esa-sceva/satcom-chunk-collection huggingface dataset:
# Substitution of Pork Fat with Beeswax-Structured Oleogels in Semi-Smoked Sausages
## 3 Results and Discussion
### Physicochemical Characteristics of Semi-Smoked Sausages
\begin{table}
\begin{tabular}{l c} \hline \hline
**Name of Fatty Acid** & **Quantity, \%** \\ \hline
**Saturated fatty acids, \%** & **20.141 \(\pm\) 1.007** \\ \hline C 14:0 myristic & 0.190 \(\pm\) 0.010 \\ \hline C 15:0 pentadecanoic & 0.026 \(\pm\) 0.001 \\ \hline C 16:0 palmitic acid & 12.168 \(\pm\) 0.608 \\ \hline C 17:0 margarine & 0.027 \(\pm\) 0.001 \\ \hline C 18:0 stearic & 7.114 \(\pm\) 0.356 \\ \hline C 20:0 arachidonic & 0.074 \(\pm\) 0.004 \\ \hline C 23:0 tricosanic & 0.540 \(\pm\) 0.027 \\ \hline
**Monoussaturated fatty acids, \%** & **17.248 \(\pm\) 0.862** \\ \hline C 16:1 (cis-9) palmitoleic & 0.037 \(\pm\) 0.002 \\ \hline C 17:1 (cis-10) margarineoleic & 0.016 \(\pm\) 0.0004 \\ \hline C 18:1 (cis-9) oleic & 17.101 \(\pm\) 0.855 \\ \hline C 20:1 (cis-11) eicosene & 0.094 \(\pm\) 0.005 \\ \hline
**Polyunsaturated fatty acids, \%** & **62.611 \(\pm\) 3.131** \\ \hline C 18:2n6c linoleic & 62.568 \(\pm\) 3.128 \\ \hline C 18:3n6Y-linolenic & 0.044 \(\pm\) 0.002 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The fatty acid composition of oleogel. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Indicator** & **Control** & **OG7** & **OG10** & \(p\)**-Value** \\ \hline Moisture, \% & 55.26 \(\pm\) 0.86 & 57.23 \(\pm\) 0.92 & 56.80 \(\pm\) 1.14 & \textgreater{}0.50 \\ \hline Protein, \% & 19.81 \(\pm\) 0.35 & 19.52 \(\pm\) 0.23 & 19.49 \(\pm\) 0.31 & \textgreater{}0.25 \\ \hline Fat, \% & 18.05 \(\pm\) 0.27 & 17.90 \(\pm\) 0.33 & 17.83 \(\pm\) 0.17 & \textgreater{}0.25 \\ \hline Carbohydrate, \% & 3.25 \(\pm\) 0.35 & 3.26 \(\pm\) 0.11 & 3.30 \(\pm\) 0.15 & \textgreater{}0.25 \\ \hline Ash, \% & 2.46 \(\pm\) 0.04 & 2.39 \(\pm\) 0.03 & 2.47 \(\pm\) 0.04 & \textgreater{}0.25 \\ \hline Energy value, kCal/kj/100 g & 254.62/1065.33 & 252.18/1054.36 & 251.58/1050.18 \\ \hline pH & 6.17 \(\pm\) 0.09 & 6.08 \(\pm\) 0.10 & 6.13 \(\pm\) 0.08 & \textgreater{}0.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Physicochemical properties of semi-smoked sausages expressed as percentages (mean values \(\pm\) standard deviation). The pH value of the sausages varied from 6.08 to 6.17. The experimental sausages with oleogels showed a slight deviation in pH value compared to the control sample (\(p<0.05\)). Other studies on the partial replacement of pork fat with structured vegetable oils in meat products also did not show significant variations in pH [33; 55; 57].
## 3 Challenges and the State of the Art Progresses
### Architectural Choices behind Performance and Security Concer
#### 3.3.1 Public vs. Private/Federated  
Public blockchain, also called permissionless blockchain, allows anyone to participate without permission. The consensus protocols of public blockchain are often based on PoW, PoS or the hybrid. With public blockchain, anyone can run a public node on its own device, join the network, and participate in the transactions. Transactions in public blockchain are transparent but anonymous/pseudonymous, and can be read by any participant in the chain.  
Private blockchain, also called permissioned blockchain, is used in a single organization to process transactions internally. In private blockchain, read permissions can be public or granted to a restricted group of participants, but write permissions are only granted to the organization. Private blockchain works in a trusted environment, and is much faster and cheaper than public blockchain because only the owner keeps the write permissions and no consensus has to be reached. Thus, it is much less secure and closed as compared to public blockchain and suffers from the security breaches as that of a centralized system.  
Federated blockchain or blockchain consortium is opposed to public blockchain and very similar to private blockchain, but there is a significant difference. Federated blockchain operates under the leadership of a group instead of a single organization, where multiple entities establish a decentralized system as that in public blockchain. Similar to private blockchain, write permissions are only granted to trusted entities and others are not allowed to participate in the process of verifying transactions. The read permissions can be public or restricted to the participants. Therefore, instead of only one organization being in charge of private blockchain, there are multiple entities working together to make the system more distributed. Thus, federated blockchain is faster and more scalable, and provides more transaction privacy compared with public blockchain; and it is more decentralized and secure compared with private blockchain. Federated blockchain is mostly used in the banking sector.  
We summarize the comparison of public blockchain with private/federated blockchain in Table 3.
# Building Damage Assessment Based on Siamese Hierarchical Transformer Framework
## 2 Methods  
### Overall Architecture  
The overall architecture of the proposed SDAFormer is shown in Figure 1, which consists of two stages: building localization (Stage 1) and damage classification (Stage 2). In Stage 1, a branch of SDAFormer, the segmentation network based on Swin Transformer, is used for building localization. This branch uses only the pre-disaster images as input and generates the building location masks. In Stage 2, both the pre-disaster and post-disaster images are fed into the siamese SDAFormer branches, respectively. Damage assessment is performed in Stage 2. Each branch of SDAFormer constitutes a Swin Transformer-based U-Net-like network. The weights are initialized from the trained weights of Stage 1 and shared between the two branches to better exploit the correlation between the pre- and post-disaster images.  
Figure 1: An overview of the SDAFormer framework. It is composed of two stages: (**a**) Stage 1: building localization, (**b**) Stage 2: damage classification.
## 2 Theoretical Analysis
### Algorithm for Damage Localization  
In this study, a novel algorithm with multiple BP-ANNs is proposed to discern between damaged and undamaged regions of a bridge. The comprehensive workflow of the damage localization algorithm is depicted in Figure 3, with each step elaborated as follows:  
**Step 1: Dataset Acquisition**  
For a newly constructed or recently maintained bridge, sensors are strategically placed on several key cross-sections. These sensors are capable of measuring vital parameters such as displacement, inclination, strain, or acceleration. However, in this particular study, we have elected to utilize only inclinometers for our analysis.  
**Step 2: Data Reconstruction for Multiple Intact Bridge Cross-Sections**  
We focus on two critical cross-sections, particularly those near the bridge columns, as their health condition can be more readily monitored compared to other sections. Multiple BP-ANNs are established using the method described in Section 2.3. The transfer matrices for these two base sections are simulated using these BP-ANNs.  
**Step 3: Data Reconstruction for the Damaged Bridge**  
Equipped with the simulated transfer matrices from Step 2, an online damage detection and localization system can be effectively constructed. The responses at the same cross-sections are continuously reconstructed by multiplying the simulated transfer matrices with the measured responses from the two base cross-sections on the damaged bridge.  
**Step 4: Damage Localization through Comparative Analysis**  
The reconstructed responses at multiple cross-sections are then compared against the actual measured responses. As detailed in Section 2.2, significant divergences between the reconstructed and measured responses may indicate the presence of damage at a particular cross-section.  
\begin{table}
\begin{tabular}{c c} \hline \hline First hidden layer & 128 neurons \\ \hline Second hidden layer & 32 neurons \\ \hline Batch Size & 100 \\ \hline Epoch & 500 \\ \hline Active function & ReLU \\ \hline Learning rate & 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configurations of the BP-ANN.  
Figure 3: Flowchart of the proposed algorithm (CS is short for “responses measured at a cross-section”).  
In contrast to existing studies that rely on influence line functions, our proposed algorithm utilizes traffic loading-independent mode shape functions. This innovation allows for online monitoring under real traffic conditions with multiple vehicles, thereby eliminating the need for the restrictive single-vehicle inspection approach. Furthermore, our method surpasses existing research that depends on mode shape identification by reducing the need for a large number of sensors and a high signal-to-noise ratio (SNR). Additionally, our approach is founded on general physical properties of bridges, making it more accessible and broadly applicable to practical engineering scenarios. It is anticipated that the proposed method will offer greater convenience and versatility for real-world online bridge monitoring systems.
# Modeling and Parameter Calibration of Morchella Seed Based on Discrete Element Method
## 3 Results and Discussion
### Discussion  
The seeds of morel mushrooms have the characteristics of soft surface and adhesive flow, involving complex interaction environments. This study used a combination of theoretical, simulation, and experimental methods, including \"experimental determination of seed material properties\", \"discrete element simulation of seed group contact parameters\", \"quadratic regression model study of contact parameter significance\", and \"physical experiments for reliability verification\", to calibrate the three most significant influencing parameters of morel seeds on the angle of repose: seed\(-\)seed rolling friction coefficient, seed\(-\)seed surface energy, and seed\(-\)steel rolling friction coefficient. The results showed  
Figure 7: Test results: (**a**) Measured repose angle. (**b**) Simulated repose angle.  
similar patterns to those of [PERSON] et al. (2020), [PERSON] et al., and [PERSON] et al. (2019); [PERSON] et al. (2020). However, comparative studies have found that unlike wheat seeds, rapeseed seeds, etc., the static friction coefficient between morel seeds has no significant effect on the angle of repose, which can provide a reference for studying particles with surface softness and adhesive flowability, and further improve the regularity model of particle contact parameters.
# Analysis and Application of Lining Resistance to Water Pressure in Tunnel through Karst Cave
## 2 Failure Mode of Grouting in Tunnels Crossing High-Pressure Karst Caves
### Analysis of Tunnel Safety Thickness for Different Calculation Cases
#### 3.2.3 Analysis of Safety Thickness of the Composite Structure  
Unlike hydraulic tunnels, railroad and highway tunnels also need to maintain a dry and water-free operating environment. Taking the New Yuanliang Mountain Tunnel side-passing cavern as an example, when there is no remaining surrounding rock between the tunnel expansion and the cavity, only the grouted stone body resists the water pressure in the cavity; when the rainy season comes, the water pressure in the cavity may increase. In order to ensure the safety of construction, in addition to grouting reinforcement outside the outline of the tunnel expansion, it was also necessary to first apply a temporary second lining structure; at this time, the temporary second lining, the initial support, and the grouted stone body were considered as a composite structure, and the resistance to water pressure was studied. In the numerical calculation model, the thickness of the grouted stone body was 5 m, the thickness of the primary support was 0.2 m, and the thickness of the second lining was varied as 0 m, 0.4 m, 0.8 m, 1.0 m, and 1.2 m. The stratigraphic parameters, model excavation, and cavity water pressure application methods used in the calculations were as described above.  
Numerical simulations were conducted to study the hydraulic pressure resistance of the composite structure at different secondary lining thicknesses, and the results are shown in Table 2. Comparing the new results with the old results, it can be found that the composite structure could be increased from 0.35 MPa to 0.68 MPa by applying only the initial support structure in the case of 5 m thickness of the grouted stone body at the tunnel periphery, which was 1.94 times higher than before. The application of initial support and secondary lining can greatly improve the water pressure resistance of the tunnel structure and grouting reinforcement circle. It can be found that a larger lining thickness led to a stronger composite structure resistance to water pressure; when the secondary lining thickness reached 1.2 m, the composite structure's resistance to water pressure could be increased to 4.44 MPa.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Thickness of Secondary Lining (m)** & **0** & **0.4** & **0.8** & **1.0** & **1.2** \\ \hline Critical water pressure (MPa) & 0.68 & 1.51 & 2.30 & 3.56 & 4.44 \\ \end{tabular}
\end{table}
Table 2: Water pressure resistance of composite structures with different second lining thicknesses.  
Figure 17: Trend of pressure with grout thickness at different locations in the numerical model: (**a**) characteristics of water pressure variation with grouting thickness; (**b**) characteristics of earth pressure variation with grouting thickness.
## 2 Architecture and Operational Analysis  
### Architecture of Intersections  
In the traffic management process at adjacent intersections with different cycles, the offset between the two intersections exhibits orderly variations as the signal cycle progresses. Such variations lead to periodic fluctuations in traffic flow between the two intersections, severely affecting traffic flow. It also causes vehicle queue length and delay time to increase significantly, further aggravating traffic congestion. It is necessary to avoid the offset between the two intersections falling into mismatched ranges to achieve optimized coordinated control. At the same time, the number of queued vehicles and those arriving before the queue dissipates in the through lanes of the downstream intersection needs to be reduced to minimize the waste of coordinated green light time. This, in turn, can improve the efficiency of the through traffic in the transition section.  
This study selected two adjacent intersections in Fuzhou, China, as the research subjects: Pushang Avenue and Hongwan Middle Road, as well as Pushang Avenue and Jianxin Middle Road (see Figure 1). The chosen survey date was 20 September 2023, and the survey times were during the morning peak from 7:30 to 8:30 and the evening peak from 17:30 to 18:30. During the survey period, there were no traffic accidents on the roads or in the surrounding areas.  
The architecture of the adjacent intersections analyzed in this study is shown in Figure 2. The architecture involves two intelligent traffic signals that use advanced 5G communication and perception technologies to obtain real-time information on the traffic signal phase, traffic flow, and road environment for the two intersections. Using data fusion, analysis, processing and interaction, and information exchange between vehicle-to-vehicle and vehicle-to-road, active control and guidance can be achieved. The main process involved collecting and preprocessing traffic information using radar-camera-integrated devices, traffic signal controllers, and roadside RSUs. The data were then transmitted to a cloud platform for analysis and optimization. Strategies such as extending green light durations, dynamically adjusting phase offsets, and guiding vehicle speeds were used to generate signal control and speed guidance plans. The signal control plan was implemented by adjusting signal timing via an upper-level computer, and the speed guidance plan was communicated to on-board units (OBUs) or roadside displays through vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication systems optimizing traffic coordination and improving operational efficiency.
## 4 Results and Discussion  
### Comparison of the Dielectric Constants Estimated by Different Methods  
The results of the dielectric constants of the asphalt concrete layer by the Coring Method, Reflection Amplitudes Method, and The Common Mid-Point Method are shown in Table 3. For the Coring method, the dielectric constant calculated ranged from 4.55 to 5.67 with an average value of 4.94. The coefficient of variation was 8%.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Test Content**} & \multicolumn{3}{c}{**Density (g/cm\({}^{3}\))**} \\ \cline{2-4}  & **Sample 1** & **Sample 2** & **Sample 3** \\ \hline Test Point 1 & 2.34 & 2.39 & 2.34 \\ Test Point 2 & 2.37 & 2.41 & 2.44 \\ Test Point 3 & 2.29 & 2.50 & 2.43 \\ Test Point 4 & 2.37 & 2.51 & 2.54 \\ Test Point 5 & 2.50 & 2.44 & 2.42 \\ Mean of Test Points & 2.37 & 2.45 & 2.44 \\ Coring Sample & 2.35 & 2.36 & 2.35 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Density calibration results of the non-nuclear density gauge.  
Figure 5: Location and pavement structure of the field test. (**a**) Location and structure; (**b**) Test area.  
Based on the Reflection Amplitudes Method, the dielectric constant ranged from 2.56 to 6.73 with an average value of 3.96. And the coefficient of variation was 39%, considerably compared to the Coring Method. In general, the dielectric constant calculated by the Reflection Amplitudes Method are lower than the coring method [2]. The highest error between the dielectric constant, determined from the Coring method and the Reflection Amplitudes Method for each sample, occurred at sample 2 with \(-\)53%.  
The dielectric constant calculated using the Common Mid-Point Method, ranged from 4.83 to 5.48 with an average value of 5.01. The coefficient of variation was 5%. Compared to the dielectric constant results from the Coring Method, the highest error occurred at sample 4 with 7%.  
As listed in Table 2, compared to the three calculation methods, the Reflection Amplitudes Method had the highest coefficient of variation when analyzing all five points. The Coring Method and the Common Mid-Point Method had similar average values and coefficient of variation for calculating dielectric constants of the asphalt concrete layer. The Coring Method was the most accurate way because it had a real parameter from the cores. However, the Coring Method was also inefficient with coring in the test area.  
According to the results of calculation, the Common Mid-Point Method showed reasonable agreement with cores and acquired dielectric constants of the asphalt concrete layer in all the areas. In this study, the Common Mid-Point Method was then recommended.
# Precision Agriculture: A Remote Sensing Monitoring System Architecture +
## 4 The Network Layer  
### The Link Layer  
In precision agriculture WSN communication protocols and technologies are used to support the connection between sensor nodes in the network and to provide a channel for communication between the coordinator node and the gateway. According to the type of application, such as precision farming, field irrigation management or greenhouse crop management, the sensor network topology and communication demands may differ. Hardware and software characteristics may also affect the choice of communication technology to be used between the nodes. Each node uses a routing protocol [18] in the view of transferring the data collected to the coordinator node.  
Based on many experimental studies on agricultural fields, there is not an ideal combination of a specific communication technology and a routing protocol. Discovering and keeping up with routes in WSNs is a quite demanding task since energy restrictions and alterations in node status, such as failure may cause sudden changes in the network topology. It is a fact that the wireless routing solution for agriculture applications should be highly energy-efficient, scalable, and autonomous. Up until now routing tactics proposed in the literature for WSNs employ specific methods such as data aggregation, clustering, different node role assignment and data-centric methods.  
The basic goal is to build each smart monitoring system upon application appropriate networking technologies to operate efficiently with minimum energy consumption. Once the coordinator node obtains the data it forwards the flow of information to the gateway to reach the main server, where the database is located. However, in some cases the coordinator node can be substituted by a base station to obtain the collected data using a Wi-Fi connection as presented in [2], or another cellular communication technology.
# High-Resolution and Wide-Swath 3D Imaging for Urban Areas Based on Distributed Spaceborne SAR
## 2 Materials and Methods
### Range Ambiguity Resolution Algorithm
#### 2.3.2 Range Ambiguity Calculation  
The ratio of the echo intensity of the ambiguity signal to the echo intensity of the main signal is an important index for measuring the range ambiguity of the system, which mainly comes from the ambiguity signal with a difference between the sum of the distance from the same target to the main satellite and the sub-satellites is equal to the integer multiple of the PRF. The geometric diagram of range ambiguity is shown in Figure 6.
###### Abstract  
This study aims to explore the methodology for assessing landslide susceptibility by using machine learning techniques based on a geographic information system (GIS) in an effort to develop landslide susceptibility maps and assess landslide risk in the Yiyang region. A landslide dataset in Yiyang was constructed after 16 landslide predisposing factors were identified across four categories, topography, geology, environment, and hydrometeeorology, through factor state determination and multicollinearity analysis. A Blending ensemble model was created and achieved higher prediction accuracy by fusing predictions from Random Forest, CatBoost, and XGBoost with logistic regression used as the meta-learner, thus deriving the importance coefficients of the landslide predisposing factors and their contribution rates. The Blending ensemble model achieved high predictive accuracy with an AUC value of 0.8784, demonstrating balanced and stable performance characteristics. With the addition of the rainfall factor, the AUC value of the Blending ensemble model has increased by 0.1199. In combination with the information value method, this model was applied to assess landslide susceptibility and rainfall-induced landslide risks in Yiyang City, demonstrating its validity. In addition, experimental validation confirmed the prediction and evaluation accuracy of the GIS-based Blending ensemble model. Results showed that the frequency ratio (FR) of historical landslide occurrences in high-susceptibility and extremely high-susceptibility zones in Yiyang City exceeded 1, indicating strong consistency between the landslide risk classification and actual distribution of historical landslides. The landslide susceptibility maps created for Anhua County, Heshan District, and Taojiang County in Yiyang City may provide support for the early warning and prevention of landslides and land-use planning in this region. The proposed methodology may be of reference value for improving natural disaster prevention and risk management.  
landslide susceptibility; GIS (geographical information system); predisposing factors; blending ensemble model; machine learning +
Footnote †: journal: Applied sciences  
Article