esa-sceva/satcom-chunk-collection huggingface dataset:
# Applicability Study on Modified Argillaceous Slate as Subgrade Filling for High-Speed Railway
## 3 Experimental Evaluation of Cement/Lime-Modified Soil
### Results
#### 3.1.3 Particle-Size Distribution for Natural and Modified Soil  
Figure 2 shows the grain-size distribution curves of natural and modified soil WAS (+5% cement). The grain distribution curve of WAS moves to the left after being modified, showing a decrease in clay content. This may be attributed to: (1) the hydration products of cement producing a cementation effect on soil particles and agglomerating soil particles; (2) the PH value increasing to a certain extent, stimulating the activities of SiO\({}_{2}\) and Al\({}_{2}\)O\({}_{3}\) in clay minerals, and reacting with Ca\({}^{2+}\) in solution to form calcium silicate and calcium aluminate series minerals. These new minerals have the same characteristics. With cementing power, they encapsulate the surface of clay particles and coagulate small clay particles into large particles together with the hydration products of cement. Similar observations have been reported while studying the effect of lime on expansive clays [25; 45].
## 4 Experimental Results and Discussions  
### Single-Beam with Local SSDI  
First, we measure the natural frequencies of the system. A response test is conducted while the piezoelectric patches are open-circuit. The excitation frequency varies from 0 Hz to 400 Hz and the amplitude remains the same. The waterfall plot of the response is given in Figure 11, where we can identify that the first two resonant frequencies of the beam without SSDI circuit are 50 Hz and  
Figure 10: Setup of the double-beam test rig: (**a**) the schematic plot; (**b**) the photo of the test rig where each beam is individually excited; (**c**) the auxiliary equipment.  
280 Hz, respectively. Note that Figure 11 shows the results of the short-time Fourier transform of the response signal, so we have time (s) in the \(z\)-axis. The first resonance is coincidently around 50 Hz (the alternating frequency), and this is validated by FEM (finite element method) computing when we tested the SSDI shunt [38]. The components of the higher harmonics (100 Hz and 150 Hz) may come from the error from the signal generator or some nonlinear aspects such as friction.  
Then we test the steady-state response of the system by fixing the excitation frequency at a constant value for a relatively long time to cancel the free vibration. Figure 12a illustrates the evolution of the voltage on the PZT1 with and without SSDI circuit when the excitation frequency is set to 50 Hz (near the first-order resonance): the solid line corresponds to the open circuit, and the dotted line to the SSDI shunt. It can be seen that the controlled voltage has the similar square-wave like form to that given by theory (see Figures 4 and 5).  
Figure 11: Sweeping frequency waterfall of the beam with open-circuit condition.  
Figure 12: Photos of the two-beam test rig: (**a**) evolution of the voltages on PZT1 (50 Hz) with and without SSDI; (**b**) voltage-responses of PZT1 and PZT2 with SSDI shunt. The voltage of PZT2 is used an indicator of the beam’s displacement.  
The evolution of the voltage on the PZT2 is plotted in Figure 12b. Note that this voltage is proportional to the displacement at the point where the piezoelectric materials are bonded. As a comparison, the controlled voltage signal on PZT1 is plotted again in this figure. We can see that the voltage of PZT1 is reversed once the voltage of PZT2 arrives an extreme. The displacement responses (voltage on PZT2) of the beam with and without SSDI circuit are measured under excitation of 50 Hz, and are compared in Figure 13. The displacement amplitude of the system with SSDI shunt is reduced significantly (about 30%) compared with that of the uncontrolled case.  
All these experimental results proved that the integrated switching circuit designed for SSDI effectively realizes the function we expect, that is, the voltage converts at the moment where the displacement arrives extreme. Furthermore, we can also see that the switch is still working when the displacement amplitude of the host structure is very small.
## 8 Combination Therapy with _Salmonella_ Further Improves Tumor-Regression  
In recent years, bacteria-mediated tumor therapy is no longer centered on using _Salmonella_ alone as a means of combating cancer. An increasing number of studies have now been reported to maximally exploit the potential of _Salmonella_ in combination with other therapeutic strategies to combat cancer [1; 7].  
The ability of _Salmonella_ to sensitize multi-drug resistant tumors have encouraged researchers to use various cancer drugs such as cisplatin or 5- Fluorouracil combined with _Salmonella_ treatment [41; 51]. A combined _Salmonella_ therapy and cyclophosphamide drug treatment improves tumor regression and significantly decreased tumor micro-vascularization in melanoma model [82]. Recently, [PERSON] et al. [83] demonstrated that _Salmonella_ treatment in B-cell non-Hodgkin lymphoma (B-NHL)-bearing mice enhanced the effect of chemotherapy using CHOP (cyclophosphamide, doxorubicin, vincristine, and prednisone). CHOP treatment in mice model prior to _Salmonella_ treatment revealed enhanced NK cell cytotoxic activity and a significantly higher lymphoma-specific humoral and cellular immune responses compared to _Salmonella_ alone nor CHOP alone treatment [83].  
In another study, adoptive T cell therapy was combined with _Salmonella_ treatment, in which intravenous injection of either viable or heat killed (HK) _Salmonella_ enhanced the proliferation of adoptively transferred OT-1 T cells and significantly improved tumor regression compared to OT-1 T cells alone [84]. Despite the successful regression of tumor by HK _Salmonella_ combined with adoptive T cell therapy, one mouse died after 16 days post-treatment indicative of potential increase in toxicity. The observed mortality may be due to elevated levels of IL-6 and other pro-inflammatory cytokines which can be addressed by applying neutralizing antibodies [84].  
The hypoxia-targeting ability of _Salmonella_ was used in tandem with photothermal therapy using the carbon-based nanomaterial, polydopamine [85]. In the study, _Salmonella_ strain VNP20009 was coated with polydopamine, designated as pDA-VNP, to act as photothermal agent. The efficacy of photothermal therapy is mediated by photothermal agents, such as pDA, to induce elevation of local temperature by converting incident light into heat [86]. The findings suggest that the targeting ability of _Salmonella_ successfully delivered polydopamine to the tumor site and that near-infrared irradiation induced significant increase in temperature in the tumor site enough to cause tumor cell decay. In vitro cytotoxicity revealed significantly lower viable B16F10 cells in pDA-VNP post-irradiation group compared with VNP20009 alone [85]. Similar findings were observed in melanoma mouse model as indicated by apoptotic and necrotic cells and confirmed by infrared thermal imaging of mice showing significant increase of temperature in pDA-VNP with irradiation [85].
## 3 Test Results
### Strain Intensity  
The total vertical loading versus axial strain curves of all specimens are plotted in Figure 9, in which the horizontal axis represents the axial strain (\(\varepsilon\)) of joints, brace, and chord members, where positive indicates tensile strain and negative indicates compressive strain, and the vertical axis represents the total vertical loading (\(P\)) applied to the loading points at one-fourth and three-fourths of the top chord members with 1000 mm in distance. It is illustrated from the comparison that the axial strains of the top chord members in compression are much smaller than the yield strain (1297 \(\times\) 10\({}^{-6}\)) of the steel materials, whereas the axial strains of the bottom chord members in tension are smaller than the yield strain of the steel materials, and the axial strains of the diagonal brace members are close to the yield strain (1565 \(\times\) 10\({}^{-6}\)) of the steel materials, as shown in Figures 9 and 10 for top chord members, bottom chord member, and diagonal brace members, respectively.  
For the top chord members in compression, as shown in Figure 9a-d, the initial slopes of the test curves of specimens T-HW, T-HN, and TS-AS are almost identical, which are smaller to specimen TS-FC. Furthermore, the axial strains of specimens T-HW, T-HN, and TS-AS under the same load level are almost identical, which are larger than that of the specimen TS-FC. For the bottom chord member in tension, as shown in Figure 9e, the test curves of these four specimens are nearly identical. It is shown from the comparison that the filled concrete makes a greater contribution to the top chord members in compression than to the bottom chord member in tension. For the diagonal brace members, as shown in Figure 10, the axial strains of all specimens under different load levels are nearly identical. In addition, the strains at crown positions on the joint 'p' of the specimens T-HW and T-HN vary greatly, which means there is a case of stress concentration, whereas the strains at the same position of specimens TS-AS and TS-FC vary uniformly, as shown in Figure 11. It is shown from the comparison  
Figure 8: Total vertical loading–deflection curves of specimens. (**a**) Midspan (transducer D1); (**b**) End joint ‘p’ (transducer D3).  
that the reinforcing methods of adding a half outer sleeve on each joint and filling concrete into the top and bottom chord members could help to improve the force condition of joints. On the other hand, the strains at saddle positions on joint 'n' of the four specimens vary uniformly.  
Figure 9: Total vertical loading–axial strain curves of chord members. (**a**) Top chord ’hj’; (**b**) Top chord ’fh’; (**c**) Top chord ’gi’; (**d**) Top chord ’eg’; (**e**) Bottom chord ’no’.
# LIDAR Point Cloud Registration for Sensing and Reconstruction of Unstructured Terrain
###### Abstract  
When 3D laser scanning (LIDAR) is used for navigation of autonomous vehicles operated on unstructured terrain, it is necessary to register the acquired point cloud and accurately perform point cloud reconstruction of the terrain in time. This paper proposes a novel registration method to deal with uneven-density and high-noise of unstructured terrain point clouds. It has two steps of operation, namely initial registration and accurate registration. Multisensor data is firstly used for initial registration. An improved Iterative Closest Point (ICP) algorithm is then deployed for accurate registration. This algorithm extracts key points and builds feature descriptors based on the neighborhood normal vector, point cloud density and curvature. An adaptive threshold is introduced to accelerate iterative convergence. Experimental results are given to show that our two-step registration method can effectively solve the uneven-density and high-noise problem in registration of unstructured terrain point clouds, thereby improving the accuracy of terrain point cloud reconstruction.  
LIDAR; point clouds; unstructured terrain; registration; improved ICP algorithm +
Footnote †: journal: Applied Sciences
## 4 Cutter Wear Mechanism and Calculation Model Considering Thermal Effect  
### Abrasive Wear Mechanism and Archard's Wear Law  
The actual wear process is the result of the combined action of many different forms of wear. The essence of wear is the loss of material from the surface of the alloy material under cutting loads. The majority of the geotechnical material on the tunnelling face contains some types of mineral particles, such as quartz and abrasive particles. Based on tribological theory, a simplified cutting model of shield cutter wear was developed to quantify abrasive wear and calculate the amount of cutter ring wear [22; 32]. Scholar [PERSON] [22] unified and simplified a large number of irregularly shaped microscopic abrasive particles into a cone model and obtained the abrasive wear calculation model shown in Figures 13 and 14.  
Figure 12: Movement trajectory of shield cutter and cutter–rock cutting mode. (**a**) Cutter cutting motion trajectory, the arrow is the advancing direction. (**b**) Schematic of cutter cutting rock, red circle is cutting groove.  
Figure 13: Mechanism of plasticity removal via abrasive wear.  
According to the plastic cutting theory, conical grains move on a relatively soft cutter surface. Assuming that the abrasive grains are subjected to a normal load \(F_{P}\), the \(m\) abrasive grain load is \(F_{m}\). The volume of abrasive marks removed plastically from the cutter surface is \(\Delta V\), the half angle of the conical grain model is \(\beta\), and the depth of abrasive cutting is \(h_{P}\). The relative sliding distance of the cutter-rock is \(L\).  
The rock grit is harder than the cutter ring alloy material, and the normal load presses the cone grit into the surface of the softer material. Relative sliding causes the surface to undergo micro-plastic cutting removal. Friction and abrasive extrusion act to plough cut wear abrasions, forming grooved wear marks. Relevant statistical studies have shown that plastic removal of abrasive wear accounts for approximately 82.9% of total wear and is the main form of wear.  
Macro-volumetric wear volume is determined by the form of micro-wear. Scholars' studies on wear mechanisms use the wear volume as an evaluation criterion. According to [PERSON]'s wear law, the material wear volume is independent of the sliding rate and shows a linear relationship with both the contact squeeze load as well as the sliding distance, expressed as:  
\[\Delta V=K\frac{FL}{H} \tag{6}\]  
where \(\Delta V\) is the volumetric wear of the cutter ring per unit sliding distance; \(F\) is the external contact load; \(H\) is the hardness of the cutter alloy surface material; and \(K\) is the dimensionless material wear coefficient.
## 1 Introduction
The main contributions the present paper aims to provide are:  
* A practical comparison of the efficacy of various signal-based condition monitoring techniques, from the most elementary and general-purpose statistical parameters to complex cyclostationary methods, touching all the three phases involved in predictive maintenance, included signal pre-treatment strategies;
* A detailed study of IMS bearing dataset 1 that echoes the results of prolific research works on the theme, but proves to be innovative for the ample number of techniques applied successfully, highlighting fault signatures with remarkable clarity, without resorting to data-driven techniques;
* A concrete proof of the validity of two of the most fruitful tools used for the study, which are Cepstrum Pre-Whitening (CPW) and the cyclostationary technique of the Improved Envelope Spectrum (IES), never applied before for the analysis of IMS dataset 1, to the best of the Authors' knowledge. The paper is structured as follows: Section 2 contains the description of the experimental test rig from which the dataset is derived, and a brief introduction of the techniques used for its analysis; Section 3 provides the most relevant results coming from their application; Section 4 is a recapitulation on the level of efficacy proved by each method and the major achievements they have been able to offer; and finally in Section 5 an overall conclusion is drawn.
## 4 Methodology of APSO-LS
### Adaptive Perturbation PSO with Local Search (APSO-LS)
This logic ensures that UAVs can complete their assigned tasks without energy depletion, while also respecting the capacity constraints of the charging stations. The fitness of each particle is evaluated using the objective function \(J^{th}\), which combines the makespan \(T_{\text{max}}^{h}\) and the total energy consumption \(\sum_{i=1}^{N_{\text{U}}}E_{i}^{h}\), as defined in Section 3. Additionally, penalties are imposed for any constraint violations, such as incomplete task allocation or energy depletion. The pseudo-code of the APSO-LS algorithm proceeds according to Algorithm 1. The integration of the rolling horizon framework with the APSO-LS algorithm provides a robust solution to the dynamic UAV task allocation and path planning problem, balancing efficiency, adaptability, and energy management. ```
1:Input: problemData
2: Initialize population of particles (route + workload encoding)
3: Initialize velocities, perturbation rate \(\rho\), temperature \(T\)
4: Evaluate fitness of each particle using \(J^{h}\)
5: Set personal best \(P_{\text{best}}\) and global best \(g_{\text{best}}\)
6:while stopping criterion not met do
7:for each particle \(p\)do
8: Update velocity using (10)
9: Update position using (11)
10: Decode solutions into routes and allocations
11: Repair infeasible routes (energy and station capacity constraints)
12: Evaluate fitness using \(J^{h}\)
13:if fitness improves then
14: Update \(p_{\text{best}}\)
15: Decrease \(\rho\)
16:endif
17:endfor
18: Update \(g_{\text{best}}\) if a better solution is found
19: Apply simulated annealing local search to \(g_{\text{best}}\):
20: Generate neighbor by 2-opt swap + workload adjustment
21: Accept if \(\Delta J<0\) or \(\exp(-\Delta J/T)>rand\)
22:\(T\leftarrow\alpha T\)
23: Adjust perturbation rate \(\rho\) if no improvement for 5 iterations
24:endwhile
25:return\(g_{\text{best}}\)
```  
**Algorithm 1** APSO-LS Algorithm
# EEG Pattern Classification of Picking and Coordination Using Anonymous Random Walks
## 3 Data Processing and Analysis
### Coherence Pattern Classification Based on Anonymous Walk Approach
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Predicted Classes**} & \multicolumn{1}{c}{**True**} & \multicolumn{1}{c}{**False**} \\  & & **Resting State** & **Picking and Coordination** & **Positive Rate** & **Negative Rate** \\ \hline \multirow{3}{*}{**True Classes**} & Resting State & 592 & 8 & 98.66\% & 1.34\% \\ \cline{2-5}  & Picking & & & \\ \cline{1-1}  & and & 6 & 234 & 97.50\% & 2.50\% \\ \cline{1-1}  & Coordination & & & \\ \hline Positive Predicted Value & 98.99\% & 96.69\% & **Prediction Accuracy (826/840)** \\ \hline False Discovery Rate & 1.01\% & 3.31\% & **98.33\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Binary classification results—random forest using bag of node degrees. Figure 4: Number of possible anonymous walks in relation to walk length. Random walks varied in length from 2 to 7, where the parameters \(\varepsilon=0.1\) and \(\delta=0.01\) determined m, the number of random walks per a graph embedding, according to Equation (3). Our dataset included 240 observations divided equally between the two classes, picking and coordination. In order to avoid over-fitting, we worked with a three-fold cross-validation method; each fold was equally distributed between classes. For each random walk length, we trained a specific classifier optimized by the various random forest parameters (number of trees, trees max depth and minimal samples per leaf). The best classification result was achieved for an anonymous walk of length 5 (corresponding to a probability vector with 52 dimensions; see Figure 4) with accuracy of 90.42% (see Table 3). Thus, by using random walks we were able to dramatically improve the classification of the task-related classes (picking and coordination). By using the bag of nodes technique PPV and TPR were both \(\sim\)60% (Table 1), whereas by using random walks the PPV and TPR both increased to \(\sim\)90% (Table 3). Hence, using a technique that captures the graph distribution has enabled classifying a complex task with a much higher level of accuracy.
## 3 Model and CFD Methods
### Spatial Motion Simulation
#### 3.3.3 Spatial Motion Result  
The numerical simulations regarding the six-degree-of-freedom spatial motion have a time interval of \(\Delta_{t}\) = 0.01 s, and the total duration is t = 200 s. To fully activate the SUBOFF model's motion characteristics, the vessel performs CFD six-degree-of-freedom numerical navigation motion under specified operating conditions, i.e., the propeller rotates at \(n\) = 15 rad/s, while the horizontal rudder \(\delta_{s}\) and the vertical rudder \(\delta_{r}\) move in a sinusoidal pattern. Considering that the submarine standard equations are applicable for low-amplitude motion, the amplitude of the rudder angle was determined as 10 degrees, and the motion law of the rudder angle was given by \(\delta_{s}\) (\(\delta_{r}\)) = 10 sin(2\(\pi\)/30\(\cdot\)t). The horizontal and vertical rudders have a motion period of 30 s. It took 126.4 h to compute it in the server utilizing three blades, with each blade having 128 GB of memory and 20 cores.  
During the simulation of SUBOFF spatial motion, the collected velocity and angular velocity data were used to identify the hydrodynamic coefficients. Figure 8 depicts the model's temporal curves for the velocity and angular velocity in different directions in the  
Figure 6: Relative position of the virtual propeller on the model.  
Figure 7: Open-water performance curve of propeller DTMB 4382.  
body coordinate system. Figure 9 presents an illustrative diagram of the motion trajectory of the SUBOFF model.  
Figure 10 presents a schematic diagram of the submarine's vortex structures at different times. The wake region of the propeller is represented by a circular shape, which distinguishes it from the physical model and the discretized propeller vortex structure. Additionally, this figure illustrates horseshoe vortices caused by the hull, tip vortices, and hub vortices at different time points.  
Figure 8: Time history curves of the SUBOFF model’s velocity and angular velocity. (**a**) X direction velocity \(u\); (**b**) Y direction velocity \(v\); (**c**) Z direction velocity \(w_{\text{z}}\) (**d**) roll rate \(p\); (**e**) pitch rate \(q\); (**f**) yaw rate \(r\).  
Figure 10: Vortex structures at different time points.  
Figure 9: Spatial motion trajectory obtained from the virtual experiment.
However, these shortcomings can be overcome by copolymerization of acrylonitrile (AN) with other acrylic monomers, such as methacrylic acid, acrylic acid, vinyl acetate, glycolyl methacrylate, N-vinylimidazol, hydroxy ethyl methacrylate, vinyl pyrrolidone, acrylamide, or vinyl chloride [49; 57; 58; 59; 60; 61]. Another advantage of PAN copolymers for preparing composite materials is their ability to coagulate in non-solvents. This makes them suitable for developing materials of various shapes and sizes using a less complex method known as wet phase inversion (WPI). In this approach, the copolymer solution is poured dropwise in the coagulation bath (containing the non-solvent) to form beads [62], or it can be poured onto suitable surfaces, followed by immersion in the coagulation bath, to form films and membranes [63]. [PERSON] et al. [64] used this technique to prepare zeolitic imidalozulate framework ZIF-8/polymer composite beads, and [PERSON] et al. [65] prepared hollow carbon beads, followed by carbonization, for oil sorption. [PERSON] et al. also used WPI to develop poly(lactic acid)/activated carbon beads to retain Rhodamine B [66]. In contrast, [PERSON] et al. developed RM-chitosan beads with adsorptive features toward three types of ions, i.e., fluoride, chromate, and phosphate [15]. Therefore, starting from the presented literature, this work aimed to develop copolymer composite beads based on PAN copolymers and RM, capable of removing pesticides efficiently by an adsorption/oxidation mechanism. For this purpose, the study considered a model pesticide, 2,4-dichlorophenoxyoxyacetic acid (abbreviated 2,4-DCFA), a hard-biodegradable pesticide commonly used in plant protection. Its effect is very harmful if it reaches water sources, the half-life being several weeks under aerobic conditions and over 120 days under anaerobic conditions [67; 68; 69; 70; 71]. Among the procedures for removing this pesticide, photocatalytic degradation, ultrasounds, electrocoagulation, adsorption on activated carbon [71], and removal by iron nanoparticles or magnetic materials (magnetite) [33] have been reported so far.