esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 High Dynamic Infrared (IR) Radiometry Based on a Constant Reference
### Atmospheric Transmittance Correction Based on a Constant Reference  
In Equation (5), \(\tau_{atm}\) and \(L_{path}\) can be obtained by atmospheric transmittance calculation software such as MODTRAN4.0 and CART (Anhui Institute of Optics and Fine Mechanics, Anhui, Anhui, 2009) [13]. Under general conditions, the multiband radiometer can measure the profile of aerosols and an automatic meteorological data logger can provide data of visibility, temperature, and humidity [14; 15; 16]. Considering this, MODTRAN4.0 computes the transmittance of the spectrum with a spectral resolution of 1 cm\({}^{-1}\) and CART the local atmosphere, then the average transmittance can be acquired with the combination of the two models under the operating band [17]. It is observed that the measurement of the transmittance by software is indirect [18]. Sometimes, the accuracy of this method can hardly be guaranteed.  
To further improve the accuracy of IR radiometry, a novel method by which the atmospheric transmittance could be acquired directly is proposed.  
The target emits radiation: (1) some of it is absorbed by the atmosphere \(\tau_{abs}\); (2) another part passes through the atmosphere \(\tau_{atm}\); and (3) a last portion is scattered by atmosphere \(\tau_{scat}\). Thus:  
\[\tau_{atm}+\tau_{abs}+\tau_{scat}\ =\ 1 \tag{6}\]  
where \(\tau_{abs}\) is the ratio of absorption, and \(\tau_{scat}\) is the ratio of scatter.  
In the pure atmosphere, aerosols can be ignored in the horizontal direction and at low angles of pitch; especially, for the transmission of IR radiation, the scatter is minimal compared with the transmittance and absorption [19]. Thus, \(L_{path}\) can be expressed as:  
\[L_{path}\ =\ (1-\tau_{atm})L(T_{e}) \tag{7}\]  
where \(T_{e}\), is the ambient temperature. Suppose that the reference is characteristic of uniformity and has a fixed temperature \(T_{m}\).  
Therefore, the atmospheric transmittance \(\tau_{atm}\) can be obtained according to Equations (5) and (7) as:  
\[\tau_{atm}\ =\ \frac{(G_{i,j}(T_{m})-G_{in})}{t}-G_{\text{out}}-R_{i,j}L_{ \text{path}} \tag{8}\]  
Equation (8) states that if a standard radiance has been available, the transmittance could be easily obtained. Therefore, atmospheric transmittance correction based on a constant reference for high precision and wide dynamic IR radiometry is proposed in this study. This method is part of the direct measurement, which has the characteristic of high accuracy. Compared with the method by a standard blackbody as a reference, our method only takes advantage of a constant reference; thus, it is fit for the requirements of IR radiometry in the outfield
## 5 Conclusions  
We demonstrated that the MAPLE technique is an appropriate approach for obtaining biomimetic thin films containing combinations of the biopolymer polyvinylpyrrolidone, flavonoids (quercetin dehydrate and resveratrol), and systemic antifungal agents (amphotericin B and voriconazole) that exhibit chemical structures similar to those of the input materials. The surface features of the thin films were evaluated using AFM, which revealed no observable grain boundaries and a lack of large material agglomerates. Agar diffusion assays confirmed the antifungal activity of MAPLE-deposited films containing polyvinylpyrrolidone-voriconazole and polyvinylpyrrolidone-quercetin dihydrate-voriconazole against _C. parapsilosis_ and _C. albicans_. These results indicate the potential use of polyvinylpyrrolidone polymers for developing voriconazole delivery systems that maximize the therapeutic efficiency of the antifungal agent at the infection site, as well as minimize systemic toxic effects of voriconazole. Additionally, coatings containing both voriconazole and quercetin dehydrate exhibited improved efficiency against _C. albicans_ biofilms.  
The authors individual contributions are listed below: conceptualization, R.C., A.M.G., M.C.C., R.J.N., D.B.C.; methodology, I.N., A.I.V., R.C.; C.P.; software, A.M.G., M.C.C.; validation, R.C., M.C.C., R.J.N., D.B.C.; investigation, A.F., A.M.G., M.C.C., R.D.B., D.Y., M.T.; resources, R.C., R.J.N.; writing--original draft preparation, I.N., R.C.; A.M.G., M.C.C.; writing--review and editing, R.C., M.C.C., R.J.N., D.B.C.; visualization, R.C., A.M.G., M.C.C., R.J.N., D.B.C.; supervision, R.C., M.C.C., R.J.N., D.B.C.; project administration, R.C.  
This work was supported by a grant of Ministry of Research and Innovation, CNCS - UEFISCDI, project number PN-III-P4-ID-PCE-2016-0884, within PNCDI III.  
Authors declare no conflicts of interest.
# Experiment and Modeling on Macro Fiber Composite Stress-Induced Actuation Function Degradation
## 5 Conclusions  
In the present study, the actuation function degradation degree of the MFC actuator undergoing cyclic loads was experimentally measured. Actuation function degradation experiments with four different stress amplitudes of cyclic loads have been implemented. Experimental results show that, when undergoing cyclic load with a stress amplitude of 12 MPa corresponding to a dynamic strain amplitude of the MFC is 400 \(\mu\varepsilon\), no more than 10% degradation of piezoelectric actuation function has been observed. For a cyclic load with a stress amplitude of 18 MPa, 24 MPa and 30 MPa, the corresponding dynamic strain amplitudes of MFC are 600 \(\mu\varepsilon\), 800 \(\mu\varepsilon\) and 1000 \(\mu\varepsilon\), respectively. The piezoelectric actuation function of the MFC actuator degenerates with the increase of loading cycles. Stress amplitude of the cyclic load is the key factor which determines the degenerate rate and degree of the MFC actuation function.  
The higher the stress amplitude of the cyclic load is, the faster the degenerate rate will be. For certain loading cycles, the higher the stress amplitude of the cyclic load is, the greater degradation degree of the MFC actuation function will be.  
Based on the measured data for the actuation function degradation of the MFC actuator undergoing cyclic load, an RBF neural network learning algorithm was adopted to establish a model for predicting the actuation function degradation of the MFC actuator undergoing arbitrary cyclic load within the linear stress limit range. Using the established actuation function degradation prediction model, the piezoelectric actuation function index \(A\) of the MFC actuator can be obtained for arbitrary cyclic load within the concerned stress amplitude limit for given cyclic loading cycles. Meanwhile, the actuation function degradation process of the MFC actuator undergoing arbitrary cyclic load within the concerned stress amplitude range can be graphically shown by using the degradation data provided by the established model. More importantly, with the help of this prediction model, it is possible to predict the function lifespan of the MFC actuator considering complex stress conditions in a practical service environment.  
Figure 11: Predicted actuation function degradation curves (red solid line) of the MFC actuator undergoing cyclic load with stress amplitudes of 16 MPa, 20 MPa, 22 MPa 26 MPa and 28 MPa.  
**Author Contributions:** Funding acquisition, W.W Investigation, W.W and Z.Z. Project administration, W.W. Writing--original draft, W.W. and Z.Z.; Writing--review & editing, W.W. and Z.Y.  
**Funding:** This work was supported by the National Natural Science Foundation of China [Grant No. 11502208].  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Fast Maritime Target Identification Algorithm for Offshore Ship Detection
## 3 Methodology
### Process of FMTI  
The FMTI algorithm is proposed in this paper for the detection of maritime targets, and its specific process is described subsequently. When there are one or more targets (including multiple targets) in the image to be recognized, the network is required to make a judgment for each prediction frame. Thus, the model divides the process into the following three steps.  
1. The classified image is gridded and there are the Bounding Boxes (Bbox) in the grid cell. Each Bbox contains five features, (x, y, w, h, \(\textit{Score}_{\textit{confidence}}\)). Where (x, y) is the offset of the Bbox center relative to the cell boundary, (w, h) denotes the ratio of width and height in the whole image, and \(\textit{Score}_{\textit{confidence}}\) is the Confidence Score. \[\textit{Score}_{\textit{Confidence}}=\textit{Pr}(object)\times\textit{GIOUT }_{\textit{pred}}^{\textit{true}}\] (1)  
_Pr(object)_ means whether the target exists or not. The existing value is 1, and the opposite value is 0.  
\[\textit{Pr}(object)=\left\{\begin{array}{ll}1,\textit{ Exist.}\\ 0,\textit{ None.}\end{array}\right. \tag{2}\]  
The _GIOU_[39] was optimized from the IOU, (Figure 1A). The intersection of Prediction and Ground Truth is shown by IOU. Where \(\textit{Area}(\textit{pred})\) denotes the area of the detection boxes and \(\textit{Area}(\textit{true})\) denotes the area of the true value.  
\[\textit{IOU}_{\textit{pred}}^{\textit{true}}=\frac{\textit{Area}(\textit{ true})\cap\textit{Area}(\textit{pred})}{\textit{Area}(\textit{true})\cup\textit{ Area}(\textit{pred})} \tag{3}\]To calculate _GIOU_, it is necessary to find the smallest box that can fully cover the Prediction box (_Area(pred)_) and the Ground Truth box (_Area(true)_), named _Area(full)_. The schematic diagram is indicated in Figure 1. \[GIOU_{pred}^{true}=IOU_{pred}^{true}-\frac{Area(full)\backslash Area(true)\cup Area (pred)}{Area(full)}\] (4)
2. The second step is feature extraction and prediction. Target prediction is performed in the final layer of the fully connected. If the target exists, the Cell gives the _Pr(class1 object)_, and the probability of each class in the whole network is calculated, then the detection \(Score_{confidence}\) is calculated. The comprehensive calculation is as \[Conf_{object_{i}} =Pr(Class|object)\times Score_{Confidence}\] (5) \[=Pr(Class|object)\times Pr(object)\times GIOU_{pred}^{true}\] \[=Pr(Class_{i})\times GIOU_{pred}^{true}\]
3. Setting the detection limitation of \(Score_{confidence}\), adjusting and filtering the borders with scores lower than the default value. The remaining borders are the correct detection boxes and the final judgment results are outputted sequentially.
## 2 Classification of Phase Change Materials (PCMs)
### Organic PCMs
#### 2.2.2 Fatty Acids, Esters, Polyols, and Derivatives  
Non-paraffin materials are the most extensive group of PCMs for latent heat storage, and the most commonly used are fatty acids, alcohols, esters, and polyols. [PERSON] et al. considered that among the different groups of PCMs, the non-paraffin materials are the most suitable for energy storage applications [49].  
Among non-paraffin materials, fatty acids have recently attracted attention due to their excellent properties, including suitable chemical and thermal stability, high latent heat of transition, lower vapor pressure, appropriate melting temperature range, low volume change, little or no supercooling during phase transition, non-toxic, and non-corrosive [50].  
Fatty acids are bio-based compounds derived from vegetable oils and animal fats by separation techniques or produced by specified processes from triacylglycereeds [51]. Fatty acids are widely used as surfactant and co-surfactant agents for emulsification and gelation in food, cosmetic and pharmaceutical applications. They are also used as thermal energy storage materials in buildings [52], air conditioning [53], and smart textiles [54].  
The results of some studies that investigated the phase change temperature and latent heat of the most known fatty acids are listed in Table 1. The thermal analysis results of the same fatty acids are slightly different in the literature. This difference is probably due to the operating conditions such as experimental temperature, the purity of materials, and different measurement techniques. In addition, the phase change temperature of fatty acids and fatty acid esters depends on the skeleton's number of carbon atoms.  
However, fatty acids have low thermal conductivity. This shortcoming is overcome when composite materials were prepared by combining fatty acids with materials exhibiting high thermal conductivity, such as expanded graphite (EG), silica fume (SF), and activated montmorillonite (a-MMT). Moreover, the phase change temperature could be adjusted by mixing two or more materials for low-temperature heat storage applications [55].
## 2 System Description
### Flight Coordination Control Unit  
For the flight coordination control unit, there are two main tasks: calculation and conversion tasks. The calculation task includes operating different algorithms and using data from the Drone Mapper device to calculate the basic parameters required for the UAV to perform its flight missions. The parameters include the flight speed, flight direction, destination coordinates, etc. The conversion task operates conversion of the data parameter output from the previous step into commands that can be recognized and executed by the UAV, and then these commands are sent to the flight controller. Different algorithms need to be designed and implemented for different flight missions in this unit. This is a highly creative and customized process.  
In general, a multiple input-output (I/O) algorithm implemented in this unit can be mathematically expressed by  
\[(O_{1},O_{2},\cdots,O_{M})=\mathcal{A}_{n}(\mathbf{I}_{n,1},\mathbf{I}_{n,2},\cdots, \mathbf{I}_{n,N}), \tag{1}\]  
where \(\mathcal{A}_{n}\) presents the designed algorithm performed by UAV\({}_{n}\) for the flight operations, including but not limited to collision avoidance and flight formation. \(\mathbf{I}_{n,k}\) for \(k\in\{1,\cdots,N\}\) denotes the received shared information sent from UAV\({}_{k}\) to UAV\({}_{n}\), and includes longitude, latitude, etc. In the present systems, \(\mathbf{I}_{n,k}\) can be further expressed by  
\[\mathbf{I}_{n,k}=\big{\{}\alpha_{n,k},t_{n,k},x_{n,k},y_{n,k},z_{n,k},v_{n,k}, \theta_{n,k},r_{n,k}\big{\}}, \tag{2}\]  
where \(\alpha\) and \(t\) denote the identification and flight time. \(x\), \(y\), and \(z\) are defined as the longitude, latitude, and altitude of the UAV. \(v\) and \(\theta\) represent the flight speed relative to the ground and the flight direction. \(r\) is the alert radius of the UAV. An example of the shared information format sent from the Drone Mapper device described in Section 2.1 is listed in Table 2. The output parameters \(O_{m}\) for \(m\in\{1,\cdots,M\}\) can be the UAV's flight speed, flight direction, destination coordinates, etc. The number of algorithm outputs \(M\) and definitions \(O_{m}\) are decided by flight missions for each UAV. Actually, the algorithm output \(O_{m}\) cannot be recognized and directly used by the UAVs. Hence, we implement a conversion function in the flight coordination control unit to translate \(O_{m}\) into commands that the UAV can recognize. A functional block diagram and a hardware photograph of the developed flight coordination control unit are shown in Figure 6 and Figure 7, respectively.  
Figure 5: An illustration of frame structure for the V2V communication protocol.
# GIS-Based Flood Assessment Using Hydraulic Modeling and Open Source Data: An Example of Application
## 4 Discussion
### Floods: Challenges for Agriculture, Grasslands, and Anthropogenic Spaces  
Floods impact the physical, chemical, and biological properties of soil, influencing its structure, porosity, texture, nutrient dynamics, microbial communities, and biogeochemical cycles, with significant implications for soil health, agricultural fertility, and ecosystem functioning [91; 22; 92; 93].  
The impact of floods on arable land is critical, as these areas are essential for agricultural production and the subsistence of local communities. On flooded lands, chemical and biological changes in the soil can lead to a significant decline in fertility and increased difficulties associated with agricultural management [22]. For instance, nutrient losses or the accumulation of toxic metabolites (ethanol, acetaldehyde) due to water stagnation directly affect crop yields. This issue is particularly relevant for the 392 hectares of arable land in the analyzed area, which require the implementation of remediation strategies such as controlled drainage or chemical soil improvement.  
Flooded forested areas (231 hectares) and grasslands (202 hectares) are also affected by flood-induced changes. Water stagnation can cause physiological stress in plants, including disruptions in respiration, photosynthesis, and nutrient absorption [94; 95]. These processes can contribute to a decline in plant biodiversity and reduce ecosystems' capacity to maintain critical functions such as carbon storage or soil erosion prevention.  
Flood-prone intravillan lands (46 hectares) are less extensive, but the impact on these areas involves significant social and economic challenges. Flooding in these zones can lead to infrastructural damage, disruption of local economic activities, and increased restoration costs. These effects are exacerbated by the duration and frequency of floods, underscoring the need for integrated risk management plans.  
Flooding significantly affects plant growth, crop yields, and quality, with variable losses depending on the crop type, growth stage, and duration of exposure [22]. Consequently, this study highlights the complexity of flood impacts on different land-use types and emphasizes the importance of implementing adaptive strategies to mitigate economic and ecological losses.
## 2 Materials and Methods
### Example 1: Simply Supported Beam with a Concentrated Load
According to the first order of shear deformation theory, with \(S_{i}\) being the shear force at the \(i\)th node, the bending vertical deflection at mid-span (node 2) \(v_{b,2}\), the vertical deflection due to shear at mid-span \(v_{s,2}\), the bending rotation at the left support (node 1) \(w_{b,1}\) and the shear rotation at the same support \(w_{s,1}\) might be expressed using the Equations (5)-(8), respectively:  
\[v_{b,2}=\frac{S_{1}*L^{3}}{3*E*I} \tag{5}\]  
\[v_{s,2}=\frac{S_{1}}{2*A_{v}*G}*L \tag{6}\]  
\[w_{b,1}=\frac{S_{1}*L^{2}}{4*E*I} \tag{7}\]  
\[w_{s,1}=\frac{S_{1}}{A_{v}*G} \tag{8}\]  
A comparison of the vertical deflections and the rotations obtained using [PERSON]'s beam theory and the results (with and without shear effects) found using Midas/Civil is summarized in Table 2.  
Analysis of Table 2 shows that all the methods provided the same values of \(v_{b,2}\) (33.34 mm) and \(w_{b,1}\) (0.010 rad). When the shear deformations were considered in Midas/Civil, additional deformations \(v_{s,2}\) were obtained. The values of these deformations (20.00 mm) corresponded with those calculated using [PERSON]'s beam theory. Nevertheless, this was not the case for the shear rotations, as no additional rotations (\(w_{s,1}\)) were obtained in Midas/Civil when the shear effects were included. The error in this rotation (0.005 rad) represented the 50% bending rotation at the support (\(w_{b,1}\)). The results of this example illustrate the important role that shear rotations might play and how they are systematically neglected in commercial software. In order to fill this gap, a new simulation method for the calculation of the real rotations in beams is proposed in the following section.
## 4 Model Validations
### Fixed-Pad Hydrostatic and Hybrid Journal Bearings  
The test data for the water-lubricated, three-recess hybrid fixed-pad journal bearing reported in Ref. [39] validate the proposed bulk flow model for hydrostatic/hydrodynamic hybrid operations. The analyzed bearing has an axial length and a diameter of 80 mm (L/D = 1) with a radial clearance of 125 \(\upmu\)m. The circumferential angle and width of the recess are 90\({}^{\circ}\) and 50 mm, respectively, and the recess depth is 10 mm [39]. The low viscosity of water may lead to flow turbulence at high operating speeds and significant fluid inertia at recess edges. Figure 5 compares the predicted static load plotted against the eccentricity ratio with the test data from Ref. [39] at 0 rpm, i.e., pure hydrostatic operation. In general, the predictions agree well with the test data, with a mean square error of 0.3%.  
Figure 3: (**a**) Density and (**b**) viscosity of supercritical carbon dioxide (sCO\({}_{2}\)) vs. pressure at 37 \({}^{\circ}\)C. Comparison between the developed model and that in NIST [12].  
Figure 6 shows the predicted centerline pressure ratio (\(P/P_{s}\)) compared to the test data from Ref. [39] at rotor speeds of 5 and 8 krm with a static load of 356 N applied at 270\({}^{\circ}\). Note that the figure shows one-third of the bearing geometry and film pressure between two externally pressurized flow supply tubes located circumferentially at 210\({}^{\circ}\) and 330\({}^{\circ}\). The predicted pressure ratios are compared to test data measured at 11 locations from 210\({}^{\circ}\) to 310\({}^{\circ}\) with an increment of 10\({}^{\circ}\). The film pressure ratio is higher at the recess regions. The sudden pressure drop at the entrance of the film land is attributed to the increase in the flow inertia with an increase in rotor speed. The predictions agree well with test data with mean square errors of 0.04% and 0.05% for 5- and 8-krm rotor speeds, respectively, thus validating the proposed bearing model for hybrid operations.  
Figure 4: Static load vs. eccentricity ratio for compressible and incompressible fluid flows compared to the predictions in Ref. [16] and those obtained using computational fluid dynamics (CFD). Rotor speed = 60 krm.  
Figure 5: Static load vs. eccentricity ratio for a fixed-pad three- recess hybrid journal bearing compared to test data in Ref. [39]. \(P_{s}\) = 0.4 MPa, and rotor speed = 0 rpm (hydrostatic operation).
## 2 Materials and Methods
### Cost-Benefit Analysis: Decision Making Based on Absolute PPMs  
Structure service life calculations and analytically derived performance models are the basis for optimal decision making when creating pavement rehabilitation plans, which are based on the results of a cost-benefit analysis [41].  
Three economic indicators, namely, the payback period, internal rate of return, and net present value, are employed to assess the economic feasibility of each rehabilitation scenario. It is essential to monetize socio-economic costs and benefits associated with pavement surface rehabilitation to determine these indicators accurately. The benefits are linked to the disparity in pavement surface quality and its degradation between scenarios with and without rehabilitation. These benefits encompass both internal, i.e., user-related, and external benefits [42]. For this case study, internal benefits such as road user operation costs and travel time costs were utilized, as they can be quantified using the methodology endorsed by the World Bank. External benefits, including environmental savings and broader economic impacts, were excluded due to the subjectivity associated with available monetization techniques, as deemed by road administration authorities. The overall road user benefits considering factors like proposed rehabilitation technology, investment costs, road administrator expenses, rehabilitation timing, and discount rate can be computed using Equation (9).  
\[RUB=\sum_{t=1}^{z}[(RUC_{DS}-RUC_{DN})\cdot k_{DEG}\cdot k_{ATG}]_{t} \tag{9}\]  
where \(RUB\) is road user benefits in (EUR); \(RUCDS\) is road user costs in the \"do something\" variant in (EUR); \(RUCDN\) is road user costs in the \"do nothing\" variant in (EUR); \(RDEG\) is the coefficient of unevenness degradation function; and \(RATG\) is the annual transportation growth coefficient.  
The degradation coefficient \(RDEG\) significantly influences the economic results on the basis of which decisions will be made. The value of the coefficient represents the \(p(x)\) value of longitudinal unevenness in particular years described in Section 3.2, and the value is derived from either an exponential or, preferably, polynomic function.  
Figure 9: Longitudinal unevenness PPM—exponential and polynomial.