esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed Model
### Multiyear Model  
The CRF fails to adequately represent all costs in the long term. Moreover, while renewable energies are costly in the initial period, their long-term operating costs are lower than diesel generation. Therefore, a multi-year formulation was proposed to address this issue.  
Unlike the one-year model, the multi-year model does not assume symmetric data for each year. Instead, it allows for the inclusion of demand and fuel cost growth through an increasing geometric progression. Additionally, it introduces new concepts, such as equipment degradation through a decreasing geometric progression, as the generation capacity diminishes over time. Consequently, the dispatch strategy is not simulated for just one year but for the entire time horizon [25].  
The modeling remains consistent with the deterministic model. However, in this case, the input parameters for demand and fuel cost will increase over subsequent years according to Equation (30):  
\[value_{first\_year}*(1+\Lambda)^{year} \tag{30}\]  
where \(\Lambda\) represents the growth rate. For wind and solar generation, no growth is considered, but they are replicated each year for the first year. The degradation of the equipment of Equations (31) and (32) is presented as a reduction of the nominal capacity (rated power) of each of them over time. It is calculated based on the rated power in year 1, and for subsequent years, the capacity will decrease by subtracting the rate \(\Delta\) and raising it to the corresponding year, for both generators and batteries.  
\[p_{kty}=p_{kt1}*(1-\Lambda_{k})^{y} \forall k\in\mathcal{U},\forall t\in\mathcal{T},\forall y\in \mathcal{YEARS} \tag{31}\] \[soc\_max_{y}=soc\_max_{l1}*(1-\Lambda_{l})^{y} \forall l\in\mathcal{B},\forall y\in\mathcal{YEARS} \tag{32}\]  
Therefore, the CRF is now removed, and all costs from year 0 to the final year of the time horizon are aggregated and divided by the generation to calculate the LCOE. The model is tested by assuming all installations occur starting from year zero. Consequently, there might be oversized solutions initially to accommodate the growth in the final years. Environmental forecasts can be derived from the presented calculations, which utilize the first year as a reference. Alternatively, users can input the entire time horizon if they possess their own forecasts.
# The Effect of Silica-Filler on Polyurethane Adhesives Based on Renewable Resource for Wood Bonding
## Materials and Methods  
### Materials  
Two biopolyols were examined as components for PUR wood adhesives: A--polyglycerol synthesized by ring opening polymerization of glycerol carbonate and 1,1,1-tris(hydroxymethyl)-propane (TMP) at 6/1 molar ratio [36; 37]. The product was a highly viscous light-brown liquid. NMR spectrum was recorded on a Varian VXR 400 MHz (Palo Alto, CA, USA). 1H NMR (400 MHz, DMSO-\(d_{6}\)), \(\delta\) (ppm): 4.91-4.20 (m, 10H, H\({}_{2}\)O, OH), 3.75-3.10 (m, 38H, polyether backbone), 1.36-1.16 (bs, CH\({}_{2}\)CH\({}_{3}\)), 0.77 (bs, CH\({}_{2}\)CH\({}_{3}\))--see Figure S1. GPC in water: \(M_{\rm n}\) = 563.47, \(M_{\rm w}\) = 1036.63, \(D\) = 1.83 (Viscotek (Malvern Panalytical Ltd., Malvern, UK) system with a triple detector array (TDA) 305 and divinylbenzene (DVB) Jordi column (JordiLabs LLC, Mansfield, MA, USA). It is a structural analogue of the polyglycerol characterized in our previous work [37]. The other commercial sucrose-based polyether polyol labeled as C was manufactured by Oltchim (Valcea, Romania) and used as obtained. Basic properties of the polyols are shown in Table 1. Polymeric methylenediphenyldiisocyanate (PMDI) was purchased from Huntsman Co., (Arlington, TX, USA) and used as received. Isocyanate content in PMDI was 36%, with 4,4\({}^{\prime}\) MDI isomer content lower than 75%. The viscosity of PMDI is 290 mPa-s at 20 \({}^{\circ}\)C, and average functionality 2.7. Viscosity of the adhesives was controlled by addition of colloidal hydrophilic silica (SiO\({}_{2}\), surface area 200 \(\pm\) 25 m\({}^{2}\)/g manufactured by Evonik Industries AG,Essen, Germany) as a filler: 2.0 wt% for in polyol A and 6.0 wt% for polyol C were needed to reach practical viscosity.
## 4 Discussion
### Comparisons of LC\({}_{50}\) Values with Those of Other Species of Molluscs
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Molluscs** & **Species** & **Water Hardness** & **Live** & **Test** & **LC\({}_{\text{50}}\)** & **References** \\  & **(mg/L)** & **Stage** & **Duration** & **(mg/L)** & **References** \\ \hline Bivalves & _Corbicula fluminaa_ & 64 & NA & 96-h EC\({}_{\text{50}}\) & 6.04 & [PERSON] et al. [104] \\ _Actinonais pectorosa_ & 170 & Glochidia & 96-h EC\({}_{\text{50}}\) & 0.31 & [PERSON] et al. [104] \\ _Medionidus corradicus_ & 170 & Glochidia & 96-h EC\({}_{\text{50}}\) & 0.57 & [PERSON] et al. [104] \\ _Phychobranchus_ & & 170 & Juveniles & 96-h EC\({}_{\text{50}}\) & 0.21 & [PERSON] et al. [104] \\ _fascolaris_ & 170 & Juveniles & 96-h EC\({}_{\text{50}}\) & 0.27 & [PERSON] and [PERSON] [97] \\ _Utterbacia imbecillis_ & 60 & Juveniles & 96-h EC\({}_{\text{50}}\) & 0.44 & [PERSON] and [PERSON] [97] \\ _Utterbacia imbecillis_ & 60 & Juveniles & 96-h EC\({}_{\text{50}}\) & 0.36 & [PERSON] and [PERSON] [97] \\ _Utterbacia imbecillis_ & 80 & Juveniles & 96-h EC\({}_{\text{50}}\) & 0.59 & [PERSON] and [PERSON] [97] \\ _Villosa nebulosa_ & 170 & Glochidia & 96-h EC\({}_{\text{50}}\) & 0.66 & [PERSON] et al. [104] \\ _Actinonais pectorosa_ & 40 & Juveniles & 96-h EC\({}_{\text{50}}\) & 0.26– & McCann [105] \\ _Actinonais pectorosa_ & 160 & Juveniles & 96-h EC\({}_{\text{50}}\) & 1.19– & McCann [105] \\ _Villosa iris_ & 50 & Juveniles & 96-h EC\({}_{\text{50}}\) & 0.34 & McCann [105] \\ _Villosa iris_ & 160 & Juveniles & 96-h EC\({}_{\text{50}}\) & 1.12 & McCann [105] \\ _Villosa umbrans_ & 43 & Juveniles & 96-h EC\({}_{\text{50}}\) & 1.30 & [PERSON] et al. [98] \\ _Villosa nebulosa_ & 43 & Juveniles & 96-h EC\({}_{\text{50}}\) & 0.44 & [PERSON] et al. [98] \\ _Donax faba_ & 29.9 ppt & Adult & 96-h EC\({}_{\text{50}}\) & 3.61 & Din and Ong [81] \\ _Anadara granosa_ & 29.5 ppt & Adult & 96-h EC\({}_{\text{50}}\) & 7.76 & Din and Ong [81] \\ _Mod
## 2 Autonomous Driving in Adverse Weather Conditions
### Algorithmic Challenges in Adverse Weather
#### 2.2.3 Path Planning and Decision-Making Complexities  
Adverse weather also affects perception and complicates path planning and decision-making processes. Reduced visibility, decreased road friction, and the unpredictable behavior of other road users in bad weather all contribute to making these tasks more complex. [PERSON] et al. [45] provided a comprehensive survey of autonomous driving, including the challenges in path planning and decision-making under adverse conditions. They highlighted the need for adaptive algorithms to adjust their behavior based on current weather conditions and the associated uncertainties.
## 3 Results  
### Color Matching Ability  
The present study evaluated the color matching ability of single-shade OM resin against a standard restorative material, Filtek-Z 350 (FT). The one-way ANOVA showed significant differences among the study groups (\(p<0.01\)). The \(\Delta\)E (color difference) values for the four shades of the two composites were compared (Table 1) for shade matching within each material. The highest mean \(\Delta\)E among the study groups was in shade B1 (17.806 \(\pm\) 1.8722) for OM, while the lowest was in FT shade for A3 (2.47 \(\pm\) 0.6225). Moreover, in the FT group, the highest \(\Delta\)E among the acrylic teeth and restoration samples was for B1 (9.0956 \(\pm\) 0.7721), while the lowest was for shade A3 (2.47 \(\pm\) 0.6225). A similar pattern was observed among the OM groups, with shade B1 showing the highest \(\Delta\)E and shade C3 displaying the lowest \(\Delta\)E. Furthermore, a comparison between the 4 shades demonstrated significant differences among the 2 materials, Filtek and OM (\(p\) < 0.01) (Table 1). Overall, the shade and color matching ability levels of FT, OM-C, and OM-UC were significantly different. A statistical comparison among these groups for a specific shade showed significantly different \(p\) values (B1: <0.0001; B2: <0.0001; A3: <0.0001; C3: 0.001) (Table 1).  
Table 2 presents a comparison of \(p\) values among the study groups for color matching. Comparing FT (control) with OM-C showed significantly better color matching for FT for shades BI (\(p\) 0.008) and A3 (\(p\) 0.004). However, color matching among FT and OM-C was comparable for shades B2 (\(p\) 0.478) and C3 (\(p\) 0.051). FT showed better color matching than OM-C for groups B1, B2, and A3; however, OM-C showed better color matching than FT for shade C3 (Table 1). OM-C showed significantly better color matching (lower \(\Delta\)E) in comparison to OM-UC specimens for all tooth shades (\(p\) < 0.01). FT showed significantly better color matching (lower \(\Delta\)E) in comparison to OM-UC specimens for shades B1, B2, and A3 (\(p\) < 0.01); however, shade C3 showed comparable color matching ability for FT and OM-UC (\(p\) > 0.01).
# Calibration Method of Orthogonally Splitting Imaging Pose Sensor Based on General Imaging Model
## 3 Results
### Calibration and Test  
According to the selected shape parameter, the RMS of the calibration data set and test data set is shown in Table 2. It can be seen from the experimental results that the calibration method based on the general imaging model can meet the calibration requirements of the orthogonally splitting imaging pose sensor. And the MQ function is more suitable as an interpolation function.
# Assessment of Drought Severity and Vulnerability in the Lam Phaniang River Basin, Thailand
## 2 Materials and Methods
### Assessment of Drought Hazard and Vulnerability
#### 2.6.3 Calculation of Drought Hazard Index  
By integrating the weights and ratings of various categories using Equation (1) for seven rain gauges, the Drought Hazard Index (DHI) was then calculated for 3-, 12-, and 24-months SPEI values for 2000-2017 and 2021-2100 under both RCPs 4.5 and 8.5 (see Figure 3 for detailed procedure).  
\[\mathrm{DHI}=\sum_{i=1}^{N}W_{i}\times R_{i} \tag{1}\]  
where N refers to the number of SPEI values for selected time interval, and \(W_{i}\) and \(R_{i}\) represent the weights and ratings for each drought category, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**SPEI Value** & **Classification** & **Weights Assigned** \\ \hline \(-1.00\) to \(1.00\) & Near normal or mild (M) & 1 \\ \(-1.50\) to \(-1.00\) & Moderate (MO) & 2 \\ \(-2.00\) to \(-1.50\) & Severe (S) & 3 \\ \(-2.00\) or less & Extreme (E) & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification of SPEI index and weighting scheme for drought hazard assessment [28].  
Figure 2: Weights and rating scores based on cumulative distribution function of SPEI for the calculation of Drought Hazard Index (DHI).  
Subsequently, the DHI values were rescaled to a scale of 0-1 and evenly divided into four groups using the Jenks Natural Breaks method (Table 2). Based on the DHI value of each grid, the drought hazard map was then generated by using the Inverse Distance Weighting (IDW) interpolation method with ArcGIS 10.5 software. The obtained drought hazard maps were then overlaid on the sub-district map of the Loei, Nong Bua Lamphu, and Udon Thani Provinces, for creating sub-district scale drought hazard maps, in which the calculation of the area-weighted average DHI for each sub-district was also performed.
## 2 Establishment of the Model  
### Physical Model  
The shape of the traditional vertical-lifting underwater monitoring device such as the self-sustaining profile detection drifting buoy is basically cylindrical, and the shell diameter is generally 0.17 m [30]. In order to cooperate with the cylindrical shell and make full use of the heat transfer area of the phase change equipment, a cylindrical phase change heat exchanger is designed in this paper. The inner diameter is set to 0.22 m, and the outer diameter is designed in two different sizes, which are set to 0.3 m and 0.26 m, respectively. Because there is a certain space on the outer side of the shell of the platform and the inner side of the cylindrical phase change heat exchanger, the inner and outer sides of the phase change heat exchanger can be used as the heat-exchange surface. In addition, in order to compare the advantages of cylindrical equipment in the heat transfer process, this paper also analyzes the phase change process of cylindrical phase change equipment and compares it with the same volume of cylindrical phase change heat exchanger. Because n-hexadecane has the characteristics of suitable phase change temperature, high volume expansion rate, stable performance and wide source [38; 39], this paper uses hexadecane as a phase change material, and in order to improve the thermal conductivity of n-hexadecane. Graphite with a volume ratio of 10% was added to n-hexadecane. The physical parameters of phase change materials are shown in Table 1.  
The research materials show, that under the pressure of 60 MPa, paraffin can still produce a volume expansion rate of 9% [40; 41]. In this paper, the volume expansion rate of n-hexadecane is 8% for the sake of conservation. Therefore, in order to obtain a volume expansion of 1 L, the volume ratio of n-hexadecane and graphite composite phase change with a volume ratio of 9:1 is obtained. The volume is 13,889 mL, so the lengths of the two cylindrical phase change heat exchangers with an inner diameter of 0.22 m, and an outer diameter of 0.3 and 0.26 m are 0.425 and 0.924 m, respectively, and 0.45 and 0.95 m are taken in the simulation. The unit model of the cylindrical phase change heater is shown in Figure 1, where PCM represents the phase change material.
# Research on the Compound Jamming Method against FDA-MIMO Beamforming
## 2 FDA-MIMO Basic Principle and Jamming Model
### Basic Principle of FDA-MIMO
\begin{bmatrix}1\\ e^{2\pi f_{0}d_{R}\sin\theta/c}\\ \vdots\\ e^{2\pi f_{0}(N-1)d_{R}\sin\theta/c}\end{bmatrix}  \tag{12}\]  
The transmit-receive antenna pattern at the spatial target point \((r_{0},\theta_{0})\) can be expressed as  
\[ \begin{array}{ll}B&=\left|\mathbf{w}_{D}{}^{H}\cdot\mathbf{S}\right|^{2}\\ &=\left|\sum\limits_{n=0}^{N-1}e^{2\pi f_{0}\frac{n\mu_{R}(\sin\theta-\sin \theta_{0})}{c}}\right|^{2}\times\left|\sum\limits_{m=0}^{M-1}e^{2\pi\left[- \Delta f_{m}\frac{2(r-r_{0})}{c}+f_{0}\frac{n\mu_{T}(\sin\theta-\sin\theta_{0} )}{c}\right]}\right|^{2}\end{array}  \tag{13}\]  
From Equation (13), it can be seen that by adopting the FDA-MIMO structure with multiple matched filters at the receiver side, the generated antenna pattern is range-dependent.
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classes** & **Precision** & **Recall** & **f1-Score** & **Support** \\ \hline AK30 & 0.992 & 0.958 & 0.976 & 106 \\ \hline AMP10 & 0.966 & 0.968 & 0.966 & 54 \\ \hline AMP2 & 0.992 & 0.96 & 0.974 & 66 \\ \hline ATM30 & 0.962 & 0.992 & 0.978 & 72 \\ \hline Aug-30 & 0.776 & 0.86 & 0.818 & 42 \\ \hline C30 & 0.946 & 0.968 & 0.954 & 67 \\ \hline CAZ10 & 0.784 & 0.94 & 0.854 & 29 \\ \hline CD2 & 0.916 & 0.906 & 0.91 & 100 \\ \hline CIP5 & 0.978 & 0.912 & 0.946 & 103 \\ \hline CN10 & 0.952 & 1 & 0.976 & 62 \\ \hline CN30 & 0.926 & 0.892 & 0.91 & 58 \\ \hline CRO30 & 0.994 & 0.988 & 0.994 & 38 \\ \hline CTX5 & 0.922 & 0.974 & 0.946 & 63 \\ \hline E15 & 0.85 & 0.898 & 0.876 & 33 \\ \hline ETP10 & 0.856 & 0.966 & 0.906 & 72 \\ \hline FC10 & 0.938 & 0.862 & 0.898 & 90 \\ \hline FEP30 & 0.824 & 0.556 & 0.654 & 14 \\ \hline FOX30 & 0.936 & 1 & 0.968 & 23 \\ \hline IMI10 & 0.73 & 0.634 & 0.68 & 40 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average values of the CNN model’s 5-fold cross-validation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classes** & **Precision** & **Recall** & **f1-Score** & **Support** \\ \hline LEV5 & 0.93 & 0.862 & 0.896 & 96 \\ \hline MRP10 & 0.884 & 0.846 & 0.864 & 47 \\ \hline NOR10 & 0.91 & 0.966 & 0.938 & 110 \\ \hline P1 & 0.856 & 0.84 & 0.848 & 45 \\ \hline
**Accuracy** & & & 0.92 & 1430 \\ \hline
**Macro avg.** & 0.90 & 0.90 & 0.90 & 1430 \\ \hline
**Weighted avg.** & 0.92 & 0.92 & 0.92 & 1430 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._  
Figure 14: Error matrix of the best fold. The experimental studies and visualizations indicated that higher success rates can be achieved in the field of classification of antibiotic disks coded with different shapes and codes through using the correct model approach, the correct number of epochs, and avoiding overfitting. Classification success was achieved by our CNN model with a precision rate of 0.9, an f1 score of 0.9, and an accuracy rate of 0.92 for the 23 classes in our dataset.
# Comparison of Lab vs. Backcalculated Moduli of Virgin Aggregate and Recycled Aggregate Base Layers
## 1 Introduction
In addition, almost all pavement analysis/design procedures (including the MEPDG) use a single modulus value to represent the stiffness of the unbound layers; therefore, the nonlinear stress-dependent behavior of unbound materials has not been considered in calculating the conversion factors in the literature. As the nonlinear stress-dependent behavior of unbound materials is already measured in laboratory testing by varying the applied confined and deviatoric stress, multi-load FWD testing is an alternative method to capture such nonlinear behavior in the field. The main objective of this study is to compare the laboratory-measured \(M_{R}\) of unbound recycled aggregate base (RAB) and virgin aggregate base (VA) layers (at the optimum moisture content) at the time of construction with their corresponding backcalculated \(E_{FWD}\) over three years of their service life. The analysis is based on the vertical strain response of four test sections under a singular load level, using the finite element (FE) and layered-elastic analysis (LEA) approaches. This approach offers a fresh perspective by analyzing how the pavement response under linear and nonlinear models, both in laboratory and field settings, can impact the correlations between the \(M_{R}\) and \(E_{FWD}\), especially for RABs. The current study takes the nonlinear behavior of unbound layers into account through the FE-based pavement response model and multi-load FWD testing.
## 2 Material and Methods
### Measurement Protocol  
The experiment was conducted as follows (see Figure 1c). Samples were put in the climate chamber and the temperature was set to a given value for about 2 days. Elastic parameters were monitored during thermal relaxation and found to reach an equilibrium value in about 1.5 days, thus ensuring thermal equilibrium before the NRUS measurements were performed roughly 5 days after. Afterwards, temperature was changed to a new value. Initial temperature was 278 K and was increased by 5 K at each step.  
Each NRUS acquisition (i.e. for one drive amplitude) consisted of the following steps (and discussed in detail in the following subsection): choice of drive amplitude and injection of a chirp signal; measurement of the received signal; derivation of the resonance curve; and determination of strain amplitude, velocity, and damping. The NRUS measurement consisted of repeating the procedure and varying the drive amplitude. In this work, a baseline loading/unloading amplitude protocol was chosen (also shown in Figure 1c), which consists of alternating high conditioning and low baseline amplitudes, keeping the latter constant (\(A_{\text{baseline}}\) in the order of tenths of volts) and increasing the first (\(A_{\text{cnd}}\)) in equal steps up to a maximum value of the order of a few volts (loading phase) and then decrease it back to \(A_{\text{baseline}}\). A total of 21 steps for \(A_{\text{cnd}}\) were chosen.  
Each NRUS sequence had the same duration and each probing had a fixed temporal length \(\Delta t=50\) ms, with pauses between successive sweeps at different drive amplitudes to ensure accurate timing control, fundamental for a correct evaluation of slow dynamic effects.  
Figure 1: Experimental set-up. (**a**) Image of the suspended samples; (**b**) schematic description of the set-up. Input (schematic) and output (real data) signals are shown in insets; (**c**) representation of the experimental protocol. In the inset, the drive amplitude protocol is shown: conditioning amplitudes (\(A_{\text{cnd}}\)), increasing up to \(A_{\text{max}}\), are alternated with constant baseline amplitudes (\(A_{\text{baseline}}\)).  
The baseline and maximum conditioning amplitude differed for each temperature/sample and were chosen to guarantee output amplitudes (i.e., strain) in similar ranges.
# Vibration-Based Fingerprint Algorithm for Structural Health Monitoring of Wind Turbine Blades
## 4 Results  
The fingerprint algorithm was tested by using measurements from several sensors, which were mounted on two test turbines as discussed in Section 2. The results are presented using the methods described in Section 3.6 for visualising fingerprints both separately per sensor and jointly for several sensors. The following section is split into results regarding Turbine 1 (Section 4.1), results regarding Turbine 2 (Section 4.2) and a joint evaluation for sensors on both turbines (Section 4.3). Parameters of the fingerprint algorithm were adjusted separately for sensors of both test turbines to account for: (i) varying vibration characteristics of the turbines; and (ii) varying size and data quality of sensor measurements (see Table 1). However, parameters were kept constant for sensors of the same turbine to enable comparability of results. Selected parameters are discussed subsequently and the reader is referred to Section 3 for a detailed description of the full set of algorithm parameters.  
Figure 11: Fingerprint results for Sensors \(S1\)**(top)** and \(S2\)**(bottom)** mounted on the blade tip of test turbine 1. **(Left)** Linear-scale spectrogram with colour corresponding to logarithmic amplitude. **(Middle)** Extracted fingerprints, both Type 1 (\(V1\)) and Type 2 (\(V2\)) vibrations displayed. **(Right)** Sensor positions on the blade.  
Figure 12: Fingerprint comparison for Sensors \(S1\) and \(S2\). Separate visualisation for Type 1 (**left**) and Type 2 (**right**) vibrations. Solid lines represent vibrations only occurring in the fingerprint of one sensor, while dotted multi-coloured lines represent vibrations occurring jointly in both fingerprints. Error bars show locations of associated peaks with their length representing peak prominence.
## 3 Implementation of X-IoCA for SAR Missions: SAR-IoCA
### Feedback Information System for SAR Missions
#### 3.2.1 Ros-Fis  
ROS-FIS consists of dedicated hosts (physically located in the MEC centers) which monitor and process, in real time, the information published on topics from the hosts in IoRT, with all of them being interconnected and linked to the ROS master node, hosted in the cloud. In addition, the cloud host could perform processing tasks. Connections between remote hosts are made over the 5G network. ROS-FIS separates the computational load on remote equipment (between the fog and the cloud), taking advantage of the 5G network, especially the high bandwidth and low latency. In addition, ROS-FIS controls the flow of information (outgoing and incoming) between devices in the 5G network, since all ROS nodes are associated with a public IP address of the 5G pilot network. Each subscription to a topic generates an uplink of this information at the other end of the network, i.e., at the information source or publishing nodes. This is because the request to a different public socket is associated with a service provided, thus being able to control the traffic between the existing 5G devices.