esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Brake pad wear forecasting, due to its complex nature, is very difficult to describe using engineering formulas. Therefore, the aim of this publication is to create high-quality brake pad wear forecasts based on three stochastic quantitative models based on multiple regression models (linear model, inverted linear model, and power model). The matrix of explanatory variables was extracted from the controllers of 29 vehicles: A--the driver's style of using the brake pedal specified on a 4-point scale and B--the number of vehicle load ranges specified on a 5-point scale. Methodology: A matrix of explanatory variables was obtained over a 2-year period from trucks carrying oversize loads via OBD2 socket. The trucks operated under similar operating conditions. The created models were verified in terms of their fit to the source data and by analyzing the residuals of the models. It should be emphasized that only the linear model met all the required criteria. The inverted linear and power-law models were rejected. Results: The verified linear model is characterized by very small MAPE errors. The model was validated on 4 trucks and the brake pad wear prediction errors ranged from \(-0.39\%\) to \(7.03\%\).  
wear estimation; brake pads prediction; truck operation; multiple regression Article
## 2 Related Work
The rapid development of drone charging technology [23; 24] has opened up a new possibilities to charge sensor nodes, making charging scheduling more efficient and flexible. Many researchers have started considering the use of drones to charge sensors wirelessly in sensor networks. For example, due to space constraints on vehicles, [PERSON] et al. [17] explored the uses of drones to replenish nodes in bridge monitoring. [PERSON] et al. [22] investigated the possibility of charging nodes using drones equipped with wireless chargers while minimizing the number of drone positions by adjusting their altitude, which affects power harvesting. [PERSON] et al. [18] described a multi-drone wireless charging method that jointly optimizes the route association and charging routes to maximize total charging utility and devised an efficient approximation algorithm with a \(1/3\alpha\) approximation ratio to solve the bounded route association problem, which is proven to be NP-hard. [PERSON] et al. [19] proposed a novel data collection scheme using drones as mobile collectors, capable of meeting requirements in both small- and largescale scenarios. [PERSON] et al. [20] focused on wirelessly charging sensor nodes via drones and optimized the scheduling of drones to maximize the minimum energy received by all nodes. [PERSON] et al. [21] presented a method using drones to charge nodes for their proposed concept of the period area coverage problem. [PERSON] et al. [25; 33] proposed hybrid charging considering the metrics of both drones and vehicles, enabling a WRSN equipped with a collaborative charging system to be applied in large and complex regions. Although some research has focused on charging nodes using vehicles or drones, there are still some shortcomings, such as offroad and speed constraints for vehicles, and limited battery capacity for drones. To overcome these limitations, this work proposes a novel scheme that employs a single vehicle and multiple drones to charge nodes simultaneously. Furthermore, we designed a charging schedule for hybrid charging modes based on on-demand architecture. This work is different from the previous work. To the best of our knowledge, we are the first to consider a charging system including vehicles and drones with the LDFM scheduling scheme. Table 1 compares the charging vehicles and scheduling methods used in our proposed model with those in the previous literature.
# The EGNOS Augmentation in Maritime Navigation +
###### Abstract  
The objective of this work is the evaluation of the performances of EGNOS (European Geostationary Navigation Overlay System) augmentation system in maritime navigation by comparing them with those obtained by other positioning methods as Single Point Positioning (SPP) and Differential Global Positioning System (DGPS). Preliminarily, EGNOS performances in an open-sky context were evaluated through static data downloaded by EGNOS RIMS (Ranging and Integrity Monitoring Stations) located in Rome. Then, for the maritime test carried out onboard a boat in the Gulf of Naples, two dual-frequency receivers were used: Xiaomi Mi 8 smartphone and u-blox ZED-F9P multi-band GNSS (Global Navigation Satellite System) receiver, both in kinematic mode. At last, IMO (International Maritime Organization) requirements, established in IMO Resolution A.1046 (27), that a SBAS (Satellite Based Augmentation System) system in particular scenarios (coastal, inland-water, harbor navigation and ocean waters) must respect, were verified.  
maritime; EGNOS; positioning 2022222775[[https://doi.org/10.3390/s22030775](https://doi.org/10.3390/s22030775)]([https://doi.org/10.3390/s22030775](https://doi.org/10.3390/s22030775))
# Drivers' Psychomotor Reaction Times Tested with a Test Station Method
## 2 Method
### Course of the Test in Nominal Conditions  
The possibility of examining the sensitivity of the proposed measurement method to detect the reaction time change depending on the kind of stimuli was the main topic of the study. An assumption was made that the stimuli reaching the driver would be diversified for their location against the vehicle. Additionally, the differences in the measured reaction time for turning and braking were checked.  
The purpose of the study was to get the answers to the following questions:  
Figure 3: View of the control application screen.  
1. Is it possible to observe and measure the following aspects in the tests: - Diversification of the response time depending on the expected reaction--braking, turning or braking and turning simultaneously? - Diversification of the driver's response time depending on the direction from which the stimulus arrives? 2. Are the potentially measured differences in the reaction times confirmed in the literature data obtained with other methods? The test schedule included long-term tests consisting of several dozen pseudorandomly sequenced stimuli coming from different directions. The signs were arranged around the vehicle so that they could provide visible signals (Figure 2): - In the center of the field of vision. - At the extremes of the field of vision, on the left and right. - In the rear view mirror (the sign is placed behind the vehicle). In response to the stimuli, the following reactions were expected from the driver: turning the steering wheel left or right, pushing the brake, or a combined reaction of braking and turning. The tests applied to a single driver while more numerous tests were supposed to confirm the phenomena statistically. At the beginning of the tests it was assumed that the study material to be collected should be as extensive and diversified as possible. Appropriate statistical processing of the results should enable initial evaluation of the factors which affect the reaction time. Groups consisting of several dozen drivers were examined using tests that included 50 stimuli coming at intervals lasting from 1 to 30 s. This helped to collect a few thousand responses. Grave errors were eliminated from the results. They constituted 4% of the responses and had unrealistically long reaction times of over a few seconds--Figure 4. The values occurred as a result of a specific reaction time measurement method in which the driver does not know whether his/her maneuver was accepted by the measurement system (the sensors purposefully do not provide feedback and switching the light sign off is the only signal informing about shifting to the next period of waiting for the subsequent stimulus).  
The measurement application helped to make an initial assessment of the averaged values of a particular driver's reaction and referring his/her result to the whole studied population owing to collection of global data.
## 3 Mobile Network Security Evolution and the Evolution of Threat Landscape
### Fifth-Generation Security Architecture
#### 3.3.2 Network Slicing Security  
The advent of network slicing is one of the defining features of 5G networks, allowing operators to create isolated \"slices\" of the network for different applications or services: (1) Isolation of slices: Each network slice operates as an autonomous entity with its own resources and network functions. This enables the implementation of slice-specific security policies. (2) Adaptive security measures: The security protocols can be tailored for each slice, accommodating varied requirements ranging from low-latency, high-reliability slices to slices designed for mMTC. (3) Resource allocation: The dynamic resource allocation capabilities of network slicing also extend to security resources, allowing for real-time adjustments in response to detected threats [56; 57; 58; 59].
# A Poisson Process-Based Random Access Channel for 5G and Beyond Networks +
## 3 Existing Contention-Based Random-Access Mechanism
### RRC Connection Setup (\(\mathit{eNB}\to\mathit{UE}\))  
This phase is also known as CRP, in which an eNB acknowledges the UE after decoding the RRC request. RRC link configuration messages are sent using the dedicated RNTI. After this, an acknowledgment is submitted by the active UEs to the eNB and proceed with data transmission. However, once the limit of retransmission attempts is approached, the colliding UEs must wait to begin a new CB-RA process.
## 4 Material and Methods  
### Shallow Seismic Refraction Method  
The shallow seismic refraction technique was used to investigate unknown subsurface areas in detail. This technique is based on Snell's law, which regulates how waves that are propagating over layers of differing seismic wave velocities are refracted. Once those waves' arrival times are measured at the surface, they can be inverted to determine the characteristics of the rock they traveled through [19; 20; 21]. Rock velocities, depths, and thicknesses are the main outputs of this technique. The seismic refraction tomography approach for site investigation is more powerful when it is compared with traditional electrical resistivity tomography [22]. Seismic waves produced by man-made sources, such as sledgehammers, travel across a medium in the seismic refraction method and are bent at interfaces where the density (acoustic impedance) or the seismic velocity varies. Waves returning to the surface after traversing various depths in the subsurface are recorded by different geophones arranged in a straight line. The seismic velocity in the subsurface and the depth of the interfaces are determined by measuring the amount of time that passes between the break and the recording of a seismic signal. Simple assumptions about the velocity structure are made in conventional analyses of seismic refraction data sets; however, these assumptions are at variance with the lateral discontinuities, observed heterogeneity, and gradients [23].  
P-wave refraction demonstrated its effectiveness in determining the depth to the bedrock within the framework of site assessment research [24; 25; 26]. However, horizontal shear (SH) wave refraction is employed to investigate subsurface layers only to certain depths and in the right circumstances [27]. Combining P- and S-wave refraction techniques improves our knowledge of subsurface geology or provides supplemental data. Because changes in saturation, elastic moduli, or porosity impact P- and S-waves differently, they are crucial for differentiating various lithological compositions [28; 29]. In order to provide subsurface volumetric imaging of lithologic and geotechnical variables, P- and S-wave seismic refraction tomography is a powerful tool. Recent studies demonstrated that seismic refraction tomography, which uses both P- and S-waves, is used to systematically evaluate V\({}_{\text{s}}\)[30] and geotechnical variables that are more sensitive to variations in the subsurface soil characteristics [30; 31]. Additionally, V\({}_{\text{s}}\)[30] is used to establish the NEHRP site class of a location [32].
## 2 Synthetic DAS Signal Generation Methodology
### Transport Microsimulation (PTV VISSIM)  
Microscopic transport models simulate individual vehicle movements and interactions within a defined network. We utilize PTV VISSIM for its detailed modeling capabilities. The key steps tailored for generating DAS-relevant inputs include the following:  
1. Network Creation: Define the road network links with 3D coordinates (including heights/gradients) and relevant attributes (lanes, speed limits, etc.). Include geophysical parameters (Poisson's ratio \(\
u\), Young's modulus \(E\)) as user-defined attributes for each link section to represent homogeneous ground conditions. Define traffic control elements (signals and signs) realistically.
2. Fiber Layout Codification: Precisely define the 3D coordinates of the existing or desired fiber-optic cable layout relative to the transport network. This is crucial for calculating load effects. In VISSIM, this can be modeled as a non-traffic link chain (Figure 5).
3. Vehicle Parameter Enrichment: Define the vehicle mix, including types relevant to DAS (cars, trucks, bicycles, etc.). Use the Heavy Good Vehicle class to define all vehicles in order to be able to assign a weight distribution to each type. Crucially, enrich standard VISSIM vehicle models with DAS-significant parameters: number of axles/wheels, wheelbase, track width, wheel positions relative to vehicle front, and estimated load percentage per wheel. This allows calculating the position and load of each wheel, even for vehicle types where VISSIM's default axle representation is insufficient.
4. Coordinate Transformation: Convert VISSIM's internal coordinates to a global system like WGS 84 (latitude, longitude, and altitude) using user-defined attributesand scripts. This facilitates calculating distances between vehicle wheels and fiber segments consistently (code available; see page 17).
5. Simulation Runs: Execute simulations with varying traffic densities (e.g., low, intermediate, and high flows) and random seeds to generate diverse scenarios. Set an appropriate simulation time step (e.g., 0.1 s).  
**Data Extraction and Preparation:** The simulation output provides vehicle trajectories (positions over time) along with their enriched characteristics (wheel loads, dimensions) and associated link geophysical properties. Combine simulation results with vehicle type details (mass, wheel configuration). Calculate the perpendicular distance (\(y\)) from each wheel to the nearest point on the fiber-optic cable, as well as the distance along the fiber (\(x\)) from a reference start point to that nearest point, for each timestamp. This prepared dataset serves as input for the physics-based signal modeling.  
Figure 2: Block diagram of the methodology to generate synthetic signals simulating DAS measurements from transport models, and example applications.
# Statistical Modeling of Cultural Differences in Adopting Autonomous Vehicles
## 2 Models for Public Opinion about AVs
### Background
#### 2.1.2 Cultural Differences  
[PERSON] [39; 40] defined culture as the collective programming of the mind that distinguishes the members of one group or category of people from others. There are a variety of individuals in each collective, and characteristics of individuals vary according to the cultural characteristics of each collective. There have been many attempts to explain cultural characteristics, and one of them is [PERSON]'s 6-D model.  
In the 6-D model, cultural differences are explained by six cultural dimensions: Power Distance, Individualism, Masculinity, Uncertainty Avoidance, Long-Term Orientation, and Indulgence. First, Power Distance (PDI) represents the degree to which less powerful members of a society accept the hierarchy or inequality in the society. In societies with high Power Distance, people are not hostile to inequality, and accept their place in the society. Second, Individualism (INDV) means the preference for a loosely knit social framework. People in individualistic cultures take care of themselves, while people in collectivistic cultures care more about the group than the individual. Third, Masculinity (MAS) represents the degree to which the members of a society pursue achievement and competition. A masculine society prefers achievement, heroism, and assertiveness. Fourth, Uncertainty Avoidance (UAI) expresses the degree to which the members of a society feel comfortable in novel and unknown situations; countries with strong Uncertainty Avoidance are less tolerant of ambiguity. Fifth, Long-Term Orientation (LTO) is a dimension of the society's position on traditions and norms. Societies with high Long-Term Orientation encourage thrift and efforts for the future. Lastly, Indulgence (INDU) refers to a society in which the basic and natural human desires related to enjoying life are satisfied freely.  
[PERSON]'s 6-D model has been used mainly to explain how cultural differences affect the introduction of IT innovation [40; 41; 42; 43; 44; 45; 52; 53], and it has also been recently applied to transportation technology [50; 51; 54]. [PERSON] et al. [50] studied whether the cultural dimension, especially individualism, is related to the adoption of fuel efficient vehicles. They determined that collectivists are more inclined to adopt fuel efficient vehicles, which are related to solving environmental problems, because they tend to help the group's interests or goals. They confirmed that UAI and MAS mediate the relationship between green self-identity and attitude toward electric car adoption. Finally, [PERSON] et al. [54] stated that the higher the UAI, the slower the hybrid car adoption.
## 3 Proposed Cumulative QoE Model
### Memory Effects  
Memory effects refer to the influence of historical/past experiences on the perceived video quality. Primacy and recency are two common effects which were investigated in numerous studies [11; 13; 15]. In addition to these factors, the effect of forgetting curve characteristic and repetition are also considered in our proposed model. The next parts of this subsection discusses the role and mathematical function of these factors. Based on that, a memory weight is proposed for the cumulative QoE model.
## 4 Result and Discussion  
### Result  
Figure 2 shows the distribution of wildfire zones during 2018 and 2019 over the Brazilian Amazonian landscape. As clearly visualized in Figure 2, the wildfire intensities and spread have significantly increased during 2019, compared to the previous year.  
Even though several wildfire events took place in the Brazilian Amazon landscape, we have observed a common general trend of GPP for the year 2018 and 2019 (see Figure 3). Such condition indicates that the wildfire events did not hamper the general trend of GPP of the Amazon forest, but did affect productivity negatively in small-scale scenarios. The highest GPP in 2018 was observed at site 2, with 507.05 gC m\({}^{-2}\) yr\({}^{-1}\), and the lowest GPP was observed at site 1, with 181.88 gC m\({}^{-2}\) yr\({}^{-1}\), whereas the highest GPP and lowest GPP for the year 2019 were recorded at site 2 and site 4, with 457.67 gC m\({}^{-2}\) yr\({}^{-1}\) and 147.38 gC m\({}^{-2}\) yr\({}^{-1}\), respectively (see Figure 4). However, a significant drop was observed in GPP in terms of productivity for the year 2019 compared to 2018 in all other 11 pilot spots except site 3 (See Figure 5). Site 11, site 12, and site 8 recorded the highest decreases in GPP, with rates of \(-\)120.11 gC m\({}^{-2}\) yr\({}^{-1}\), \(-\)99.32 gC m\({}^{-2}\) yr\({}^{-1}\), and \(-\)79.70 gC m\({}^{-2}\) yr\({}^{-1}\), respectively. The details of a site-wise decrease in GPP in 2019 compared to 2018 have been provided in Figure 5.
# Hand Posture Recognition Using Skeletal Data and Distance Descriptor
## 3 Proposed Method
### Point Pair Descriptor  
Let \(P_{c}\) be the palm center, \(n_{c}\) normal to the palm at point \(P_{c}\), \(P_{i}\) the tip of the \(i\)-th finger, and \(n_{i}\) the vector pointed by that finger (Figure 1).  
The relative position of vectors \(n_{c}\) and \(n_{i}\) can be described by three values according to Formulas (1)-(3) [43]:  
\[\alpha_{i}=acos(v_{i}\cdot n_{i}), \tag{1}\]  
\[\phi_{i}=acos\left(u\cdot\frac{d_{i}}{|d_{i}|}\right), \tag{2}\]  
\[\Theta_{i}=atan\left(\frac{w_{i}\cdot n_{i}}{u\cdot n_{i}}\right), \tag{3}\]  
where the vectors \(u\), \(v_{i}\), and \(w_{i}\) define the so-called Darboux frame [44]:  
\[u=n_{c}, \tag{4}\]  
\[v_{i}=\frac{d_{i}}{|d_{i}|}\times u, \tag{5}\]  
\[w_{i}=u\times v_{i}, \tag{6}\]  
and \(\cdot\) denotes the scalar and \(\times\)--vector products. The Point Pair Descriptor consists of 15 features calculated for each finger using Formulas (1)-(3):  
\[V=[\alpha_{1},\phi_{1},\Theta_{1},\alpha_{2},\phi_{2},\Theta_{2},\alpha_{3}, \phi_{3},\alpha_{4},\phi_{4},\Theta_{4},\alpha_{5},\phi_{5},\Theta_{5}]. \tag{7}\]  
The features were normalized to the interval [0; 1].  
Features \(\alpha\) and \(\Theta\) can be interpreted as pan and yaw angles between vectors pointed by fingers and palm normals. Feature \(\phi\) is an angle between vectors pointed by fingers and the line connecting this vector with the initial point of palm normal. PPD is an alternative to other angular-based features describing hand skeleton. Such features are most often angles corresponding to the orientation of each fingertip projected onto the palm plane [15; 18], between lines connecting the palm center with the fingers [45], between adjacent bones in the same finger, or between adjacent fingers [24]. Unlike PPD descriptor, these features do not use normals to the palm calculated at its center to determine angular relations.
# Profiling Taste and Aroma Compound Metabolism during Apricot Fruit Development and Ripening
## Materials and Methods
### Fruit Quality Traits Determination  
Peel color at different developmental stages was measured using a Hunter Lab Mini Scan XE Plus colorimeter (Hunter Associates Laboratory, Inc., Reston, VA, USA). The CIE L*a*b*color scale was adopted, and the data were expressed as L*, a*, b*, C*, H, four random measurements per fruit were made and a mean value was obtained from ten fruits per replicate.  
Flesh firmness was measured at the equator of the fruit using a penetrometer (Model: HL-300, Xianlin Non Detection Device Co., Ltd., Nanjing, China) with an 8 mm diameter head. Ten fruit were used as one replicate and two measurements were made on opposite sides of each fruit after removal of a 1 mm thick slice of peel, and three replicates were used for every time point samples.  
TSS and TA measurements were conducted on juice samples collected from ten fruit for one replicate, and three replicates were used. TA measurements were performed by titrating 10 mL of juice with 0.2 M NaOH until reaching a pH of 8.2, and TA values were expressed as mmol L\({}^{-1}\) H\({}^{+}\). TSS values on the opposite parts of each fruit were measured with a hand-held refractometer (Model: B32T Brix Meter, Guangzhou Ruiqi Trade Co. Ltd., Guangdong, China).  
The production of ethylene by the fruit was measured according to method described by [PERSON] et al. [21]. Ten fruits as one replicate was placed in a sealed 1 L glass container for one hour and three replicates were used for every time point samples. Gas samples (1 mL) were then extracted from the containers and injected into an Agilent 7820A gas chromatograph (GC; Agilent Technologies, Inc., Santa Clara, CA USA) fitted with a stainless steel Supelco Porapak-Q column (2 m in length, o.d., 3.175 mm; mesh size, 80/100; Supelco, Bellefonte, PA, USA) and a flame ionization detector (FID). Nitrogen was used as the carrier gas. The column temperature was maintained at 80 \({}^{\circ}\)C, the injector temperature at 150 \({}^{\circ}\)C, and the detector temperature at 200 \({}^{\circ}\)C.