esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Surface Deformation Induced by Ultra-Shallow-Buried Pilot Tunnel Construction
## 3 Case Validation of Deformation Predictions in Shallow-Buried Tunnels
### Analysis of Results
#### 4.2.2 Horizontal Displacement of Ground Surface  
As shown in Figure 19, the ground surface near the pilot tunnel centerline on both sides exhibits horizontal displacement toward the axis. In Figure 15, the positive/negative values denote the horizontal displacement direction: positive aligns with the x-axis positive direction, and negative indicates the opposite. The surface horizontal displacement curve is centrally symmetric, featuring three zero points and four extreme values within the plotted range. The first point where horizontal displacement is zero lies at the pilot tunnel axis. The horizontal displacement curve is symmetric along the positive and negative x-axes. For the positive x-axis, as the distance x from the pilot tunnel axis increases, surface horizontal displacement toward the axis occurs and gradually increases, reaching the first extreme value of \(-\)17.23 mm at 4.8 m. The displacement then decreases, reaching the second zero point at 9.7 m. Beyond this, surface horizontal displacement occurs away from the axis, increasing to an extreme value of 30.61 mm at 17.4 m, then decreasing and tending to zero at infinity. Within the range (\(-\)9.7 m, 9.7 m), all surface horizontal displacements point to the tunnel axis (toward the excavation center). In regions (\(-\infty\), \(-\)9.7 m) and (9.7 m, \(\infty\)), displacements depart from the tunnel axis, moving away from the excavation center.  
Figure 18: Surface curvature deformation of Beijing Pilot Tunnel.  
As shown in Figure 20, the ground surface near the pilot tunnel centerline on both sides exhibits compressive deformation. In Figure 19, positive values denote horizontal compressive deformation, while negative values indicate tensile deformation. The overall horizontal deformation curve is axisymmetric, featuring four zero points and three extreme values within the plotted range. Directly above the pilot tunnel axis, the extreme value of horizontal compression deformation is 5.51 mm/m. The horizontal deformation curve is symmetric along the positive/negative x-axes. For the positive x-axis, as the distance x from the pilot tunnel axis increases, the compression deformation value decreases, reaching the first zero point at 4.8 m--corresponding to the first extreme value point of the surface horizontal displacement curve. As the horizontal distance increases further, tensile deformation values rise, reaching a second extreme point of \(-6.13\) mm/m at 10.4 m. The horizontal tensile deformation then decreases gradually, reaching the second zero point at 17.4 m (corresponding to the extreme value of the horizontal displacement curve). Beyond this, horizontal deformation reverts to compression and tends to zero at infinite distance.
## 2 Numerical Procedure
### Numerical Method  
The governing equations for a laminar 3D isothermal flow are the mass, momentum and energy conservation equations, given by:  
\[\frac{\partial\rho}{\partial t}+\
abla\times\left(\rho\overset{\rightarrow}{V }\right)\ =0 \tag{1}\]  
\[\frac{\overset{\rightarrow}{V}}{\partial t}+\left(\overset{\rightarrow}{V} \times\
abla\right)\overset{\rightarrow}{V}\ =\ -\frac{1}{\rho}\
abla p+v\
abla^{2}\overset{ \rightarrow}{V}+\overset{\rightarrow}{f} \tag{2}\]  
\[\rho\left[\frac{\partial h}{\partial t}+\
abla\times\left(h\overset{ \rightarrow}{V}\right)\right]\ =\ -\frac{Dp}{Dt}+\
abla\times\left(k\
abla T\right)+\phi \tag{3}\]  
where \(\rho\) is the density, \(t\) is the time, \(\overset{\rightarrow}{V}\) is the velocity vector, \(p\) isthe pressure, \(v\) isthe constant of kinematic viscosity, \(\overset{\rightarrow}{f}\) is the body force per unit mass, \(h\) isthe enthalpy, \(k\) isthe thermal conductivity and \(T\) is the temperature. For the fluid, the energy conservation promotes a balance between the different mass transport and heat transfer mechanisms (convection and conduction) due to the effect of viscosity, pressure and density variations [25], represented by Equation (4):  
\[\rho C_{p}\frac{\partial T}{\partial t}+\rho C_{p}u\
abla T\ =\ \alpha_{p}T\left(\frac{ \partial p_{a}}{\partial t}+u\
abla p_{a}\right)+\tau:G+\
abla\times\left(k \
abla T\right)+Q_{f} \tag{4}\]where \(C_{p}\) is the heat capacity at a constant pressure, \(u\) the velocity field, \(\alpha_{p}\) is the thermal expansion coefficient, \(p_{a}\) the absolute pressure, \(\tau\) the viscous stress tensor, \(G\) the strain rate tensor and \(Q_{f}\) the heat source. For the solid, energy conservation leads to the Fourier law:  
\[\rho C_{p}\frac{\partial T}{\partial t}\ =\ \
abla\times(k\
abla T)+\mathrm{Q}_{f} \tag{5}\]  
It is worth mentioning that, as explained in the Introduction, the Navier-Stokes method with the first-order slip boundary condition can give results consistent with those obtained by numerical simulations based on the Boltzman model for fluids with a Knudsen number between 0.01 and 0.1. So, for this Knudsen number, the Navier-Stokes method with a slip boundary condition is adequate to model the nanofluids' regimes. As the flow is laminar, this approach is also valid for the characteristic scale of the microchannels addressed here.
## 2 Ospec Performance Verification
### Fabrication of PSC Beam Specimens  
After the long-term performance verification of the strand specimens was completed, the beam specimens were fabricated to examine the behavior of OSPC strands installed in actual concrete structures. The dimensions of the specimens were based on a cross section of 300 mm in width \(\times\) 520 mm in height at the center, and the dimensions were 300 mm in width \(\times\) 600 mm in height at the support, considering the anchorage. As shown in Figure 8, the main reinforcement bars and strands were arranged inside the specimen, and the effective depth of the tendon was 460 mm. The strands were divided into composite and non-composite specimens according to the attachment method, with three strands being arranged for each specimen, and one of them was replaced with an OSPC strand. Figure 9 shows the production of the specimen and the introduction of the prestressing force. If the wavelength data measured by the optical fiber sensor of OSPC Strand were converted into a prestress force, the maximum prestress force was 184.1 kN for the composite specimen and 179.5 kN for the non-composite specimen. A total of six specimens were produced in combination with specimens for different purposes, but only the results of specimens classified as composite and non-composite specimens were used in this study.  
The changes in prestressing force were observed for about 1600 days in the specimens after prestressing and preparation for long-term measurement. The place where the specimen was located was an area that experiences severe temperature changes, and the range of temperature change over the four seasons was about 40 \({}^{\circ}\)C. The long-term measurement started around December when the temperature was low, and the change in the wavelength of light emitted from the optical fiber sensor due to the change in temperature according to the seasons was converted into a prestress force, as shown in Figure 10 below. The process of converting the optical wavelength value of the optical fiber sensor into a prestress force was applied as in the study by [PERSON] et al. (2018). As shown in Figure 11, there was a slight difference in the wavelength values of the optical fiber sensors in the composite and non-composite specimens, but the change was in line with the trend of seasonal temperature change.  
Figure 8: Dimensions and appearance of the long-term performance test specimens.  
Figure 10: Change in prestress force according to temperature.  
Figure 9: Fabrication and prestressing of long-term performance test specimens (**a**) Fabrication of specimens, (**b**) Prestressing and measurement, (**c**) Fabricated specimens, (**d**) Prestressing graph.
## 2 Materials and Methods
### Clinical Procedure  
Ethical clearance was approved from the institutional review board (IEC 859/2016). This study was registered in Clinical Trial Registry of India (CTRI/2017/10/010303) and carried out at the Department of Conservative Dentistry & Endodontics, Manipal College of Dental Sciences, Manipal, Karnataka, India. Following a detailed explanation of the experimental study purpose, clinical procedures, and possible risks, informed consent was obtained from each patient. Absence of decay, restorations, trauma, periapical lesions, and periodontal pathologies was assured throughout clinical and radiographical examination of all teeth. Teeth which could not be isolated under a rubber dam were excluded. The pulp sensibility of all the experimental teeth was tested using thermal (cold test, Endo-Frost, Coltene, Cuyahoga Falls, OH, USA) and electric stimuli (Parkell Electronics Division, Farmingdale, NY, USA). In each patient, local anaesthesia (Septodont, Saint-Maur-des-Fosses, France) was performed, followed by rubber dam isolation, and cleansing of the surrounding regions (clamp, dam, and tooth) using a 0.2% chlororekidine gluconate solution. High speed diamond burs (Horico Dental, Berlin, Germany) under water coolant were used to prepare class I occlusal standardized cavities with an occlusal depth of 3 \(\pm\) 0.2 mm, mesiodistal width of 4 \(\pm\) 0.5 mm, and buccolingual width of 3 \(\pm\) 0.2 mm. After exposure of the pulp in the cavity centre on the pulpal floor using a sterilized round bur under water cooling, haemostasis was obtained by application of gentle pressure using a saline solution moistened cotton pellet. The tooth was then randomly treated (coin toss method) with either ProRoot MTA (Dentsply Sirona) or RetroMTA (BioMTA). The same procedure was applied on the contra lateral premolar. Both MTA cements were prepared and applied according to manufacturer's instructions. After application of the pulp capping material, a thin layer or resin-modified glass ionomer cement (Ionolux, VOCO GmbH, Cuxhaven, Germany) was placed over the tested capping materials as a liner and the access hole was restored using a universal resin composite (Filtek Z350 XT, 3M ESPE, St. Paul, MN, USA). All the procedures were carried out by a single operator, experienced in performing vital pulp therapy. All patients were recalled after 30 and 60 days for teeth extraction and histopathological analysis. In this period, if the patient experienced any pain in the restored test with the test agents, it was extracted immediately.
## 3 Proposed System
### Software Module
#### 3.1.2 Detection Framework of DataMatrix-ArUco-Hybrid Pattern
After all DM regions are traversed, the temporary DAH pattern set contains several DAH pattern candidates. Only the DAH pattern that satisfies the following conditions will be accepted: it must contain at least one DM region and one ArUco region, and these two regions must not be located diagonally from each other. We exclude those invalid DAH patterns and output the filtered set as the detection result.
## 4 Results
### Attack Model  
False data injection is a type of cyber attack in which manipulated or false information is deliberately introduced into a system to mislead its operations or decision-making processes. It is commonly employed to compromise the integrity of data in power systems, sensor networks, and other critical infrastructures.  
In this study, to simulate various electricity theft patterns, six different false data injection attacks are implemented [32, 33], as shown in Table 2. \(X=\{x_{1},x_{2},\ldots,x_{48}\}\) represents the electricity consumption data for a day, and \(\bar{x}_{t}\) denotes the data at time \(t\) after being subjected to an attack. \(\Delta t=random(0.2,0.8)\times 48\) is the randomly chosen time period throughout a day. \(t_{start}=random(0,48-\Delta t)\) is the randomly selected starting times, needing to meet that \(t_{start}+\Delta t\leq 48\). These attacks are broadly categorized into two types: reduced consumption attacks and load profile shifting attacks [1]. Types 1-4 primarily reduce the electricity consumption recorded by the meter, while Types 5 and 6 shift electricity usage from high-price periods to low-price periods.  
The physical interpretations of these six attacks are as follows: FDI1: Electricity consumption recorded at each time point is scaled down by a randomly selected proportion. FDI2: A constant value is subtracted from the electricity consumption data. FDI3: Electricity consumption values exceeding a preset threshold are capped at the threshold value. FDI4: Consumption data within a randomly selected time interval during the day are replaced with zeros. FDI5: The total daily electricity consumption is evenly distributed across all recording points and reduced by a fixed proportion. FDI6: The entire daily electricity  
Figure 4: Autocorrelation of a normal sample and an abnormal sample. (**a**) A normal sample. (**b**) An abnormal sample.  
consumption profile is inverted. Additionally, Figure 5 illustrates examples of both normal electricity consumption data and data affected by each of these six attack types.
## 3 System Model and Problem Formulation
### Threat Model  
Despite the benefits of the collaboration among edge devices, there are critical threats and trust issues. One major problem is how to defend against free-rider attacks. To be specific, we consider two categories of free-riders: (1) Fake contributors: These free-riders typically deceive the system by submitting fabricated local model weights. Fake contributions can include random model parameters, a combination of existing model weights and existing model weights altered with additive noise. (2) Weak contributors: Each free-rider trains a local model based on a private local dataset. However, the submitted local model barely improves the performance of the global model. Weak contributors may use noisy or very small amounts of data during training. Free-riders contribute very little while still reaping the benefit of the aggregated global model in every round.  
To better clarify the impact of free-riding attacks, we classify possible consequences into three categories: unfair reward allocation, delay of service and poor performance of the global model. Unfair reward allocation is due to the fact that all honest contributors and free-riders receive the same and up-to-date global model, while their contributions are distinct. Delay of service is obvious since the inclusion of free-riders takes system resources, such as communication bandwidth, consensus overheads, etc. Furthermore, it is catastrophic to aggregate local models submitted by free-riders into the global model. The convergence speed and test accuracy of the global model can be degraded significantly [7]. Therefore, eliminating free-riders is a critical need for a robust and fair DML system.  
Existing defensive methods [6; 7; 8] assume a trusted model aggregator to conduct free-riding detection, which is not applicable to DML. Detecting free-riders in a peer-to-peer network is challenging because each participant has limited information about the true positive contributions of others, which are based solely on their private local dataset. To solve the problem, we aim to use a decentralized approach to defend against free-rider attacks. We aim to develop an incentive mechanism that ensures every participant leaves the system with a global model reflecting their incremental contributions to DML.
## 5 Empirical Case Study: Courier Company in Bangkok
### Towards Balancing Vehicle's Total Working Time
#### 5.4.1 A Motivating Example  
Recall from Section 5.3.1 that Phase II of Problem B--12 p.m. exited with an unsuccessful termination where three vehicles violated the working time limit. The results suggest that the 480-min time allocated may not be sufficient. To find an appropriate working time limit, we rerun Phase II (Algorithm 2) with several different working time limit parameters \(\tau\) on Problem B--12 p.m. The results of this experiment are given in Figure 8. The dot plot in Figure 8 presents the amount of violation by each vehicle. The x-axis represents the working time limit (\(\tau\)) set as a parameter for the route adjustment algorithm (Algorithm 2), and the y-axis represents the amount of time violation. Each cross (+) corresponds to the violation amount of each vehicle route in the solution plan. There are a total of 15 vehicles in this problem. A positive violation amount (above zero line) means that a particular route violates the provided working time limit while a negative violation amount means that the vehicle time is under the working time limit. Obviously when \(\tau=480\), we see that three crosses are lying above the zero level violation amount. Hence, three vehicle routes (out of 15) violated the working time limit set by the company, consistent with our previously obtained result.  
Figure 7: Effect of assigning a customer in a random fashion versus ascending order of travel time to the nearest non-violating vehicle in Step 4 of Algorithm 2. Red: Phase I; blue: Phase II with ascending order assignment; black: Phase II with random assignment.  
A few observations can be drawn from Figure 7(a). First, it can readily be seen that if we use the working time limit of 500 min as the route adjustment algorithm parameter, vehicle times are most balanced. The more we decrease (to the left) or increase (to the right) the working time limit from this point, the further away the route becomes from a balancing situation. This is demonstrated by the red curve in Figure 7(b) which calculates the route balance (difference between the longest and shortest route times). Another important observation is that the number of swaps in Algorithm 2 tends to be highest at the optimal working time as shown by the blue curve. This happens because if the working time limit was set too low (to the left of the optimal duration), the route adjustment algorithm will quickly terminate with unsuccessful termination status. On the other hand, if the working time limit is too high (to the right of the optimal duration), the algorithm will also quickly terminate but this time with a successful termination status as it is easier for a vehicle to satisfy a high working time limit (hence no violation). Whenever either of the two cases occurs, only few locations will be swapped before termination.
## 4 Eliminating the Low Weight Non-Structured Codewords
### More Examples  
The (57,3,5), (126,3,5), and (204,3,5) Tanner LDPC-CCs are respectively defined by the polynomial syndrome former matrices  
\[\begin{bmatrix}1&1&D^{35}\\ D^{8}&D^{43}&D^{45}\\ D^{19}&D^{3}&D^{13}\\ D^{57}&D^{9}&D^{30}\\ D^{33}&D^{2}&1\end{bmatrix},\begin{bmatrix}1&D^{28}&D^{116}\\ D^{7}&D^{101}&D^{36}\\ D^{63}&D^{81}&1\\ D^{58}&D^{72}&D^{14}\\ D^{18}&1&D^{126}\end{bmatrix}\text{and}\begin{bmatrix}1&1&D^{171}\\ D^{86}&D^{85}&1\\ D^{97}&D^{9}&D^{106}\\ D^{90}&D^{145}&D^{177}\\ D^{204}&D^{168}&D^{40}\end{bmatrix}.\]  
Using the distance spectrum estimation algorithm in [17], each of them contains five codewords with the minimum weight 24. Applying Lemma 1 to each of the above three codes, we find that the five codewords calculated in [17] are all base structured ones. In other words, these LDPC-CCs do not contain any non-structured codewords of weight 24. Compared to the situation of the (21,3,5) code, the reason that the (21,3,5) code contains a non-structured codeword of weight 24 can be explained by the difference in the super codes.  
Splitting the polynomial syndrome former matrix, we obtain following super codes  
\[\begin{bmatrix}1&D^{35}\\ D^{8}&D^{45}\\ D^{19}&D^{13}\\ D^{57}&D^{30}\\ D^{33}&1\end{bmatrix},\begin{bmatrix}1&D^{28}\\ D^{7}&D^{101}\\ D^{63}&D^{81}\\ D^{58}&D^{72}\\ D^{18}&1\end{bmatrix}\text{and}\begin{bmatrix}1&1\\ D^{86}&D^{85}\\ D^{97}&D^{9}\\ D^{90}&D^{145}\\ D^{204}&D^{168}\end{bmatrix}\]  
for the (57,3,5), (126,3,5), and (204,3,5) Tanner LDPC-CCs, respectively. By computer search, each of the above super codes contains 10 codewords of weight 6. Confirmed by Lemma 1, all of them are base structured codewords. The weight 24 structured codewords of the (57,3,5), (126,3,5), and (204,3,5) codes can be derived by summing the periodically shifted weight 6 base structured codewords of the corresponding super code.  
Compared to the case of the (21,3,5) code, the (57,3,5), (126,3,5), and (204,3,5) codes do not have any non-structured codewords of weight \(24\). It is due to the elimination of non-structured codewordsof weight 6 in the corresponding super code. Based on this observation, we conclude that when designing LDPC-CCs eliminating low weight non-structured codewords in the super codes can decrease the number of low weight non-structured codewords in the mother code.
## 4 Results and Discussion
### Results of the Landslide Susceptibility Mapping
Figure 5b revealed that no landslide is located within the very low susceptibility areas; 15% of the landslide are situated within the low susceptibility areas; 26% within the moderate suitability areas; 30% within the high susceptibility areas; 30% within the very high susceptibility areas. The result of the landslide assessment highlighted that the majority of the occurred landslides  
Figure 5: Landslide susceptibility map by the VR model and RIRA method: (**a**) landslide susceptibility classes in the Yangou watershed, (**b**) distribution of susceptibility over the landslide at susceptibility map (%), and (**c**) histogram of landslide susceptibility index value. (60%), are situated within the zones of high and very high susceptibility, while the part of landslide volume was accounting for 87% of the total landslides. Landslide susceptibility is predicting the probability of landslides' occurrence regardless of their magnitude which is, however, a critical piece of information in mass movement erosion control. However, the previous study revealed small and medium mass movements often have much higher transport rate of debris than large ones [21]. Both the volume and frequency of landslides were considered in our study; the landslide mitigation and control areas can be further prioritized in the watershed from the map. The high and very high susceptibility area must receive higher priority in the watershed for large landslides, while the low susceptibility risk area can be excluded in the observation. Moreover, the area of low and medium susceptibility can be paid more attention to, as well as the soil erosion caused by landslides. The results of a zoning plan can also reduce the workload of field investigations and move the focus of the actual work in watershed management.