-dependent photocurrent measurements were performed on 265 nm-emitting DUV LEDs under zero-bias conditions. Sharp near-band-edge (\(\sim\)265 nm) absorption was observed in addition to broad (300-800 nm) visible-range absorption peaks in the photocurrent spectrum, while the current direction of these two peaks were opposite to each other. In addition, the current direction of the photocurrent in the visible wavelength range was reversed when a certain forward bias was applied. This bias-induced current reversal displayed a slow recovery time (\(\sim\)6 h) when the applied forward voltage was removed. Furthermore, the recovery time showed strong temperature dependency and was faster as the sample temperature increased. This result can be consistently explained by the presence of hole traps at the electron-blocking layer and the band bending caused by piezoelectric polarization fields. The activation energy of the defect state was calculated to be 279 meV using the temperature dependency of the recovery time.  
AlGaN; deep ultraviolet; light-emitting diode; photocurrent spectroscopy; anomalous photocurrent 2022131233123 Al composition, including high threading dislocation density due to a low lateral growth rate and differences between the lattice constant and the thermal expansion coefficient in the sapphire substrate and the AlN buffer layer. Moreover, p-GaN layers are usually grown on top of p-AlGaN layers since ohmic contact on p-AlGaN is difficult to achieve. A complex electron-blocking layer (EBL) structure is required due to the relatively low p-doping concentration in p-AlGaN compared to in GaN-related blue LEDs. However, the EBL can generate defects around the active region in the UV LEDs [3; 4; 7; 8; 9; 10; 11; 12]. Hence, AlGaN DUV LEDs may exhibit many defective states near the active region. This is a major factor impacting the efficiency of LEDs. Studies on the point defects or complex defect states in the active region and their origin have only recently been conducted, and studies on the defects are focused on threading dislocation using electron microscopes.  
In this study, the trap states and defects in the active region in AlGaN DUV LEDs are investigated using wavelength-dependent photocurrent spectroscopy. We observed an anomalous photocurrent reversal and its temporal recovery. This phenomenon was attributed to the photocurrent generated by Al vacancies in p-AlGaN and hole traps at the EBL and p-AlGaN interface. Furthermore, we verified our hypothesis by analyzing the change in temporal recovery depending on temperature, and the activation energies of the trap states were obtained.
## 2 System Model
### Cognitive Radio Wireless Sensor Network Energy Consumption Model
#### 3.3.3 Cognitive Radio Unit Energy Consumption
Energy consumption \(\mathrm{E}_{rx}\) for receiving \(B_{ld}\)-bits of each local decision is given as:  
\[\mathrm{E}_{rx}(B_{ld})=\ B_{ld}E_{ec} \tag{16}\]  
Let \(R_{max}\) denote the maximum radio range of clusterhead \(CH_{j}\), then the consumed energy \(\mathrm{E}_{bd}\) for broadcasting the final cooperative decision can be expressed as:  
\[\mathrm{E}_{bd}(B_{ld},d_{j,R_{max}})=\ B_{ld}(E_{ec}+E_{am}\ d_{j,R_{max}}) \tag{17}\]  
Let \(\mathrm{E}_{dp}\) denote energy consumption for processing a \(B_{ld}\) bits packet received from each member node \(MN_{i}\) for decision fusion. Energy cost for clusterhead \(CH_{j}\) cooperative channel sensing is the energy consumed for sensing a set of channels, energy consumed for receiving \(m_{n}\) member nodes' local decisions, energy consumption for processing the decisions and energy consumption for broadcasting the final cooperative decisions which is given as:  
\[E_{CH}^{cs}=\underbrace{\mathrm{E}_{cs}(n_{z},T_{cs})}_{\underset{Sensing}{ \mathrm{{channels}}}}+\underbrace{\sum_{ \begin{subarray}{c}\epsilon=1\\ \mathrm{{Decisions}}\\ {Receiving}\end{subarray} }^{m_{n}}}_{\underset{Receiving}{\mathrm{{ receiving}}}}+\underbrace{\sum_{ \begin{subarray}{c}\epsilon=1\\ \mathrm{{Decisions}}\\ {Processing}\end{subarray} }^{m_{n}+1}}_{\underset{Decisions}{\mathrm{{Decisions}}}}+ \underbrace{\mathrm{E}_{bd}\big{(}B_{ld},d_{j,R_{max}}\big{)}}_{\underset{ Broadcasting}{\mathrm{{copy.Decision}}}} \tag{18}\]  
Therefore, total energy cost for cooperative channel sensing \(E_{total}^{cs}\) can be expressed as:  
\[E_{total}^{cs}=\sum_{j=1}^{k}(\sum_{\epsilon=1}^{m_{n}}E_{MN}^{cs}+E_{CH}^{cs})\]  
\[=\sum_{j=1}^{k}(\sum_{\epsilon=1}^{m_{n}}(E_{rp}(B_{ld},\mathrm{d}_{i,j})+2E_{ rx}(B_{cd})) \tag{19}\]  
\[+\sum_{\epsilon=1}^{m_{n}+1}(E_{cs}(n_{z},T_{cs})+\mathrm{E}_{dp}(B_{dp}))+E_{ bd}(\mathrm{B}_{cd},d_{j,R_{max}}))\]
# Blockchain-Based Distributed Computing Consistency Verification for IoT Mobile Applications
## 6 The Proposed BDCA
### Data Updating
```
1:i = 1;
2:while i <= r do
3:\(Pl_{i}\)= \(\{(k,\gamma_{vol_{i}}-1)\}\);
4:\(\Gamma_{i}=\gamma_{vol_{i}}\);
5:Y\({}_{i}=h_{vol_{i}}\);
6:i = i + 1;
7:endwhile
8:\(Point\_Sets=\{1,2,...,r\}\);
9:for i = 1, 2,..., t do
10:for j = 1, 2,..., r do
11:if AVF[i][j] == NULL then
12: continue;
13:else if AVF[i][j] is a leaf or non-leaf node \(\
u_{x}\)then
14:\(\Gamma_{i}=\Gamma_{i}+r_{\
u_{x}}\);
15:if\(l_{l_{x}}==1\)then
16:\(Y_{i}=H_{i}(\Gamma_{i}||Y_{i}||h_{l_{x}})\);
17:\(PI_{i}=PI_{i}(\alpha)-r_{\
u_{x}}\);
18:else if\(l_{l_{x}}==0\)then
19:\(Y_{i}=H_{1}(\Gamma_{i}||h_{l_{x}}||Y_{i})\);
20:\(PI_{i}=PI_{i}(\beta)+r_{\
u_{x}}\);
21:else\(l_{l_{x}}==2\)
22:return 0;
23:endif
24:else AVF[i][j] is a point \(Point_{\delta}\), \(\delta\in(1,r]\)
25:\(Pl_{\delta}=PI_{\delta}(\beta)+\Gamma_{i}\);
26:\(\Gamma_{i}=\Gamma_{i}+\Gamma_{\delta}\);
27:Y\({}_{i}=H_{1}(\Gamma_{i}||Y_{i}||Y_{\delta})\);
28:\(PI_{i}=PI_{i}(\alpha)-\Gamma_{\delta}\);
29:\(PI_{i}=Union(PI_{i},PI_{\delta})\);
30: remove \(\delta\) from \(Point\_Sets\);
31:endif
32:endfor
33:endfor
34:if\(PI_{1}\
eq\{(d_{1},d_{1}-1),(d_{2},d_{2}-1),...,(d_{r},d_{r}-1)\}\)then
35: return 0;
36:else if\(\Gamma_{1}\
eq k\) or Y\({}_{1}\
eq h_{root}\)then
37: return 0;
38:else
39: return 0;
40:endif
```  
**Algorithm 3** Batch verification. _Deletion:_ Assume that \(DO_{S}\) wants to delete the \(i\)-th leaf node at \(ts_{2}\) (e.g., delete \(m_{4}^{*}\) in Figure 3c). * \(DO_{S}\) generates the data updates request \(req=\{ts_{2},delete,i,AFV_{1\times t},h_{root}^{*},pk_{DO_{S}}\}\) and updates the BV-MHT on the path from the node \(v_{i}\) to the root. Then, \(DO_{S}\) sends it to \(SC\). * \(SC\) verifies and updates the BV-MHT in a similar way as above, and generates a data update transaction \(Tx_{2}\) encrypted with AGMS. Then, \(Tx_{2}\) is sent to \(TC\) for data updating. \(Tx_{2}=[^{*}Update^{*},req]\)  
* \(TC\) updates the data in a similar way as above. _Insertion:_ Assume that \(DO_{S}\) wants to insert a new node after the \(i\)-th leaf node at \(ts_{2}\) (e.g., insert \(m_{4}^{{}^{\prime}}\) after \(m_{4}\) in Figure 3d). * \(DO_{S}\) generates the data updates request \(req=\{ts_{2},insert,i,h_{i}^{{}^{\prime}},m_{i}^{{}^{\prime}},AFV_{1\times t},h _{root}^{*},pk_{DO_{S}}\}\) and updates the BV-MHT on the path from the node \(v_{i}\) to the root, where \(v_{i}^{{}^{\prime}}=\{l_{i},r_{i}+1,H_{1}(r_{i}+1||h_{i}||h_{i}^{{}^{\prime}})\}\). Then, \(DO_{S}\) sends it to \(SC\). * \(SC\) verifies and updates the BV-MHT in a similar way as above, and generates a data update transaction \(Tx_{2}\) encrypted with AGMS. Then, \(Tx_{2}\) is sent to \(TC\) for data updating. \(Tx_{2}=[^{*}Update^{*},req]\)  
* \(TC\) updates the data in a similar way as above.
# Simulation and Validation of a Steering Control Strategy for Tracked Robots
###### Abstract  
Tracked inspection robots have demonstrated their versatility in a wide range of applications. However, challenges arising from issues such as skidding and slipping have posed obstacles to achieving precise and efficient trajectory control. This paper introduces a method to determine the steering parameters of robot based on the surrounding obstacles and road information. The primary objective is to enhance the steering efficiency of tracked robots. The corresponding relationship between the track speed, driving force and track steering radius of the tracked robot is obtained. Considering the influence of track skid and slip, relationship models about the steering radius and traveling speed of the robot are established. The minimum and maximum steering radii in the obstacle avoidance process are analyzed, and a mathematical model of the relationship between the steering angle of the robot and the distance between the side obstacles is established. The trajectory deviation model of the tracked robot is established, and a principle analysis of the LiDAR ranging is completed. This lays the foundation for a steering measurement and control system for tracked robots. ADAMS(2020) software is used to establish the multi-body dynamics model of the tracked robot, and three different obstacle-avoiding steering control strategies are designed for the robot in a simulated environment with space obstacles. The simulation experiment demonstrates that the robot achieves more efficient obstacle avoidance steering through the use of differential steering, leading to a decrease in both track skid and slip rates. Through the simulation experiment, it can be seen that the robot uses differential steering to complete the obstacle avoidance steering movement more efficiently, and the track skid and slip rates are smaller. The simulation results are used to complete the steering control experiment of the tracked robot on different road surfaces. The results show that by adjusting the track driving parameters, the robot can effectively complete the obstacle-avoiding steering movement by using the differential steering control strategy, which verifies the accuracy of the steering control strategy.  
tracked robot; obstacle avoidance; skid and slip; virtual prototype +
Footnote †: journal: Applied Sciences
# Effects of Vehicular Communication on Risk Assessment in Automated Driving Vehicles
## 2 Sensor Characteristics and Information Fusion
### Information Fusion of Radar and V2V Communication
To perform a track-to-track association for the data coming from two independent sources in local filter, \(\mathbf{\overline{x}}_{radar}\) and \(\mathbf{\overline{x}}_{v2v}\), the first step is to regard how close one track is to another so that association decisions can be made. A validation region is the volume in state space where tracks are expected. This validation region is usually expressed in terms of the Mahalanobis distance. For example, Figure 4 shows an example of a radar measurement validation. The reference (true) position of the target-vehicle is shown in black line which is target vehicle trajectory. A V2V/Radar fusion result is expressed by blue dot, which is a current estimated target-vehicle location. The estimation uncertainty is shown to be a three sigma ellipse in red. The red circles are radar measurements, of which only the nearest to the current estimate are used to update the baseline. The advantage of using a validation area based on the Mahalanobis distance instead of an Euclidean distance is that it takes the uncertainty in each of state variables with different units, into account. Given two tracks, radar and V2V with mean estimates and covariances represented by \((\mathbf{\overline{x}}_{radar}[k]\), \(\mathbf{\overline{p}}_{radar}[k])\) and \((\mathbf{\overline{x}}_{v2v}[k]\), \(\mathbf{\overline{p}}_{v2v}[k])\), the Mahalanobis distance is defined as:  
\[\chi_{radar,v2v}=\sqrt{(\mathbf{\overline{x}}_{radar}[k]-\mathbf{\overline{x} }_{v2v}[k])^{T}\cdot(\mathbf{\overline{p}}_{radar}[k]+\mathbf{\overline{p}}_{ v2v}[k])^{-1}\cdot(\mathbf{\overline{x}}_{radar}[k]-\mathbf{\overline{x}}_{v2v }[k])} \tag{2}\]  
Both the uncertainty of the state estimate and that of the measurement influence the Mahalanobis distance. The smaller these uncertainties are, the higher the associated distance. For the experimental sections, the Mahalanobis distance threshold was empirically set to 5, in order to get a good rejection of outlier radar measurements, due to other vehicles and scatter the state estimate and that of the measurement influence the Mahalanobis distance. The smaller these uncertainties are, the higher the associated distance. For the experimental sections, the Mahalanobis distance threshold was empirically set to 5, in order to get a good rejection of outlier radar measurements, due to other vehicles and scatter. Data association techniques are used for relating measurements to targets [20]. Unlike the Nearest-Neighbor approach, where only the measurement which has the  
Figure 4: Radar measurements (red) need to be associated with the current relative position estimate (blue). The Mahalanobis distance takes also the current estimation uncertainty (blue ellipse) into account. smallest Mahalanobis distance to the target is used to update the measurement; in probabilistic data association all measurements in the validation region are taken into account.
# Applying Machine Learning to DEEC Protocol: Improved Cluster Formation in Wireless Sensor Networks
## 3 Implementation  
The choice of K-NN and K-Means clustering algorithms as the core algorithms for cluster head selection in this work was motivated by their unique strengths in addressing WSNs' dynamic and heterogeneous nature. K-NN is inherently proximity-based, making it highly effective for grouping nodes in environments where network topology may change due to node failures or mobility. On the other hand, K-Means is a widely used unsupervised learning algorithm that excels at partitioning nodes into spatially balanced clusters by minimising intra-cluster distances. This property is particularly advantageous in heterogeneous WSNs, where the even distribution of nodes and workload is critical for prolonging network lifetime and preventing energy holes. Compared with other ML techniques, such as supervised classifiers or reinforcement learning, K-NN and K-Means offer lower computational complexity and do not require labelled data or extensive training, making them practical for resource-constrained sensor nodes. K-NN and K-Means were chosen for their lightweight, unsupervised nature, making them ideal for WSNs where computational resources and labelled data are limited. In contrast, more complex or supervised ML models would impose significant computational and data collection burdens, making them less practical for real-time, distributed sensor networks. Cluster head (CH) selection is a critical factor in the energy efficiency and longevity of Wireless Sensor Networks (WSNs). This section presents a comparative analysis of three  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  
CH selection strategies: the original probabilistic method, the K-Nearest Neighbours (K-NN)-based approach, and the K-Means clustering-based approach. The evaluation focuses on key performance metrics, including total network energy, energy efficiency, CH-to-base station (CH-BS) distance, cluster head count stability, and network lifetime. The simulation for the original and modified protocols used the same parameters as shown in Table 2.