esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Fabrication of OSC Devices  
In the present work, the devices with the configuration ITO/PEDOT:PSS/active layer/LiF/Al were fabricated (where ITO is indium tin oxide and PEDOT:PSS is poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (Clevios P VP AI 4083)). The ITO glass was cleaned with a sequential ultrasonic treatment in detergent, deionized water, acetone, and isopropyl alcohol for 15 min each and subsequently dried in an oven for 5 h. The ITO-coated glass substrates were pre-treated in a UV-ozone oven for 15 min. A layer of PEDOT:PSS (\(\sim\)30 nm) was spin-coated on top of the ITO-coated glass substrates at 4000 rpm and annealed at 100 \({}^{\circ}\)C for 10 min to remove residuary water in the PEDOT:PSS layer. The active layers were spin-cast at 3000 rpm from a D/A/A solution in CF and \(o\)-DCB with a total solids concentration of 15 and 40 mg mL\({}^{-1}\), respectively, followed by thermal annealing at 120 and 150 \({}^{\circ}\)C, respectively. The average thickness of the active layers using CF (\(\sim\)100 nm) and \(o\)-DCB (\(\sim\)200 nm) were measured with an Alpha-Step IQsurface profiler. A LiF (-0.5 nm) and Al (-100 nm) layer were deposited via thermal evaporation on the active layer under a vacuum of \(\sim\)10\({}^{-6}\) Torr. The effective area of all devices was measured to be 9 mm\({}^{2}\). The current density vs. voltage (_J-V_) curves of the devices were measured using a computer-controlled Keithley 236 source measure unit. The characterization of un-encapsulated solar cells was carried out in air under AM 1.5G illumination (100 mW cm\({}^{-2}\)) using a xenon lamp-based solar simulator. The simulator irradiance was characterized using a calibrated spectrometer, and the illumination intensity was set using an NREL-certified silicon diode with an integrated KG1 optical filter. The external quantum efficiency (EQE) was measured using a reflective microscope objective to focus the light output from a 100 W halogen lamp outfitted with a monochromator and optical chopper (PV Measurements, Inc., Point Roberts, WA, USA). The photocurrent was measured using a lock-in amplifier, and the absolute photon flux was determined using a calibrated silicon photodiode.
## 6 Results and Discussion  
### Analysis Results  
The analysis results of the control performance indices of the building with TMD or ISAMD control under excitation of 26 near-fault and far-field ground motion records are shown in Figures 8-13. The optimal control performance indices of the TMD and ISAMD are listed in Table 3. Table 3 reveals that the optimal control performance indices of the building under control of the TMD or ISAMD were relative to the frequency ratio of the control mass block \(\gamma_{\text{f}}\). The ISAMD, without additional auxiliary dampers or springs and only one spring and active joint, switched the status of \"Unlock\" to \"Lock\".  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline \multicolumn{1}{c}{**Parameter**} & \multicolumn{1}{c}{**J1**} & \multicolumn{1}{c}{**J3**} & \multicolumn{1}{c}{**J5**} & \multicolumn{1}{c}{**J6**} \\ \hline \multirow{2}{*}{TMD} & Value & 0.72 & 0.48 & 0.88 & N.A. \\ \cline{2-6}  & min. at _rf_ & 0.95 & 0.95 & 0.95 & N.A. \\ \hline \multirow{2}{*}{ISAMD \(W_{\text{V}}\):\(W_{\text{A}}=1\):0} & Value & 0.65 & 0.40 & 0.85 & 5.43 \\ \cline{2-6}  & min. at _rf_ & 1.59 & 1.43 & 1.67 & 2.19 \\ \hline \multirow{2}{*}{ISAMD \(W_{\text{V}}\):\(W_{\text{A}}=\omega_{0}:\sqrt{2}-1\):} & Value & 0.62 & 0.38 & 0.91 & 3.79 \\ \cline{2-6}  & min. at _rf_ & 2.1 & 2.1 & 1.45 & 5.78 \\ \hline \multirow{2}{*}{ISAMD \(W_{\text{V}}\):\(W_{\text{A}}=\omega_{0}\):1} & Value & 0.65 & 0.38 & 0.91 & 3.87 \\ \cline{2-6}  & min. at _rf_ & 2.79 & 2.27 & 1 & 4.90 \\ \hline \multirow{2}{*}{ISAMD \(W_{\text{V}}\):\(W_{\text{A}}=\omega_{0}\):\(\sqrt{2}+1\):} & Value & 0.67 & 0.38 & 0.93 & 3.98 \\ \cline{2-6}  & min. at _rf_ & 2.79 & 2.90 & 1.2 & 2.12 \\ \hline \multirow{2}{*}{ISAMD \(W_{\text{V}}\):\(W_{\text{A}}=0\):1} & Value & 0.68 & 0.4 & 0.94 & 3.71 \\ \cline{2-6}  & min. at _rf_ & 3.25 & 3.25 & 1.36 & 3.42 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The optimal control performance indices for the building with TMD or ISAMD under excitation of 26 seismic records.  
Figure 8: Response ratio of Structure with TMD under 26 seismic excitations [45].  
Figure 10: Cont.  
Figure 9: Response ratio of Structure with the ISAMD with \(\text{W}_{\text{V}}\text{:}\text{W}_{\text{A}}=1\); without auxiliary damper and spring under 26 seismic excitations.  
Figure 11: Response ratio of Structure with the ISAMD with W\({}_{\text{V}}\):W\({}_{\text{A}}=\omega_{0}:1\), without auxiliary damper and spring under 26 seismic excitations [45].  
Figure 12: Cont.  
Figure 10: Response ratio of Structure with the ISAMD with W\({}_{\text{V}}\):W\({}_{\text{A}}=\omega_{0}:\sqrt{2}-1\), without auxiliary damper and spring under 26 seismic excitations.  
Figure 12: Response ratio of Structure with the ISAMD with \(\mathrm{W_{V}}\):\(\mathrm{W_{A}}=\omega_{0}:\sqrt{2}+1\), without auxiliary damper and spring under 26 seismic excitations.  
Figure 13: Response ratio of Structure with the ISAMD with \(\mathrm{W_{V}}\):\(\mathrm{W_{A}}=0\):1, without auxiliary damper and spring under 26 seismic excitations.
## 1 Introduction
### Factors Influencing Wear Rate  
The wear of disc cutters must be considered in the context of the tribological system in which three basic components interact: the disc cutter, the rock material, and the environment. From a tribological point of view, the cutting tool is a solid body, the rock material serves as a counter body, and the tunneling conditions form the surrounding environment [39]. Figure 3 is a schematic representation of the tribological system in the case of TBM tunneling. The wear rate of TBM disc cutters is influenced by several factors, including rock properties, tunneling parameters, disc cutter design, and maintenance practices [35,40]. Rock properties such as hardness, abrasiveness, and mineral composition affect wear rate, with harder and more abrasive rock causing faster wear [24,41,42]. Tunneling parameters such as thrust force, torque, cutter head rotation speed, and rate of advance also affect the wear rate, as higher speeds and forces increase contact pressure and friction [43]. The design features of disc cutters, such as shape, size, material composition, and edge geometry, play a role in the wear rate as they affect cutting efficiency and friction [43,44]. Proper maintenance practices, including the timely replacement of worn disc cutters and regular inspections, have a significant impact on wear rate and overall performance. Accurate predictions of disc cutters wear are therefore of great significance to extending cutter life and improving TBM excavation efficiency [45].  
Figure 2: Schematic representation of material chipping, one of the abnormal types of wear in TBM disc cutters.
## 3 Results and Discussion
### Optical and Electrical Properties
In Figure 7b we can see a broadband reduction in the reflectivity of the sample produced by the Si symmetric cathode when compared with that fabricated using Pt electrode. Another interesting note is that the reflectance dips at the VIS-NIR region is blue shifted while that at the UV is red shifted. The reflectance dip at the UV region is attributed to the resonance effect in PSi and redshifts with the pore size increase [46; 47]. The PSi layer consists of air, silicon, and silicon dioxide, forming a compact structure with an effective refractive index. This composition influences the resulting optical response and affects the shift in the interference pattern [48; 49; 50]. These two features are important when applying such structures in photodetecors and chemical sensors within these spectral ranges. We did not observe any significant changes in the optical reflectance spectra due to the small diameters of the fabricated nanopores for sample S1. However, Figure 8 shows a broadband reduction in the optical reflectance for the symmetric Si electrode case of the PSi layer on sample S3. This decrease in optical reflectance is attributed to the higher porosity of the PSi layers fabricated by the Si electrode (24.7%) compared to the porosity of the PSi layer fabricated by the Pt electrode (22.6%), as mentioned in the morphology section (see Figure 4). \begin{table}
\begin{tabular}{c c c c c} \hline
**Samples-S1** & **I0 (mA)** & **n** & **R\({}_{s}\) (k\(\Omega\))** & **R\({}_{sh}\) (k\(\Omega\))** \\ \hline using Pt cathode & \(4.80\times 10^{-4}\) & 5.44 & 1.0 & 42.38 \\ \hline using Si cathode & \(2.15\times 10^{-4}\) & 4.79 & 1.08 & 42.82 \\ \hline \end{tabular}
\end{table} 
Table 2: Schottky diode characteristics for samples fabricated using the two setups.
## 4 Analysis and Discussion
### PUF-Based Authentication Challenges and Solutions
#### 4.4.6 Challenge 6: Eavesdropping  
It is challenging to implement intricate security algorithms on cost-sensitive devices because they have limited computational capacity. As a result of this restriction, the likelihood of espionage and other intrusions increases. In [114], the authors introduced the concept of PUF sensors, which work on authenticated sensing protocols, thus reducing the chances of eavesdropping and man-in-the-middle attacks. An experimental demonstration of this concept was carried out with the help of a ring oscillator PUF.
# Phase Risk Analysis of Overhead Lines Under Complex Icing Conditions
## 3 Finite Element-Based Analytical Model of Phase-to-Ground Line Spacing for Ice-Covered Lines  
### Ground Arc Pendant Modeling and Solution Methods  
#### 3.1.1 Calculation of the Sag of the Conductor-Ground Line  
The shape of the overhead conductor and ground line, as shown in Figure 3, is often described using a hanging chain model. In the catenary model of the transmission line, the conductor regarded as an ideal flexible cable is subjected to a uniformly distributed load  
Figure 2: Changes in the spacing of ground conductors after de-icing.  
across the diagonal span. A coordinate system is defined with the lower suspension point A as the origin, the \(x\)-axis perpendicular to the specific load, and the \(y\)-axis parallel to the specific load. Subsequently, the oblique parabolic equation of the overhead transmission line is derived as follows:  
When the two suspension points are at different heights, it is usually assumed for calculation purposes that the left suspension point \(A\) is at the origin. The horizontal distance from the lowest point \(O\) to the left suspension point is a, and the horizontal distance to the right suspension point \(B\) is \(b\).  
When the two suspension points are at the same height, the length of the entire span is the following:  
\[L_{h=0}=2\frac{\sigma_{o}}{q}\text{sinh}\frac{lq}{2\sigma_{o}} \tag{2}\]  
The equation of the catenary is as follows:  
\[y=\frac{h}{L_{h=0}}\left[\frac{2\sigma_{o}}{q}\text{sinh}\frac{qx}{2\sigma_{o} }\text{cosh}\frac{q(l-x)}{2\sigma_{o}}\right]-\sqrt{1+(\frac{h}{L_{h=0}})^{2} }\left[\frac{2\sigma_{o}}{q}\text{sinh}\frac{qx}{2\sigma_{o}}\text{sinh}\frac {q(l-x)}{2\sigma_{o}}\right] \tag{3}\]  
The sag \(f_{x}\) at any point \(C\) in the overhead line stall is:  
\[f_{x}=\frac{h}{l}x-\frac{h}{L_{h=0}}\left[\frac{2\sigma_{o}}{q}\text{sinh} \frac{qx}{2\sigma_{o}}\text{cosh}\frac{q(l-x)}{2\sigma_{o}}\right]+\sqrt{1+( \frac{h}{L_{h=0}})^{2}}\left[\frac{2\sigma_{o}}{q}\text{sinh}\frac{qx}{2 \sigma_{o}}\text{sinh}\frac{q(l-x)}{2\sigma_{o}}\right] \tag{4}\]  
In the equation, \(h\) represents the height difference of the conductor in meters (m); \(q\) denotes the unit load; \(x\) is the horizontal coordinate of the selected point; \(l\) is the span length of the conductor in meters (m); \(L_{h=0}\) indicates the length of the entire span when the two suspension points are at the same height, measured in meters (m); and \(\sigma_{o}\) represents the horizontal component of the stress at various points along the transmission line.  
The hanging chain line equation accurately calculates conductor sag and is widely used. However, it becomes ineffective under complex working conditions such as uneven ice accumulation and strong winds.
## 3 Toward Next-Generation Public Safety Networks  
The migration from LMR systems to next-generation public safety networks requires the introduction of essential solutions and features to ensure mission-critical grade performance. Hence, 3 GPP has performed additional standardization efforts for the support of public safety services over cellular networks starting from its Rel-12 [5].
## 4 Evaluation and Results
### Machine Learning Applied to Data Analysis
#### 4.2.1 Decision Tree Analysis: Dominant Features by STP Type
* Spanish HPs are between SPs and TPs but tend to be closer to the SP type, although this is not definitive. Figure 6: Decision tree and rule description for: (a) 10 core features with significative differences among STP types; and (b) 9 core features with significative differences among STP types. However, there are a few TPs performing as SPs and vice versa. Specifically, based on the decision tree analysis, five TPs present features considered closer to the SP type and two SPs show features closer to the TP type.
## 4 Hierarchical Incremental Maps  
This section presents the method that we propose to create topological maps incrementally. The starting point is a set of images captured from the same area of the environment, and a node composed of these images. The aim of the process is to build a hierarchical and incremental map, where visually similar zones are detected, compacted and represented as a node. It will be carried out by clustering images with similar features. Broadly speaking, the hierarchical map is built in such a way that when a group of new images do not belong to a previously visited node, a new node is created with them. The hierarchical incremental map is updated and extended every time the robot captures a new image or group of images. A summary of all the parameters needed to follow the process is included in Table S3 and some tunable parameters that can modify and improve the mapping results are shown in Table S4.  
When a newly acquired image \(I_{q}\) arrives, firstly, a Node Level Loop Closure is performed with the nodes \(N^{C}=\{N_{1},N_{2}...,N_{C}\}\) currently contained in the map. \(N^{*}\) will represent the set of candidate nodes, so if the node \(N_{i}\) leads to the loop closure, \(N_{i}\) is elected as candidate node and it becomes part of \(N^{*}\). After retrieving the set of candidate nodes to which the image \(I_{q}\) may belong, secondly, an Image Level Loop Closure is performed with the images that belong to its nodes \(I^{N^{*}}\).  
To carry out these two processes, the position and orientation descriptors are obtained from image \(I_{q}\). Descriptors should be able to retrieve properly both the candidate nodes and the image that better matches \(I_{q}\) among the images contained in the candidate nodes. At this point, position and orientation descriptors will be only compared with the reference images contained in the nodes which were selected after Node Level Loop Closure \(I^{N^{*}}=\{\cup_{N_{i}\in N^{*}}I^{N_{i}}\}\), where \(I^{N_{i}}\) is the set of images belonging to the node \(N_{i}\) and \(N^{*}\) is the set of nodes selected in the Node Level Loop Closure process. If the Image Level Loop Closure process is successful, a unique image is retrieved as match. If the retrieved image \(I_{i}\) fulfills the Prominence Condition (Section 4.3) and the Centroid Condition Section (4.4), \(I_{q}\) is added to the corresponding node.  
Figure 3: Process to build the gist descriptor using horizontal cells.  
Additionally, it is possible that no node is selected in the Node Level Loop Closure process and \(N^{*}\) becomes an empty set \((N^{*}=\emptyset)\). When several consecutive images produce this result \((N^{*}=\emptyset)\) in the Node Level Loop Closure process, a new cluster is created, expanding the hierarchical incremental map. Figure 4 shows a schematic overview of the hierarchical mapping method. The next subsections describe in detail these processes.
## 5 Case Study  
The tool was also applied in a geological survey of a hydropower project. It was a large engineering project in a complicated geological condition. The valley and slope were steep, and the angles of slopes were generally 30-90\({}^{\circ}\). Mudslides, landslides, and land collapses are common in the area. Thus, the bad geological conditions have a negative influence on the project site selection. It is necessary to assess the potentially unstable area after impounding. Moreover, with the rise in the water level, there may be new geological hazards. We should record geological information and make a preliminary analysis, such as surface drawing and accumulation body estimation, in this area. Additionally, we should extract information from images. Figure 14 shows the traditional geological survey in hydraulic engineering.  
Figure 13: Block instability analysis.  
The obtained information is loaded in a 3D modeling software to verify the proposed approach. As shown in Figure 15, the geological survey was mainly performed along the river. All kinds of geological information were recorded. Based on the recorded geological information, 3D analysis was performed in the tool, especially for accumulation bodies. The results are shown in Figure 16.  
Figure 14: Traditional fieldworks in hydraulic engineering.  
Figure 15: Results of a geological survey.  
The geological information in 2D images was also converted into 3D information in the geological survey. Photo stitching was applied to show the effectiveness. The two strata, I and II, were recorded in the tool, as shown in Figure 17a,b. According to the actual coordinates, the two structures were loaded into 3D modeling software. Figure 17c shows the spatial relationship between the two structures. In Figure 17d, two photos were stitched to show the real positions of strata I and II, which proved the function's effectiveness.  
Figure 16: Results of accumulation body and occurrence estimation.  
Figure 17: _Cont._  
Figure 17: Two-dimensional image interpretation: labelling (**a**) strata I and (**b**) strata II; (**c**) 3D models of strata I and II loaded in a 3D modeling software; (**d**) two stitched images of strata I and II.
# An Automatic Navigation System for Unmanned Surface Vehicles in Realistic Sea Environments
## 7 Conclusions  
In this paper, a complete ANS is presented with PPS (DTFMS) and CAS (FCS-MPC) in a realistic sea environment. First, the TFMS method is proposed to model the static sea environment and path plan, which can be adjusted by \(Saturation\) to weigh security and path length. Then, according to FCS-MPC theory, a CAS is developed to track trajectory and avoid collision based on a prediction model of the USV. The dynamic target point and security evaluation come from path planning and an environmental model of the PPS. Next, the proposed DTFMS method with dynamic domain models (a path re-planning algorithm), is able to well represent the dynamic vessels including the own and target vessels. There, dynamic domain models are changed according to the state of motion of the vessels and the environment update time. Finally, by comparing the static and dynamic tests, the simulation results validated the effectiveness of the proposed ANS in simulated and realistic sea environments.  
Future work will firstly consider the problem that the practicability of ANSs can be further increased for real fully-autonomous USV experiments. First, to model a more real sea environment, the sea environment model needs additional altitudes and surface currents. Second, the collision avoidance control algorithm also needs to consider the problem of energy consumption and environmental influences (wind, waves and current). Thirdly, the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) should be considered in ANSs.  
This work was partially supported by \"the Nature Science Foundation of China\" (Grant Number 51609033), \"the Nature Science Foundation of Liaoning Province of China\" (Grant Number 2015020022) and \"the Fundamental Research Funds for the Central Universities\" (Grant numbers 3132014321, 3132016312 and 3132017133).  
The work presented here was completed through the collaboration of all authors. [PERSON] designed, analyzed, and wrote the paper. [PERSON] guided the full text. [PERSON] conceived the idea. [PERSON] and [PERSON] analyzed the data. All authors approved the manuscript.  
The authors declare no conflict of interest.
###### Abstract  
In this paper, a four-dimensional (4D) dynamic cooperative path planning algorithm for multiple unmanned aerial vehicles (UAVs) is proposed, in which the cooperative time variables of UAVs, as well as conflict and threat avoidance, are considered. The algorithm proposed in this paper uses a hierarchical framework that is divided into a 4D cooperative planning layer and a local threat avoidance planning layer. In the cooperative planning layer, the proposed algorithm, named dynamic priority rapidly exploring random trees (DPRRT*), would be used for the 4D cooperative path planning of all UAVs involved in a given task. We first designed a heuristic prioritization strategy in the DPRRT* algorithm to rank all UAVs to improve the efficiency of cooperative planning. Then, the improved RRT* algorithm with the 4D coordination cost function was used to plan the 4D coordination path for each UAV. Whenever the environment changes dynamically (i.e., sudden static or moving threats), the proposed heuristic artificial potential field algorithm (HAPF) in the local threat avoidance planning layer is used to plan the local collision avoidance path. After completing local obstacle avoidance planning, the DPRRT* of the 4D cooperative planning layer is again called upon for path replanning to finally realize 4D cooperative path planning for all UAVs. The simulation and comparison experiments prove the feasibility, efficiency, and robustness of the proposed algorithm.  
multiple unmanned aerial vehicles (multi-UAVs); four-dimensional (4D) dynamic cooperative path planning; rapidly exploring random trees (RRT*); artificial potential field (APF)