esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Data Collection Techniques  
For each of the seven sites, a 3 km \(\times\) 3 km grid of systematically distributed points were prepared and overlaid the images. This resulted in 228 sample points within each site. Systematic sampling design was used because of its potential to uniformly cover the entire sites and thereby produce more precise estimates [15]. Each of the two observers interpreted a total of 4788 observation points (228 observation points per site per image type \(\times\) seven sites \(\times\) three image types). Having the grid point at the center, a square of 0.5 ha (about 70 m \(\times\) 70 m) was created for all of the 228 grid points within each site. This cell layer was the unit of interpretation of the LULC class. This cell size was chosen to match with the minimum area of the national and FAO definitions of forest. Within these 0.5 ha cells, a systematic grid consisting of 49 squares of 2 m \(\times\) 2 m in size, was arranged. These small squares were used in the visual interpretation to observe the presence/absence of tree crowns, and then used for canopy cover determination. The canopy cover of a single cell of 0.5 ha was determined using the count of points that coincided with the crowns of trees divided by 49. The images were clipped to 1 km \(\times\) 1 km tiles for each of the 228 points, and centered at each point to have a better overview of the surrounding land cover during the interpretation. A package was written in R language to develop a graphical user interface that was used to display images for each point in a completely randomized manner, and record and save the results of the image interpretation. The 0.5-ha cell with 49 grid points was enabled to be zoomed in and displayed together with the clipped image (Figure 2). In addition, the land-cover type, time of acquisition for the selected image, and elapsed time (time from launching a location to saving results) were recorded.  
A given LULC type within the 0.5 ha was determined visually based on the IPCC definitions of LULC categories [15] i.e., forest land, cropland, grassland, wetland, settlements, and other land; see the IPCC guidelines for details. The thresholds for forest land were 0.5 ha area and 20% canopy cover in the current study. Furthermore, interpretation decisions were made following a hierarchy of rules [34] (Figure 3). The proportions of points that fell on tree crowns (if any) were determined, regardless of the LULC class types. Among the potential images for interpretation, the newest image was used. Two persons performed the interpretation, and the average of the interpretation results was used in further analyses.  
Figure 2: The graphical user interface used for visual image interpretation. (**A**) A window for opening new image and recording the interpreted data; (**B**) 1 km \(\times\) 1 km image with a point at the center; (**C**) a zoomed into 0.5-ha cell with the 49 points (2 m \(\times\) 2 m squares).
# How to Make Augmented Reality a Tool for Railway Maintenance Operations: Operator 4.0 Perspective
## 2 Theoretical Background
### Operator 4.0: Augmented Operator  
Maintenance operations, in which the human workforce has a crucial role, need instruments able to manage complex systems. Nowadays most of the lower-skilled human jobs were eliminated and replaced by technology. This results in the remaining jobs becoming more complex, comprehensive, and increasing the importance of interdisciplinary cooperation [24]. Consequently, numerous complex, high-precision processes are and will be managed manually. However, many of them could collaboratively be executed by humans and interactive technology cooperation.  
Several factors are important regarding work satisfaction and dissatisfaction, including the work environment, work organization, and whether the work is interesting [18]. The operator's cognitive ergonomics, interests, and expertise should be included carefully since interaction with the real and digital systems is established. During this process, the operator's physical and mental workloads are affected by both system and process features as well as by external factors. Operators produce a subjective experience depending on the surrounding environment, individual skills and characteristics, and task features [24].  
In an augmented environment, the Operator 4.0 typology considered is the augmented operator [25]. More specifically, the augmented operator owns a superior knowledge of the working environment which is not only derived from the daily interactions related to the maintenance task or procedures, but it incorporates a variety of value-added contents that are suited to augment his/her skills and abilities to perceive and act within the working environment [26]. Operator 4.0 is expected to benefit from the information and guidance generated within the virtual context. Applicable resources that usually may not be available should be made available directly using interactive technology. Besides this, a remote consultation tool to get easy and fast information and advice, and a tool for safety and security enhancement has to become available. The AR technology ensures lowering the cognitive load of the operator, as the operator does not have to manually search for filter  
Figure 2: Added value augmented reality (AR) offers maintenance procedures adapted from Campbell [22].  
according to the current context, or interpret information on a screen, rather they can visualize it directly on the target object [27].  
From a business perspective, maintenance KPIs can be analyzed that allow managers to have a proper overview of workstations and production lines in real-time monitoring [27]. It becomes possible to identify, analyze, diagnose, and resolve errors to keep maintenance processes moving towards operational efficiency.
# Actuation Mechanisms and Applications for Soft Robots: A Comprehensive Review
## 2 Actuation Materials and Mechanisms for Soft Robots
### Shape Memory Alloy (SMA)  
SMA is a specific alloy that, after being deformed, can return to its initial shape when heated [66; 67]. As shown in Figure 2, the effects of the shape changes can be categorised into three types: one-way shape memory effect (OWSME), two-way shape memory effect (TWSME), and pseudo-elasticity [68; 69]. Under a certain temperature, stress, and strain, SMA can exhibit two different phases, three different crystal structures (detwinned martensite, austenite, and twinned martensite), and six possible transformations [70]. These phenomena create OWSME, TWSME, and pseudo-elasticity, respectively.  
Typical SMAs include Ni-Ti-based (nitinol), Fe-based, Cu-based, etc. Ni-Ti-based SMA is the most commonly used shape memory material, owing to its high thermal stability, strain recovery, and corrosion resistance [71; 72]. That is, in terms of heat resistance, transformation, and recovery stress, Fe-based and Cu-based SMAs are not as good as Ni-Ti-based SMAs, so they are less used to design actuations of soft robots [73; 74; 75]. Ni-Ti-based functional components with shape memory effects can be flexibly produced using  
Figure 2: SMA phases and crystal structures [68; 69].  
additive manufacturing processes [58], which supports the design of small, exquisite, highly automated, and reliable soft robots. Therefore, Ni-Ti-based SMA has attracted increasing attention and has been widely applied in biomedical, aerospace, robotics, mechanical, and other fields [76]. SMA-based actuators can be mainly divided into the spring form and the wire form. More technical details are given below.
# Real-Time Control System for Model Railway Based on SIMIS W Interlocking System
## 2 System Architecture
### Hardware  
Figure 2 shows the control system's hardware architecture. The top layer consists of several desktop computers that run the ILTIS-N operator workstations and the REST API communication server. The second layer includes the SIMATIC S7-1200) and S7-300 PLCs. These PLCs collect and process data from the model railway and control track elements, including signals, switches, and level crossings.  
The SIMATIC S7-1200 PLC includes a CPU 1214C DC/DC/DC processor module and multiple local and remote I/O modules. The remote I/O modules communicate with the CPU via the ET 200 SP interface, which utilizes the Profinet interface. This PLC controls all signals and switches on the model railway through custom-designed interface modules and communicates with the desktop PCs over the laboratory LAN.  
The SIMATIC S7-300 PLC consists of a CPU 313C-2 DP and I/O modules SM323 and SM322. This PLC collects and processes data from the track circuits, which it then transmits to the S7-1200 PLC via a hardwired connection.  
Figure 1: Simplified topology of model railway [8].  
Figure 2: Hardware architecture of model railway control system [8].
## 2 Materials and Methods
### Construction satP of the Deletion Strains and the Complemented Strains  
The _satP_ gene knockout was conducted with the suicide vector, pRE112. Briefly, the genome of the strain to be knocked out was amplified by PCR using UP-F/R and DOWN-F/R primers (Supplementary Table S1), respectively, to obtain the upstream and downstream regions of the _satP_ gene. Then, the PCR products were mixed as the template for the next overlapping PCR, and UP-F and DOWN-R were used as primers to amplify the template. Finally, the amplified PCR product (1673 bp) was cloned into the PRE112 vector, and no base mutation was identified by sequencing. Then, the plasmid pRE112-_satP_ was successively transformed into DH5\(\alpha\)-\(\lambda\)pir and _E. coli_ WM3064. The single colony was inoculated into 5 mL BHI broth, then inoculated into the BHI agar medium containing 10% sucrose, and cultured overnight at 37 \({}^{\circ}\)C. The recombinant strain without the _satP_ gene and pRE112 plasmid was screened (thus creating the _satP_ mutant of the \(\Delta\)_PmS_, \(\Delta\)_PmR_ and \(\Delta\)_PmHR_ deletion strains). The complemented strain, _CPmS_, was constructed with the expression plasmids pBBR1-MCS, by homologous recombination.
## 1 Introduction
For example, the group of [PERSON] et al. [41] report that micro-engineered cell scaffolds provide the exact conditions for successful human osteoblasts' primary cell line differentiation into osteocytes with a limited number of cells seeded, which, according to the authors, represents a functional strategy to create a patient-specific tissue model. Research from [PERSON] et.al. [42] has demonstrated that the combination of multi-scale structures with core-shell design of the scaffolds could successfully mimic the cortical bone structure. They have used human adipose-derived stem cells (hADSCs) cells as a very appropriate choice for autologous stem cells in order to boost bone regeneration. Furthermore, hADSCs can be efficiently derived from adipose tissue by minimally invasive surgery. Nevertheless, for primary experimental evaluation, the use of a standard immortal cell line is still preferred, because of the higher reproducibility of the results obtained, lower cost, and uniform cell type. Cells can be grown and expanded over longer periods of time, in standard culture conditions, with no need for special media or additives [38]. To the best of our knowledge, the influence of laser parameters, like scan speed, laser power, and repetition frequency, of the pulses has extensively examined [43; 44; 45; 46; 47], whereas fewer data exist concerning the influence of the number of applied laser pulses (spot scanning regime) on the processing quality of ceramic samples, which differs from the regime of linear-scanning. The development of various microstructures, in the form of bumps or pit-like, is a consequence of the selection of pulse to pulse overlap ratio [48]. The literature survey has not indicated studies related to the effect of laser-induced processing via a selection of the preferred number of applied laser pulses (N) on ceramic biomaterial. In this study, laser-based methods utilizing two regimes of texturing \"spot to spot\" scanning and \"continuous line scanning\" of surface modification were applied to improve the morphological properties of the surface of \(\beta\)-TCP pellets for proper cell surface environment creation. The microstructured scaffolds were investigated by confocal laser scanning microscopy (CLSM), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), Fourier-transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD) analyses. Live/dead assay analysis, Alamar Blue Assay, and actin staining of the mouse calvaria osteoblasts (MC3T3) cultured on ceramic scaffolds were performed as a part of preliminary cell seeding experiments for evaluating the effect of the patterned ceramic surface on cytotoxicity and MC3T3 orientation behavior from day 1 to 7.
# Radio Signal Modulation Recognition Method Based on Deep Learning Model Pruning
## 5 Experimental Analysis  
The simulation tool for our experiment is Keras 2.0, based on Linux. The batch size is set to 256. The optimizer is Adam, and the learning rate remains Keras' default value. The maximum number of epochs is set to 100. The hardware uses an NVidia Tesla P4 (8 GB memory) GPU, with 12 GB of memory. All the data are calculated by testing three times to average it, and the loss value is calculated. The effects of five parameters on the proposed model are shown and discussed below. Our model is also compared with some of the existing models as well.
## 4 Pharmacological Activities of _Z. montanum_ Extracts and Compounds
### Antibacterial Activity  
_Z. montanum_ essential oil showed potent antibacterial activity against a number of Gram-positive and Gram-negative bacteria. Compared to methanolic extract, chloroform extract showed significant antimicrobial activity against a wide range of pathogens [66]. The rhizomes of _Z. montanum_ are reported to be rich in essential oil effective against a range of pathogenic bacteria including _Escherichia coli_, _Ikbeisella pneumonia_, _Salmonella paratyplii_, _S. typhi_ and _Shigella flexneri_[27]. _Z. montanum_ oil showed potent antimicrobial activity against seventy-four microbial strains with most potent activity against bacteria as such _Bacillus subtilis_, _E. coli_, and _Salmonella typhi_ evaluated by disc-diffusion broth dilution method [19]. [PERSON] et al. reported significant antimicrobial activity of the essential oil of _Z. montanum_ against Gram-negative _Acinetobacter baumannii_ strains by agar disc-diffusion tests [67]. Sesquiterpenes, monoterpenes and diterpenes from _Z. montanum_ showed various degrees of antimicrobial action against _B. cereus_, _Staphylococcus aureus_, _E. coli_, and _Pseudomonas aeruginosa_[68].
# Research on Microstructure and Mechanical Properties of Laser Welding of BR1500 HS Joints
## 3 Results and Discussion
### Microhardness  
#### 3.3.1 Effect of Welding Speed on Microhardness  
Welding process parameters and quenching process have a great impact on the microhardness of welded joints. When the laser power is 2600 W, the microhardness of the joint before and after quenching at different welding speeds is shown in Figure 8. As can be seen from Figure 8a, before quenching, the microhardness of FZ, CGZ, and FGZ is higher than BM and IRZ due to a large amount of martensite. The microhardness of the former is greater than 400 HV, while that of BM is only about 200 HV. This also explains why the strength of the joint is higher than that of BM before quenching. As the welding speed increases, the width of FZ and HAZ gradually decreases, which can also be seen in Figure 9. It can be seen from Figure 8a that all samples exist in the softened area in the HAZ. The hardness drops to a minimum in the critical zone and then rises linearly. The softening occurs near the base metal in the HAZ because of the area between the peak temperature \(A_{\text{C1}}\)\(\sim\)\(A_{\text{C3}}\) in the HAZ is not completely quenched and the martensite in the area below the temperature \(A_{\text{C1}}\) is tempered. The two parts jointly form the softened area and the softest spot appears in the interface between the quenching zone and the tempering zone. In Figure 8a, when the laser power is constant and the welding speed increases, the minimum hardness increases, and the width of the softened area decreases. The width of the softened area reflects the isotherm distribution of the temperature field and the minimum hardness reflects the thermal cycle of each temperature point. As the welding speed increases, the temperature field elongates in the welding direction, the area below \(A_{\text{C3}}\) becomes narrower. Therefore, the softened area becomes narrower. The high welding speed will shorten the maintenance time of each temperature point and the cooling rate will be fast. The original structure will be less affected, so the minimum hardness will increase [28].  
Figure 8b shows the microhardness of the joint after quenching at different welding speeds. After quenching, the microstructure of the FZ, HAZ, and BM transforms into martensite and a small number of retained austenite with uniform grains. As a result, the microhardness of all joint areas becomes more uniform and is between 400 HV and 500 HV. The microhardness of BM increased significantly from about 200 HV to about 475 HV, while the microhardness of FZ, CGZ, and FGZ increased slightly after quenching.  
Figure 7: Tensile fracture morphology of samples No. 5\({}^{\sharp}\): (a) base materials (BM); (b) joint.
## Abbreviations and Parameters  
The following abbreviations and parameters are used in this manuscript:  
\[\begin{split}\text{BER}&\text{bit error rate}\\ \text{DDSIC}&\text{decision-directed successive interference cancellation}\\ \text{DVB}&\text{Digital Video Broadcasting}\\ \text{FTN}&\text{Faster Than Nyquist}\\ \text{MMSE}&\text{maximum likelihood sequence estimation}\\ \text{SDR}&\text{Software-defined radio}\\ \text{SSSSE}&\text{Sequential Character Sequence Estimation}\\ \text{SSSgbKSE}&\text{Sequential Character Sequence Estimation with go-back-K Sequence Estimation}\\ \text{$a(t)$}&\text{amplitude pulse}\\ \text{$A_{0}$}&\text{maximum value of amplitude pulse}\\ \text{$f_{0}$}&\text{carrier frequency}\\ \text{$T$}&\text{transferring time of one information bit}\\ \text{$N$}&\text{number of signals in sequence}\\ \text{$R=1/\xi T$}&\text{bit rate}\\ \text{$L$}&\text{parameters that determine signal duration}\\ \text{$d$}&\text{modulation symbol}\\ \text{$R_{n}$}&\text{normalized symbol rate}\\ \text{$T_{s}$}&\text{observation interval}\\ \text{$\mu$}&\text{channel transfer coefficient}\\ \text{$M$}&\text{Base of the channel alphabet}\\ \text{$Q$}&\text{number of subsequent signals}\\ \text{$q$}&\text{number of used subsequent signals in algorithm}\\ \text{$I$}&\text{number of previous signals}\\ \text{$E_{b}$}/\text{$N_{0}$}&\text{signal-to- noise ratio per bit}\\ \text{$p$}&\text{serial number of symbol in the transmitted sequence}\\ \text{$N_{s}$}&\text{number of samples per bit}\\ \end{split}\]
# A 200-MS/s 10-Bit SAR ADC Applied in WLAN Systems
###### Abstract  
This paper introduces a new high-performance successive approximation register (SAR) analog-to-digital converter (ADC) designed for high-speed and low-power wireless local area network (WLAN) applications using a SMIC 55 nm 1p8m CMOS process. The design employs several innovative techniques, including an improved bootstrap switch with high linearity, a 4-reference voltage method to minimize capacitive digital-to-analog converter (CDAC) mismatch, a kickback-canceling comparator to eliminate kick-back noise, and redundant design-assisted window-opening SAR logic to decrease conversion time. Experimental results reveal that the proposed ADC achieves an impressive signal-to-noise and distortion ratio (SNDR) of 55.3 dB and a spurious-free dynamic range (SFDR) of 66.6 dB at a sampling rate of 200 MHz with Nyquist frequency input while consuming a power of 2.8 mW at a 1.2 V power supply. This corresponds to a figure-of-merit (FoM) value of 29 fJ/conversion-step. Thanks to the incorporation of the 4-reference voltage method, the ADC demonstrates a significant area advantage compared to other designs with similar FOM values utilizing more advanced processes, occupying a mere 0.008 mm\({}^{2}\) of core area.  
SAR ADC; WLAN; kickback noise; mismatch Article  
A 2002313704  
comparator input referred noise [7]. Another challenge is that the successive approximation serial structure limits the speed. These challenges require researchers to develop innovative design techniques to overcome them.  
The following Figure 2 illustrates the block diagram of a conventional SAR ADC, which mainly includes a sample and hold (S/H) block, a comparator, SAR logic, and a CDAC.  
This paper presents a novel SAR ADC designed for WLAN applications with a 200 MHz sampling rate and 10-bit resolution that consumes minimal power and occupies a small area. To achieve these goals, the design utilizes several innovative techniques, including an improved bootstrap switch with high linearity, a 4-reference voltage method to minimize CDAC mismatch, a kickback-canceling comparator to eliminate kickback noise, and redundant design-assisted window-opening SAR logic to decrease conversion time. The paper is structured as follows: Section 2 outlines the proposed SAR ADC architecture, which includes a high-linearity and high-speed design strategy. In Section 3, the individual building blocks are discussed in detail. Section 4 describes the test results. Finally, the discussion section compares the performance of the proposed ADC with other similar works, while the conclusion section summarizes the main contributions of this paper.
## 5 Experiments and Results
### Analyses of Parameters' Impact on Data Cleaning Operations
#### 5.3.1 Effect of the Frequency Threshold  
The effect of the frequency threshold \(f\) is limited due to the fact that most of the stations correctly log their status. Given a total number of 284 stations, Table 6 reports the number of infrequent stations (i.e., the stations with many missing data) given the threshold parameter \(f\) and a _varianceThreshold_\(=5\) (this is the default value for _varianceThreshold_).  
Therefore the effect of the frequency threshold is limited. As a consequence of this analysis, we set the frequency threshold \(f\) to 0.9 for our framework, which means that only the stations for which their status is available in less than 90% of the timestamps are discarded. In other words, at most 10% of missing log data are allowed for each of the considered station.