esa-sceva/satcom-chunk-collection huggingface dataset:
# Time Classification Algorithm Based on Windowed-Color Histogram Matching
## 2 Related Works
### Sky Detection-Based Algorithms  
To estimate the time period of a day, the sky area contains meaningful information due to the change of sun light. Therefore, we noted that sky region extraction is an important module as the pre-processing of time classification. There are some works to detect the sky region in an image [32; 33; 34; 35; 36].  
[PERSON] et al. used a histogram to extract the sky region [32]. However, there are several problems. First, it takes time to detect the sky region. Second, it often fails to detect the extract sky region. Therefore, we employ Mask R-CNN, a well known semantic segmentation way to resolve these problems [19]. [PERSON] et al. have made their own dataset using metadata and classified the time segment using deep-learning when an input containing a sky region was given [37].  
However, it also has the following problems: First, the metadata is not exact. Second, there is an ambiguity in the categories. Third, the accuracy of classifying the time stamps was not good due to two previous reasons. Therefore, we are going to use only the sky region of the image to classify time variation. We propose an efficient algorithm to classify time by analyzing the color histogram of the sky region.
## 5 Experimental Results and Analysis
### Experiment 2: Analysis of Cylindrical Roller Bearing Faults
After generating the fault feature vectors by the improved fast spectral correlation, the ELM and SVM were used to train and classify the fault feature vectors respectively. Figure 19 shows the results of classification using ELM and SVM. The classification accuracy of ELM is 83.3333% and the classification accuracy of SVM is 84.1667%. It shows that PSO-RF has a higher accuracy.
# A Review of Nanomaterials' Effect on Mechanical Performance and Aging of Asphal Mixtures
## 4 Effect of the Modification with Nanomaterials in the Mechanical Performance of Asphalt Mixtures
### Nanosilica
improvement in retained Marshall stability from 83% to 88% (Figure 4); 9% increase in indirect tensile strength (Figure 3); small improvement in indirect tensile strength ratio from 80% to 82% (Figure 5); 14% increase in stiffness modulus and better fatigue life (97% increase in number of cycles) in fatigue tests at 1000 \(\upmu\)m/m controlled strain; 9% increase in flexural tensile strength and 25% increase in flexural strain in the three-point bending test. [PERSON] et al. [97] studied an asphalt mixture (NMAS 19 mm) modified with 7% nanosilica. The asphalt mixture was produced with limestone aggregates, limestone filler, 60/70 (control binder) and 5.5% binder content. The nanosilica modified mixture presented 56% increase in Marshall stability (Figure 2), increase in retained Marshall stability from 85% for control to 93% for nanosilica modified (Figure 4), 11% increase in indirect tensile strength (Figure 3), and increase in indirect tensile strength ratio from 88% for control to 93% for nanosilica modified (Figure 5). [PERSON] and [PERSON] [23] studied a porous asphalt mixture (NMAS 12.5 mm). For the mixture production, the authors used limestone aggregates, limestone filler, several combinations of additives (glass fiber, lime powder, polypropylene fiber, SBS and nanosilica), 60/70 asphalt binder and the binder contents 4.5%, 5.5%, and 6.0%. For the tested additives combinations, the introduction of 2% and 4% nanosilica modifications caused reduction in binder drain down, reduction in permanent deformation, and increase in indirect tensile strength. The effects of the nanosilica increased according with the dosage increase. In a following study, [PERSON] et al. [98] evaluated the performance of a porous asphalt mixture (NMAS 12.5 mm), produced with limestone aggregates, limestone filler, lime (0.5% by weight of mixture), and 60/70 asphalt binder modified with 4.5% SBS. The binder contents of the asphalt mixture were 5% and 6%. The authors study the modification with 2% nanosilica alongside with 0.2% glass fiber and basalt fiber. The introduction of 2% nanosilica increased indirect tensile strength, improved the resistance to moisture sensitivity, and reduced abrasion (Cantabro test). The use of silica nanoparticles to modify the asphalt mixture revealed to cause an overall improvement in its mechanical performance. Generally, the studies found in literature indicate higher Marshall stability, higher indirect tensile strength, enhanced water sensitivity (indicated by higher indirect tensile strength ratio and/or higher retained Marshall stability), higher stiffness (higher resilient modulus or stiffness modulus), lower permanent deformation, and better fatigue resistance.
## 2 Materials and Methods
### Outcome Measures  
The primary outcome was the changes in the range of motion (ROM). To measure the ROM of the knee and the hip joint of participants, a motion analyzer (Motion analysis, UINCARE-82B, Seoul, Korea) was used. The ROM was determined by measuring the distance between the X-axis, Y-axis, and Z-axis of the participant's joint with the camera in the motion analyzer. The UINCARE motion analyzer allows for measuring the ROM with one camera without attaching a marker that is different from the conventional marker-attached motion analyzer [21]. The knee flexion and extension ROM were measured in the sagittal plane with the lateral epicondyle of the femur as the axis, the greater trochanter middle line as the stationary arm, and the line from the femur to the lateral malleolus as the moving arm. Participants were instructed to wear short pants on the testing days in order to reduce bias during the measurements. Additionally, all irrelevant objects were removed from the testing site to minimize the risk of injury.  
Figure 2: AR-based training session. (**A**) The second-week training session in sitting position. (**B**) The third- and fourth-week training session in the standing position.  
The secondary outcome was muscle strength. A digital manual muscle tester (Lafayette Hand-Held Dynamometer Model-01165, Lafayette Instrument Company, Lafayette, IN, USA) was used to measure the strength of hip flexion, hip extension, knee flexion, and knee extension.  
The third outcome was the pain score, which was measured by the visual analog scale (VAS), which is a frequently used and simple method for the assessment of the variation in the intensity of pain. This uses a horizontal line, 100 mm in length, marked on the left (score 0) as 'no pain' and on the right (score 10) as 'unbearable pain'. Lastly, the balance ability (dynamic balance) was assessed using motion analysis with single-leg support while swaying in different directions: anterior-posterior (AP), superior-inferior (SI), and medial-lateral (ML). The measurement was conducted on a firm surface with the non-affected limb as support first in order to be familiar with the movements. All the outcomes were recorded before the surgery, 3-4 days after TKA, and then at the end of the 4-week training program.
## 4 Simulation Results and Discussions
### Many-Objective OPF Problem  
For solving OPF problems comprising more than three objective functions, the problems become MaOPF problems. In this section, all four objective functions which are cost, emission, transmission loss, and VSI are simultaneously considered as the objective  
Figure 7: Three-dimensional Pareto fronts for: (**a**) case 17; (**b**) case 18.  
functions and solved by MaMPA in both tested systems. The fuzzy decision method is applied to provide the best-compromised solutions from the Pareto fronts for each test system since the Pareto fronts for more than three objectives are difficult to plot, and the hypervolume values are found to compare the Pareto fronts.
# Challenges, Threats, Security Issues and New Trends of Underwater Wireless Sensor Networks
## 3 Threats and Challenges in Underwater Wireless Sensor Networks (UWSNs)
### Homing Attack
#### 3.3.4 Transport Layer  
The transport layer of UWSNs is responsible for the reliable transport of packets. Typical DoS attacks at this layer include the _desynchronization attack_ and _synchronization flooding attack_.  
In a desynchronization attack, a malicious attacker interrupts active connections between nodes by transmitting forged packets with bogus sequence numbers or control flags that desynchronize endpoints. For UWSNs, synchronization is very important and difficult, moreover, the global positioning system (GPS) is not suitable [2]. Hence, GPS-free synchronization mechanisms are used. A malicious attacker may forge messages carrying wrong sequence numbers to disturb synchronization between nodes, which can affect the accuracy of the synchronized clocks and the efficiency of scheduled operations. Worse, all GPS-free time synchronization schemes are vulnerable to wormhole attacks. If the attacker combined a desynchronization attack with a wormhole attack, it could severely disrupt communication and cooperation of the whole network. For example, wormhole, Sybil, or replay attacks may cause fake measurement results on ranges or round-trip time (RTT) between legitimate nodes, which are important parameters for time reference alignment. Header or full packet authentication can defeat such an attack.  
Synchronization Flooding Attack  
When a protocol is required to maintain a state it becomes vulnerable to memory exhaustion through flooding. An attacker may repeatedly make new connection requests until the resources required by each connection are exhausted or reach a maximum limit. A very common form of DoS attack involves sending a large number of common packets, e.g., transmission control protocol (TCP), internet control message protocol (ICMP), and user datagram protocol (UDP), which aimed at a single destination. The huge traffic deluge caused by these packets leads the network to no longer be able to distinguish between legitimate and malicious traffic.  
Using connectionless transport protocols is a feasible countermeasure, but at the expense of lack of necessary transport-layer functionality to applications. The primary defense against this attack is synchronize sequence numbers (SYN) cookies [56], which encode information from the client's TCP SYN message and return it to the client to avoid maintaining state at the server. Unfortunately,this technique calls for high computational capacity and causes extra messages overhead, which makes it undesirable for UWSNs.
# Analytical Study on the Load-Bearing Performance of RC Beams Subjected to ASR Expansion
## 4 Load-Carrying Capacity of RC Beams with ASR Deterioration
### Modeling of RC Beams  
#### 4.2.1 Analysis Target and Model  
The rectangular RC beam (Figure 19) and its analytical model (Figure 20) are shown in the figure. To reduce the computational load, a quarter of the beam was modeled and analyzed by halving the beam span and depth directions, respectively. This study uses a three-point bending loading of a flexure-fracture RC beam as an example for analytical investigation. Because the mesoscale model described in Section 2 was used, the concrete portion was modeled from three phases: mortar, coarse aggregate, and ITZ. The rebar was modeled as an elastoplastic body, according to [PERSON], instead of a truss element, which is commonly used for the analysis of combined deterioration (rebar corrosion). The elements used were four-node hexahedral elements for the mortar, coarse aggregate, and rebar, and six-node interface elements for the ITZ. D10 bars were used for the tensile main bars and \(\Phi\)6 bars for the compressive main bars. Additional \(\Phi\)6 bars were placed as shear reinforcement bars with a span of 100 mm. The boundary conditions defined for the analytical model were fixed in the y-direction at a position 100 mm from the edge of the model, in addition to the symmetry plane constraint. For the rebars, solid elements were used to create a mesh that faithfully reproduced the ribs and nodes of the deformed steel bars. The total number of nodes was 599,987, and the total number of elements was 2,620,082. In this analysis, complete adhesion was assumed at the interface between the rebars and concrete.  
Figure 18: ASR aggregate expansion procedure.
# Bond Fatigue of TRC with Epoxy Impregnated Carbon Textiles
## 1 Introduction  
Textile reinforced concrete (TRC) has been under investigation for about two decades now. During this time, it was used--among other applications--for the construction of several bridges, e.g., [1; 2; 3; 4; 5; 6; 7; 8; 9]. Whilst the pedestrian bridges were built without prior separate fatigue investigations, the fatigue resistance of the road bridges was tested in the laboratory on true scaled structures. However, it would be uneconomical to perform a fatigue test on an entire structure every time. Therefore, it is necessary to have a closer look at the fatigue behavior of textile reinforced concrete. This is among other things the objective of the research projects C\({}^{3}\)-V1.2 and C\({}^{3}\)-V2.1, belonging to the research program C\({}^{3}\)-Carbon Concrete Composite [10].  
Currently, there are already some investigations on the tensile fatigue behavior of TRC. In [11; 12; 13; 14], tensile fatigue tests with different carbon textiles were carried out, and in [15], the tensile fatigue behavior of an alkali-resistant glass textile was investigated. Since the high tensile strength of technical textiles is only advantageous when the occurring forces can be transmitted from the concrete to the textile, the bond behavior of TRC should not be ignored. As the authors don't know any research concerning the topic of bond fatigue of TRC, our own investigations were carried out and are presented in this paper.  
For the investigation of bond fatigue, a suitable test setup was developed [16]. Whilst here flexible carbon textiles with a styrene-butadiene impregnation were tested, the applicability of the test setup also for stiff carbon textiles with an epoxy impregnation was proved in [17]. On the basis of this research, more precise investigations on the influence of load level and anchorage length on deformation, stiffness, and number of cycles to failure were done. The results are presented in the following sections.
## 2 Methodology and Case Study
### Speed Data for Analysis  
Measures were taken in the selected urban streets with a speed radar and a speed gun during various days. According to the specifications, both devices have a maximum error of accuracy of 5%. Measures were taken during the day, with day-light conditions in all the segments. None of the data was collected at night. During the speed measurements, weather conditions were not extreme: heavy rain, high wind, snow. Sunny or cloudy days were selected, with light rain in some cases for short periods.  
For each measuring point, the following variables were calculated: the maximum (\(V_{\text{max}}\)) and minimum speed (\(V_{\text{min}}\)), the average speed (\(V_{\text{m}}\)); and, the 85 th percentile of the speed distribution  
Figure 3: Schemes of traffic calming measures and measuring point in selected streets in Basauri (Spain): (**a**) Eluterio Villaverde Street; (**b**) Gernika Street.  
(\(V_{85}\)), i.e., the speed below which 85 percent of vehicles in the traffic stream travel or, in other words, the speed exceeded by 15% of the vehicles. As the important point is to know the success of placing vertical TCMs in sequence, both directions of the street were analyzed together, not distinguishing between directions.
## Appendix A
#### Single-stage case  
As regards the single-stage case (single turbocharger), with reference to Figure A1, the WIT convention is the following:  
* Subscript \"0\" marks all the room/atmospheric/ambient-condition quantities;
* Subscript \"1\" marks all the compressor-inlet quantities;
* Subscript \"2\" marks all the compressor-outlet quantities;
* Subscript \"3\" marks all the air-receiver quantities;
* Subscript \"4\" marks the exhaust-gas manifold temperature, calculated as the average of all the exhaust-gas manifold temperatures (each single temperature is marked through an additional subscript that specifies the cylinder number). It is worth noting that the exhaust-gas pressure is not calculated due to its significant fluctuation;
* Subscript \"5\" marks all the turbine-inlet quantities. It is worth noting that, in this case, the pressure value is stationary and can be measured;
* Subscript \"6\" marks all the turbine-outlet quantities.  
As regards measurement units, the WIT uses the following:  
* Room/atmospheric pressure \(p_{0}\) [mbar];
* Compressor-inlet pressure \(p_{1}\) [mbar];
* Compressor-outlet pressure \(p_{2}\) [bar];
* Receiver pressure \(p_{3}\) [bar];
* Turbine-inlet pressure \(p_{4}\) [bar];
* Turbine-outlet pressure \(p_{5}\) [bar];
* Temperatures [\({}^{\circ}\)C].  
The standard for measurement units simplifies the measurement process of quantities, but it also has some disadvantages. Indeed, since the SI unit for pressure is Pascal [Pa] and for temperature is Kelvin [K], proper transformations are needed.
## 1 Introduction
The immersion depth of the polishing ribbon is typically less than 0.5 mm, and the positioning error of the robot is typically greater than 0.2 mm without compensation [14; 15; 16]. Therefore, the robotic deterministic polishing process with a low MRF flow rate (less than 1000 mL/min) and low ribbon height (less than 2 mm) requires high normal positioning precision. To achieve a high convergence efficiency for robot magnetorheological polishing, it is indispensable to explore the influence of robot positioning error on the removal function and develop an appropriate trajectory compensation strategy. To cope with the problems mentioned above, based on the current MRF unit design, a lightweight and intelligent design is conducted to match the load and precision requirements of robotic magnetorheological finishing. The magnetorheological finishing actuator for robots is developed using constant-force control to create a consistent and regulated material removal rate throughout the polishing operation. Different from a standard magnetorheological finishing unit, the actuator is capable of sensing and altering the polishing condition. Moreover, due to high flexibility and superior adaptability, the intelligent robot is able to take the place of the current commonly used high-precision machine tool. This furthers the promotion and application of robotic magnetorheological polishing technology with a low manufacturing cost and extends the robotic magnetorheological finishing to large-size and high-gradient freeform components. The remainder of the paper is arranged as follows: Section 2 presents the magnetorheological removal function model and clarifies the influence of robotic normal positioning error on the stability of the removal function. Section 3 presents a robotic magnetorheological polishing system using an active force control technique. Section 4 verifies the effect of robotic MRF polishing based on constant force control by experiments. Finally, the conclusions are presented in Section 5.
## 3 Results and Discussion
### Optimization Studies of Different 'Green' Extraction Methods
#### 3.5.3 Process Optimization of Accelerated-Assisted Extraction (ASE)  
ASE is gaining wider attention nowadays due to its environment-friendly processes and sustainability. The WOF sample was extracted with ASE using three different temperatures (60, 90, and 120 \({}^{\circ}\)C) for a total time of 30 min, applying three extraction cycles. The use of static cycles helps to maintain a favorable extraction equilibrium by adding fresh solvent during the extraction process. According to [PERSON] et al. [70], the addition of a fresh solvent positively affects the extraction of phenolic compounds from sea buckthorn leaf and berry extracts when three extraction cycles are applied.  
The phenolic compounds in the WOF extracted by ASE revealed a decrease with an increase in temperature. This means that the use of low temperatures under ASE results in high antioxidant activity in WOF extracts with a superior yield of oleuppein. Similarly, other authors found that the extraction temperature had a negative effect on the lignan glucoside contents extracted by ASE equipment [71]. The optimum temperature for ASE phenolic extraction from WOF was achieved at 60 \({}^{\circ}\)C. Similar to our findings, most of the available literature reports about olive-related products apply low temperatures. Specifically, [PERSON] et al. [72] applied an ASE temperature of 70 \({}^{\circ}\)C for the recovery of an oleuppein-rich extract from olive pomace, while [PERSON] et al. [73] used a much lower temperature of 30 \({}^{\circ}\)C for olive tree leaves. However, [PERSON] et al. [27] reported that temperature showed no significant correlation for phenolics when studying the effect of solvent and temperature on ASE extraction of phenolics in fresh olive fruit (_Olea europaea_).
## 3 Results and Discussion
### Mathematical Models Validation  
Based on the observations made before, it is clear that it is not so simple to find a light recipe that can optimize at the same time all the basil properties at each growth phase. Therefore, the desirability function has also been calculated for each time period, according to the information provided in Section 2.4 and Table 5. Results regarding the desirability function are shown in Figure 11, where contour plots concerning the function have been provided. In these graphs, similar to the other contour plots, it is possible to immediately identify the area of optimum (in yellow) that would suggest the best light recipe for each time period. For the first time period (15D) the best condition is due to the interaction of a restrained HR:DB ratio (25%) with the lowest distance (60 cm) and the presence of White LEDs (Figure 11a). Considering the information reported in Table 1, the overall HR:DB ratio for this best condition is equal to 44%. It is the same for the second time period (20D), even if in this case a larger yellowish area can be observed (Figure 11b). In the third time period (25D), a difference from the previous ones arises as the White LEDs should be avoided (Figure 11c). Finally, the fourth period (30D) is the one less affected by the factors investigated here since only distance plays an effective role, meaning that all the LEDs can be shut down without damaging basil growth (Figure 11d).  
Figure 11: Contour plots of the desirability functions related to different periods: (**a**) 15D; (**b**) 20D; (**c**) 25D; (**d**) 30D.  
The numerical results of this evaluation are reported in Table 9, as well as experimental data concerning the desirability function validation. In particular, to experimentally validate the guidelines obtained from the results' analysis, an additional growth run was carried out having these main features: (i) light source--plants distance fixed at 60 cm; (ii) fixed photoperiod of 16h/day; and (iii) light recipe varying during the growth period in accordance with the results from Figures 7-10 and Table 8. The employed light recipes are summarized in Table 10.