esa-sceva/satcom-chunk-collection huggingface dataset:
# Building Damage Assessment Based on Siamese Hierarchical Transformer Framework
## 4 Discussion
### Limitations  
Although SDAFormer has shown superior performance in the experiments, it still has two limits.  
* First, with the process of urbanization, it is important to assess urban disaster-affected areas. However, it is still difficult to comprehensively assess the damage of the multi-story buildings for our proposed framework. The information of a building from the satellite image is limited to roofs, while damages on walls cannot be effectively detected. In some cases, these tall buildings are projected into irregular shapes due to the satellite imaging angle, and the sides of the buildings are shown in the satellite images, which may lead to ambiguity of the network. In addition, the shadows of the buildings may change with the satellite acquisition time. However, in the xBD dataset, most of the building objects are low buildings, which weakens the model's ability to detect tall buildings.
* Second, the training samples are imbalanced in terms of damage levels and the disaster categories, as shown in Table 2 and Figure 12. The imbalanced training set may affect the training process of the network. Due to the difficulty in acquiring paired high-resolution pre-disaster and post-disaster remote sensing images, the xBD dataset is the first building damage assessment dataset with high-quality annotations. Therefore, it requires a large amount of work to expand the size of the building damage assessment dataset.
# Inverse Modeling of Seepage Parameters Based on an Improved Gray Wolf Optimizer
## 3 Inverse Model of Seepage Parameters  
### The Objective Function  
The aim of the inverse model is to determine the hydraulic conductivity for each partition while minimizing the value of the objective function. The objective function was constructed by hydraulic head and leakage in this paper, improving the reliability of the simulation results. Suppose that the hydraulic conductivity of each medium is isotropic. Denote by \(\mathbf{K}=[k_{1},k_{2},\ldots,k_{n}]\) the combination of hydraulic conductivity, in which \(k_{i}\) represents the hydraulic conductivity of the \(i\)th media. The number of piezometers and measuring weirs are indicated by \(m\) and \(n\), respectively. \(\mathbf{H}=[H_{1},H_{2},\ldots,H_{M}]\) is expressed as a sequence of hydraulic head measurements. Similarly, \(\mathbf{Q}=[Q_{1},Q_{2},\ldots,Q_{N}]\) is a series of leakage volume measurements. \(H_{i}(\mathbf{K})\) and \(Q_{j}(\mathbf{K})\) are the simulated hydraulic head and leakage volume by finite element method. The mathematical model for the inverse problem is established, as shown in Equation (13).  
\[\begin{split} minf=\left(\sum\limits_{i=1}^{M}\frac{\|H_{i}(\mathbf{K })-H_{i}\|_{2}^{2}}{\|H_{i}\|_{2}^{2}}\right)^{\frac{1}{2}}+w\left(\sum \limits_{i=1}^{N}\frac{\|Q_{j}(\mathbf{K})-Q_{j}\|_{2}^{2}}{\|Q_{j}\|_{2}^{2}} \right)^{\frac{1}{2}}\\ \text{s.t.}\ \mathbf{K}_{min}\leq\mathbf{K}\leq\mathbf{K}_{max}\end{split} \tag{13}\]  
Here \(\mathbf{K}_{min}\) and \(\mathbf{K}_{max}\) are the lower and upper bounds of hydraulic conductivity values, respectively. The range of hydraulic conductivity can be roughly determined by geological data and engineering experience. \(w\) is a weight factor for balancing the hydraulic head and the leakage volume. In this paper, the simulated leakage value is estimated by the flow rate and area of the overwater cross-section. It is suggested that the value of the weighting factor is set small considering an error between the simulated value and the measurement at the shoulder part of the dam. [PERSON] [23] compared the relative errors of hydraulic head and leakage volume at different weights. The results show that the simulated values are in good agreement with the measurements, and the minimum value of relative error is reached at the condition of \(w=0.02\). The finding was applied in this paper.  
The objective combination of hydraulic conductivity was obtained by the searching process. The search process was accelerated by IGWO. The objective combination of hydraulic conductivity was applied to simulate the seepage field of the dam during operation.
# A Low-Profile HF Meandered Dipole Antenna with a Ferrite-Loaded Artificial Magnetic Conductor
## 3 Experimental Verification
### Experimental Results of the Designed Antenna with the AMC  
In order to verify the performance of the designed antenna, the receiving power capabilities of the proposed antenna with the AMC and the reference monopole antenna [28] are measured and compared. Figure 11 shows the receiving power measurement setup. The distance between the receiving antennas (the proposed antenna with the AMC and the reference monopole antenna) and the transmitting antenna (MP1 DXTR80, SUPER ANTENNA [29]) used in this experiment is 130 m. The receiving antennas are located on the rooftop of a ground vehicle. The receiving power of the reference monopole antenna was measured with a \(1000\times 1000\) mm\({}^{2}\) ground plane. The ground plane was implemented using copper tape on top of Styrofoam. The proposed antenna with the AMC was also on Styrofoam to protect it and the ground vehicle. The transmitting power was generated by a signal generator (E4438C, Keysight) and the receiving power was analyzed by a signal analyzer (FSV signal and spectrum analyzer, Rohde & Schwarz).  
Figure 12 shows the measured receiving power results of the proposed antenna with the AMC and the reference monopole antenna. The transmitted power was set to 17 dBm and the power was transmitted from 20 MHz to 30 MHz at 2 MHz intervals. From these results, it can be confirmed that the received power of the proposed antenna with the AMC is higher than that of the reference monopole antenna on the HF band of 20-30 MHz.  
Figure 10: Simulated realized gain on the \(+z\)-axis of the meandered dipole antenna with the AMC.
## 3 Results and Discussion
### Fixed-Bed Column Adsorption Studies  
The breakthrough curve obtained for the Dowex 21K XLT was similar for nitrate and phosphate and the column was saturated after 45 h (Figure 4). However, for sulphate it was not saturated as the resin had a higher adsorption capacity for this anion compared to phosphate and nitrate. The breakthrough adsorption capacities for nitrate, phosphate and sulphate calculated from the breakthrough curves were 13.3, 14.9 and 29.7 mg/g, respectively. The order of the adsorption capacities obtained in the column experiment is the same as that in the batch experiment (sulphate > phosphate > nitrate).
# Combining Optimization and Simulation for Designing a Robust Short-Sea Feeder Network
## 4 Optimization and Simulation Model
### Performance-Improving Strategies
#### 4.3.1 Enhancing Route Robustness  
The first type of strategy tries to enhance the routes' robustness during the route generation procedure, i.e., before starting the optimization-simulation process. Additional slack (or idle time) is added to some or all of its legs when generating routes. By doing so, a vessel might still be able to synchronize in an ocean hub, even if it has to slow down due to harsh weather. Conversely, adding too much slack can cause excessive waiting times and, as such, increase the operational cost for the vessels. Therefore, determining the amount and place of additional slack while ensuring a sufficient robustness level is an important trade-off to assess. This strategy is comparable to the current practice of airlines where delayed routes can recover at night when no flights are scheduled and the network resets [36].  
Figure 5: Flow chart of the simulation model. The solutions provided by the master problem are simulated week-by-week over a long period using historical data. When a violation is detected, a penalty cost is added to the operational costs of the solution.  
Similarly, the robustness of the routes can be enhanced by considering seasonality in the schedules. Due to the difference in weather conditions between summer and winter, it might be beneficial to operate dedicated routes for each season. However, the system needs to operate the same fleet of mother and daughter vessels during the whole year, regardless of the season.
## 5 Performance Evaluation and Analysis
### Network-Related Performance Measure
#### 5.2.2 Relation of Overhead and Recovery Time  
To figure out the relation of _Overhead_ and _Recovery Time_, we calculate how many messages can pass to a given link. According to the variables definition in Table 7, N ants (packets or messages) occur per cycle. Considering the round trip of the packet and assuming that request packet size and response packet size are the same, the message flows twice in total. So, _the number of messages per second_**M** can be written as (13):  
\[\text{M}=2\text{NC}^{-1} \tag{13}\]  
The _Link Overhead_ can be easily obtained by multiplying **M** by the size of the packet **T** as (14):  
\[\begin{array}{l}\text{O}=\text{TM}\\ =2\text{TNC}^{-1}\end{array} \tag{14}\]  
Figure 18: Ant Colony System, Source Update. (**a**) Time series graph; (**b**) Histogram.  
It is not easy to calculate (14) because the size of the packet \(\mathbf{T}\) is variable. But the upper bound of _Link Overhead_\(\mathbf{O}_{\mathrm{upper}}\) can be easily obtained by substituting MTU from \(\mathbf{T}\).  
_Recovery Time_\(\mathbf{R}\) can be expressed using ACO parameters listed in Table 7. First, _Link-error detection time_\(\boldsymbol{T_{e}}\) and _Shortest Path Search time_\(\boldsymbol{T_{s}}\) are random variables. Both _Link-error detection time_ and _Shortest Path Search time_ can be expressed in multiples _cycle_. This is because the computation time of the ACO algorithm is proportional to the cycle. \(\boldsymbol{T_{e}}\) and \(\boldsymbol{T_{s}}\) are expressed as  
\[T_{e}=\mathrm{pC} \tag{15}\]  
\[T_{s}=\mathrm{qC} \tag{16}\]  
Also (14) can be re-expressed as (17) using (15) and (16):  
\[\mathrm{R}=(\mathrm{p+q})\mathrm{C} \tag{17}\]  
where \(\mathbf{p}\), \(\mathbf{q}\) are discrete random variables depending on the selected ACO algorithm. Finally, (18) can be obtained by multiplying the (14) by the (17):  
\[\mathrm{O*R}=2\mathrm{TN}(\mathrm{p+q}) \tag{18}\]  
Even the values of \(\mathbf{p}\), \(\mathbf{q}\) are random variables, but they can be treated as constants since they are very small in the measurement. Therefore, the right side of Equation (4) can be treated as a constant. From Equation (4), we can see that the overhead increases exponentially with decreasing Recovery Time.
## 2 First-Order Element as a Basic Part of the Network  
In the first phase of the research, the first-order analog element was selected as the fundamental network component. It was characterized by two outputs: low-pass and high-pass. This can be described by the following system of equations:  
\[T\cdot f_{T}^{-1}(t)\cdot\frac{dy(t)}{dt}+y(t)=k_{\infty}\cdot f_{k}(t)\cdot x (t)z(t)=T_{g}\cdot\frac{dy(t)}{dt}, \tag{1}\]  
where:  
\(x(t)\)--input signal subjected to filtration;  
\(z(t)\)--high-pass output signal;  
\(y(t)\)--low-pass output signal;  
\(T_{d}\)--time of derivative (defines the lower limit of the high-pass frequency);  
\(T\)--time constant (defines the upper limit of the low-pass frequency);  
\(f_{T}^{-1}(t)\)--function varying the time constant coefficient;  
\(f_{k}(t)\)--function varying the gain coefficient;  
\(k_{\infty}\)--gain coefficient with the time approaching infinity.  
The results of the preceding research [13; 14; 15] have shown that filters with time-varying parameters work undoubtedly better in the time domain than filters with time-invariant parameters. The quality of their operation is determined by the time after which the error value of the output signal is no greater than the assumed accuracy, \(\alpha\). Replacing the time constant with the time function and the gain coefficient with the gain function significantly shortens the duration of the transient state without changing the filters properties in the frequency domain.  
The functions of the gain factor and the time constant are described as follows:  
\[f_{k}(t)=f_{k}(t)\cdot[d_{k}-(d_{k}-1)\cdot h_{k}(t)];\] (  
In order to transform the continuous-time system model mentioned above to the discrete-time system, the bilinear transform was used. The bilinear transformation maps the s-plane (Laplace transform) to the z-plane (Z transformations). Although this transformation is non-linear, it is useful considering that it maps the entire s-plane axis to the z-plane unit circle. After specifying the requirements for the designed digital filter, using the applicable mapping, the proper equation forms were obtained. Obtained equation:  
\[y(n)=b_{0}x(n)+b_{1}x(n{-}1)-a_{0}y(n)-a_{1}y(n{-}1), \tag{4}\]  
where the low-pass filter coefficients are:  
\[\begin{array}{c}a_{0}=2{\cdot}T+T_{f};\\ a_{1}=2{\cdot}T-T_{f};\\ b_{0}=k{\cdot}T_{f};\\ b_{1}=k{\cdot}T_{f};\end{array}\]  
and the high-pass filter are:  
\[\begin{array}{c}a_{0}=2{\cdot}T+T_{f};\\ a_{1}=2{\cdot}T-T_{f}\\ b_{0}=2{\cdot}T{\cdot}k;\\ b_{1}=2{\cdot}T{\cdot}k.\end{array}\]  
The next step was to implement prepared elements and connect them into the network. Simulations were carried out in order to analyze operation of the network, its speed, and its effectiveness.
# Performance Analysis of BDS-5G Combined Precise Point Positioning
## 3 Results and Discussion
### Dynamic PPP
#### 3.3.2 Geometric Configuration  
Figures 13 and 14 show the results obtained for 5G base stations with different geometrical configurations participating in the dynamic PPP under Scenes 1 and 2 (from top to bottom, for XYZ directions and tropospheric zenith wet delay). Figure 15 shows the statistics of the mean convergence time and the STD of the errors for the six initializations in a day.  
As shown in Figures 13 and 14, the various configurations had significant advantages in terms of the convergence speed in their dominant directions, and convergence was achieved in a short time. Again, in Figure 15, there was a significant improvement in the STD of the errors after convergence. Scenes 1 and 2 showed that the error STD of various configurations in their corresponding dominant directions was reduced to within 2 cm.  
From this, we can conclude that in the case of dynamic PPP, the introduction of 5G dominant geometry can also achieve very fast convergence in the dominant direction and obtain a low-error STD. Especially in the case of a small number of satellites, dynamic PPP is difficult to converge at times, or there is a large error after convergence. At such times, the introduction of 5G observations can accelerate the convergence and correct the positioning error.
## 3 System Architecture, Software Services and Deployment
### Data Engineering on the Fog Layer
#### 3.3.1 Generic Data Processing  
Once the stream processing pipeline is formally established, all inbound data are processed based on a predefined set of data processing functions. This practice renders edge devices as the \"primary\" processing points for all sensor-oriented data, triggering a series of steps that effectively handle and extract all meaningful information from the recently obtained datasets. The first step of the overall data processing pipeline is to track the existence of improper, fragmented or erroneous data that will adversely compromise the coherence of any time series. It is critical that outliers are identified (i.e., by checking new observation points that appear to be somehow irrelevant to normal values). The origin of these outliers often derives from transmission errors, which are sometimes frequent on the sensor device, but should be deleted on time to not \"pollute\" the dataset. The second step applies an ongoing, but predefined time window that allows averaging and smoothing out potential fluctuations. The third step is introduced to handle temporary sensor device disconnections, which may lead to missing values and consequently disrupted datasets. Moreover, a dedicated algorithm for identifying data gaps and inserting mean values for complementing the datasets is also implemented.
# Numerical Simulation-Based Study of Controlled Particle Deposition Technology for Wafer Surfaces
## 3 Results and Discussion
### Aerosol Sample Monodispersity  
In this study, SEM was used to verify the monodispersity of the PSL suspension. The suspension was first diluted to a specific concentration, after which 1 mL was dispensed onto a clean wafer surface. Following air-drying in a cleanroom, SEM images were conducted to traceably validate the particle sizes of the dried solids. Figure 9 illustrates the SEM characterization results for four characteristic particle sizes: 70 nm, 100 nm, 140 nm, and 200 nm. The scanning results show that the particle sizes of all samples align with their nominal values, with an error of less than 0.5%. The particle size distribution meets traceability requirements, demonstrating the suitability of the suspension as a raw material for preparing monodisperse aerosols and enabling the generation of initial aerosols in the 70-200 nm range.  
As illustrated in Figure 10, the positions of the prominent peaks in the particle size spectra align with the expected particle sizes. However, additional peaks appear at smaller particle sizes. This phenomenon is related to the charge balance of the monodisperse aerosol sample.  
Taking the 200 nm monodisperse aerosol as an example, when the X-ray neutralizer reached charge equilibrium, the aerosol contained 200 nm particles carrying one, two, or three charges. The corresponding electrical mobility (Z) follows the relationship \(Z_{1}<Z_{2}<Z_{3}\), meaning that particles with more charges exhibit higher electrical mobility. Since electrical mobility determines how particles are classified in the SMPS, multiply charged larger particles appear at positions corresponding to smaller single-charged particles, leading to additional peaks in the SMPS results [22].  
According to the Boltzmann charge equilibrium principle, larger particles have a higher probability of carrying multiple charges. As a result, the secondary peaks become more pronounced for 140 nm and 200 nm aerosol samples. Additionally, for the 70 nm and 100 nm aerosols, peaks also appear at sizes larger than the prominent peaks. This may be  
Figure 8: Deposition distribution contours for different-sized particles.
## 4 Discussion  
Due to the underwater environment, the sub-sea tunnel's initial support and secondary lining may bear a certain water pressure. However, determining the water pressure has always been a problem for researchers [29; 30; 31]. When the outer layer of the tunnel structure is permeable, the tunnel structure bears hydrostatic pressure. Many studies show that the water pressure borne by sub-sea tunnels is not hydrostatic pressure and is far less than hydrostatic pressure [32; 33]. Practice shows that if the total hydrostatic pressure were loading the tunnels, the lining of many sub-sea tunnels, such as Seikan Tunnel [34], would have been damaged due to excessive stress. In fact, the lining is not damaged. In this way, the water pressure is reduced due to a grouting layer outside the tunnel structure and relatively complete surrounding rock. Therefore, combined with the Qingdao sub-sea tunnel, this paper studies the reduction phenomenon of lining water pressure and puts forward the corresponding reduction coefficient of tunnel lining water pressure for reference. It is worth noting that the proposed pressure reduction coefficients in Tables 4-6 can be used for a horseshoe tunnel only, and with the values that were previously defined for the thickness and permeability of the tunnel support and the water height above the tunnel. Future research can then expand the simulations for other tunnel sections (circular, for instance).
## 4 LoRaWAN Issues and Recent Solutions
### Power Consumption
The drawbacks considered were the size of the beacon (10 bytes) and retransmission times of the packet loss (one time only). In future work, the proposed protocol must be strengthened further by creating personalized retransmission policies. In [33], the authors proposed an optimized energy model for sensor nodes based on LoRa/LoRaWAN technologies. The proposed models were evaluated to reduce the loss of energy by the sensing unit, MCU unit, and transmission unit. The energy of each unit was modeled individually to compare the consumed energy by each part of the LoRa node. The proposed model shows the effect of the selection of hardware and software for the sensor. Additionally, it shows the effect of transmission acknowledgement and the choice of different LoRa/LoRaWAN parameters. Therefore, it is very important to optimize these parameters to reduce the energy consumption of the end devices, such as the spread factor, payload size, bandwidth, and coding rate. The findings showed through the numerical results that with distinct LoRa/LoRaWAN parameters, the consumed energy shifted. Class A was most energy-efficient in LoRaWAN, which is the basis of the model. However, the study only evaluated the power consumption in the hardware parts of the LoRa nodes and did not suggest an approach to enhance the LoRa radio, merely mentioning the effect of ACK and parameter selection. The optimized energy models could be further enhanced in future work by LoRa power management algorithms to optimize the sensor node's lifetime. In [40], FREE is proposed as a fine-grained scheduling scheme for reliable and energy-efficient data collection. FREE computed the transmission schedule as a slot time (i.e., the transmitted data must be buffered in a scheduled time slot instead of transmitted directly). The study maximized the overhead phase, which was necessary to manage the allocated frequency channels, spreading factors, transmission powers, and time slots and synchronize the network. Collisions are the big issue of lost and retransmitted data, resulting in more power loss and representing a bottleneck of the standard LoRaWAN protocol. FREE overcomes this issue by utilizing different spreading factors and grouping acknowledgements to achieve scheduled concurrent transmissions. The solution was simulated by the LoRaSim simulator and compared with other methods for confirmable and unconformable transmissions. The results proved that FREE makes deployment quite scalable, accomplishing this with roughly 100% data delivery and a battery lifetime increased by 2 additional years until it became over a 10-year battery lifetime, regardless of the transmission type or network size. The drawback of this work is the excessive fairness between the devices in terms of power consumed. In order to use the allocations recorded in the previous data sets, this would entail a different allocation method.