esa-sceva/satcom-chunk-collection huggingface dataset:
# An Effective Multi-Label Feature Selection Model Towards Eliminating Noisy Features
## 5 Experimental Evaluation
### Example 3: Embedding Label Correlations  
Label correlation information is important for multi-label learning. In the following experiments, we estimate the preserved label correlation information of the selected feature subset \(\mathbf{\hat{F}}\) as follows:  
\[C(\mathbf{\hat{F}})=\frac{1}{k^{\prime}(k^{\prime}-1)}\left\|\frac{1}{n^{2}} \mathbf{Y}^{T}\mathbf{X}_{\mathbf{\hat{F}}}\mathbf{X}_{\mathbf{\hat{F}}}^{T} \mathbf{Y}-\mathbf{S}\right\|_{F}^{2}, \tag{17}\]  
where \(\mathbf{X}_{\mathbf{\hat{F}}}\) denotes the instances characterized by \(\mathbf{\hat{F}}\) and \(\mathbf{S}\) is the label correlation matrix of the original data. Intuitively, Equation (17) measures the residue scale of label correlation information in the original  
Figure 1: Classification redundancy: (**a–c**) are the classification redundancies produced by the feature selection approaches on the emotions, birds, and enron datasets, and the lower of the redundancy is the better.  
and reduced feature spaces. A lower value indicates more information preserved. In other words, more label correlation information can be embedded in the feature selection process in this situation.  
Similarly to the configuration in Section 5.2, we take emotions, birds, and enrol as the benchmarks and record \(C(\hat{\mathbf{F}})\) of the \(k^{\prime}\) features selected by each approach, where \(k^{\prime}\in\{d/10,2d/10,\ldots,9d/10\}\). As shown in Figure 2, ELC is better at preserving the class correlation information than the other multi-label feature selection approaches. Actually, the majority of the existing multi-label feature selection approaches take the label correlation information into consideration to some extent. In contrast to these approaches, ELC quantitatively measures this correlation information and maximally embeds it into the feature selection process. This characteristic, which has already been proved in Property 2, can be further revealed by the experimental results in this section.
## 1 Introduction  
The potato tuber moth, _Phthormaae operculella_ (Zeller), is a major pest of Solanaceous crops, including potato, tomato, and eggplant [1; 2; 3]. _P. operculella_ larvae are commonly known as 'potato tuber worms', and they inhabit plant growth sites and are found in tissues such as leaves, stems, and fruits [4; 5]. _P. operculella_ larvae cause problems mostly during the storage and distribution of agricultural commodities because they can reduce quantities and lower quality [6; 7; 8]. Secondary damage occurs in crops with wounds created by the larvae because fungi and bacteria grow in the wounds [9]. In addition, increased number of _P. operculella_ are expected to occur in some regions of China and India due to climate change, so there is a need for a method to prevent massive damage caused by this pest during potato cultivation and storage [10].  
_P. operculella_ is regulated as a quarantine pest by the European and Mediterranean Plant Protection Organization (EPPO) and in Taiwan [11]. To control this insect pest, fumigants such as methyl bromide (MB), phosphine (PH\({}_{3}\)), and ethanolinitrile (EDN) are used. However, MB was designated as an ozone layer-depleting substance in the Montreal Protocol, and its use has been banned worldwide. In addition to chemical methods, physical methods such as the application of extreme temperature, controlled atmospheric conditions, and irradiation have been employed as alternative methods [12; 13].  
Ionizing energy such as X-rays is being applied to quarantine pests overseas [14]. Whereas fumigation treatment requires a long treatment time and leaves pesticide residues behind, ionizing energy does not present these problems [15]. In addition, ionizing energy,including X-rays, cannot immediately kill insect pests but is instead aimed at preventing their reproduction by inducing abnormal development and sterility [16; 17; 18; 19; 20; 21; 22; 23; 24; 25]. When an insect pest is irradiated with ionizing energy, the DNA is damaged, but the damaged DNA stops the cell cycle, and recovery is attempted [26]. If the degree of damage is low, recovery to a normal level will occur, but if the degree of damage is high, normal conditions cannot be restored [18; 22; 23].  
In this study, the effects of X-rays on the development and reproduction of _P. opercuella_ were investigated in each developmental stage. In addition, DNA damage and recovery were studied in _P. opercuella_ following the application of X-rays. Based on this approach, we aimed to provide basic data on the results of X-ray-based pest control techniques in agricultural products.
## 4 Case Study and Results
### Experimental Setup
#### 4.1.2 UAV Characteristics and Parameters  
The UAV considered in the simulations is a quadcopter of similar scale to the DJI Phantom series, selected for its compact footprint and suitability for urban deployment.  
Table 3 summarizes the average specifications used in the model.
# I-BIM Applied in Railway Geometric Inspection Activity: Diagnostic and Alert
## 4 Railway Inspection Activity
### Degradation of Railway Track Components
#### 4.1.1 Track Gauge and Cant  
The railway gauge is the distance defined between the inner faces of the heads of both rails. The track gauge is the minimum space between points P1 and P2 that is measured in a horizontal orientation with the top tangent of the railheads (Figure 6a). The ZP horizontal measure is located below the top tangent of the railheads. The value of ZP in the harmonized standard is 14 mm [32]. The track gauge is an indicator of the construction quality and the state of track maintenance. When the value of this parameter is not in conformity to the standards, it reflects the wear and degradation of the materials.  
The cant of a railway track is the height variation degree between the two rails. The railway cant is measured as the elevation difference concerning the outer and inner rails (Figure 6b). This important parameter is usually controlled in order to guarantee the railway's security and a smooth raid condition. The longitudinal cant variation supports the establishment of the tolerance limits of the track and the adequacy of maintenance plans [33]. The cant data are measured to evaluate eventual rail tracks' deficiencies. A correct geometry measurement can support the identification of geometric irregularities over the rail surfaces, caused by material abrasion, inadequate fasteners or even an irregular rail base platform [34].  
In curved railway tracks, the outer rail is elevated, providing a banked turn. This allows trains to navigate curves at higher speeds and reduces the pressure of the wheel flanges against the rails, minimizing friction and wear. The outer rail of a curved railway track is generally raised over the inner. The difference in elevation between the outer and inner rails is referred to as the cant, and in a curved track this difference is more evident.  
Figure 6: Measurement of the railway gauge (**a**) [32] and cant (**b**) [33] parameters.
## 2 Materials and Methods  
### Filter Coffee Sample Extraction Methods  
One coffee variety was used for all analyses: C. _anabica_ from Gardelli Specialty Coffee, Kakindu natural, Kenya, with medium roast, terroir Kiambu (Kenya), from variety \"arabica\" cultivar SL34, cultivated at 1800-2000 m a.s.l. Coffee was produced by small farmers, processed using the natural method, and dried on raised African beds. This type of coffee has a quality score of 89.00. Moreover, the beans were roasted with a customized solid-drum roaster. The package of coffee beans (250 g) was opened immediately before serving to avoid oxidative damage. The beans were ground with a professional grinder (Mythos one, [PERSON], Belforte del Chienti, Italy) before each extraction. The grind was measured using the Mastersizer 3000 Aero Series dry disperser (Malvern PANalytical Ltd., Grovewood, UK): the grind was 1030 \(\pm\) 2.13 \(\upmu\)m for Pure Brew (PB), 1290 \(\pm\) 3.81 \(\upmu\)m for French Press (FP), 800 \(\pm\) 3.21 \(\upmu\)m for AeroPress (AP), and 945 \(\pm\) 2.81 \(\upmu\)m for V60. All samples were prepared with commercial natural water (Nerea). This water was selected for its mineral salt content (161 mg/L dry residue) associated with its salt balance. The chemical composition of the water was indeed suitable to be an effective extractant; according to the SCA parameters, the water must have a dry residue in the range of 75-250 mg/L and an ideal level of 100-150 mg/L [10; 11]. This range ensures that the water contains enough minerals for proper extraction without overwhelming the coffee with excessive minerals.  
The presence of minerals in the water was assessed by the producer (Nerea, Castellanenglo sul Nera, MC, Italy). Magnesium concentration was 0.9 mg/L, and sodium concentration was 1.18 mg/L. Sodium should ideally be below 10 mg/L. Excessive sodium can lead to a salty taste. Regarding water hardness, it refers to the concentration of calcium ions. In coffee preparation, extremely soft water can result in under-extraction and flat flavors, while very hard water might lead to over-extraction and bitterness, so moderately hard water is generally considered ideal for coffee brewing. The SCA suggests a calcium hardness level of 50-175 ppm. The water used in this study reported a calcium concentration of 58 mg/L. Water pH is also an important parameter in coffee extraction. Water that is too alkaline or too acidic can impact the extraction process and alter the coffee's flavors. The recommended pH range for brewing water is 6.5 to 7.5. The water used in this study had a pH of 7.45.  
Each filter coffee extraction was replicated to obtain six samples of each method. The specific procedures used to obtain the four filter coffee samples are described in the following sections.
## 4 Analysis of Experimental Results
### Automatic Kelock Performance Test  
When the system is locked, the incident light of the detector is repeatedly blocked with a photocatalyst to unlock it. Figure 14 shows the changes in the error signal and SAS signal, respectively, during the automatic relocking of the system. When blocked, the system unlocks, the SAS signal becomes zero, and the error signal jitter increases. When the photocatalyst is removed, the system is automatically relocked, the error signal fluctuates around zero, and the SAS signal's amplitude jumps to the peak's tip at the locking point \(T_{3}\). The time required for automatic relocking is calculated to be 1.66 \(\times\) 105/9997 \(\times\) 0.2 = 3.32 s.
# Research of Park and Ride Parking Spaces Tiered Pricing Methodology Based on Subway Ride Distance
## 3 Results and Discussion
### Factor Analysis  
In establishing the mixed logit model, the choice of the P&R mode is used as a reference group to fit the preferences for car, metro, and taxi options. The fitted coefficients indicate the preference for choosing a certain transportation mode relative to the P&R mode under the influence of explanatory variables. The McFadden's R2 values for the two models, respectively, stand at 0.247 and 0.201, underscoring the models' robust goodness of fit. In presenting the results, this paper will showcase the goodness-of-fit tests for the models, along with the modeling outcomes for mode attributes and individual characteristics, daily travel habits, and travel considerations. These findings will be presented in Tables 3-5, respectively. Furthermore, a separate discussion of the results will be provided for each category.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Variable**} & \multicolumn{1}{c}{**Mode**} & \multicolumn{2}{c}{**Fixed Pricing Strategy**} & \multicolumn{2}{c}{**Tiered Pricing Strategy**} \\  & & & **Coef. (\(p>|z|\))** & **Odds Ratio** & **Coef. (\(p>|z|\))** & **Odds Ratio** \\ \hline \multirow{3}{*}{Mode attributes} & Pkm & — & — & \(-0.148\) (0.363) *** & 0.863 \\  & Walking distance & \(-0.936\) (0.951) *** & 0.392 & \(-0.292\) (0.637) ** & 0.747 \\ \hline \multirow{6}{*}{Individual characteristics} & \multirow{3}{*}{Gender} & PT & \(-0.568\) *** & 0.567 & \(-0.458\) *** & 0.633 \\  & & Car & — & — & — & — \\  & & Taxi & \(-0.738\) *** & 0.478 & \(-0.323\) * & 0.724 \\ \cline{2-6}  & \multirow{3}{*}{Age} & PT & \(0.220\) * & 1.246 & \(-0.438\) *** & 0.645 \\  & & Car & — & — & \(-0.215\) * & 0.806 \\ \cline{1-1}  & & Taxi & — & — & \(-0.241\) * & 0.786 \\ \cline{1-1}  & \multirow{3}{*}{Occupation} & PT & \(-0.903\) *** & 0.405 & \(-0.394\) ** & 0.675 \\ \cline{1-1}  & & Car & — & — & 0.240 * & 1.271 \\ \cline{1-1}  & & Taxi & — & — & 0.269 * & 1.308 \\ \hline \multirow{6}{*}{Purpose} & PT & \(0.640\) *** & 1.896 & 0.278 * & 1.321 \\ \cline{1-1}  & Car & \(0.426\) *** & 1.532 & 0.218 * & 1.244 \\ \cline{1-1}  & Taxi & \(0.376\) ** & 1.456 & 0.829 *** & 2.290 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of mode attributes and individual characteristics (values in parentheses indicate the standard error of the random parameters).
# Strategies for Reducing Automobile Fuel Consumption
## 3 Vehicle Design and Technology
### Improved Fuel Economy by Reducing Rolling Resistance (Tire Technology)  
In a vehicle, tire dynamics play a critical role in energy efficiency. The _rolling resistance_ (RR), a result of the tire's interaction with the road, significantly impacts the vehicle'sfuel consumption. Understanding the mechanisms behind rolling resistance helps in developing tires that contribute to fuel economy and environmental sustainability. This involves exploring various factors from hysteresis in the tire materials to aerodynamic effects and the role of tire pressure. Contemporary advancements in tire technology, including material composition and tire labeling systems, offer promising avenues for reducing rolling resistance and, consequently, fuel consumption. A detailed breakdown of the contributions of different rolling resistance aspects is presented in Table 2.  
Furthermore, if the tire pressure is 40% lower than the recommended level, fuel consumption can rise up to 8%. It is important to mention that tire pressure can naturally be reduced monthly by one to two psi during normal operating conditions. Additionally, ambient temperature fluctuations have a direct effect on tire pressure: a 5 \({}^{\circ}\)C temperature variation corresponds to a 2% change in pressure. A series of studies [51; 54; 55; 56; 57] have focused on the intricate relationship between tire pressure (P) and rolling resistance (RR), considering different proportionality relations, such as (1/P), (1/P)\({}^{0.5}\), and linear and quadratic relations involving the vehicles' service load (Z), speed (V), and pressure (P). The proposed correlation is expressed as RR \(\propto\) P\({}^{\alpha}\), where the value of the \(\alpha\) index typically ranges between \(-0.3\) and \(-0.5\) for contemporary radial tires.  
The use of _nitrogen_ (instead of regular air) in tires offers the advantage of reducing tire pressure loss [55]. Laboratory tests have shown that new tires inflated with nitrogen experience approximately two-thirds less pressure loss compared to those inflated with air, both under static and dynamic loads. This difference can be attributed to the higher diffusivity ratio of oxygen through the rubber, making nitrogen a more effective option for maintaining tire pressure over time.
# Magnetic Self-Healing Composites: Synthesis and Applications
## 4 Applications
### Biomedical
formed crosslinked hydrogels from oxidized pectin/chitosan/nano \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\) via [PERSON]'s base reaction as a carrier for anticancer drugs [107]. [PERSON] et al. investigated magnetic thermoreversible self-healing hydrogels where the self-healing properties emerge as a result of the electrostatic attraction between positively charged chitosan and negatively charged cellulose. The proposed scaffold showed a controlled in vitro drug release under an external magnetic field [108]. [PERSON] et al. showed magnetic self-healing hydrogels performing as excellent underwater glues. The proposed metal-coordinated telechelic hydrogels offer a wide tuning of their mechanical properties thanks to the possibility of crosslinking with a wide gamma of di- and trivalent ions. The resulting formulations are interesting candidates for biocompatible medical wound seals [109]. Most of the reported magnetic self-healing hydrogels are based on polysaccharides such as chitosan or gelatin. Other sources such as proteins have exhibited excellent self-healing efficiency and mechanical performance [110; 111; 112]. To our best knowledge, the combination of protein-based self-healing hydrogels with magnetic particles is still an unexplored field. All the reported magnetic self-healing hydrogels have shown randomly dispersed magnetic particles involving an isotropic distribution. In contrast, the intrinsic properties of biological tissues as muscle or skin, such as enhanced mass transfer, surface lubrication, force generation and enhanced cell proliferation [113], arise from well-defined hierarchical, anisotropic microstructural arrangements. Consequently, some efforts have been carried out to overcome this drawback of traditional hydrogels by preparing anisotropic magnetic hydrogels. [PERSON] et al. prepared anisotropic self-healing magnetic hydrogels by submitting the uncured suspension to an external magnetic field during the polymerization step, as shown in Figure 8a. The resultant anisotropic material exhibits enhanced rheological properties when submitted to a magnetic field, emerging as a promising soft anisotropic material [76]. [PERSON] et al. prepared anisotropic magnetic self-healing material with both magnetothermal and photothermal responses. The magnetic particles absorb NIR irradiation, promoting the healing step through phase transformation. As can be observed in Figure 8b, the applied magnetic field can assemble the magnetic NPs as photonic crystals, exhibiting iridescent structural colours [77]. To date, no detailed studies on how the anisotropic arrangement of magnetic particles in self-healing hydrogels influences the healing properties are available. This would be of great interest since the particle assembly and packing are directly related to the polymer chains' mobility and the dynamic groups' rearrangement. Figure 8: _Cont._
## 2 Methodology
### LV Model
#### 2.1.2 Pressurant Mass and Tank Calculations  
The selected pressurant is Helium, He, and the pressurant tank material is Ti 6 Al-4V [30]. The initial tank pressure, \(p_{He}\), is set at 450 bar. The number of moles of He required to pressurize the fuel and oxidizer tanks is calculated using the Ideal Gas Equation:  
\[nHe_{f}=\frac{p_{tot}\times v_{f}}{RT} \tag{16}\]  
\[nHe_{ox}=\frac{p_{tot}\times v_{ox}}{RT} \tag{17}\]  
where R is the molar gas constant, 8.314 J/Kmol, and temperature, T, is assumed at room temperature, 298.16 K. The volumes for fuel (\(v_{f}\)) and oxidizer, (\(v_{Ox}\)) were previously calculated using Equation (8).  
The ratio of He left in the tank, \(ratio_{He_{off}}\), is assumed to be 2. The pressure of He left in the tank, \(p_{He_{off}}\) is given by the following.  
\[p_{He_{off}}=p_{tot}\times ratio_{He_{off}} \tag{18}\]  
The minimum number of moles of \(He\), (\(nHe\)), therefore, required to pressurize both propellant tanks is calculated as follows.  
\[nHe=\frac{\frac{(nHe_{f}+nHe_{ox})\times p_{He}}{p_{He_{off}}}}{\frac{p_{He}}{ p_{He_{off}}}-1} \tag{19}\]  
The mass of He, \(M_{He}\), is obtained via converting the number of moles of He to kg.  
\[M_{He}=\frac{4 nHe}{1000} \tag{20}\]The Ideal Gas Equation is again used to calculate the volume of He, \(v_{He}\), in the tank. This is assumed to be the inner tank's volume. The pressurant tank is assumed to be spherical; therefore, using the standard equation for volume of a sphere, the inner radius is calculated via the following.  
\[r_{HeTank_{i}}=\sqrt[3]{\frac{v_{He}}{4\pi}} \tag{21}\]  
Given the allowable stress, S (assumed 1100 MPa), and joint efficiency, E (0.9), of the material and the equation from [30], the tank's thickness, \(t_{HeTank}\), is determined as follows.  
\[t_{HeTank}=\frac{p_{He}\times r_{HeTank_{i}}}{2S\times E-0.2p_{He}} \tag{22}\]  
The tank's outer radius, \(r_{HeTank_{o}}\), is simply the following.  
\[r_{HeTank_{o}}=r_{HeTank_{i}}+t_{HeTank} \tag{23}\]  
The He tank outer, \(v_{HeTank_{o}}\), and inner \(v_{HeTank_{i}}\) volumes are calculated using the volume of a sphere and the respective inner and outer tank radii given in equations. The mass of the pressurant tank, \(M_{HeTank}\), is therefore calculated below with an additional 20% mass as the safety margin.  
\[M_{HeTank}=\frac{1.2(v_{HeTank_{o}}-v_{HeTank_{i}})}{\rho_{HeTank}} \tag{24}\]
# Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis
## 5 A Taxonomy for Gait, Balance, RoM Analysis
### Gait Analysis  
One of the main goals of physical therapy and rehabilitation is that ambulatory patients achieve independent walking. Physical therapists use _gait analysis_ to determine what causes patients to walk the way that they do. The goal of clinical gait analysis is to assist in plan of care decision-making for patients to help ameliorate the gait deviations so that the patient may walk more efficiently and independently [105]. Additionally, gait analysis in research studies the natural history of change in walking over time in neurodegenerative diseases or changes in gait after implementing interventions [12,13].  
Walking is the result of a cyclic series of movements that can be characterized by a description of its fundamental unit, the _gait cycle_. A gait cycle is the time period of events during locomotion in  
Figure 2: Taxonomy of Wearable Sensor Technology for Gait, Balance, and ROM Research.  
which one heel makes contact with the ground and that same foot makes contact with the ground again; a single gait cycle is also known as _stride_. A step is the sequence of events that occurs within successive heel contacts of opposite feet. Step and/or stride detection is the first task that researchers try to accomplish when using wearable sensors to conduct gait analysis. The most common method to perform step and/or stride detection using IMU and smart devices is by using peak detection algorithms on accelerometer data [28; 35; 48; 36]. Step and/or stride detection is easier when using insole pressure sensors since the pressure applied to the sensor when the foot is in contact with the ground is higher than when it is not in contact with the ground [31; 46; 52; 54; 69; 73]. However, people that are affected by neurological disorders tend to shuffle and/or drag their feet when walking, performing short and dragged steps, which makes it harder to detect heel strikes or foot contact [106].