esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 System Implementation
### Hardware Installation  
(1) Build a WSN monitoring system server  
We set up a military warehouse WSN monitoring system server in the National Defense University Research Building (located in Taoyuan City), as shown in Figure 11. It comprises the following hardware devices: Microsoft IIS 7.0 web server, and Microsoft SQL 2012 system database server, as well as two types of transmission (Ethernet and GSM transmission modules) to receive the information returned by the remote smart gateway (located in New Taipei City).  
* Server specifications: CPU: Intel(R) Core(TM)2 Duo T9300 @ 2.5 GHz; RAM: 3.00 GB; HDD: 500 GB.
* GSM transmission model specifications: The A8000-GPRS/GSM USB Modern is a GSM/GPRs model designed to realize the advantages of M2M. It supports four communication frequencies (850/900/1800/1900 MHZ).  
Figure 11: WSN monitoring system server.  
Figure 10: The system implementation architecture.  
2 Setup a WSN system in the selected military warehouse  
The hardware included a smart gateway (the exterior and interior of which are shown in Figures 12 and 13) and wireless sensing nodes (Sensors 1 and 2-5), as shown in Figure 14. Sensor 1 is placed at the entrance of the warehouse to collect indoor environmental parameters; Sensors 2-5 are placed in the oil and gas storage tank, and can real-time collect the temperature, humidity, pressure and power of the sensor battery for safe storage of sensitive weapons. The sensors in this system have passed an electromagnetic compatibility test (MIL-STD-461E RE102) [13], transport vibration test (MIL-STD-810E) [14], high-temperature operation test (23 to 60 \({}^{\circ}\)C), and low-temperature operation test (\(-10\) to 23 \({}^{\circ}\)C) by National Chung-Shan Institute of Science & Technology.  
Figure 12: Exterior of the smart gateway.  
Figure 13: Interior components of the smart gateway.  
* Smart gateway configuration: We used ARM9 DMA-2443L as the platform to design and develop the embedded gateway.
* Configuration of sensor nodes: We employed MICRO TECH WAN-1 sensors developed by the National Chung-Shan Institute of Science & Technology. These nodes collect and transmit data from the WSN via mini USB ports.
# Digitalizing Industrialized Construction Projects: Status Quo and Future Development
## 4 Qualitative Analysis
### Construction Phase
#### 4.3.2 Construction Scheme Planning  
Construction scheme planning involves the adept use of computer-aided tools, virtual reality (VR), and other digital technologies to meticulously strategize project delivery. This process entails detailed schedule estimation, risk assessment, performance tracking, and disruption detection. All aim at achieving the visualization, simulation, and optimization of the construction program.  
In developing the construction schedule, numerous influencing factors must be considered. This can be achieved through employing VR and 4D BIM [67]. [PERSON] [68] introduced a framework integrating BIM, sensors, and machine learning to streamline construction scheduling. [PERSON] et al. [69] further devised an interactive computer-aided assembly planning method grounded in a Monte Carlo simulation and logical algorithms. This method accounted for variables such as the number and type of fixed/mobile cranes and their potential locations, ranking each simulation result based on criteria including assembly operation cost, assembly time, and the number of precast elements not yet mounted.  
To consider construction schedule risks, [PERSON] et al. [70] developed a hybrid dynamic model to assess the dynamic impact of the interrelationships of various risk variables on assembly performance. The model reflects the actual project schedule and performs risk analyses for factors such as the inefficient verification of precast elements due to unclear labeling, misplacement due to carelessness, owner crane failure and maintenance, etc.  
To ensure a smooth project delivery, ongoing construction program planning remains imperative even after construction has begun. A digital twin applied to the planning, scheduling, and execution of component assembly can realize real-time monitoring and dynamic control, forming a two-way virtual-physical interaction [71]. Additionally. [PERSON] et al. [72] employed image processing algorithms, in combination with time-lapse photogrammetry, to achieve the on-site installation progress measurement of prefabricated wood buildings. [PERSON] et al. [73] also used computer vision methods to detect real-time construction site disruptions. To sum up, computer vision techniques provide more accurate construction progress monitoring, while digital twin techniques focus on overall construction progress planning and control.
# Research on the Influence Mechanism of Foam Slurry Wall Formation
## 2 Design of the Numerical Simulation Scheme
### Selection of Foam Slurry Proportions  
To investigate the impact of foam slurry proportioning on the formation of foam slurry wall, computational analyses were performed under different swelling ratios and foam concentrations (Table 3). Given that a high bentonite content increases the slurry's density and viscosity, reducing its flowability and potentially causing slurry pump overload [23; 24], the swelling ratio was controlled to be within 10%, with values of 2.5%, 5%, and 10%. Based on preliminary tests, when the foam concentration is between 10% and 30%, the foam structure is compact and has a longer half-life (i.e., better stability). Therefore, in the computational analysis, foam concentrations of 0% (pure bentonite slurry without foam), 10%, 20%, and 30% were selected. It should be noted that to focus on studying the effects of bentonite and foam content on slurry wall formation, the influence of different types and concentrations of foam agents on slurry performance was ignored, and the slurry viscosity was set as a constant.
## 6 Results and Discussion
### Analysis of Delay and Distance  
The analysis of delay and distance using the BEA-SSA method compared to GWO [26], MOFPL [22], PRO, SSA [36], BES [37], ROA, HGS, SSO, RCSO [21], and FCR [20] is shown in Figures 4 and 5. The examination was performed for two sets of nodes (100 and 200). The delay for transferring packets must be the smallest for good performance of the system. Likewise, the distance between the CH and the BS should be less for good performance of the system. Here, the delay is symbolized in seconds. In Figure 4, it is noticed that, as the round count rises, the delay also increases. At first, at 0 th round, the delay for 100 nodes using BEA-SSA is only \(1\times 10^{-7}\). As the round increases, i.e., at the 2000 th round, the delay value for 100 nodes using the BEA-SSA technique is 1.5 \(\times\) 10\({}^{-7}\) s. Similarly, as the round count rises, the distance also increases. At the 0 th round, the distance for 200 nodes using BEA-SSA is 27. As the round increases, i.e., at the 2000 th round, the distance value for 200 nodes using the BEA-SSA technique is 47. Moreover, the BEA-SSA scheme is superior to the compared GWO [26], MOFPL [22], PRO, SSA [36], BES [37], ROA, HGS, SSO, RCSO [21], and FCR [20] models. To choose the best CH in WSN, an improved Deep Convolutional Neural Network (DCNN) predicts the energy level. The RSSI, PDR, and residual energy are all high for the Bald Eagle Assisted SSA (BEA-SSA) model. The WSN uses the BEA-SSA to determine which CH is the best. These developments are attributable to the BEA-SSA-based CH's deployment and the improved energy evaluation and prediction using DCNN. This enhancement is due to the improved energy evaluation and prediction using DCNN.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Metrics** & **BEA-SSA** & **GWO [26]** & **MOFPL [22]** & **PRO** & **SSA** & **BES** & **ROA** & **HGS** & **SSO** & **RCSO [21]** & **FCR [20]** \\ \hline Best & 0.007 & 0.001 & 0.009 & 0.002 & 0.003 & 0.002 & 0.001 & 0.003 & 0.002 & 0.002 & 0.002 \\ Worst & 0.597 & 0.597 & 0.598 & 0.598 & 0.598 & 0.598 & 0.598 & 0.597 & 0.597 & 0.597 \\ Mean & 0.220 & 0.211 & 0.209 & 0.237 & 0.228 & 0.208 & 0.229 & 0.213 & 0.214 & 0.213 & 0.211 \\ Median & 0.170 & 0.159 & 0.161 & 0.217 & 0.196 & 0.158 & 0.199 & 0.167 & 0.167 & 0.167 & 0.162 \\ STD & 0.187 & 0.193 & 0.195 & 0.194 & 0.193 & 0.196 & 0.192 & 0.192 & 0.192 & 0.194 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistical Analysis on BEA-SSA compared with other models for 100 nodes.  
Figure 4: Delay analysis using BEA-SSA compared with other models [20; 21; 22; 26] for node counts of (**a**) 100 and (**b**) 200.  
Figure 5: Distance analysis using BEA-SSA compared with other models [20; 21; 22; 26] for node counts of (**a**) 100 and (**b**) 200.
# Autonomous Mobile Robot Navigation in Sparse LiDAR Feature Environments
## 5 Experimental Results
### Trajectory-Tracking Accuracy Experiment  
This experiment will verify the trajectory-tracking accuracy of the proposed method in this paper. The experimental method sets two preset paths. The first is the Double-L-shaped path, as shown in Figure 11(a), and the second is the S-shaped path, as shown in Figure 11(a). The coordinates reached by the mobile robot during navigation and the trajectory errors of the preset paths are recorded. The experiment is repeated 10 times on each path from the same starting point. The Model Predictive Control (MPC) and the original Pure Pursuit (PP) are used to compare in this paper, as shown in Table 2. Because the starting point is joystick migration, there is a slight artificial error at the starting point, and the error data are calculated after 5 s. The results verified that the maximum error of the improved Pure Pursuit is within 45 mm, with a 77% improvement rate compared to the original Pure Pursuit, while our method has a similar error rate as the MPC method.  
Figure 11: The preprocessing for 2D LiDAR images.  
Figure 10: (a) Mobile robot receives point-cloud range. (b) Point-cloud is drawn on picture.  
Figure 12: The path error comparison between MPC (purple), original PP (blue) and proposed improved PP (green) methods in Double-L-shaped path (red): (**a**) Trajectory comparison chart. (**b**) MPC trajectory error path. (**c**) Original PP trajectory error graph. (**d**) Improved PP trajectory error graph.  
Figure 13: The path error comparison between MPC (purple), original PP (blue) and proposed improved PP (green) methods in S-shaped path (red): (**a**) Trajectory comparison chart. (**b**) MPC trajectory error path. (**c**) Original PP trajectory error graph. (**d**) Improved PP trajectory error graph.
## 3 Simulation and Results  
We consider an 88 m depth waveguide with a negative gradient SSP as shown in Figure 1. It consists of a 10 m isothermal mixed layer with a sound speed of 1533 m/s. The sound speed reduces linearly to 1478 m/s at a depth of 40 m and then remains unchanged until the bottom at 88 m. The bottom sound speed is 1650 m/s, the bottom density is 1.76 g/cm\({}^{3}\), and the bottom attenuation is 0.8 dB/\(\lambda\). We assume that the source speed is \(v_{0}=2.0\) m/s, the range of CPA is \(r_{0}=1000\) m, the time of CPA is \(t_{0}=1000\) s, the source depth is 50 m, a narrowband signal of 350 Hz is emitted, and the depth of the receiver is 70 m.  
Figure 1: Typical shallow-ocean sound field environment for simulation.  
The received sound pressure is calculated using the Kraken normal mode model [19]. The relationship between the real part of the sound pressure and time is shown in Figure 2a. After zooming in to reveal the details, it can be seen from Figure 2b that the real part of the sound pressure exhibits obvious periodicity. According to the above theoretical derivation, this period is related to the average horizontal wavenumber and the radial velocity.  
In the simulation, the observation period is set to be \(T=0.5\,\)s, and Equation (3) is calculated using the 128-point FFT. The horizontal wavenumber spectrum of the first-time step is obtained and shown in Figure 3. It can be seen that the maximum value of the spectrum appears at \(k_{r}=2.651\). We set the average phase velocity to be \(\overline{v}_{p}=1478\,\)m/s and obtain \(\overline{k}=1.4879\,\)m\({}^{-1}\). Then an estimated value  
It can be seen from the above analysis that the range localization result obtained by the simulation data is good. The processing result of the SWellEx-96 experimental data is given below.
## 5 Bio-Phytoremediation by Microorganisms
### Microbias Mechanisms for Desalination  
Microbes, such as fungi and prokaryotes, which belong to the domains bacteria and Archaea, can adapt to a wide variety of changes in external osmolarity. Until recently, it was believed that Archaea (Halobacteria's family) were the only active aerobic heterotrophs at or near NaCl saturation [40]. In addition, the majority of halophyte studies have mainly focused on the physiological and genetic control of salt tolerance [78]. In particular, halophilic bacteria are acclimated to grow appropriately in hypersaline conditions. Certain strategies are used by these bacteria in order to survive and grow. Osmotic imbalance is recognized by an osmosensor, which could be macromolecules performing structural alterations to adapt to the shifting extracellular functions of water or modifications in cell structure [90].  
The first strategy is to accumulate potassium/chloride ions in order to maintain cell intracellular osmotic equilibrium under high-salt environments [91]. This is a frequent adaption in very anaerobic halophilic bacteria [92]. However, the majority of halotolerant and moderately halophilic bacteria employ a second strategy: compatible solute condensation. These complicated solutes (which include sugars, polyols, amino acids, carbohydrates, and their derivatives) may be acquired from the medium or produced by organisms [93].  
Pumping ions out of the cell is another mechanism used to avoid excessive salt amounts in the cytoplasm. Na\({}^{+}\)/H\({}^{+}\) antiporters, such as NhaA, an antiporter found in _E. coli_ and other bacteria [94], help to pump out excess sodium ions [78]. Current analysis of salt-induced alterations in proteome maps showed a greater complexity of microorganism osmolyte-accumulating pathways. For example, under salt stress, _Halomonas_ sp. AAD12 revealed substantial differences in the expression of proteins implicated in osmoregulation, stress reaction, energy production, and transport [95].  
To deal with internal high salt amounts, fungi also accumulate compatible solutes. This has been studied in the salt-sensitive yeast _S. cerevisiae_ and in several halotolerant filamentous fungi; it was shown that the majority of glycerol is generated as salt amounts increase [96]. Numerous _H. werneckii_ genes that react to moderately and excessively high salt quantities were found using subtractive hybridization [97]; 13 of them had no homology to sequences in databases, demonstrating that the black yeast has extremely particular adaptations to hypersalinity.
# Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks
## 4 Experiment
### Artificial Neural Network Data Poisoning Attack
#### 4.3.1 Evaluation of the Artificial Neural Network before a Random Label Flipping Attack  
The neural network model used in this experiment is a feedforward artificial neural network with eight layers and an output layer. The activation function used is ReLU, and the output layer uses the sigmoid activation function. The model is trained using the binary cross-entropy loss function and optimised using the Adam optimiser. The Early Stopping callback is used to avoid overfitting by monitoring the training loss and stopping the training if it does not improve for five consecutive epochs; the results of the model show that it achieves high accuracy on both the training and validation sets, with a training accuracy of 0.994, a validation accuracy of 0.9936, and a test accuracy of 0.866, as shown in Figure 10b. The model was trained for 50 epochs, which was stopped early as the loss did not improve for five consecutive epochs. These results indicate that the model can learn the patterns in the input data accurately and generalise well to unseen data. Figure 10a shows the loss versus validation loss graph.  
Figure 9: (**a**) Logistic regression output with distance-based flipping attack with threshold of 0.5; (**b**) random forest output with distance-based flipping attack with threshold of 0.5; (**c**) support vector machine output with distance-based flipping attack with a threshold of 0.5; and (**d**) XGB output with distance-based flipping attack with a threshold of 0.5.  
The classification report shows the performance metrics of the model for each class and its overall accuracy. Precision is the ratio of true positives to predicted positives, recall is the ratio of true positives to actual positives, and F1 Score is the harmonic mean of precision and recall. Support is the number of instances of each class, 0.0 denotes normal traffic, and 1.0 denotes attack traffic.  
The report shows that the model has a high accuracy of 0.87, meaning it correctly classified 87% of instances. It also has a high F1 Score of 0.86, achieving a balanced performance across both classes, as shown in Figure 10c. And with a higher precision for normal labels than attack labels it made fewer false positive errors for normal labels than class attack labels. However, the model has a higher recall for attack labels than normal labels, and the classification report indicates that the model is effective for network anomaly detection on a non-poisoned dataset.
# A Medical Image Segmentation Network with Multi-Scale and Dual-Branch Attention
## 4 Experiments and Results
### Results
#### 4.3.2 CVC-ClinicDB Dataset Comparison Results  
The results of the CVC-ClinicDB dataset are shown in Table 2, and the qualitative comparative results of the six models are shown in Figure 5. The results show that MSDA-Net showed improved values of ACC, Dice, Precision, and IoU. Compared with UNet, MSDA-Net obtained an improvement of 1% Acc, 3.1% IoU, 1.4% Dice, 0.7% Precision on the CVC ClinicDB-dataset. Additionally, Infer-T reduces the time by 1.12 s compared to UCTransNet. From Figure 5, it can be seen that our proposed method can accurately predict the location and boundaries of intestinal polyp lesions, and its prediction results closely align with ground truth values.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Parameter** & **FLOPs** & **Acc** & **Dice** & **Precision** & **IoU** & **Recall** & **Infer-T** \\ \hline U-Net [11] & 14.75 M & 25.22 G & 0.970 & 0.911 & 0.905 & 0.842 & 0.926 & **1.79 s** \\ U-Net++ [25] & 36.63 M & 105.85 G & 0.971 & 0.907 & **0.930** & **0.894** & 0.904 & 2.86 s \\ MultiResUNet [45] & **7.25 M** & **14.30 G** & 0.966 & 0.896 & 0.926 & 0.822 & 0.885 & 2.36 s \\ AttentionU-Net [12] & 34.88 M & 16.70 G & 0.969 & 0.901 & 0.893 & 0.834 & 0.925 & 3.33 s \\ TransU-Net [46] & 105.28 M & 25.35 G & 0.972 & 0.911 & 0.913 & 0.824 & 0.916 & 4.73 s \\ UCTransNet [47] & 63.36 M & 36.12 G & 0.968 & 0.901 & 0.910 & 0.827 & 0.904 & 4.36 s \\ Ours & 99.47 M & 19.86 G & **0.979** & **0.918** & 0.925 & 0.871 & **0.938** & 3.41 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative comparisons with various models on the 2018 Data Science Bowl dataset.  
Figure 4: Visual comparison of image segmentation results of the 2018 Data Science Bowl dataset.
## 3 Data and Data Processing  
Our study of the Karapinar sub-basin relies on three data types, InSAR, continuous GNSS, and groundwater level. The InSAR data provide high spatial resolution (3-20 m) but moderate temporal resolution (12-16 days) observations of the study area. The continuous GNSS observations were acquired in a single site and provide high temporal resolution of ground movements, which are used to verify the InSAR observations. The groundwater level data were acquired in a single site with high temporal resolution. The groundwater level data were used to reveal the relationship between land subsidence and groundwater.
# PVDF-TrFE Film on PDMS Substrate for Energy Harvesting Applications
## 2 Experimental Details  
### Fabrication of the Flexible Piezoelectric Energy Harvester  
To fabricate the P(VDF-TrFE) films, first the co-polymer powder of P(VDF-TrFE) (50/50, Piezotech) was dissolved in \(N\),\(N\)-dimethylformamide (DMF) solvent. The solution was then stirred for 12 h at 40 \({}^{\circ}\)C to synthesize 13 wt % of P(VDF-TrFE) solution. To make polydimethylsiloxane (PDMS) substrate (3.5 cm (W) \(\times\) 3.5 cm (L) \(\times\) 0.15 cm (H)), the liquid PDMS (weight ratio of elastomer to cross linker used was 10:1) was poured into square mold and then directly degassed to remove bubbles in a vacuum desictor for 1 h. After that, it was heated in air at 100 \({}^{\circ}\)C for 30 min. Ti/Ni (30 nm/150 nm) metal stack (2 cm \(\times\) 2 cm) was deposited at 100 \({}^{\circ}\)C by electron beam evaporation on the PDMS substrate to form the bottom electrode. Heat plays an important role in this fabrication process, as it improves the conductivity (by lowering resistance) when the Ti/Ni metal stack (2 cm \(\times\) 2 cm) is deposited on the PDMS substrate directly. The PDMS substrate, owing to its tensile strength, was stretched while it was exposed to heat (100 \({}^{\circ}\)C), and first metal (Ti, 30 nm) was permeated into the surface of PDMS substrate, and then second metal (Ni, 150 nm) was finally deposited on the Ni/PDMS. Since cooling produces metal layer overlap, it makes the metal stack more stable under bending and vibration conditions from external force.  
Next, a solution of the P(VDF-TrFE) was spin-coated on the PDMS substrate. A thickness of 7 \(\upmu\)m was obtained by rotating the spin coater at 3500 rpm for 60 s. The DMF solvent was evaporated by pre-heating the device at 60 \({}^{\circ}\)C for 10 min. The film was subsequently heated in air at 140 \({}^{\circ}\)C for 2 h. A second layer of Ti/Ni (30 nm/150 nm) was deposited at 100 \({}^{\circ}\)C by electron beam evaporation to form the top electrode. To perform \"poling\" of the P(VDF-TrFE) film, an electric field of 100 MV/m was applied for 1 h to align the dipoles in the film [25]. The electric field of 100 MV/m was reached gradually by increasing the field by 10 MV/m in every 5 min to avoid breakdown due to sudden application of voltage. All the fabrication steps are shown in Figure 1.