esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Time Tampering in TTHCA
Let \(t_{i}\) = 1,600 ns for all \(i\) and \(\Delta T_{j}\) = 8 ms for all \(j\), where \(j=i+1\). If \(RTT_{WH}\) = 16.0048 ms then \(RTT\) = 56.0112 ms. For this PM I-B scenario, the HC is 5 and \(\Delta T_{TOT}\) = 40 ms, so from Equation (2), source node A calculates \(PTT\) = 8.0056 ms giving \(PTT\)/\(HC\) = 1.60112 ms. If it is assumed \(R\) = 250 m, then from Equation (3) the upper bound for \(PTT\)/\(HC\) = 833 ns which means TTHCA will successfully detect the wormhole. Using Equation (4), it can be determined that both I\({}_{2}\) and I\({}_{5}\) are able to prevent detection by increasing \(\Delta T_{TOT}\) within the range:  
\[16.002867\text{ ms }\leq\Delta T_{F}\leq 16.011200\text{ ms}\]This means the time tampering window is only 8.33 \(\upmu\)s wide and while this is a stringent constraint, if synchronized clocks are being used by both M\({}_{1}\) and M\({}_{2}\), it is still realistically an achievable design tolerance. Analysis for a wide range of network and wormhole attack conditions reveals that a sufficient and necessary condition for a wormhole to avoid being detected is to uphold either Equations (6) or (7) and (8). In this PM I-B example, both M\({}_{1}\) and M\({}_{2}\) will calculate \(\Delta T_{F}=16.003133\) ms which implies the tampered value falls within the window Equation (4) to avoid being discovered. In these circumstances, the false measurement \(\Delta T_{TOT}=56.003133\) ms so from Equation (2), the source node A measures \(PTT=4\),033 ns and \(PTT/HC=806\) ns meaning this wormhole route will go undetected by TTHCA.
## 3 Results
### Microstructure and Phases
#### 3.3.2 Crystallographic Phases  
X-ray diffraction (XRD) was used to identify the crystalline phases present in the deposits formed in the holiday region and on the surface of the TSA coatings.  
All the hydrated oxides/hydroxides of Al have the formula Al(OH)\({}_{3}\), but they vary in the crystallographic polymorphs or polytypes, i.e., Bayerite, Doyleite and Norstrandite, which can be generally named as Gibbsite. Bayerite is monoclinic, whereas Doyleite and Nordstrandite are triclinic.  
From the EDX analysis it was apparent that the two elements that comprised the majority of the deposit were Mg and Ca. The composition of the deposit did not change significantly with the area of damage. To understand the corrosion mechanism on exposed steel and TSA, XRD was carried out on the deposit formed on the TSA surface and the deposit found in the damage/holiday region on top of steel.  
The deposit collected from the TSA surface was predominantly Gibbsitic, with some indication of the presence of amorphous hydrated oxides of aluminium. The crystalline peaks correspond to Norstrandite, Doyleite and Bayerite (Figure 7a).  
Figure 6: EDX analysis of the deposit found on the surface of TSA.  
The deposit collected from the holiday/damage region showed Aragonite and Brucite phases present, as expected (Figure 7b). Some peaks of Gypsum were also found along with that of Bayerite, Bassanite and Halite (Figure 7b).
# Simulation of a Narrowband Power Line Communications System over Medium Voltage
## 4 OFDM in PLC Transmission  
The IEEE P1901 standard for PLC systems proposes two filter bank multicarrier (FBMC) techniques, namely F-OFDM and W-OFDM [5; 15]. Here, these two modulation options are studied for NB-PLC transmission via non-branched MV power lines in the attempt to determine the maximum reach of NB-PLC signal transmission.
# An Efficient Void Aware Framework for Enabling Internet of Underwater Things
## 3 The Proposed Efficient Void Aware Routing Protocol for Enabling Internet of Underwater Things
### Terminology Definition  
This section defines terminologies that help in understanding the proposed algorithms and scenarios provided in this paper.  
1. [label=.]
2. Void area: the area without any sensors.
3. Actual void node: the node \(n_{i}\in U\) that without shallower candidate set in its Routing Table (_RT_). \[(\forall n_{i}\in U|n_{i}.RT=\varnothing\Rightarrow n_{i}\to ActualVoid)\] (1)
4. Intuitive void node: the node \(n_{i}\in U\) can be considered as an intuitive void node in two scenarios:  
Figure 2: Communication void problem in EVA, (**a**) optimal route with more neighbour nodes in the range, (**b**) void problem with limited number of neighbor nodes in the range.  
If the number of \(ActualVoid\) higher than the number of \(OrdinaryNode\) in \(RT\) and \(OrdinaryNode\) does not contain one node at least with \"very good\" link quality.  
\[\begin{array}{l}(\forall n_{i}\in U|n_{i}.RT\
eq\varnothing\ \&\ V_{n_{i}}\geq O_{n_{i}}\ \&\&\ \forall O_{n_{i}}\in U|n_{i}.RT.distance\
eq\textit{``VGLink''}\Rightarrow\\ n_{i}\to IntutiveVoid)\end{array} \tag{2}\]  
If the \(RT\) is not empty and all candidate sets are \(AcualVoid\).  
\[(\forall n_{i}\in U|n_{i}.RT\
eq\varnothing\ \&\ S_{n_{i}}-V_{n_{i}}=\varnothing \Rightarrow n_{i}\to IntutiveVoid) \tag{3}\]  
\[\begin{array}{l}\text{d.}\ \  
a hello packet between neighbors within the transmission range. Then, link quality is computed and added to the routing table. In the second phase, the void detection algorithm is designed to identify the actual and intuitive void nodes. The last phase has the responsibility to forward the data packet using the ordinary nodes considering suppressing the void nodes from joining the forwarding process. We want to clarify that the three phases of the execution of the proposed EVA framework are distributed network-wise and sequential in order nodes-wise. It means, the framework is executed in different void network regions in distributed nature. However, at each void node, the three phases including data collection, void node identification, and intelligent data forwarding are executed sequential in order.
## 4 Results
### D Inversion Model  
The generation of a high-resolution uniform grid using drones has enabled the creation of three-dimensional models that visualize the magnetic susceptibility by applying an advanced inversion technique [30]. The resulting model has been built using the VOXI Earth Modelling software from OASIS Montaj. This tool has greatly simplified the identification and spatial delimitation of the magnetic bodies of interest.  
The results obtained from this modelling process (Figure 5) show the existence of two geological bodies with contrasting magnetic susceptibilities. These findings are of great relevance for the discrimination of the different geological formations present in the study region. It is noteworthy that the lowest magnetic susceptibility corresponds to Formation 1, while the highest susceptibility is associated with Formation 2. It is noteworthy that the metagranites, quartz Schists and/or ultramylonites exhibit a markedly lower iron content compared to the orthogneisses, which is essential for the accurate interpretation of our geological model. Moreover, this distinction manifests itself continuously as one goes deeper into the subsurface.
# QoS-Guaranteed Radio Resource Management in LTE-A Co-Channel Networks with Dual Connectivity
## 3 Proposed Schemes
### HetNet Congestion Indicator  
Due to the dual connectivity, each piece of user equipment is connected to the Macro BS. The handover problem between the HeNB will be processed by the Macro BS. We propose a new HetNet Congestion Indicator (HCI) cell selection scheme to achieve the load balance, considering not only the signal quality of the UE, but also the number of remaining resources of each base station. In addition to the cell selection, we also use HCI to adjust the type of the UE, either MUE or PUE.
# Structural Design and Study of an Integrated Cutter System Based on Machine Operation
## 4 Verification of the Anti-Loosening Performance of the Integrated Cutter System
### Experimental Procedure  
This experiment mainly involved the experimental equipment shown in Figure 12. Figure 12a shows a resistance strain gauge, which was responsible for measuring the strain change in the screw. Figure 12b is the strain collector, which was responsible for receiving and recording the strain information output from the strain gauges. The strain collector selects INV3062A from the China Orient Institute of Noise & Vibration (Beijing, China), and its main technical indexes are as follows: sampling rate: 0.5 Hz\(\sim\)204.8 kHz adjustable; AD precision: 24-bit \(\Delta\)-sigma mode; number of analog input channels: 16; dynamic range: 120 dB. Figure 12c shows a constant torque wrench, which can apply rated torque on the screw. Figure 12d shows the equal-scale eccentric integrated cutter prototype. Figure 12e shows the electrodynamic vibration test system, model IPA180L/H1859A. Its main technical indexes are as follows: rated sinusoidal thrust: 16,000 kgf; frequency range: 2-2000 Hz; rated displacement: 51 mm; rated speed: 1.8 m/s; rated acceleration: 981 m/s\({}^{2}\); maximum load: 1600 kg.  
Figure 12: Experimental equipment: (**a**) resistance strain gauge; (**b**) strain collector; (**c**) constant torque wrench; (**d**) equal-scale eccentric integrated cutter prototype; (**e**) vibration test system.  
Firstly, the strain gauges were pasted and the strain collector was connected. Its pasting position is shown in Figure 13, where three strain gauges were pasted on both sides of the screw along the axis direction. Among them, S1, S2 and S3 represent the left-side screw, and S4, S5 and S6 represent the right-side screw. Then, by consulting the bolt preload comparison table, it can be seen that a torque of 1650 N\(\cdot\)m should be applied to the M30 bolts. Then, the constant torque wrench shown in the figure was used to tighten the experimental sample by applying a torque of 1650 N\(\cdot\)m and observe whether the strain gauges worked properly. After confirming that the strain gauges had not failed, the test specimen could be assembled on the vibration test bench and the vibration test could be started. Finally, the vibration test was started with the selected working conditions, and changes in strain values were recorded by sampling regularly. The vibration direction is shown in Figure 14: 8 h of continuous vibration in the normal load direction and 3 h of continuous vibration in the lateral load and rolling load direction.
## 2 Materials and Methods  
Before delving into the details of the proposed system model, it is essential to provide an overview of the underlying concepts and methodologies. In this section, we outline the framework for the investigation and implementation of the joint communication and sensing (JCAS) system. The proposed system model aims to achieve seamless integration of communication and radar functionalities, enabling simultaneous operation while addressing challenges such as interference and performance balance.
# A Robust Scheduling Framework for Re-Manufacturing Activities of Turbine Blades
## 4 Robust Scheduling Framework
### Modeling Uncertainty
#### 4.2.2 Uncertainty Affecting the Process Steps  
In re-manufacturing, the characteristics of the parts to be processed, e.g., their level of wear, could entail the need of operating different processes. A typical situation is the need of executing a rework.  
The execution of rework activities can be modeled in different ways, grounding on the specific characteristic of the process and re-manufacturing environment as well as with respect to the available data. A first approach is incorporating possible rework activities in the standard process, thus, modeling the distribution of processing time to take into consideration the possibility of longer times due to the need to operate a rework. Although simple, this approach is clearly giving up in modeling the actual sequencing of operations. In fact, rework activities are usually operated after a verification of the standard process. Thus, they are usually operated at a later temporal stage. Putting work and rework operations in the same scheduled activity is not correctly modeling the fact that, in the meanwhile, other activities can be processed. A second approach is hypothesizing that all batches need to be reworked, which is a really common occurrence in the re-manufacturing of turbine blades. Thus, an additional set of activities is considered, for each batch of blades to be processed). These are then added to the original RCPSP introduced in Section 4.1, considering the associated processing times as well as precedence constraints.  
Another approach considers that not all the possible rework activities are always operated. Thus, their occurrence is modeled through a Bernoulli distribution while the associate processing time is still defined by a probabilistic distribution, thus:  
1. A RCPSP model is defined considering both work and rework activities.
2. The processing time of rework activities is obtained through the convolution of the occurrence and processing time distributions described above.
# Towards an Optimized Blockchain-Based Secure Medical Prescription-Management System
## 4 Implementation and Test of Blockchain-Based Prescription Management System
### IoT System  
Expanding on the prior sections, once the prescription details are registered, the DApp generates an exclusive QR code tailored to that specific prescription. This QR code acts as a repository for the prescription's information, encapsulated in the form of black and white squares. For the implementation of this data-encoding mechanism, we have opted for an IoT system, consisting of a **Raspberry Pi** board and a **Pi camera**, presented in Figure 5, specifically designed for **QR code scanning**.  
Figure 4: User authentication and add prescription interfaces.  
Figure 5: IoT system composed of Raspberry Pi and Pi camera.  
This setup enables pharmacists to swiftly scan and authenticate the pertinent prescription data, thereby facilitating seamless purchase procedures.  
Using a QR code with a Raspberry Pi-based IoT system offers several benefits:  
* Real-time processing: Raspberry Pi's processing power enables real-time QR code scanning and information extraction. This responsiveness is critical for applications that require immediate access to data.
* Ease of programming: Raspberry Pi supports various programming languages, simplifying the development of QR code-scanning applications. This versatility accommodates developers with different coding backgrounds.
* Camera module compatibility: Raspberry Pi camera modules are well suited for capturing images, including QR codes. Their compatibility ensures optimal image quality and reliable QR code recognition.
* Scalability: Raspberry Pi-based solutions are scalable, making them suitable for environments with varying scanning demands. Adding more units to the setup can accommodate increased QR code scanning requirements.
* Low energy consumption: Raspberry Pi boards are energy-efficient, making them suitable for continuous operations without significant power consumption.  
We chose to utilize a Raspberry Pi equipped with a camera and QR code reader due to its potential to establish a robust connection between the physical world and blockchain technology. The Raspberry Pi is adept at efficiently capturing, processing, and performing real-time transmission of information encoded within QR codes. This capability enables not only the extraction of essential data, but also seamless interaction with a blockchain platform.
# Optimization Algorithm for 3D Smooth Path of Robotic Arm in Dynamic Obstacle Environments
## 2 Basic Model
The attractive potential field guides the robot towards the target point, while the repulsive potential field keeps the robot away from known obstacles. However, traditional APF tends to suffer from the local minimum problem when dealing with complex environments, especially those with multiple adjacent or densely distributed obstacles. This means that the robot may get stuck on a non-global optimal path and be unable to bypass obstacles to reach the target point. In more complex multi-obstacle environments, APF often fails due to the need for complex repulsive forces. Some methods [31; 32] modify the repulsive force function to allow path planning in environments with multiple static obstacles. [PERSON] et al. [33] proposed an adaptive potential field method by adjusting repulsive force parameters to avoid local minima. [PERSON] et al. [34] utilized APF to control the robotic arm to avoid dynamic obstacles under environmental constraints and addressed the issue of robot body protection during obstacle avoidance. Although methods based on APF address the requirement of real-time obstacle avoidance, they are mostly used for point-to-point path planning. In environments with complex static and dynamic obstacles, the robot's movement during obstacle avoidance is often unpredictable, leading to inefficiencies. Regarding the strengths and weaknesses of RRT and APF, local path replanning enables RRT to avoid dynamic obstacles. However, to achieve better real-time performance, the length of the replanned path must be minimized. Long paths reduce the efficiency of task execution by the robot. [PERSON] et al. [35] proposed a path optimization method called vector field RRT (VF-RRT), which uses APF to guide RRT searches and obtain optimal results. [PERSON] et al. [36] introduced environmental potential field RRT (EPF-RRT), utilizing APF functions to avoid static obstacles and plan collision-free paths. [PERSON] et al. [37] added a target deviation to the hybrid method of APF and RRT to enhance planning convergence. [PERSON] et al. [38] combined bidirectional RRT with APF, reducing path planning time and optimizing path length. [PERSON] et al. [39] proposed the P-RRT* algorithm, which integrates potential fields with RRT* to accelerate the convergence of RRT. [PERSON] et al. [40] further improved P-RRT by adjusting the weights between potential vectors and random node vectors to avoid local minima when planning lime picking paths. The tuning strategy improves the efficiency of RRT* in searching for viable paths by avoiding the generation of paths in local minimum areas by random sample nodes.
## 3 Research Methods
### Analysis 1: Path Modeling  
A path analysis, a structural equation modeling method, was conducted to test the hypothesized relationships among the multiple variables. This analysis was performed using SPSS Amos ver. 23. Path analysis enables a set of equations within a model to be computed concurrently and must only include measured variables. The chi-square goodness-of-fit test and other fit indices were used to establish the consistency between the observed model and the expected model. A comparative fit index (CFI) score larger than 0.95 [60], a goodness-of-fit statistic (GFI) larger than 0.90 [61], and root mean square approximation (RMSEA) of less than 0.06 [61; 62] were used as indicators of a good model fit. Results of the path analysis showed a good fit (\(X^{2}=19.556\); \(p=0.050\); \(X^{2}/df=1.787\); GFI = 0.992; CFI = 0.990; RMSEA = 0.036) and provided support for all hypotheses except H13. Twelve of the thirteen hypothesized paths were significant at the \(p\leq 0.01\) level and were supported by the data. Table 4 shows the path coefficients of each hypothesised relationships.  
In Figure 2, solid lines signify significant path coefficients, and the dotted line indicates the non-significant path. The model predicted a 27% variation in mobile security practices (R\({}^{2}=0.27\)), with security inconvenience as the strongest determinant (\(\beta=-0.37\)). Expected outcome (R\({}^{2}=0.23\)) is significantly affected by security awareness (\(\beta=0.12\)), security  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Factor Loading** & **Cronbach’s Alpha** & **Mean** & **Std. Dev.** \\ \hline Factor 1: Security monitoring & & 0.927 & 3.168 & 1.280 \\  & ST1 & 0.885 & & \\  & ST2 & 0.862 & & \\  & ST3 & 0.824 & & \\  & ST4 & 0.814 & & \\ \hline Factor 2: Security training & & 0.909 & 3.515 & 1.251 \\  & SM1 & 0.870 & & \\  & SM2 & 0.815 & & \\  & SM3 & 0.808 & & \\  & SM4 & 0.769 & & \\ \hline Factor 3: Security awareness & & 0.875 & 4.166 & 0.992 \\  & SA1 & 0.885 & & \\  & SA2 & 0.852 & & \\  & SA3 & 0.800 & & \\  & SA4 & 0.783 & & \\ \hline Factor 4: Job characteristic & & 0.941 & 4.406 & 1.015 \\  & JC1 & 0.945 & & \\  & JC2 & 0.930 & & \\  & JC3 & 0.928 & & \\ \hline Factor 5: Usage behavior & & 0.631 & 4.407 & 1.423 \\  & UB1 & 0.783 & & \\  & UB2 & 0.757 & & \\  & UB3 & 0.737 & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Factor loadings and descriptive statistics for security training, security monitoring, security awareness, job characteristics, and usage behavior.  
monitoring (\(\beta=-0.11\)), usage behavior (\(\beta=0.12\)), and security inconvenience (\(\beta=-0.39\)). Security inconvenience is mainly predicted by security awareness (\(\beta=0.20\)), followed by usage behavior (\(\beta=-0.16\)) and security monitoring (\(\beta=-0.09\)).
## 4 Conclusions  
An analytical technique capable of distinguishing the effects of axial and bending displacements and shear deformation on the natural frequencies of three-layered beams was proposed in this study. The effect of these strain energies was calculated using the relation between the maximum normal strain and kinetic energies by applying the shape functions of the displacements defined by the developed transfer matrix. The transfer matrix method is used to determine the eigenpairs of three-layered beams with a soft core, and the efficacy of the method is demonstrated through a comparison of the results computed using the proposed method and those discussed in previous works.  
From the analyzed results, all the bending-dominated modes of top and bottom layers were accompanied by the axial displacements because of the existence of a core layer, whereas the axial-dominated modes were uncoupled with the bending displacements. Moreover, the axial displacements have a significant effect, even in bending-dominated modes.  
The proposed method has been considered a reasonable assumption for practical problems, but the method is somewhat limited for applications because the mass of the core layer was ignored, and the shear deformation beam theory was not considered in the top and bottom layers. For a wide range of applications for such beams, the mass and shear deformation in all layers should be considered in future studies.  
Figure 8: Variation in the first five mode shapes of soft-core three-layered beams with the cantilevered end condition with respect to the variation of _L/h_.
# Numerical Study on Transient Annular Pressure Caused by Hydration Heat during Well Cementing
## 4 Numerical Simulations and Analyses
### Annular Pressure under Different Wellbore Diameters  
The annular pressure under different wellbore diameters is simulated using the models established in this paper. As shown in Figure 14, the annular pressure increases with the wellbore diameter. The wellbore with a smaller diameter is easily affected by the environment and loses more heat to the surrounding formation. Therefore, the wellbore with a smaller diameter has a lower temperature and a lower annular pressure. In addition, the higher temperature accelerates the cement hydration rate. Thus, the annular pressure increases faster when the wellbore diameter is larger. According to the simulation results, the maximum annular pressure with a 269.9 mm wellbore is only 2.0 MPa, while the maximum annular pressure with a 320.7 mm wellbore is 7.9 MPa.