esa-sceva/satcom-chunk-collection huggingface dataset:
## The Methodology of Numerical Modeling
### Validations for the Chosen Models  
A terse illustration of the validations performed for the three models is as follows. Firstly, the initial tube temperature distributions obtained from the FVM model were validated against temperature measurements using Type-K thermocouples in an in-house test bench [32]. The validations were carried out with a 120 kW self-recuperative burner operating in flameless combustion mode under on/off firing. The air-ratio for all the experiments was set at 1.1. A maximum quantitative difference of 3% was observed between temperatures on the radiant tube surface obtained from the simulation and the experiment. Furthermore, the maximum difference of the calculated energy flows from the measured values was less than 5%, except in the case of the wall losses. The reported deviations were accounted for in the simplifications of the FVM model, chosen to keep computational times practicable. Secondly, the obtained final tube temperature distributions from the CHT model were validated against temperature measurements in the same in-house test bench [35]. The cooling sections of the test bench were set up to represent a strip, having a width of 825 mm and a uniform temperature of 600 \({}^{\circ}\)C, passing on both sides of the radiant tube. The calculated temperatures from the CHT model deviated a maximum of ca. 5.3% quantitatively from the measurements. In addition, a small qualitative shift of the temperatures on the tube's first leg was observed, in the direction away from the burner opening.  
Finally, the FEM model was validated against deformation measurements for the case of an isolated tube, by means of the point- and profile-laser sensors [30; 31]. The calculated integral longitudinal tube deformations deviated less than 5% from the measured data. The local longitudinal deformation results from the FEM model also confirmed the measurements on the tube. This supported the fact that the creep model used as part of the FEM model ensures repeatability of results. As the creep model [34] was derived empirically by means of isothermal and anisothermal creep tests on lab-scale specimens, no additional validation of the creep model was necessary.  
It could be noted that a detailed mesh sensitivity analysis was not carried out within the scope of this work. However, the numerical results were validated against the corresponding experimentally-derived data for a wide range of burner operating conditions. This indicated that the selected discretizations in the FVM, CHT, and FEM models were of sufficient quality.
# Breast Cancer Diagnosis Using YOLO-Based Multiscale Parallel CNN and Flattened Threshold Swish
## 2 Material and Methods
### Experimental Setup and Implementation  
The experiments were conducted on a Windows 10 computer equipped with an Intel Xeon E5-2643 3.3 GHz CPU, 32 GB of RAM (Intel, Santa Clara, CA, USA), and an NVIDIA Titan X GPU with 12 GB of memory (NVIDIA, Santa Clara, CA, USA). For model weight optimization, we utilized the adaptive moment estimation method, setting the momentum at 0.9 and the decay at 0.0005. The batch size was set to 32. The network underwent training for 145 epochs, starting with a learning rate of 0.001. This rate was specifically chosen based on our experimental conditions and the datasets used. Notably, we adjusted the learning rate to 10% of its initial value at the 20 th and 70 th epochs to optimize training efficacy. To gauge the lesion detection proficiency of our model, we measured the mean Average Precision (mAP) at an Intersection over Union (IoU) threshold of 0.5.
# Calculation Method of Earth Pressure Considering Wall Displacement and Axial Stress Variations
###### Abstract  
Current earth pressure calculation methods suffer from certain limitations because they do not consider the effect of retaining wall displacement. In this study, the soil behind the wall is assumed to be in a plane strain state, and drawing upon nonlinear elastic constitutive theory, an earth pressure calculation method is proposed, capable of considering both axial stress and wall displacement. To account for changes in soil modulus with confining pressure, the tangent modulus from the Duncan-Chang nonlinear model is introduced. Depending on the direction of the principal stress behind the retaining wall, the static earth pressure point, the major principal stress inflection point, and the minor principal stress second inflection point are determined. The conditions for the existence of the second inflection point are also given. These specific points, together with the limit earth pressure point, divide the earth pressures acting on the wall into six regions. The study provides earth pressure calculation formulas for T (translation) mode, RBT (rotation about a point below the base) mode, and RTT (rotation about a point above the top) mode based on the characteristics of wall displacement distribution in each mode. The proposed method exhibits good agreement with the test results, offering an effective approach for accurately calculating earth pressures related to displacement.  
wall displacement; earth pressure; axial stress; special earth pressure point; displacement mode +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Virtual Material-Based Acoustic Model
#### 2.1.1 Equivalent Shear Modulus  
The ultrasonic attenuation generated from friction or adhesion between contacting asperities has a significant effect on the accuracy of acoustic model. The contacting asperities of the whole rough surface are misaligned to some degree, which gives rise to the frictional tangential motions and adhesion of contacting asperities and contribute to the ultrasound attenuation at the interface. The shear modulus \(g\) of the single asperity can be expressed as [15]  
\[g=\frac{16G}{\pi}\Bigg{(}1-\frac{0.6S_{y}}{0.25}\frac{k_{e}}{E}\sqrt{\frac{ \beta}{\sigma}}\Bigg{)}^{\frac{1}{3}} \tag{7}\]  
\[k_{e}=\frac{3\pi\int_{0}^{\infty}(z_{n}-h_{n})\phi_{n}(z_{n})dz_{n}}{4\int_{0} ^{\infty}(z_{n}-h_{n})^{3/2}\phi_{n}(z_{n})dz_{n}} \tag{8}\]  
where \(S_{y}\) is the yield strength, \(\beta\) is the asperity radius of curvature, \(h_{n}\) is the ratio between \(h\) and \(\sigma\) (the standard deviation of the surface height distribution). \(\phi_{n}\) (z\({}_{n}\)) is the normalized probability density function of height distribution, which can be expressed as  
\[\phi_{n}(z_{n})=\frac{1}{\sqrt{2\pi}}\bigg{(}\frac{\sigma}{\sigma_{s}}\bigg{)} exp\Bigg{(}-\frac{1}{2}\bigg{(}\frac{\sigma}{\sigma_{s}}\bigg{)}^{2}z_{n}^{2} \Bigg{)} \tag{9}\]  
where \(z_{n}=z/\sigma\) is the non-dimensional parameter, \(\sigma_{s}\) is the standard deviation of asperity heights distribution. The ratio of \(\sigma/\sigma_{s}\) for the isotropic rough surfaces with a Gaussian distribution of surface heights is  
\[\sigma/\sigma_{s}=n\beta\sigma/\sqrt{\left(n\beta\sigma\right)^{2}-\gamma} \tag{10}\]  
Figure 2: Rough surface deformation under asperity interactions.  
where \(\gamma\) is a constant with \(\gamma=3.71693\times 10^{-4}\), n is the asperity distribution density, and  
\[\sigma=\sqrt{m_{0}}\;\;n=\frac{m_{4}}{6\pi\sqrt{3}m_{2}}\;\;\beta=0.375\sqrt{ \frac{\pi}{m_{4}}} \tag{11}\]  
where \(m_{0}\), \(m_{2}\), \(m_{4}\) are the spectral moments of the rough surface.  
Equation (8) can be numerically calculated with \(h_{n}\) varying in the range of [0, 3] to cover all levels of real contacts. Since \(h_{n}\) is affected by asperity interactions, the premise of achieving \(k_{e}\) solution is to obtain \(h_{n}\) under different loads.  
Accordingly, the shear modulus of the virtual material can be obtained by integrating the single asperity shear modulus with the number of contact asperities and is expressed as  
\[G_{0}=N\int_{h_{n}-d_{n}}^{\infty}g\phi_{n}(z_{n})\mathrm{d}z_{n} \tag{12}\]  
where \(N=nA_{n}\) is the total number of asperities with the mean plane of the asperity heights taken as the reference plane, \(A_{n}\) is the nominal contact area, \(d_{n}=d_{d}/\sigma\) is the dimensionless distance between the mean of asperity heights and that of surface heights. \(d_{d}\) is given by [17,21], and \(d_{n}\) can be expressed as  
\[d_{n}=\frac{4m_{2}}{\sigma\sqrt{\pi m_{4}}} \tag{13}\]
# A Low-Power WLAN Communication Scheme for IoT WLAN Devices Using Wake-Up Receivers
## 6 Simulation Results and Discussion
### Discussion on the Strengths and Weaknesses of the Proposed Scheme  
The main strength of the proposed scheme is that it efficiently reduces the overall delay for the massive uplink transmissions of IoT devices with a wake-up receiver in IEEE 802.11 ba. The overall delay reduction of massive uplink transmission can be achieved with the joint design of the UL MU OFDMA and wake-up receiver of the proposed scheme. The proposed group transmission scheduling scheme and modified EDCA parameters also provide more delay reduction and power reduction when they are working together with UL MU OFDMA.  
The main weakness of the proposed scheme is that it increases power consumption. In the case of UL MU OFDMA, power consumption increased due to increased packet transmission time by using the narrow band uplink channels. Moreover, if STAs have additional uplink data after performing UL MU OFDMA transmission in massive uplink transmission, they need to perform contention procedures for channel access, which results in increased collision probability leading to more power consumption for the additional transmission.
## 3 Energy-Efficient aDaptive hiEarchical and RobusT Architecture  
EDETA (Energy-efficient aDaptive hiEarchical and robustT Architecture) is a routing protocol originally proposed for WSN [26, 27] and recently adapted to UWSN [4]. It is a hierarchical protocol and nodes arrange themselves in clusters with one of them acting as a cluster-head (CH). The CHs form a tree structure between themselves in order to send the collected and aggregated data from the other nodes to the sink in a multi-hop manner. The protocol supports more than one sink in order to provide more scalability and some fault tolerant mechanisms.  
Operation of the EDETA protocol is divided into two phases, (i) the initialization phase and (ii) the normal operation phase. During the initialization phase, clustering is done and cluster-heads are elected. During the normal operation phase, the nodes send their data periodically, at their scheduled times, to their CHs. Finally, cluster-heads send their data to their parents until the data reaches the sink.  
In this protocol, similarly to the LEACH election mechanism [28], the CH election mechanism is distributed among the nodes in the network. Moreover, in order to distribute the energy consumption, the network structure is broken up after a certain amount of time and the initialization phase starts again. An enhanced version of EDETA, called EDETA-e (EDETA-enhanced), also allows the designers of the network to accurately plan and choose nodes acting as CHs. In this variant of the protocol the initialization phase is done only once.  
The CH election is based on a random number that must be lower than a calculated threshold, \(T(n)\), calculated by Equation (1):  
\[T(n)=\frac{c}{|N|-2c}\times\alpha,\,n\in N \tag{1}\]  
where \(c\) is desirable number of clusters in the network, \(N\) is the set of nodes in the network and \(\alpha\  
If the number randomly generated is lower than the calculated threshold, a node can become a CH only if its remaining energy is greater than value calculated by Equation (2):  
\[E(n)=E_{T}\times\frac{2T_{Config}}{2T_{Config}+MAX_{Rounds}T_{SuperFrame}},\,n\in N \tag{2}\]  
where \(E_{T}\) is the average of the remaining energy of the CHs around the node and \(T_{Config}\), \(T_{SuperFrame}\) and \(MAX_{Rounds}\) are protocol parameters that will be discussed later in Section 3.1.  
EDETA is a time-constrained protocol. As shown in the next section, the different phases of the protocol are time constrained. This way, EDETA can be used in applications in which time is an important variable.
# Real-Time Penetration Recognition Based on Voltage Signal in K-TIG Welding
## 3 Experimental Results and Discussion
### Complete and Incomplete Joint Penetration under Steady State  
In this paper, we obtained complete joint penetration and incomplete joint penetration welding seams and tracked the characteristic signal strength in the frequency range of 36-37 kHz by adjusting the welding parameters. To this end, welding experiments are conducted and the morphology of the front and back sides of the welding seams is examined and presented in Figure 6a. The welding parameters are adjusted to achieve complete joint penetration at a guaranteed welding speed of 240 mm/min using a welding current of 550 A, followed by incomplete joint penetration using a welding current of 480 A. The voltage time-domain signals collected under two different penetration conditions remain stable, with no visible changes in both. The power spectral tracking graphs of the calculated characteristic signals are plotted and shown in Figure 6b.  
Figure 5: Selection of FFT calculation window and step size.  
Figure 6: Complete and incomplete joint penetration under steady state. (**a**) Welding test plate front and back weld beads; (**b**) monitoring of characteristic signal strength during the welding process.  
From Figure 6, it can be observed that the characteristic signal strength in the frequency range of 36-37 kHz in the welding voltage frequency domain signal collected under the two experimental conditions rapidly increased from the arc starting period and then remained relatively stable until it decreased to 0 at the arc ending moment. When the welding melt state did not change during the experiment, the characteristic signal strength remained relatively stable, whether it was in complete joint penetration or incomplete joint penetration. A significantly lower overall strength of the characteristic signal was observed under the incomplete joint penetration state when compared to the characteristic signal strength interval collected under the complete joint penetration state. In-depth research on this characteristic will be conducted in this paper.
# Fine-Scale (10 m) Dynamics of Smallholder Farming through COVID-19 in Eastern Thailand
## 2 Materials and Methods
### Data  
#### 2.2.1 Field Data  
In summer of 2023, we conducted a field survey in all seven provinces of the study area to acquire crop types and their spatial location. We applied the stratified random sampling strategy, where field samples were randomly distributed across the agricultural lands. Due to logistical challenges, the samples were slightly adjusted to ensure that they were within walking distance to a local road for easy access. Each sample point was located within at least a 10 m \(\times\) 10 m area of a single crop type or a significantly dominant crop type, which was consistent with the spatial resolution of the satellite imagery used in this study (see Section 3.2). A total of 886 sample points were collected, predominantly consisting of major crop types, namely paddy rice (84), durian (57), eucalyptus (69), oil palm (85), pineapple (58), sugarcane (47), cassava (53), rubber plantation (82), mangosteen (29), and coconut (22). Additionally, samples were gathered for urban (61), water (40), forest (33), grass/shrub (39), bare ground (51), and others (76). Please refer to Section 2.3.1 for details of the classification scheme. Overall, the number of samples for each crop type roughly corresponded to its spatial coverage in the study area. However, we intentionally included additional samples for certain crops due to their high variation in species type or plantation style. For instance, Thailand boasts various durian types, such as Mon Thong, Cha Nee, and Kan Yao. Although durian's spatial coverage is much lower than several other major crops, we added extra samples to capture its variation. See sample location in Figure 2 and field photos in Figures 3 and 4. The samples also included less-prominent crops, as well as non-agriculture land cover types (e.g., impervious surface and water). To accurately identify crop types in such a diverse tropical environment, we capitalized on both high-resolution Google Earth@ satellite and street view imagery, as well as informal interviews with local farmers. This ensured that our samples were representative of all major crops, which were not only diverse in species type, but also showed high variation in plantation age or the crop growing stage.  
Figure 3: **(a1)** A mature durian tree; **(a2)** durian fruit; **(b1)** a flooded rice paddy plantation; **(b2)** rice; **(c1)** a eucalyptus tree; **(c2)** eucalyptus tree seeds; **(d1)** an oil palm plantation; **(d2)** bright red fortail palm fruit that grows on oil palm; **(e1)** a pineapple plantation; and **(e2)** pineapples. Please see the other five crop types in Figure 4.
# Diagnosing and Balancing Approaches of Bowed Rotating Systems: A Review
## 2 Materials and Methods
### Balancing/Correcting
#### 2.2.2 Modal Balancing Method  
On the other hand, the modal balancing approach has attracted more attention in balancing bowed rotor systems. [PERSON] [69] applied the modal balancing approach for a multi-bearing rotary system (consisting of fluid-film bearing). It was claimed that bowed systems with ball bearings, and also with fluid film bearings, can be balanced with this procedure. Impacts of the number of measuring points, and also their location on the balancing procedure, were investigated.  
[PERSON], [PERSON], and [PERSON] [65] balanced a warped rotary machine using the modal balancing method analytically and experimentally. Balancing a bowed rotor was performed according to the net whirl instead of the total whirl and beyond the fourth flexural (critical) speed.  
To balance a bowed rotor system, [PERSON] et al. [70] presented a complex modal balancing procedure. Although a mathematical model is required to obtain the system's modal parameters, the method's main advantage was claimed to be the lack of the need for trial runs. A working steam turbine generator and a gas turbine system with computer-generated response data were both used to test the proposed process. Three scenarios were assumed for the balancing process. First, the initial bow and single mass unbalancing behaved as a tantamount imbalance. Second, the system dynamic response at the balance velocity was subtracted from the low-level pace in the runout. Finally, the balancing procedure was completed at the critical speed by using the neighboring speed.  
[PERSON], [PERSON], and [PERSON] [71] used the model discussed in [72] for the distributed unbalance to balance a bowed rotor system at its first bending critical speed. They employed the modal balancing procedure, a single trial run, and a single balancing plan. Knowing the amplification factor at a critical speed, the modal correction mass required to balance a rotor at its first bending critical speed was calculated.
# NOMA-Based Rate Optimization for Multi-UAV-Assisted D2D Communication Networks
## 5 Simulation Experiment and Result Analysis
### Analysis of Simulation Results
Figure 11: Comparison of system throughput with and without D2D clusters for different numbers of sets. To further illustrate the effect of trajectories on system throughput, Figure 14 shows the throughput versus the number of training sets under different trajectory designs. It can be seen that the system throughput under 3D trajectory optimization is better than that of 2D trajectory optimization, randomly deployed trajectories, and circular trajectories. This result indicates that 3D trajectory optimization is able to adapt more flexibly to the complexities in the environment, thus improving the spectrum utilization and throughput of the system. Compared with other trajectory design approaches, 2D trajectory optimization improves throughput to some extent, but it fails to take full advantage of the flexibility of the spatial dimension due to its restriction to the plane, resulting in a relatively small increase in throughput, and the average increase in system throughput for 3D trajectories over 2D trajectories is 27%. Random deployment trajectories and circular trajectories do not need to consider trajectory optimization, so convergence can be reached very quickly, and the throughput performance of both of them is smoother and lower, mainly due to the fact that these two trajectory designs fail to be effectively optimized for the distribution of users, which further proves the validity of the three-dimensional trajectories of the researched UAVs. Figure 12: Single-drone 3D flight trajectory map. Figure 13: Multi-drone 3D flight trajectory map. Figure 15 shows the system throughput for different numbers of drones. In general, more drones can provide more services to users and a higher system and rate can be obtained. In the single-UAV case, convergence is easily reached. Furthermore, in terms of the increase in system throughput, there is an average increase of 43% in system throughput when increasing from two drones to three drones, while there is an average increase of 18% when increasing from three drones to four drones. This result indicates that from three drones onward, the increase in system throughput gradually decreases as the number of drones increases. This is because when the number of UAVs exceeds a certain threshold, resource and interference constraints in the system gradually become apparent, leading to a gradual decrease in the gains from more UAVs. The simulation results show that three UAVs can improve the system throughput while avoiding the marginal effect caused by too many UAVs. Figure 14: Comparison of system throughput with different trajectory designs. Figure 15: Comparison of system throughput with different numbers of UAVs.
## 3 System Model
### Problem Formulation  
Thus, the joint gateway and controller placement problem can be formally defined as follows. Given the GEO satellite \(s\) and the set of ground nodes \(\mathcal{U}\) with \(k\) satellite gateways and \(m\) SDN controllers for deployment, we aim at determining the optimal placement of gateway \(\mathcal{W}=\{w_{1},w_{2},\ldots,w_{k}\}\subseteq\mathcal{U}\) and controller \(\mathcal{C}=\{c_{1},c_{2},\ldots,c_{m}\}\subseteq\mathcal{U}/\mathcal{W}\), so as to maximize the average network reliability with \(L_{\text{cst}}\) as the network latency constraint, namely,  
\[\max_{c\in\mathcal{C},w\in\mathcal{W}}~{}~{}R_{avg}, \tag{8}\]  
\[\text{s.t.}~{}~{}L_{avg}\leq L_{\text{cst}},\,w\in\mathcal{W}, \tag{9}\]  
\[\sum_{j\in\mathcal{C}}c_{j}=m, \tag{10}\]  
\[\sum_{j\in\mathcal{C}}u_{ij}=1,~{}~{}\forall i\in\mathcal{U}, \tag{11}\]  
\[u_{ij}\leq c_{j},~{}~{}\forall i\in\mathcal{U},j\in\mathcal{C} \tag{12}\]  
\[\sum_{j\in\mathcal{C}}w_{lj}=1,~{}~{}\forall l\in\mathcal{W}, \tag{13}\]  
\[w_{lj}\leq c_{j},~{}~{}\forall l\in\mathcal{W},j\in\mathcal{C} \tag{14}\]  
\[u_{ij},w_{lj},c_{j}\in\{0,1\},~{}~{}\forall i\in\mathcal{U},l\in\mathcal{W},j \in\mathcal{C} \tag{15}\]  
The constraints of this MILP model can be divided into three categories: latency-related, placement-related, and numeric-related. Among them, Equation (9) is latency-related, which means that the average network propagation latency should meet the latency constraints. Besides, the ensuing constraint Equations (10)-(14) are placement-related where Equation (10) means there are exactly \(m\) controllers deployed separately in the network; Equation (11) means that each switch is assigned to one controller; Equation (12) means there is no control path between node \(i\) and \(j\) without a controller placed at node \(j\); Equations (13) and (14) mean similar constraints on gateways; finally, Equation (15) is the numeric constraints.  
However, finding the joint placement solution with maximized reliability is NP-hard, which means there is likely no polynomial-time algorithm that can guarantee an optimal solution. Therefore, we considered a heuristic approach by incorporating the network partition scheme to ameliorate the problem.