esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Identification of Accident-Prone Roadways
### Impact of Adverse Meteorological Conditions  
According to the Annual Report of Road Traffic Accident Statistics of the Ministry of Public Security of China [36], more than 20% of highway traffic accidents every year occur under complex meteorological conditions such as rain, snow, and fog. The data of 3108 valid traffic accidents during the actual operation of a highway in Sichuan (hereinafter referred to as SC1 highway) from 2015 to 2020 were used as the basis of the study, of which the number of accidents in rain, fog, and snow totaled 593 cases, accounting for about 19%. The types of accident patterns under the three adverse meteorological conditions were statistically analyzed, and the statistical results are shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Discriminating Indicators** & **Influence Degree** \\ \hline Rainfall & Rainfall within 24 h/mm \(\geq\) 10 mm & Certain impact \\ Fog & VisibilityL \(\leq\) 200 m & Certain impact \\ Snowfall & Snow cover thickness of 1.0 cm \(\leq\) H \(<\) 2.9 cm & Certain impact \\ \hline \hline \end{tabular}
\end{table}
Table 2: Minimum standard for risk classification of adverse weather on highways.  
Comprehensive analysis of the above data shows that under adverse weather conditions, traffic accidents are mainly in the form of collisions, accounting for about 58% of the total number of accidents. The main cause of this phenomenon is that the anti-slip property of the road surface decreases significantly in rainy and snowy weather, and the friction resistance is weakened, which leads to easy skidding of the vehicle tires, thus seriously affecting the driver's normal control of the vehicle, causing the vehicle to deviate from the intended driving trajectory, and colliding with other vehicles or stationary objects. At the same time, on foggy days with low visibility, it is difficult for drivers to accurately judge the distance from the vehicle in front of them, and the braking reaction time is prolonged, so the chances of rear-end accidents increase dramatically.  
In view of this, the identification of accident-prone road sections under specific climatic conditions and the development of targeted traffic safety measures and road improvement strategies accordingly are of great practical significance and wide application prospects for reducing the incidence of traffic accidents under adverse meteorological conditions, safeguarding road traffic safety, and enhancing the operational efficiency of roads.
## 5 Experiments
### Hybrid Localization Concept Verification
The results obtained using both algorithms differ for localization under the shower. In case of the loosely coupled version, when the user entered the shower, his estimated locations are grouped in one point. For the tightly coupled version, due to high variance of RSS measurements, some of the results are located outside the bathroom. The comparison of algorithm accuracy can also be performed based on Empirical Cumulative Distribution Function (ECDF) for localization errors. Since, during the performed experiment, the moving user was localized, it was difficult to precisely determine his true location for reference and calculate localization errors. Therefore, for accuracy comparison purposes, a trajectory error defined as the smallest distance between the localized point and the reference trajectory was used. The ECDF curves for calculated trajectory errors are presented in Figure 14.  
For both of the paths, the most accurate results were obtained with the UWB-based localization system. In case of the first path, the difference was significant (median trajectory errors equaled 0.27 m for UWB equaled, 0.45 m for loosely coupled hybrid algorithm and 0.51 for BLE-based EKF). In some cases, UWB system did not allow for accurate localization. For about 10 percent of the results the trajectory error exceeded one meter. It might have resulted from multipath propagation (in the apartment there were three large wardrobes with mirror covered doors). For the second path, the difference between UWB and hybrid algorithms was negligible. In both cases, the obtained trajectory errors were smaller when using additional ranging sensors than in case of using BLE only. The CDF curves show that a loosely coupled version of the hybrid algorithm was more accurate than a tightly coupled one but the difference was not significant. To conclude, the performed experiments have shown that using proximity sensors as an addition to BLE-based localization system allows to improve localization accuracy. The overall accuracy of the solution is lower than of the UWB based positioning systems, but given the higher cost and energy demands of the UWB based systems, the proposed concept might be a good alternative for use in some AAL solutions. Figure 14: Empirical Cumulative Distribution Function of localization trajectory error (smallest distance from reference trajectory) for: (**a**) path 1, (**b**) path 2.
# Dielectric Elastomer Actuator for Soft Robotics Applications and Challenges
## 8 Optical Application with Tunablility
### Liquid Droplet Lens  
To reduce the complex fabrication process, the liquid droplet lens using a DEA has been suggested [141]. A macro size hole is drilled in the center of the DEA, and a liquid droplet which acts as a lens is filled in the hole. In the initial state, the liquid droplet maintains its biconvex shape. From the applied voltage, the DE membrane expands in the planar area, and thereby the shape of the liquid droplet, which cause a change in the focal length. The miniature liquid droplet lens has a simple  
Figure 14: Structure illustration of all-polymeric lenses using DEAs: (**a**) a thin film active-lens [137]; (**b**) active-lens using hybrid driving force [138]; (**c**) a binary phase Fresnel lens [140].  
fabrication process, and fast response time (-540 ms). However, the liquid droplet should be controlled in micro size and should be placed in a horizontal position for imaging due to the gravitational effect.  
In this section, various types of adaptive focus tunable DEA lenses have been introduced. While DEA based tunable lenses show promising features, the controllability of the lens geometry remains a problem. Unlike hard lenses, soft lenses are prone to geometric errors on the lens surface. This may cause critical problems such as spherical aberration or image distortion. Therefore, methods to maintain and guarantee an accurate lens geometry need to be proposed to enhance the applicability and practicality of soft DEA tunable lenses.
# Detection and Classification of Human Activity for Emergency Response in Smart Factory Shop Floor
## 2 Related Works and Background Information
### Background on Machine Learning Algorithms
#### 2.5.3 Logistic Regression (LR)  
The LR is mostly applied for binary classification: datasets where y = 0 or 1, where 1 denotes the default class. For example, in predicting whether an emergency will occur or not, there are only two possibilities: that it occurs (which we denote as 1) or that it does not (0). So, if we were predicting the probability of a smart factory floor worker being sick or falling off or is in need of emergency response, we would label the emergency scenario using the value of 1 in our dataset. The algorithm follows the logistic function (S-shaped curve) given as (1)  
\[f(x)=\frac{L}{1+e^{-k(x-x_{0})}}, \tag{1}\]  
where \(x_{0}\), the \(x\) value of the sigmoid's midpoint; \(L\), the curve's maximum value; \(k\), the logistic growth rate or steepness of the curve. Despite the similarity in classification performance in both cases LR and SVM, local LR can provide class probabilities, whereas SVM is a deterministic classifier. Therefore, if the goal of the ML is classification only, each of them may be used, while LR is preferred where interpretation becomes necessary [45].
# Emerging Perspectives of Blockchains in Food Supply Chain Traceability Based on Patent Analysis
## 3 Results
### The Most Valuable Patents or Applications within Topics  
For the studied patent portfolio, forward citations and patent family sums, as well as the sum of these indicators, were analyzed (Figure 7). Because patenting of BC-based inventions intended for FSCs started relatively recently (Figure 4), the value indicators for most patents is consequently quite low. In the case of forward citation, such a result is expected, as there has not been sufficient time for this indicator to grow. On the other hand, low values of family size confirm that patents related to the application of blockchains in FSCs do not yet address the wider market.  
In order to gain insight into the potentially most influential patents from the portfolio, and particularly into their distribution among topics revealed from LDA, 16 patents with the highest scores of observed value indicators are presented (Table 3) by identified topics.  
The most outstanding patent regarding value indicators (CA2776577) is on topic C, to which patents combining blockchain and other ICT technologies in FSCs are assigned. This patent presents a system and method for establishing an agricultural pedigree for at least one agricultural product.  
The highest number of patents with relatively high-value indicators are on topic A, to which patents related to BC-supported tracing and tracking in FSCs are assigned. Such an outcome is quite understandable, since traceability in the FSC is required in food-related regulations worldwide.  
Figure 7: Assessment of the value of patents in the analyzed patent portfolio.
## 4 Physical Features and Response Characteristics  
It is clear from the literature within the field that physical features such as network size and density are regarded as important features which shape the response of a solution in the network as they are commonly used as independent variables for evaluation. This has been considered a priori to experiments and as a result no research has been directed at determining how much of an effect individual features have and if this effect is uniform over all communications solutions. Experiments conducted in this article address how each physical feature effects the ability of a communication solution to provide a level of Quality of Service (QoS) for an application while presenting data to determine the suitability of using a set of physical features as a means to model the response of a communications solution.  
The modelling process plays a major part in the framework described in Figure 2. The framework is highly dependent on how well a network can be modelled from the feature set. A number of challenges arise, however, when attempting to perform experiments which model an IoT network for this process.  
* Many physical features exist which may effect the response of a solution. The goal is to build the simplest model, with the fewest features. This set should also consist of the physical features which effect the greatest influence over the response. Determining this set is in itself challenging for two reasons: (i) Each communication solution reacts uniquely to the physical features of the network. Defining a set that incorporates the main influences for all solutions becomes more difficult; (ii) Removing features from the set may leave certain response types not covered by the model.
* Even with extensive testing on a given deployment it remains difficult to guarantee a level of QoS for an IoT network. Determining in absolute terms how a solution will perform on a particular deployment remains difficult even with effective modelling.
* The chosen features must be those which are easily obtained from the network in order to minimise the required testing on each network before deployment.  
The physical features used for testing are carefully chosen to ensure they encapsulate the main proponents determining the network response for each communication solution. In order to do this a number of different solutions, based on the RPL protocol, are tested over a range of physical features. The extent of the effect each feature plays in shaping the response is evaluated, determining which features are most prominent.
## 2 Materials and Methods
### Site Characterization and R\({}_{\rm s}\) Measurement  
In a 1/50 th ha (200 m\({}^{2}\)) circular fixed radius plot, all trees and shrubs greater than 2 cm diameter at 1.37 feet above the ground were identified and measured for diameter in September of 2018. We waited until February of 2019, for the absence of leaves, to collect tree heights to obtain better accuracy using a TruPulse 200 (Laser Technology, Inc., Centennial, CO, USA). In February, about 3% of the trees measured in September were snapped or broken by an ice storm. In order to obtain an estimate of their previous heights before the storm we created a simple linear regression based on the height and DBH data already gathered for undamaged trees of that species. Basal area, trees per acre, relative average dominance (basal area of a given species expressed as a percentage of the total basal area of all species present) and volume per hectare for all the species in each plot were calculated. Volume was estimated using the equations from [PERSON] and [PERSON] [20]. Species-specific equations were used for the following species: blackgum (_Nyssa sylvatica_ Marshall.), red maple (_Acer rubrum_ L.), black oak (_Quercus velutina_ Lam.), northern red oak (_Quercus rubra_ L.), and white oak (_Quercus alba_ L.). We used either a hard hardwood or soft hardwood equation for the other hardwood species' in the plots. Species-specific equations were used for our two conifer species, hemlock and eastern white pine (_Pinus strobus_ L.) from [PERSON] [21]. Biomass of herbaceous understory plants such as ferns was measured using a 1 m\({}^{2}\) clip plot in each 1/50 th ha plot collected on 14 August 2018 and later dried and weighed. The clip plot was taken 1 m distance from the plot center, aimed towards the first tree that we measured for DBH. Soil was sampled (0 to 10 cm) at three randomly selected locations in each 1/50 th ha plot using a soil push tube (2 cm diameter).  
The subsamples were well mixed on site and the composite sample was used for analysis. The 16 soil samples were sent to the Virginia Tech Soil Testing Laboratory to be tested for nutrients, pH, and organic matter content. We also tested for C:N ratio of the soil using a vario MAX CNS analyzer (Elementar Americas Inc., Ronkonkoma, NY, USA).  
In each of the vegetation types, we measured R\({}_{\rm s}\), and soil temperature and moisture in each plot monthly, spanning a full year, in order to get a wide range of soil temperatures and plant phenologies. Three random subsamples were collected in each plot. A Li-Cor 8100-103 gas analyzer (LI-COR Inc., Lincoln, NE, USA) with a survey chamber diameter of 20 cm was used to measure R\({}_{\rm s}\). In the same subsample locations during each visit, a digital thermometer, the AcuRite 00641, (Chaney Instrument Co., Lake Geneva, WI, USA) was used to measure soil temperature at a depth of 12 cm. A hydrosense II soil -water sensor (Campbell Scientific USA, Logan, UT, USA) was used to measure percent volumetric soil moisture from 0 to 11 cm.
## 1 Introduction
As a first experience, we used the routing protocol OLSR and AODV to validate the proposed approach, whereby exhaustive simulations with different scenarios were performed using the OpNET simulator (Optimized Network Engineering Tools). This paper is organized as follows: Section 2 introduces some related work and motivation for research. Section 3 contains an overview of the architecture for self-organization. Section 4 presents the experimental results and Section 5 discusses the results analyzed for the self-optimization and self-configuration process. Finally, Section 6 contains our conclusions and suggestions for future work.
###### Abstract
The proposed rehabilitation mechanism focuses only on finger movements in the sagittal plane. The mechanism in this study has two DOFs, uses virtual centers (through cams) and employs a parametric model to personalize the mechanism's 3D design. These design choices were made to maximize the patient finger's ROM and increase the variety and complexity of exercises during rehabilitation. With the help of parametric models and 3D printing technology, the device has a low cost compared to others, while the time needed for designing and manufacturing is also reduced. The main contributions to the rehabilitation field of this work are:  
* The ROM for each finger articulation is increased compared to the prototypes proposed in the current literature by using personalized four-bar mechanisms and cams with virtual centers. * Quickly personalizing finger rehabilitation devices that suit each patient's need through a parametric model will provide a unique work area for each individual. * The mechanism is non-invasive, having very little interference with the finger's natural movements by placing almost every part of the mechanism above the fingers and avoiding the use of physical hinges located at the articulation level. * Increments in the variety and flexibility of possible rehabilitation exercises, compared to designs mentioned before, by using two DOFs for each finger and having double action. * Reduction in manufacturing and design time and costs.
## 3 Macro-Element Application
### Linear Range
#### 3.1.2 Vibration Analysis
At last, it may be pointed out that the choice between the best approach to distribute the mass matrix is arguable because the accuracy depends on the mesh refinement. Still, a consistent strategy is adopted hereafter. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Macro–Element**} & \multicolumn{4}{c}{**Macro–Element**} & \multirow{2}{*}{**FEA**} & \multirow{2}{*}{**(**rad/s)**} & \multirow{2}{*}{**m**} & \multirow{2}{*}{**n**} \\  & \multicolumn{2}{c}{**(Lumped Mass Approach)**} & & & & & & & & & & \\ \cline{2-10}  & **N = 4** & **N = 8** & **N = 16** & **N = 30** & **N = 4** & **N = 8** & **N = 16** & **N = 30** & \\ \hline
1 & 1.276 & 1.094 & 1.026 & 1.025 & 1.244 & 1.088 & 1.064 & 1.024 & 1.000 & 19.739 & 1 & 1 \\
2 & 1.382 & 1.085 & 1.037 & 1.037 & 1.302 & 1.069 & 1.064 & 1.034 & 1.000 & 49.348 & 1 & 2 \\
3 & 1.382 & 1.085 & 1.037 & 1.037 & 1.294 & 1.069 & 1.064 & 1.034 & 1.000 & 49.348 & 2 & 1 \\
4 & 1.499 & 1.129 & 1.036 & 1.028 & 1.371 & 1.103 & 1.073 & 1.031 & 1.001 & 78.957 & 2 & 2 \\
5 & 1.405 & 1.088 & 1.047 & 1.045 & 1.276 & 1.053 & 1.063 & 1.039 & 1.000 & 98.696 & 1 & 3 \\
6 & 1.475 & 1.084 & 1.046 & 1.045 & 1.218 & 1.049 & 1.061 & 1.039 & 1.000 & 98.696 & 3 & 1 \\
7 & 1.569 & 1.143 & 1.045 & 1.038 & 1.356 & 1.099 & 1.075 & 1.036 & 1.001 & 128.30 & 3 & 2 \\
8 & 1.569 & 1.143 & 1.045 & 1.038 & 1.355 & 1.099 & 1.075 & 1.036 & 1.001 & 128.30 & 2 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Convergence study: normalised natural frequencies \((\omega/\omega_{exact})\) found for the proposed macro–model and a standard continuous Finite Element Analysis (FEA).
###### Abstract
Researchers have highlighted the advantages of synthesizing CQDs from fruit juices, particularly citrus fruits, which are predominantly acidic and rich in carboxyl groups, resulting in higher yields compared to other fruits [16]. Plasma is a fundamental state of matter that comprises photons, as well as charged, molecular, atomic, and radical species. Cold plasmas can be generated without the need for expensive vacuum systems or pumps, making them applicable in various fields, such as plasma sintering, water desalination, nanomaterial synthesis, surface modification, material deposition, and bacterial inactivation [19]. [PERSON] et al. utilized cold plasmas to synthesize CQDs, employing chloroplatinic acid and ethanol as precursors, resulting in a single-emission wavelength [20]. [PERSON] et al. achieved the creation of graphene quantum dots enveloped in gold nanoparticles through DC microplasma [21]. Blue emissive CQDs have been successfully synthesized via plasma treatment using a range of carbon precursors, including citric acid, D-fructose, sodium dodecyl sulfate, chitosan, hydrocarbons, and folic acid [21, 22, 23, 24, 25]. Notably, CQD synthesis has proven successful in both acidic and basic reaction environments. However, it is worth noting that the use of fruit juice as a precursor for CQD synthesis through the plasma method remains an unexplored area of research. The excessive use of antibiotics, both in agricultural and clinical settings, has resulted in widespread antibiotic resistance, necessitating the urgent development of new antibacterial agents. According to the World Health Organization (WHO), Gram-negative (GN) bacterial pathogens are particularly resistant to antibacterial treatments [26]. The structure of GN bacteria inherently provides greater resistance compared to Gram-positive (GP) bacteria. GN bacteria feature a slim, peptidoglycan-covered outer layer with multiple thin membrane layers that do not readily absorb external materials, making them more challenging to combat with various antibacterial agents. _Escherichia coli_ (_E. coli_), a representative GN bacteria, has been studied for its resistance to antimicrobial cationic surfactants [27]. CQDs with positive surface charges act as potent antibacterial agents against negatively charged bacteria, impairing the surfaces of bacterial cells. Consequently, CQDs emerge as promising candidates for photo-activated antibacterials capable of effectively preventing and controlling pathogenic bacteria. Notably, the environmental and health hazards posed by antibacterial chemicals, such as hydrogen peroxide and sodium hypochlorite [28], are mitigated by utilizing CQDs, offering a safer alternative to chemical antimicrobial toxicity. In this study, eco-friendly CQDs were synthesized from orange juice, serving as a green precursor through a plasma method. The optimal CQD was subjected to antibacterial testing, with the primary goal of investigating and determining the average antibacterial rate of the CQDs against _E. coli_ strains. The results indicated that CQDs exhibit potential as novel antibacterial nanoparticles.