esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[18] studied the law of surface subsidence caused by curved shield tunneling based on theory. [PERSON] et al. [19] studied the characteristics of additional stress caused by curved tunneling based on Mindlin theory. [PERSON] et al. [20] studied the influence of excavation parameters on surface subsidence under small radius curved tunnel excavation through theoretical methods and numerical simulations. [PERSON] et al. [21] established a prediction formula for land subsidence caused by shield excavation with different radii based on numerical simulation and field data. It can be seen that most scholars [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13] focus on the research of straight line tunnels, and some scholars [14; 15; 16; 17; 18; 19; 20; 21] focus on the research of surface settlement and the influence of shield parameters on surface settlement for curved tunnels. However, there are few studies on the lateral and longitudinal deformation of the surface during the excavation of curved tunnels, and very few studies on surrounding rocks and segments. Therefore, it is necessary to study the lateral and longitudinal deformation of the ground surface and the mechanical characteristics of surrounding rock and segments in the process of curved tunnel excavation. In view of this, relying on a small radius curve tunnel in a certain section of Changsha Metro Line 6, as shown in Figure 1, this paper establishes a numerical model considering the over-excavation inside the curve section, analyzes the law of ground deformation caused by shield excavation, and discusses the law of construction parameters on ground settlement, surrounding rock deformation, and segment stress. The research results provide theoretical guidance for the construction of this project.
## 3 Materials and Methods
### Data Pre-Processing and Generated Dataset
```
1: Let time_drift = 3
2: Add action = NOTACTION and confidence = 100 parameters to user events dataset
3:foruser = 0, 1, 2,...,N from users do
4:iffirst_timestamp_event of user < first_timestamp_physical then
5: Assign events variable to the events collection with a timestamp \(\geq\) first_timestamp_physical of user
6: Assign _phstacks_ variable to all the physical activities of user
7:elseiffirst_timestamp_event of user > first_timestamp_physical then
8: Assign _phstacks_ variable to the physical activities collection with a timestamp \(\geq\) first_timestamp_physical of user
9: Assign _events_ variable to all the events of user
10:else
11: Assign events variable to all the events of user
12: Assign _phstacks_ variable to all the physical activities of user
13:endif
14: for event = 0, 1, 2,...,M from events do
15: Let timestamp_event variable the value of event timestamp
16: Let p_phys_acts_coll all the physical activities between timestamp_event-time_drift and timestamp_event + time_drift
17:if p_phys_acts_coll size \(\geq\) 0 then
18: Checks all the candidates and select those that satisfy Min ([timestamp_event-timestamp_phy])
19:if Selected candidates size \(\geq\) 1 then
20: Selected the candidate that satisfies Max (count(candidate\(\in\) candidates))
21:endif
22: Assign the action and confidence parameter from the best candidate find to the events dataset
23:endif
24:endif
25:endfor
```  
**Algorithm 2** Combining smartphone events and physical activity  
After combining physical activities as events captured by the mobile device, \"summary cycles\" were computed that grouped the counts of all events and actions given during a lock or unlock. During this process, several metrics were generated, such as the cycle time, number of production type events, and average typing time. In the following sections, we provide additional details about the mechanisms that utilize the generated dataset.
# Routing Schemes in FANETs: A Survey
## 3 Taxonomy
### Challenges  
There are three main challenges that must be addressed in routing for FANETs in order to improve routing performance, such as higher QoS performance and route setup success rate, as well as lower energy consumption and number of clusters (see Section 3.5):  
* High dynamicity. In FANETs, the highly dynamic network topology, as a result of high mobility, causes low link quality. Consequently, there are frequent link disconnections and network partitions, which increase route discovery and maintenance, resulting in lower routing performance. Various mobility models (see Section 2.4) of UAVs has been designed to investigate routing [104].
* High cost. In FANETs, frequent route updates, route discovery (or to reestablish routes), route maintenance (as well as packet retransmission) due to route failure, can incur three main types of costs: (a) routing overhead (or inefficient resource utilization); (b) energy consumption; and (c) computational cost due to the time incurred for route discovery and maintenance. Multi-UAVs can be deployed to increase connectivity among UAV [7].
* Low residual energy. In FANETs, UAVs, which are powered by battery, have limited energy (a) to perform routing (i.e., route updates, discovery, and maintenance); (b) to retransmit packets due to route failure; and (c) to support long transmission range due to the comparatively long distance between UAVs. Meanwhile, UAVs with higher payloads increase energy consumption [7].
# Preparation and Characterization of Gold Nanorods Coated with Gellan Gum and Lipoic Acid
## 1 Introduction
From the structural point of view, LA is characterized by an intracycle disulfide group capable to interact with the gold surface via gold-sulfur chemistry [25; 26] and an ionizable carboxylic terminal, that confer good overall hydrophilicity. AuNRs-based drug delivery systems may be properly stabilized by small molecules, nevertheless, to increase cytocompatibility and drug loading capacity, a biocompatible polymeric coating is usually adopted. Polymeric hydrophilic or amphiphilic materials have been widely explored as AuNPs capping agents. Among them are not only synthetic polymers such as polyethylene glycol PEG-SH [27] and polyaminoacidic structures [15] but also natural polymers such as proteins [28] and polysaccharides [14]. Gellan gum (GG) is a linear polysaccharide consisting of a tetrameric repeating unit of ((D-Glucose)-(D-Glucone)-(L-Rhamnose)), with a negative net charge. It is synthesized by Sphingomonas elodea and it displays water solubility and in vivo biodegradability [29]. GG is a food additive approved by Food and Drug Administration (FDA) [30] and, as a polysaccharide, is characterized by the presence of carbohydrate units rich in hydroxyl groups able to establish hydrogen bonds with different classes of molecules [31]. This work aimed to test promising natural materials as coating agents of gold nanorods, focusing on how the choice of the material, as well as the coating strategy, could influence physicochemical properties and PTT efficacy of the final nanostructure. Concretely, three types of coated gold nanorods were prepared using lipoic acid (LA) and gellan gum (GG) as capping agents. In particular, AuNRs/LA, AuNRs/LA,GG, and AuNRs/GG-LA were produced using, respectively, the small disulfide molecule LA, a layer by layer coating of LA and polysaccharide chains of GG, or the covalent derivative obtained from esterification between GG and LA as stabilizing agents. The prepared AuNRs have been characterized by evaluating their stability in aqueous medium over time and monitoring possible aggregation through UV-VIS spectrometry. The size and surface properties as well as hyperthermia abilities after NIR irradiation were then investigated and finally, biological studies were performedon human colorectal cancer cell line (HCT116), to assay coated nanorods cytocompatibility and hyperthermia effect in vitro. The results obtained were thus compared to select the most promising candidate for potential use in cancer PTT.
# A Learning-Based Image Fusion for High-Resolution SAR and Panchromatic Imagery
## 3 Results and Discussion
### Additional Dataset  
One additional dataset was included to verify the robustness of the proposed method. The area is St. John's, Newfoundland and Labrador, which is located along the Atlantic Ocean and mainly contains the water, grass, barren land, forest, and developed structures, and the panchromatic and SAR images are acquired from the GeoEye-1 and TerraSAR-X sensors. The GeoEye-1 image was acquired on 19 August 2019; it has a 0.46 m spatial resolution and 11-bit radiometric resolution. The TerraSAR-X was acquired on 8 August 2019; it was obtained in Staring SpotLight mode with a 0.8 m \(\times\) 0.25 m spatial resolution, an ascending orbit, and HH polarization. The preprocessing was performed in the same way as that for the previously used dataset, elucidated in the previous sections, and the additional experiments were performed on two sites with a subset of 1500 \(\times\) 1500 pixels. The additional experimental images and
###### Abstract  
Separation and high recovery factor of proteins similar in molecular mass is a challenging task, and heavily studied in the literature. In this work, a systematic study to separate a binary protein mixture by charged ultrafiltration membranes without affecting membrane performance was carried out. \(\alpha\)-lactalbumin (ALA, 14.4 kDa) and \(\beta\)-lactoglobulin (BLG, 18.4 kDa) were used as a binary model system. These two proteins are the main proteins of whey, a very well-known byproduct from the dairy industry. Initially, a systematic characterization of individual proteins was carried out to determine parameters (protein size and aggregation, zeta potential) which could influence their passage through a charged membrane. Then, the influence of operating parameters (such as initial protein concentration, pH, and critical pressure) on the UF process was investigated, so as to identify conditions that limit membrane fouling whilst maximizing protein recovery factor and purity. The study permitted to identify process conditions able to fully separate ALA from BLG, with high purity (95%) and recovery factor (80%), in a single UF step. Compared to studies reported in literature, here, the main approach used was to carry out a charged UF process far from proteins isoelectric point (pI) to limit protein aggregation and membrane fouling.  
charged ultrafiltration membranes; \(\alpha\)-lactalbumin; \(\beta\)-lactoglobulin; protein fractionation; protein electrostatic interaction; protein ultrafiltration; fouling  
Received: 16 July 2021  
Accepted: 24 September 2021  
Published: 2 October 2021  
Published: **Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
# Coupled Vibration Analysis of Multi-Span Continuous Cable Structure Considering Frictional Slip
## 4 Engineering Examples  
A double-tower, three-span cable hoisting system is taken as an engineering example, as shown in Figure 10. There is a fixed pulley on the top of each of the two towers, and the pulley can slide on the main cable during construction with considering sliding friction. The coordinates of the nodes are I(0, 43.038), J(96.1, 0), KL(96.1, 95.6), K(589.05, 3), KR(589.05, 95.6), L(712.2, 29.516). The main cable adopts nine \(\varphi\)62 steel cables with a tensile strength of 1870 MPa, an area of 0.016272 m\({}^{2}\), a weight per unit length of 1.497 kN/m and a modulus of elasticity of 110 GPa. The cable tower is made of Q345b steel, with a modulus of elasticity of 206 GPa. The designed maximum hoisting weight is 150 tons, and the crane weighs 8 tons. The original length of the middle-span cable is 494.8918 m, and the horizontal force is 2105.3 kN. The original length of the left-span cable is 109.4220 m. The original length of the right-span cable is 139.6430 m. According to the wire cable manufacturer's test, the damping coefficient of the wire cable is 0.3. The design speed of the traveling crane with a load is 0.16 m/s. The friction coefficient \(\mu\) of the cable at the saddle is 0.3.
## 4 Result
### Test Results of LV Loads
To avoid no-load operation, a separate water tank system was constructed to circulate water during motor operation. Power semiconductors have specified rated voltage and current values, which represent their absolute maximum ratings. While AC voltage and current are typically expressed in root mean square (RMS) values, the ratings of power semiconductors refer to peak values; therefore, the actual usable range is lower than the rated values. Similarly to conventional distribution equipment, power semiconductors can tolerate a short-duration overcurrent. However, this allowable short-time current is limited to pulse-level durations, not in seconds, and thus cannot withstand the high inrush current of \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**HP**} & **Rated** & \multirow{2}{*}{**Pole**} & **Rated** & \multirow{2}{*}{**Efficiency**} & **Power** & \multicolumn{2}{c}{**Rated Current**} & \multicolumn{2}{c}{**Inrush**} \\  & & & **Speed** & & **Factor** & **220 V** & **380 V** & **220 V** & **380 V** \\ \hline \multirow{3}{*}{1} & \multirow{3}{*}{0.75 kW} & 2 & 3400 rpm & 73\% & 84 & 3.2 & 1.9 & 19.3 & 11.1 \\  & & 4 & 1710 rpm & 74\% & 77 & 3.5 & 2.0 & 20.7 & 12.0 \\  & & 2 & 3400 rpm & 77\% & 81 & 6.3 & 3.7 & 37.9 & 21.9 \\  & & 4 & 1720 rpm & 80\% & 77 & 6.4 & 3.7 & 38.3 & 22.2 \\  & & 2 & 3420 rpm & 80\% & 85 & 8.5 & 4.9 & 50.9 & 29.5 \\  & & 4 & 1730 rpm & 82\% & 81 & 8.7 & 5.0 & 52.2 & 30.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Induction motor load characteristics. Figure 15: Waveform of 3 kVA nonlinear load. induction motors. As a result, the capacity of the motor that can be used with the STPC must be carefully limited, taking into account the expected inrush current. The motors used in the experiment were three-phase induction motors rated at 3 HP and 5 HP. The test results are presented in Figures 16 and 17. The test results indicated that both the input and output currents of the STPC exhibited an inrush current during motor startup. Specifically, the output current of the R phase reached a peak value of up to 75 and settled to a steady state after approximately eight cycles. Considering the maximum allowable current of the MOSFET elements used in the STPC's parallel structure, the motor's inrush current must remain below this limit  
Figure 16: The test result of 3 ph induction motor (current). Figure 17: The test result of 3 ph induction motor (voltage). to ensure safe operation. As shown in the input/output voltage waveforms on the right side of Figure 16, the voltage maintained a relatively stable sinusoidal waveform without noticeable disturbances.
## 6 Non-Linear Methods
### Challenges and Future Directions  
Despite their advantages, non-linear methods are computationally intensive and require advanced expertise to implement. This complexity limits their adoption in small- and medium-sized enterprises in the food industry. Addressing these challenges requires the development of user-friendly tools and the integration of non-linear methods with emerging technologies such as artificial intelligence (AI) and machine learning.  
AI-powered optimization models can enhance the predictive capabilities of non-linear methods, enabling real-time decision-making in dynamic environments. For example, machine learning algorithms can be used to update non-linear models based on changing market conditions, ensuring continuous optimization of supply chains and production processes [61].  
Additionally, research should focus on developing hybrid optimization approaches that combine non-linear methods with other techniques, such as metaheuristics or evolutionary algorithms. These hybrid methods can address the limitations of non-linear optimization while expanding its applicability to more complex problems.  
By enabling cost savings, waste reduction, and efficiency improvements, non-linear methods contribute significantly to the SDGs outlined in the 2030 Agenda. As global challenges intensify, the continued advancement and adoption of non-linear optimization techniques will be essential for building a sustainable and equitable food system. Investing in research, tools, and training to overcome the barriers to adoption will ensure that non-linear methods remain at the forefront of innovation in the food industry.
# Passive Vibration Control of a Semi-Submersible Floating Offshore Wind Turbine
## 2 Modeling the Dynamics of Semi-Submersible FOWT
### Simplified Dynamic Model for Semi-Submersible FOWT
#### 2.1.3 Restoring Force terms of Platform  
As a floating body moves in the wave, the restoring force is composed of the hydrostatic and mooring components. The restoring force of pitch motion is mainly controlled by the hydrostatic pressure, which is linear if the rotation is small. However, the restoring force of surge is primarily provided by the non-linear mooring system. The linearization of mooring restoring forces is realized by using only the first order term. Therefore, all the restoring forces are calculated by:  
\[\begin{split} F_{\text{gg}}^{\text{restore}}&=-K_{ \text{gg}}^{\text{gg}}\cdot x_{\text{sg}}-K_{\text{P}}^{\text{sg}}\cdot \theta_{\text{P}}\\ T_{\text{P}}^{\text{restore}}&=-K_{\text{gg}}^{ \text{P}}\cdot x_{\text{sg}}-K_{\text{P}}^{\text{P}}\cdot\theta_{\text{P}} \end{split} \tag{7}\]  
Except \(K_{\text{P}}^{\text{P}}\), the other restoring stiffness components in Equation (7) mainly originate from the mooring system. Following the settings and schemes in FAST, the initial values of mooring stiffness coefficients are estimated based on the quasi-static catenary equation. Furthermore, referring to [18], we can calculate the hydrostatic restoring stiffness through:  
\[\begin{split} K_{\text{P}}^{\text{hydro}}&=\rho g \underset{S_{0}}{\iint}x^{2}dS+\rho gV_{0}z_{b}\\ K_{\text{P}}^{\text{P}}&=K_{\text{P}}^{\text{hydro }}+K_{\text{P}}^{\text{mooring}}\end{split} \tag{8}\]  
where \(\underset{S_{0}}{\iint}\) is an area integral on the waterline surface of the foundation, \(V_{0}\) denotes the displacement volume of the foundation, \(z_{b}\) is the height of the center of buoyancy to the origin of coordinates, \(K_{\text{P}}^{\text{hydro}}\) is the pitching restoring coefficient caused by hydrostatic force, and \(K_{\text{P}}^{\text{mooring}}\) denotes the pitching restoring coefficient induced by mooring system.
## 4 Technique Validation and Discussion
### Absolute Total Electron Content
The dot-and-dash blue line presents the JPL data, the black dotted line presents the CODE data, and the red solid line shows the TuRBOTEC values. Figure 8 shows distribution of differences between the vertical TEC from alternative and TuRBOTEC data. Data for IRKJ station over 2014. Panel (a) shows GIM CODE vs. TuRBOTEC; (b) Madrigal vs. TuRBOTEC; (c) IONOLAB vs. TuRBOTEC; (d) SEEMALA-TEC vs. TuRBOTEC. Panel (e) shows the distribution between the difference in JPL GIM TEC and CODE GIM TEC. (JPL vs. CODE)-distribution (e) features similar against (CODE vs. TuRBOTEC) or (IONOLAB vs. TuRBOTEC) root-mean-squares, that are 1.5 TECU vs. 1.7 or 2.1 TECU, and similar mean - -2.7 TECU vs. 2.3 or 2.1 TECU. The Madrigal data and the SEEMALA-TEC data show a high discrepancy with the TuRBOTEC: the root-mean-squares were 10-12 TECU. Panel (f) compares the techniques with (TuRBOTEC) and without constraint. It shows that most vertical TECs are the same with and without constraint. However, about 9% of vertical TECs from the TuRBOTEC estimates exceed (by more than 1 TECU) those from the same algorithm but without constraints. Figure 8: Normalized \(\Delta I_{V}\) histograms (difference between \(I_{V}\) values from alternative and TuRBOTEC data). (a) GIM CODE vs. TuRBOTEC; (b) Madrigal vs. TuRBOTEC; (c) IONOLAB vs. TuRBOTEC; (d) SEEMALA-TEC vs. TuRBOTEC. Panel (e) shows the distribution between the difference in JPL GIM TEC and CODE GIM TEC; panel (f) compares techniques with (TuRBOTEC) and without constraint. The data are for the IRKJ over 2014. The vertical TEC from the previous version of the suggested technique (without constrains and several other issues) was experimentally checked in [58; 59]. Those results also show relevant vertical TEC dynamics during space weather events.
# Examination of Landslide Susceptibility Modeling Using Ensemble Learning and Factor Engineering
## 3 Methodology
### Random Forest  
Random forests, established by [PERSON] [40], are an ensemble method that utilizes decision trees. This algorithm involves randomly sampling both instances and features from the original dataset to construct multiple decision trees, each trained on a random subset of the data. This dual randomness in selecting samples and features distinguishes random forests from traditional decision tree methods. The approach assumes that: (1) the bootstrap samples accurately represent the data distribution, (2) feature collinearity does not significantly distort the importance scores, and (3) the majority of trees capture meaningful signals rather than noise. Random forests are particularly effective for high-dimensional datasets, allowing for classification without the need for dimensionality reduction. After training, the model calculates the factor importance to assess each factor's contribution to the predictions, helping to identify the most influential variables. Despite their high accuracy and robustness, a notable limitation of random forests is their interpretability. The complexity of visualizing and interpreting the combined results of hundreds or thousands of decision trees makes them less straightforward than single decision trees.
## 4 Results and Analysis
### Analysis of Influencing Factors
#### 4.2.4 Effect of RQ  
Under both the S1 and S2 distribution, RWT increased as the \(RQ\) increased (Figure 10a). The maximum and minimum values of \(RQ\) (11 and 1.6 m\({}^{3}\)/h) were compared and the resulting flow field distributions and temperature field distribution were shown in Figure 10b. The factor of \(RQ\) had a great influence on the flow field. For the upstream waters, increasing \(RQ\) enlarged the range of current water and caused an increase in mean velocity. For the downstream waters, with the increase of \(RQ\), the pulling force resulting from the discharge of water strengthened and a light eddy appeared between the main downstream flow and the curtain. For the temperature field shown in Figure 10b, the increase of \(RQ\) accelerated the flow of higher-temperature water at the top of the curtain, thus reducing the time of water after TCC reached stability. But the influence of \(RQ\) on RWT was small. The difference of RWT in experimental tests and numerical simulation was less than 1 \({}^{\circ}\)C.  
In practical engineering, the \(RQ\)s were decided with the consideration of downstream flood control capacity, power generation capacity, the benefit of the eco-environment, and so on. \(RQ\) may have a large variation range, but the final RWT would not change a lot.
## 4 Computational Experiments  
At first, the proposed model, VRPSPD-TW-LO, has been verified in small instances by checking the feasibility and practical profitability based on a statistical test. All of the computational experiments are executed on a personal desktop computer (Intel i7-7000 3.60 GHz, 16 GB) and the solutions of VRPSDP-TW-LO and VRPSDP-TW are obtained by using Gurobi 8.1. Python API (vesion: Gurobi 8.1, GUROBI OPTIMIZATION, Berlin, German). Gurobi solver is a commercial software for solving mixed integer linear programs such as the formulation given in Section 3.2. For each instance, the code generates the mathematical model and feeds it to the Gurobi solver.