esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Friction Coefficient Based on 3D Texture Characteristics of Road Surfaces
###### Abstract
No single texture parameter or evaluation method has yet achieved the high reliability and practical usability required for global acceptance in predicting friction through non-contact measurements. This study aims to enhance the prediction of tire-road friction through contactless surface measurements. The primary contribution involves using a newly developed device that captures pavement surface details down to the microtexture level on road pavements in situ.
# Multi-Device Security Application for Unmanned Surface and Aerial Systems
## 4 Results
### Experiment Three--MAUI ROS Live
#### 4.3.1 Simulation Environment  
The test environment for phase three consists of (A) one virtual Ubuntu VM serving as ground control for ScanEagle, (B) the ScanEagle UAV VM, and (C) the physical CASSMIR USV (shown in Figure 10). The MAUI ROS Live application is installed in each environment. Once the applications were installed and compiled, we initialized MAUI ROS Live on all three environments in sequence, starting with the ScanEagle, then CASSMIR, and finally the ground station VM. The ScanEagle VM creates the MLS group, and all others join the group. The tested environments were connected to mesh network radios separately for physical layer transport.  
For the CASSMIR to join and communicate within the MLS group consisting of the ScanEagle and ground control, we added a second network adapter in the CASSMIR. This adapter was configured with a similar host IP address and network mask as the ScanEagle and the ground control station. Adding a dual network adapter allowed the CASSMIR to join the ad-hoc network without modifying its baseline internal network architecture.  
Figure 10: Physical CASSMIR USV platform.
## Appendix D Proof of Monotonicity of (25)  
Let \(a\overset{\Delta}{=}\frac{1-\tau^{2}}{\tau^{2}}\) and \(c\overset{\Delta}{=}K-1\) for simplicity, where \(a\geq 0\) and \(0<c<K\). Then, the first-order derivative of (25) is given by  
\[\frac{\partial x_{2,ZF}^{ip}}{\partial M}=\frac{\left(\log_{2}e\right)^{2}a^{2 }c}{\left(K-2\right)}\times\left(\frac{a\left(2-a\right)M^{2}+2c(a-1)^{2}M-c ^{2}(a-1)^{2}}{\left(a\left(M-c\right)+c\right)^{4}}\right).\]  
Let \(f\left(M\right)=a\left(2-a\right)M^{2}+2c(a-1)^{2}M-c^{2}(a-1)^{2}\). Then, we can consider the following three cases:  
* _Case (i)_ When \(a=2\), \(f\left(M\right)=2 cM-c^{2}\) is an increasing function of \(M\). Since the root of \(f\left(M\right)\) is \(\omega=\frac{c}{2}=\frac{K-1}{2}\), \(f\left(M\right)\) is always positive where \(M\geq K\).
* _Case (ii)_ When \(0\leq a<2\), \(f\left(M\right)\) is a convex function and the two roots are given by \[\omega_{1}=\frac{c\left(a-1\right)}{a}\text{ and }\omega_{2}=\frac{c\left(a-1 \right)}{a-2}.\] When \(1\leq a<2\), \(\omega_{1}\geq\omega_{2}\) and \(K{>}\omega_{1}\). When \(0\leq a<1\), \(\omega_{2}>\omega_{1}\) and \(K{>}\omega_{2}\). Therefore, \(f\left(M\right)\) is always positive where \(M\geq K\).
* _Case (iii)_ When \(a>2\), \(f(M)\) is a concave function and \(\omega_{2}>\omega_{1}\) and \(K{>}\omega_{2}\). Therefore, \(f\left(M\right)\) is always negative where \(M\geq K\),  
As a result, (25) is a monotonically increasing function when \(0\leq a\leq 2\), i.e., \(\frac{1}{3}\leq\tau^{2}\leq 1\) and (25) is a monotonically decreasing function when \(a>2\), i.e., \(0<\tau^{2}<\frac{1}{3}\).
## 4 Results and Discussion
### Comparative Analysis of Experimental Results
To quantify the deviation of temperatures at each monitoring point from the average value, a temperature uniformity coefficient, \(\sigma_{T}\), is introduced to assess the uniformity of the workpiece surface temperature field during the gas quenching process. The expression for this coefficient is as follows:  
\[\sigma_{T}=\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}(\overline{T_{N}}-T_{i})^{2}} \tag{12}\]  
Figure 21: Velocity contour map at the test section: (**a**) original design and (**b**) optimized design. Figure 17:0 Standard deviation of velocity for the original and optimized designs. In the formula, N represents the total number of measurement points, \(\overline{T_{N}}\) denotes the average temperature of the N measurement points, and \(T_{i}\) represents the temperature value at the i measurement point. For the original gas quenching furnace, \(\sigma_{T}\) is 4.146, while after optimization, \(\sigma_{T}\) decreases to 1.945, a reduction of 53.09%. This indicates that the temperature uniformity in the workpiece area has significantly improved following the blade optimization.
# Pixel Circuit Designs for Active Matrix Displays
## 1 Introduction
### TFT Devices
#### 1.3.1 a-Si TFT  
a-Si TFTs have been the mainstream TFT for TFT-LCD since the commercialisation of TFT-LCD in the early 1970s [29]. a-Si is an amorphous material with a low process temperature, simple production, mature technology, and low cost. The mobility affects the driving current and switching speed of the TFT. The mobility of the a-Si TFT is only 0.1-1 cm\({}^{2}\)/Vs. If a-Si TFT is used for high-current driven AM-OLED and AM micro-LED  
\begin{table}
\begin{tabular}{c c c c} \hline
**TFT** & **a-Si TFT** & **LTPS TFT** & **MO TFT** \\ \hline Microstructure & Amorphous & Polycrystalline & Amorphous \\ Max process temperature (\({}^{\circ}\)C) & 330 & 500 & 350 \\ Cost & Low & High & Low \\ Mobility (cm\({}^{2}\)/Vs) & 0.1–1 & 50–100 & 10–30 \\ PPI & Low & High & High \\ Threshold voltage (\(V_{\mathrm{TH}}\)) & Good & Worse & Good \\ uniformity & Worse & Good & Better \\ \(V_{\mathrm{TH}}\) stability & Worse & Good & Better \\ Logic power & Worse & Better & Good \\  & & LCD & LCD \\ Application & LCD & OLED & OLED \\  & & Micro-LED & Micro-LED \\ \hline \end{tabular}
\end{table}
Table 2: Features and applications of a-Si TFT, LTPS TFT, and MO TFT [27].  
Figure 1: Passive matrix drive draft **(a)** and active matrix drive draft **(b)**[24].  
displays, its drive current is typically increased by enlarging the width-to-length ratio of the TFT. However, expanding the TFT's width increases parasitic capacitance and restricts the pixel aperture ratio, leading to a low PPI [29; 30]. Moreover, a-Si TFT exhibits the worst \(V_{\text{TH}}\) stability, which compromises display stability. Due to this unresolved issue, applying a-Si TFTs to AM-OLED or AM micro-LED commercial products is challenging [30]. Therefore, a-Si TFTs are primarily used in low-resolution LCDs [31].
## 4 Applications in Optimization
### Self-Organizing Migrating Algorithm (SOMA)
#### 4.4.2 Computational Intelligence Algorithm  
Martinez-Vargas et al. [90] proposed a leader-follower optimization algorithm based on the combination of five different Population-based Algorithms (PAs) to tackle the high-dimensional optimization problem known as the Base Station (BS) deactivation problem in telephony. The general idea is to find the minimal set of active BSs in a 6G cellular network to maintain service for at least 99% of mobile users (MUs) who are reassigned due to BSs being shut down. Computational Intelligence (CI) is the key enabler for future 6G systems to autonomously manage their elements and build an intelligent wireless environment. The five follower PAs are: binary particle swarm optimization (BPSO), chaotic BPSO (BPSOC), a Genetic Algorithm (GA) with a steady-state population model (AGE), a GA with a generational model (AGG), and a binary artificial bee colony.  
Each follower algorithm searches for its best solution (that is, the best local solution) in its set of candidate solutions during some number of iterations. Meanwhile, the leader algorithm checks whether the followers' status is waiting or working. If the follower algorithms have the waiting status, the leader algorithm asks for the best local solution they have found thus far. Then, the leader gathers those best local solutions and chooses the best one among them as the best global solution. Consequently, the leader sets its status to working and sends the best global solution to the followers. When the followers receive the best global solution, they search for the worst candidate solution in their group of candidate solutions. The best global solution takes the place of the worst candidate solution (elitism). After the number of iterations is met, the leader resolves the problem, that is, it finds the lowest number of active BSs and reassigns those MUs whose BSs have been deactivated.  
Among the follower algorithms, BPSOC has a unique mechanism to move through the search space. This mechanism uses chaos sequences to improve diversity and prevent premature convergence to a local optimum. To this end, BPSOC uses a Lozi map embedded in  
\[v_{id}=\omega*v_{id}+c_{1}*v*\textbf{chaos0}*(g_{best}-x_{id})+c_{2}*\textbf{ chaos0}*(p_{best}-x_{id})\,, \tag{62}\]  
on the **chaos0** function.  
The BPSO and BPSOC algorithms have exploitation bias. The diversity trend throughout iterations in these two followers is acute. This means that the convergence velocity towards \(p_{best}\) and \(g_{best}\) is fast. Furthermore, diversity is slightly greater in BPSOC because of the Lozi map effect (this is a unique mechanism in BPSOC).  
In conclusion, Martinez-Vargas et al. found that the leader-follower optimization algorithm can find solutions even in challenging scenarios, and that BPSO and BPSOC find better solutions by performing deeper exploitation.
## 3 Metabolomics Applied to Marine Organisms in Cancer Studies
### Metabolite Changes in Treated Cancer Cell Lines
\begin{table}
\begin{tabular}{c c c c c} \hline
**Algae** & **Compound/Extract** & **Cell Lines** & **Metabolomics** & **Reference** \\ \hline _U. fasciata, G. furcata_ and _S. henslouianum_ & \begin{tabular}{c} Sulphated \\ polysaccharides \\ \end{tabular} & MKN45 and DLD & RFC & (120) \\ \hline _A. carterae_ & \begin{tabular}{c} Methanolic extract \\ and \\ \end{tabular} & \begin{tabular}{c} A549, HT29, MDA-MB-231 \\ and PSN-1 \\ \end{tabular} & \({}^{1}\)H-NMR and MS & (121) \\ \hline _I. galluna_ & \begin{tabular}{c} Crude extract \\ and \\ \end{tabular} & \begin{tabular}{c} K562, MOLT-4, U937, HL60 \\ and Burkitt\({}^{\prime}\)s lymphoma \\ \end{tabular} & \({}^{1}\)H-NMR, GC-MS & (122) \\ \hline _C. protothecoides_ and _N. oculata_ & \begin{tabular}{c} Crude extract \\ \end{tabular} & A172 and HTC116 & GC-MS & (123) \\ \hline _T. suecia_ & \begin{tabular}{c} Crude extract \\ \end{tabular} & A172 and HTC116 & GC-MS & (124) \\ \hline _N. oculata, T. suecia_ and _Chlorella_ sp. & \begin{tabular}{c} Water, ethanolic and \\ methanolic extracts \\ \end{tabular} & MCF7 and 4 T1 & \({}^{1}\)H-NMR & (125) \\ \hline _S. platensis_ & \begin{tabular}{c} Methanolic extract \\ \end{tabular} & L20 B and MCF7 & GC-MS & (126) \\ \hline _H. pluvialis_ and _D. salina_ & \begin{tabular}{c} Carotenoid fractions \\ and polar fractions \\ \end{tabular} & \
\begin{tabular}{c} HepG2, MCF7, HCT116 \\ and A549 \\ \end{tabular} & LC-MS & (127) \\ \hline _P. tricornutum_ and _S. pinnata_ &
\begin{tabular}{c} Hydrophilic and \\ lipophilic fractions \\ \end{tabular} & CHL-1 & \({}^{1}\)H-NMR & (128) \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._these crude extracts were screened to evaluate their cytotoxic activity on A-549 (lung adenocarcinoma), MB-231 (breast carcinoma) and Panc1 cell lines, demonstrating that an organic extract of the alga sampled in June exhibited caspase 3/7 activity of 2.2 for the Panc1 cancer cell line. The extracts were also tested for their general toxicity on human keratinocyte (HaCaT) cell lines, against which no activity was recorded, implying a lack of general toxicity for normal cells. In addition, results of this study showed the variation of many compounds according to the sampling month; for example, the highest content in phlorotannin was recorded during the summer period. Literature analyses reference the detection of the antiproliferative effect of _F. vesiculosus_ due to phlorotannins [117], fucoidan [131] and fucoxanthin [132]. This study highlighted the importance of the impact of the sampling season on the bioactivity and metabolite profile.
## 3 Method  
In this section we will explain in detail our method starting with how it is trained, which is shown in Figure 4. The first training step consists of extracting the poses from video and translating them into appropriate parameters:  
1. The application of standard tools (specifically Open Pose [21; 41]) to identify humans in videos and extraction of their **2D** poses. The poses are defined in terms of joint coordinates in pixels.
2. Optional: low pass filtering.
3. A sliding window based translation of the raw 2D pixel coordinates into appropriately filtered and scaled values in the body centered (hip as origin) frame of reference. This window considers 1.5 s in the past and 1.5 in the future, with a total length of 3 s.
4. Generating sliding windows of the pre-processed pose data with window size \(W\) and step \(S\).  
Figure 3: Joints used for each of the 4 regression models. Those models also receive the scaled hip speed and the change in scale. Orange boxes represent where sensors were placed during the exercises, while their surrounding white boxes describe the joints relevant for each of their regression models.  
The system is trained on a dedicated dataset that contains synchronized sensor and video data for a set of motions typical for a broad domain. The sensor data collected is used as ground truth for training the regression model. Thus, any pre-processing of the sensor data performed before it is used to compute the loss will be reflected in the synthetic sensor data that the model will learn to generate. This means that the aim of the pre-processing must be to remove from the sensor signal any information that will hamper learning by \"confusing\" the model (noise, frequencies too high to be contained in the video data), while retaining as much useful information as possible. Examples of pre-processing strategies that we investigated were low pass filtering with different cut off frequencies and scaling. After pre-processing we aggregate the ground truth sensor data into sliding windows with window size \(W\) and step \(S\) to correspond to the partitioning of the pose parameters. The sliding windows are the input to the regression model. After some experimentation we have settled on a model based on residual deep convolution neural networks that uses dilated convolutions, similar to a Temporal Convolutional Network (TCN) [42]. We train a separate model for each sensor position and channel. Once trained, the system is presented with a video of the activity and produces a corresponding sequence of simulated sensor signals.  
In the remainder of this Section we will explain in detail the training steps of our method. In Section 3.1 we go into depth about how we obtain poses for each video as well as explain the procedures and rationales behind our sensor and pose pre-processing. The motivation and configuration of our deep learning model is explained in Section 3.2, while the dataset that was used to train it, is described in Section 3.3.
## 2 Low-Power Wireless Technologies
### DASH7 Alliance Mode
NGCs use a pure ALOHA access mechanism, which does not require prior negotiation before transmission. Contrarily, GCs are based on non-persistent CSMA/CA (carrier sense multiple access/collision avoidance), which requires to check the status of the channel prior to transmission. If the channel is idle, based on assessing the energy present in the medium, the node waits for a certain amount of time and tries again. If the medium is still free, then the node starts transmitting the packet immediately. The standard defines three back-off mechanisms when the medium is busy: AIND (adaptive increase no division), RAIND (random adaptive increase no division) and RIGD (random increase geometric division). However, only RIGD, where transmission starts after a random interval and retransmission follows a \(1/(2^{n+1})\) decay function, is used in practice. The physical layer and the data-link layer of D7 AM are well known in the literature and, therefore, do not represent any major improvement over other existing low-power wireless technologies. However, the main difference between D7 AM and other existing technologies is the query system implemented at the upper layers of the stack, which is highly integrated with the lowest layers. The query system enables to restrain the response of nodes to a query based on certain upper layer parameters. For example, during synchronization, the initiating node may indicate that the query is only for nodes that have a temperature sensor. Therefore, all the nodes that do not have a temperature sensor will not synchronize and attempt to reply to the subsequent queries. Once the nodes that have a temperature sensor are synchronized, the initiating node can request data only from nodes that have a certain temperature value, for example, below 25 \({}^{\circ}\)C. On subsequent requests, the initiating node can request data from the remaining nodes, indicating the appropriate temperature values, or collect the remaining nodes using a wildcard. Thus, a D7 AM network can be seen as a distributed database system for various sensor parameters, with certain nodes actuating as the database querier and the remaining nodes actuating as dynamic entries in the distributed database. Such a distributed database system can either be queried on demand, e.g., when triggered by an external agent, or configured to react to certain events, e.g., a temperature sensor going above a certain threshold.
# Design, Fabrication and Testing of a Multifrequency Microstrip RFID Tag Antenna on Si
## 5 Fabrication and Testing  
### Fabrication  
An RFID tag antenna specimen was fabricated at the microelectronics laboratory of the Foundation for Research and Technology-Hellas and tested at the telecommunications laboratory of Hellenic Naval Academy. The microfabrication process involves metallic structure pattern transfer techniques, including optical UV lithography procedures (for the pattern transfer of CAD layout designed patterns), metal depositions using electroplating, and spin-coated stay-on dielectrics.  
Figure 12: (**a**) Simulated surface current for 2.4 GHz. (**b**) Simulated surface current for 5.8 GHz.  
The procedure begins with the standard degreasing of a Silicon substrate (76 mm/3 inches in diameter), which is a Float-Zone (FZ) Silicon with a wafer thickness of 380 \(\upmu\)m, a crystallographic orientation of (100) and, most importantly, a high resistivity of 10,000-1,000,000 \(\Omega\)-cm (like a pure dielectric substrate). The degreasing step is a standard organic solvent cleaning procedure involving the ultrasonic-agitated dipping of the Si substrate in the following order: Acetone, Isopropanol, and finally deionized H\({}_{2}\)O. The substrate is dried using nitrogen. Following this step, the AZ9260 photoresist is spin-coated with a thickness of 0.006 mm onto the substrate and thermally stabilized using a hot plate at 110 \({}^{\circ}\)C for 2 min. The transfer pattern is obtained by exposing the layout mask of the bottom inductor spiral using i-line UV lithography with the mask aligner Suss MA6, and developing the exposed photoresist with the use of the AZ826 MIF developer.  
The bottom inductor spiral electrode of the RFID is metalized with a 3 \(\upmu\)m thick copper (Cu) electroplating using the previous lithography technique. As a dielectric \"bridge\" support for the bridge interconnect that is intended to connect the center of the spiral coil to the outside electrode, the spin-on polyimide dielectric PI2525 is used. Directly above the polyimide layer, another photoresist layer is spin-coated. The previous optical lithographic technique is once more used to define and transfer the pattern of the dielectric bridge support with the co-development of both the photoresist and the underlying polyimide layer. After the selective resist stripping of the photoresist mask, the polyimide is stabilized by a thermal treatment curing procedure on a programmable hot plate with ramped heating from room temperature up to 350 \({}^{\circ}\)C under an ambient nitrogen atmosphere.  
Finally, similar to the first lithographic patterning and Cu electrodeposition process, the bridge Cu interconnect, with a thickness of 5 \(\upmu\)m, is created in order to electrically connect the inner core part of the bottom spiral inductor with the outer electrode. The fabricated RFID is presented in Figure 13a,b.
## 2 Materials and Methods
### Full-Scale SVE System
The SVE remediation began in June 2014 and ran continuously (except for brief interruptions due to power failures and technical issues) using all the extraction wells. No rebound testing was performed. In 2016, pre-acceptance tests were carried out in order to understand if the mitigation targets were achieved. A new coring campaign based on 13 samples was carried out with a variable depth (from 1 to 5 m from the ground level) in correspondence with the same cores considered during the environmental characterization, specifically S291, S291C, S293, S293A, S293B, S037, S037A, S037B, SP028, SP028C, P036, P036A, and P036B (Figure 4b). The organic contaminant concentrations were obtained by gas chromatography in accordance with the EPA method 8015D (standard deviation of measurements \(\pm 5\%\)) [30]. In addition, in 2018, another coring survey to monitor the performance of the remediation system was carried out. The efficiency rate, identified as hydrocarbon reduction in soil over the years, was calculated for each sample as:  
\[\%Eff=\frac{C_{in}-C_{fin}}{C_{in}}\times 100 \tag{2}\]  
with \(C_{in}\) the initial concentration at the EC in 2005 and \(C_{fin}\) as the final concentration at the preliminary test (PT) in 2018.