esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Model Simulation
#### 2.5.2 The Training Phases  
The BP network is built according to the above settings, and the raw data is standardized using the polar method. The 22 variables and indicators of the four subsystems of the evaluation index system were used as input vectors, and the comprehensive agricultural investment climate evaluation grade generated according to entropy method was used as output vectors. The network structure of \(22\times 15\times 1\) is selected in the BP neural network training phase. Set the maximum number of training for \(N=1000\), learning rate for \(\eta=0.01\), momentum factor for \(\alpha=0.5\), and minimum convergence error for \(\varepsilon_{\min}=10^{-4}\). Transfer function is the logsig function of the logarithm of S; training function is the traingdx function, learning function is the learndm function, and the initial value of weight matrix is given by the system. The simulation results of the BP network model are obtained from Matlab. The 10-year data of the 16 countries total 160 groups, among them 128 groups are selected as train samples, and the rest as test samples. The predicted output and error of BP neural network is shown in Figures 2 and 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Level** & **C6** & **C7** & **C8** & **C9** & **C10** \\ \hline I & [20529.11, 26054.54] & [4.41, 5.06] & [5.35, 5.91] & [75, 100] & [0.39, 0.5] \\ II & [15003.67, 20529.11) & [3.77, 4.41] & [4.79, 5.35] & [50, 75] & [0.28, 0.39) \\ III & [9478.24, 15003.67) & [3.12, 3.77] & [4.23, 4.79] & [25, 50] & [0.17, 0.28) \\ IV & [3952.80, 9478.24) & [2.47, 3.12] & [3.67, 4.23) & [0, 25] & [0.06, 0.17) \\ \hline
**Level** & **C11** & **C12** & **C13** & **C14** & **C15** & **C16** \\ \hline I & [34, 41] & [3.33, 3.83] & [0.90, 1.37] & [3.29, 4.38] & [3.41, 4.52] & [4.68, 5.48] \\ II & [27, 34] & [2.83, 3.33) & [0.43, 0.90] & [2.21, 3.29] & [2.30, 3.41] & [3.88, 4.68) \\ III & [20, 27] & [2.33, 2.83) & [\(-\)0.05, 0.43) & [1.12, 2.21] & [1.19, 2.30) & [3.08, 3.88) \\ IV & [13, 20] & [1.83, 2.33) & [\(-\)0.52, \(-\)0.05] & [0.03, 1.12) & [0.07, 1.19) & [2.28, 3.08) \\ \hline
**Level** & **C17** & **C18** & **C19** & **C20** & **C21** & **C22** \\ \hline I & [4.66, 5.16] & [4.64, 5.62] & [3.83, 4.74] & [5.33, 6.51] & [69.5, 88.4] & [7.02, 8.16] \\ II & [4.16, 4.66] & [3.66, 4.64] & [2.92, 3.83) & [4.16, 5.33) & [50.7, 69.5] & [5.88, 7.02) \\ III & [3.66, 4.16] & [2.68, 3.66] & [2.01, 2.92) & [2.98, 4.16) & [31.9, 50.7) & [4.75, 5.88) \\ IV & [3.16, 3.66] & [1.70, 2.68) & [1.10, 2.01) & [1.80, 2.98) & [13.1, 31.9) & [3.61, 4.75) \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._  
Figure 2: The predicted output of BP neural network.
# Influence of Plastic Waste on the Workability and Mechanical Behaviour of Asphal Concrete
## 3 Results
### Marshall Stability and Flow  
Figure 7 shows the results obtained in the Marshall compression tests, stability, and flow (average of four Marshall specimens), for asphalt mixtures under testing. The results reveal that different types of plastic waste incorporated in asphalt concrete increased the stability values. Although this trend was observed in all the studied blends, this increase was more visible for specimens with 4% of ABS and HDPE500. For the HDPE and LDPE(uw), stability generally increased as the plastic content increased.  
The results did not allow absolute conclusions on the combined effect of gradation of plastic waste or its stiffness on the stability and flow variation. There is also the influence of the air void content, which is generally considered an important parameter in the results of the Marshall compression tests.  
Figure 6: Variation of volumetric properties with the plastic waste content.
# Agreement Technologies for Coordination in Smart Cities
## 3 Applications
### Distributed Coordination of Emergency Medical Service for Angioplasty Patients
#### 3.4.1 EMA for Angioplasty Patients  
The emergency medical system for the assistance of patients with STEMI is made of the following participants: patients, hospitals with angioplasty facilities, medical emergency coordination centre (ECC), ambulance staff, and cardiology teams, each one being a compound of a cardiologist and one or more nurses.  
Usually, each hospital with angioplasty has assigned to it its own cardiology team(s) positioned at alert outside the hospital and obliged to come to the hospital in the case of emergency. This is because the cardiology teams' costs make a large portion of the overall costs in surgical services [55].  
Figure 11: Proposed three-level decomposition of the problem of EMS coordination for with ST-segment elevation acute myocardial infarction (STEMI) patients awaiting angioplasty.  
The objective of the proposed system is the reduction of patient delay times by distributed real-time optimisation of decision-making processes. In more detail, we modelled patient delay time, and present a three-level problem decomposition for the minimisation of combined arrival times of multiple EMS actors necessary for angioplasty. For the three decomposition levels, we propose a distributed EMS coordination approach and modify the auction algorithm proposed by [PERSON] in [PERSON] (1997) for the specific case. The latter is a distributed relaxation method that finds an optimal solution to the assignment problem.  
On the first level, agents representing ambulances find, in a distributed way, the patient assignment that minimises arrival times of available ambulances to patients. After the treatment in situ, on the second optimisation level, ambulances carrying patients are assigned to available hospitals. On the third level, arrival times of cardiology teams to hospitals are coordinated with the arrival times of patients. The proposed approach is based on a global view, not concentrating only on minimising single patient delay time but obtaining the EMS system's best solution with respect to the (temporal and spatial) distribution of patients in a region of interest.
# Assessing Abrasion Resistance in Concrete Pavements: A Review
## 1 Introduction
It is generally recognized that using appropriate finishing and curing techniques significantly improves concrete's resistance to abrasion [11]. All concrete mixtures' abrasion resistance is improved with age due to the strong correlation between compressive strength and this property [8; 12]. It is feasible to create high abrasion-resistant concrete that meets requirements by adding fibers, silica fumes, and harder aggregates, which all have an impact on mechanical qualities [13]. Although high-quality concrete can endure heavy loads for many years without suffering significant damage, it cannot tolerate the abrasive effect of debris grinding against it or continuously altering its surface. Depending on the operating conditions, abrasion erosion with a depth varying from a few centimeters to a meter or more may occur. Life cycle predictions of concrete structures should take these abrasion, degradation, and fracture characteristics into account [14]. Variations in material properties, complex tire-road surface interactions, and the absence of standardized testing methods that effectively simulate real-world conditions are limitations in understanding the abrasion resistance of concrete pavements. With an emphasis on material properties (aggregates, cement type, admixtures, and the water-cement ratio), pavement design (surface texture and reinforcement), and environmental conditions, this review paper attempts to investigate the fundamentals of abrasion resistance and standard testing procedures, analyzing new materials and methods that have been developed to improve abrasion resistance.
## 2 Methods and Observations
The baseband signal is then sampled at a rate of 1000 Hz. The characteristics of the reflected signal depend on the scattering properties of the sea surface and the footprint of the reflection zone. The footprint shape and size in turn depend on the signal incidence angle and the relative velocities of the transmitter and the receiver. In our experiments, the receiver was fixed on the mountain, while the transmitter was moving slowly during the grazing angle measurements. Thus, scattering properties of the sea surface in this setup are directly related to the sea surface roughness, which again depends on sea wave characteristics. Due to the geometry, at low elevation grazing angles, the received GPS signals do also contain the incoherent scattering processes taking place in the lower boundary layer and the reflection zone [20]. A longer distance between the receiver and the reflection zone, and a higher altitude of the receiver reduce the effect of incoherent phenomena in the measurements. Additionally, when the receiver is at higher altitudes, the straight-line tangent altitude defining the power spectral information is less influenced by atmospheric turbulence. The measurement campaign was conducted in the period from 4 October to 13 October 2004. A total of 95 occultations were successfully recorded during the campaign, having almost vertical tracks with respect to the ocean surface. According to a buoy, located southwest of Hawaii (17.14\({}^{\circ}\)N; 157.79\({}^{\circ}\)W), the mean surface wind speed (averaged over 5 min) varied between 2 m/s and 15 m/s during the period. All meteorological data for the reflection zone used in the paper originate from the US National Weather Service, NOAA. Figure 2: Signal-to-noise ratio (\(C/N_{0}\)) as function of time for a rising GPS satellite. The measurements originate from 4 October 2004 (6:56–7:21 UTC). The GPS receiver was operating in a 1000 Hz sampling mode using the open-loop approach [19; 21]. In addition to the direct GPS signal, having the highest power, the calculated sliding power spectra also identified the ocean reflected signal. This occurred from elevation angles (lower than 3 degrees) down to \(-4\) degrees, primarily due to the geometry of the experiment. The reflected signal from each GPS satellite was monitored in a time window of about 20-30 min. During this period, the ocean reflection zone moved horizontally by less than a few hundred meters. For the lower grazing angles and higher wind fields, the reflection zone widened, forming an elliptical shape of the interaction zone. Power spectra of the measured antenna in-phase (\(l\)) and quadrature (\(Q\)) amplitude components showed this effect as a spectral broadening of the main signal.
## 4 Case Study
### Aircraft Failures Requiring Repair Development
#### 4.2.3 Corrosion Failure of a Thrust Reverser Pivoting Door Actuator Fitting  
Figure 7a shows the pivoting door operation in the deployed state. These doors are open during the aircraft landing to assist the braking system. During braking, they are exposed to aggressive gasses, rain, and ice, which promote corrosion, especially in some aluminum alloys. Figure 7b shows a detail of a swing door, with an aluminum fitting indicated by a white arrow.  
In the original aircraft design, the fitting material is made of Duralumin 2024-T4, which has excellent fatigue properties, but very poor resistance to corrosion. As a result, there have been several reports of these fittings being replaced due to corrosion problems.  
The fittings replacements have been regularly undertaken with negative economic impact to airliners, with huge benefits expected from replacing the old 2024 aluminum alloy with a newer 6000 or 7000 series aluminum alloy that has better mechanical properties.
# Evaluation of Blue(r)m Mouthwash vs. Chlorhexidine
## 2 Materials and Methods
### Study Design  
In total, 50 patients were randomly allotted into two groups, study, and control, of 25 patients each using a fair coin toss method. All patients received a complete ultrasonic scaling on their first visit by an expert periodontist.  
At the baseline, the following parameters were recorded:  
1. Full-mouth Plaque Index (PI)--[22].
2. Full-mouth Gingival Index (FMGI)--[23].
3. Percentage of bleeding on probing sites (% BOP).  
Oral hygiene maintenance instructions were given to all the patients, and a similar toothbrush and dendrimice was suggested to all by the same examiner (M.K.). Subsequently, patients were instructed to use 10 mL of Blue(r)m mouthwash (Bluem Europe, Zwolle, The Netherlands) (study group) or 10 mL of 0.2% chlorhexidine mouthwash (control group) twice daily for two weeks post scaling. They were instructed to rinse thoroughly with the respective mouthwash for 30-60 s, once in the morning and once before going to bed. After that, they were instructed to spit it out and avoid oral ingestion. They were asked to refrain from eating or drinking for half an hour and to maintain an at least 45 min gap between toothbrushing and mouthwash usage [24].  
Subsequently, patients were recalled after 14 and 30 days to evaluate PI, GI, and % BOP parameters.  
The primary outcome was to assess the change in the recorded parameters after 2 weeks and 1 month from the baseline.  
The secondary outcome was to evaluate the patients' acceptance of the mouthwash and their tolerability of the side effects. A patient satisfaction survey was conducted wherein questions were asked regarding altered taste sensations, burning sensations, and the staining of teeth. The answers were recorded as \"Yes\" if present and \"No\" if absent.
# Minimization of _n_th Order Rate Matching in Satellite Networks with One to Many Pairings
###### Abstract  
This paper studies the minimization of _n_th (positive integer) order rate matching in high-throughput multi-beam satellite systems, based on one-to-many capacity allocation pairings, for the first time in the literature. The offered and requested capacities of gateways and users' beams are exploited, respectively. Due to the high complexity of the binary optimization problem, its solution is approached with a two-step heuristic scheme. Firstly, the corresponding continuous, in [0, 1], pairing problem is solved applying the difference of convex optimization theory, and then, a transformation from continuous to binary feasible allocation is provided to extract the pairings among gateways and users' beams. Comparing with the exponential-time optimal exhaustive mechanism that investigates all possible pairs to extract the best matching for minimizing the rate matching, extended simulations show that the presented approximation for the solution of the non-convex optimization problem has fast convergence and achieves a generally low relative error for lower value of \(n\). Finally, the simulation results show the importance of \(n\) in the examined problem. Specifically, pairings originated by the minimization of rate matching with larger \(n\) result in more fair rate matching among users' beams, which is a valuable result for satellite and generally wireless systems operators.  
high-throughput satellite systems; dynamic resource allocation; difference of convex optimization; quadratic optimization; heuristic minimization Article
# A Distributed Mix-Context-Based Method for Location Privacy in Road Networks
## 2 Related Work
Secondly, privacy is provided to vehicles within the zone, and there is no privacy protection outside it. Thirdly, if the zones are deployed at fixed regions, only these areas provide the privacy protection and deployment costs increase the need to build a large number of zones with infrastructure support in the road network area. Based on problems and limitations in the existing schemes for location privacy in a vehicular network, we propose a novel scheme using a crowd-based mix context that utilizes the diverse nature of vehicle traffic densities. The pseudonyms changing process depends on the number of neighbors in the transmission range and road context information. This improves the anonymisation of a target vehicle (a vehicle that an adversary wants to locate) in a crowd of similar-status vehicles in a concerned region.
# Gait Biomechanics for Fall Prevention among Older Adults
## 3 Biomechanical Factors for Falls Risks
### Biomechanics of Slipping and Coefficient of Friction (COF)  
Slipping is the second leading cause of falls, but unlike tripping, backward and sideways loss of balance can be consequences, often resulting in the worst type of injury: hip fracture [40]. [PERSON] et al. [50] reported that the one-year mortality of hip fractures is 22% for older individuals.  
Biomechanically, slipping can be described as 'horizontal foot velocity during the stance phase' when the foot is, in principle, in contact with the walking surface. After heel contact, the foot stance is not supposed to move anteriorly, but when the foot contact's force outweighs the maximum available friction, the excess anterior force generates acceleration, causing a slip. A slip can be defined by displacement, velocity or acceleration but needs to be distinguished from a micro-slip, up to 3 cm horizontal displacement, following heel contact as part of the regular gait cycle [51].  
Friction has the ability to counter-match the shear force, but depending on the interface quality, it has a maximum threshold above which the object (i.e., foot) can start moving. The maximum available friction can be computed as  
\[\text{Maximum Friction}=\text{coefficient of friction }(\text{COF})\times\text{Normal Force}\]  
where COF indicates'slipperiness of the interface' and Normal Force is the GRF component perpendicular to the walking surface, therefore, the vertical GRF component in level walking. As long as the maximum available friction is higher than the horizontal foot contact force, a slip can be prevented, but in level walking, the alternative condition is 'COF is higher than the certain threshold, known as the required coefficient of friction  
Figure 3: **(Left)** MFC histogram description, IQR = interquartile range, Q1 = 25 th percentile, Q3 = 75 th percentile; **(right)** illustration of ankle dorsiflexion to increase MFC.  
(RCOF)' [52; 53]. The RCOF is determined by walking patterns, more specifically, the ratio of GRF between horizontal and vertical components.  
\[\text{RCOF}=\left|\frac{\text{GRFhor}}{\text{GRFver}}\right|\]  
Throughout the stance phase of the gait cycle, GRF characteristics continuously change, but the initial peak RCOF is the marker for the risk of slipping widely varying between individuals and walking patterns. Immediately following heel contact, the RCOF values are often 'noisy' (Figure 4), usually disregarded from the slip-risk assessment. This noisy part of RCOF probably reflects a micro-slip, a small movement of the heel (i.e., up to 3 cm) immediately following foot contact [51]. In short, a slip-resistant interface (i.e., shoe-outsole and floor treatment) is required against the peak RCOF after heel contact ( heel slip risk in Figure 4).
# Effects of Co-Solvents on the Performance of PEDOT:PSS Films and Hybrid Photovoltaic Devices
###### Abstract  
Hybrid silicon solar cells have been fabricated by the spin coating of conductive polymer poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) as a p-type contact on textured n-type crystalline silicon wafers. The effect of adding co-solvents, ethylene glycol (EG) and dimethyl sulphoxide (DMSO), to PEDOT:PSS improves its conductivity which translates to the improved performance of solar cells. Transfer length measurements were conducted to realize optimal contact with minimal losses between the front metal contact (silver) and PEDOT:PSS. From the conductivity and device results, a 7% EG with 0.25 wt% Triton (surfactant) blend of PEDOT:PSS is found to be optimal for these cells. This current approach with a few changes in the device architecture will pave way for the further improvement of PEDOT:PSS based hybrid silicon solar cells.  
solar cell; hybrid; PEDOT:PSS; cosolvents; solution processing; carrier selective contact; contact resistance
## 4 Possible Ways to Improve WUE Remote Sensing
### Intelligent Algorithms and Big Data Platforms Boost Remote Sensing Retrieval of WUE  
Except for data fusion of multi-platform and or multi-sensor by remote sensing, integration of remote sensing and multi-process models is also an effective approach to achieve WUE remote sensing. Integration of those datasets and processes involve a large number of inputs, including remote sensing and climatic data, especially at large scales. Upscaling WUE modeling from a site to a regional level needs several climatic driving factors to be matched at the pixel level, which mainly depends on data reanalysis. A huge amount of data storage and computation on a personal computer is a big challenge. Therefore, a big data platform, for example Google Earth Engine, is a very useful tool to conduct the retrieval and analysis of GPP, ET, and WUE. The cloud-based platform provides free access to analysis through a command line operation, and continually updated satellite remote sensing data (e.g., MODIS, Landsat and Sentinel) and earth-observed climate driving data. Moreover, machine-learning algorithms can also be used for the retrieval of WUE regardless of the complexities underlying the mechanism in the coupling of the ecosystem carbon-water cycle, and simplifying complex physical models for onward simulation which were coupled in the retrieval process, such as radiative transfer models. Several machine learning algorithms, such as artificial neural networks [125], support vector machines [126], random forest [127], regression trees [128], and multivariate adaptive regression spline function [129], have been used for remote sensing retrievals of corresponding variables. Coupling the process model and machine learning methods may be a better way for remote sensing retrievals. Besides, artificial neural networks with meteorological data and remotesensing data as input and a random forest with meteorological data forcing as an input, have been proved to capture the seasonal and daily variabilities [96; 130]. Direct estimation of ecosystem WUE by intelligent algorithms inputting meteorological forcing and remote sensing parameters will be also a choice before the underlying mechanism is clear.