esa-sceva/satcom-chunk-collection huggingface dataset:
# Identification of Rice Sheath Blight through Spectral Responses Using Hyperspectral Images
## 2 Materials and Methods
### The Identification Algorithm of Rice ShB in Leaves  
See5.0 Classifier was used for identifying rice leaves infected with ShB. The idea of this algorithm is the entropy criterion that the growth of the classification tree depends on the variable with the highest entropy or amount of information [37; 38]. The entropy formula is as follows.  
\[\text{Entropy}=-\sum_{\text{j}=1}^{\text{k}}\frac{\text{n}_{\text{j}}}{\text{N }}\times\text{Log}_{2}(\frac{\text{n}_{\text{j}}}{\text{N}}) \tag{2}\]  
where N is the total number of observations, k is the number of classes in N, and n\({}_{\text{j}}\) is the number of observations belonging to the same class.  
See5.0 Classifier has many advantages, including rulesets, boosting, evaluation systems and pruning options. These options are helpful to users to make an effective informative pattern. Classifiers were generated from the second data set and evaluated by 10-fold cross-validation in the study. Evaluation indexes included the average error rate and the standard errors of the means. The average error rate is the ratio of the total number of errors on the hold-out cases to the total number of cases. The standard errors of the means are an estimate of the variability of the results.
# A Brief Review of Multipath TCP for Vehicular Networks
## 2 TCP and MPTCP
### Overview of Multipath TCP
As the transmission delay is a key performance parameter for voice recognition, it could be affected more easily by network condition in single path TCP. In MPTCP, the scheduler can shift the data transmissions from a weak signal strength subflow to a stronger one. Besides MPTCP, there are also some alternatives that can realize multiple paths transmission such as multipath QUIC (MP-QUIC) [37] and multipath transmission model for datagram congestion control protocol (MP-DCCP) [38]. MP-QUIC is the extension of QUIC, which enables multipath transmission on one QUIC connection. In [37], [PERSON] et al. implemented some experiments and show that MP-QUIC outperforms MPTCP both on lossy scenario and non-lossy scenarios. However, the effectiveness of MP-QUIC needs to be further verified in various scenarios. Similar with UDP, the datagram congestion control protocol (DCCP) is a transport layer protocol on the top of IP. It includes a congestion control mechanism and acknowledgement (ACK) by the receiver when receiving packets. However, the sender does not retransmit the lost packets. DCCP is very flexible. It can switch its options and feature even during transmissions. The multipath datagram congestion control protocol (MP-DCCP) is based on DCCP, and it supports the use of different congestion control algorithms. Similar with UDP, MP-DCCP does not consider retransmissions. [PERSON] et al. proposed an IP-compatible multipath framework for heterogeneous access networks by utilizing the MP-DCCP in [38]. The framework realized IP compatibility by integrating virtual network interfaces with MP-DCCP. The MP-DCCP supports multipath transmission across multiple DCCP tunnels simultaneously. MP-DCCP measures the capacity of each tunnel based on loss rate, bandwidth and round trip time, and schedules the data that entered from virtual network interfaces across different tunnels. A scheduling module and reordering module are applied to adjust the delay difference of tunnels for MP-DCCP.
## 4 Theoretical Analysis of Computational Complexity
### Polygonizing Planes
Thus, a conservative estimate for the number of steps \(S\) for all \(N^{2}\) rays is  
\[S\quad\leq\quad N^{2}\;+\;2\bigg{(}N^{2}+\frac{N^{2}}{2}+\frac{N^{2}}{3}+ \cdots+\frac{N^{2}}{N-1}+N\bigg{)}\quad=\quad N^{2}\;\cdot\;(1+2H_{N}), \tag{7}\]  
where \(H_{N}\) is \(N\)th partial sum of the harmonic series [23]: \(H_{N}=\sum_{n=1}^{N}\frac{1}{n}\). The number \(H_{N}\) is about as large as \(\log N\). The reason for this comes from the comparison of \(H_{N}\) and the integral \(\int_{1}^{N}\frac{1}{x}\,dx\), which can be solved analytically. Thus, it follows that \(S\in O(N^{2}\log N)\) and the complexity of case #2 is \(O(N^{2}\log N)\). **Case #3: the general case**. Due to easier exposition and without loss of generality, we assume that the plane passes through origin (i.e., \(\mathbf{r}_{P}=\mathbf{0}\)). Combining this assumption with Equations (3) and (5), we obtain the following iteration for the \(z\) coordinate:  
\[z_{n+1}=z_{n}+\big{|}n_{x}x_{0}+n_{y}y_{0}+n_{z}z_{n}\big{|} \tag{8}\]  
where \((n_{x},n_{y},n_{z})^{T}\) is the unit normal of the plane and \((x_{0},y_{0},z_{0})^{T}\) is the origin of the ray. Let us denote with \(z^{*}\) the intersection of the ray and the plane:  
\[z^{*}=-\frac{n_{x}x_{0}+n_{y}y_{0}}{n_{z}} \tag{9}\]  
Without loss of generality, we assume that \(n_{z}<0\). There are two subcases: (1) the method approaches the plane along the ray and (2) the method moves away from the plane along the ray. In the first subcase, we have \(n_{x}x_{0}+n_{y}y_{0}+n_{z}z_{n}>0\). In this scenario, it is easy to see that  
\[z^{*}-z_{n}=(1+n_{z})\cdot(z^{*}-z_{n-1})=\cdots=(1+n_{z})^{n}(z^{*}-z_{0}) \tag{10}\]  
Since \(1+n_{z}\) is between \(0\) and \(1\), we have that the number of iterations \(n\) has to be about \(O(\log N)\) so that \(D_{P}(\mathbf{r}_{n})\) becomes less than \(\frac{\sqrt{6}}{N}\) (at which point the polygonization routine is invoked and we can move to the other side of the plane). In the second subcase, we have \(n_{x}x_{0}+n_{y}y_{0}+n_{z}z_{n}<0\). Now the following holds:  
\[z_{n}-z^{*}=(1-n_{z})\cdot(z_{n-1}-z^{*})=\cdots=(1-n_{z})^{n}(z_{0}-z^{*}) \tag{11}\]Since \(1-n_{z}\) is greater than 1, at most \(O(\log N)\) iterations along the ray are needed to exit the polygonization volume. Given that there are \(N^{2}\) rays in total, the complexity of case #3 is \(O(N^{2}\log N)\).
## 4 Strain Results and Analysis
### Dimensional Analysis
#### 4.1.2 The Maximum Strain of the Pipeline  
The pipeline was impacted by a blast wave with peak particle velocity \(v_{\max}\). The deformation of the pipeline was linear elastic. The maximum strain \(\varepsilon_{\max}\) can be determined by \(D\), \(\delta\), \(\rho_{\mathrm{P}}\), \(E_{\mathrm{P}}\), \(\rho_{s}\), \(E_{d}\), \(c\), and \(v_{\max}\). These variables include the load, inertia, and compression effect of the pipeline and soil and geometric properties of the pipeline section. The function of stress on the outer surface of the PE pipe is given by:  
\[\varepsilon_{\max}=f_{c}\Big{(}D,\delta,E_{\mathrm{P}},E_{d},\rho_{\mathrm{P} },\rho_{s},c,v_{\max}\Big{)} \tag{7}\]  
Applying the \(\pi\) theorem, Equation (7) is expressed in a dimensionless form:  
\[\varepsilon_{\max}=f_{c}\Bigg{(}\frac{D}{\delta},\frac{E_{d}}{E_{\mathrm{P}} },\frac{E_{\mathrm{P}}}{\rho_{s}c^{2}},\frac{\rho_{\mathrm{P}}}{\rho_{s}}, \frac{v_{\max}}{c}\Bigg{)} \tag{8}\]  
The specific form of the function is unknown and should be determined by the measured data. In this experiment, after the parameters of \(D\), \(\delta\), \(\rho_{\mathrm{P}}\), \(E_{\mathrm{P}}\), \(\rho_{s}\), \(E_{d}\), and \(c\) were determined, \(v_{\max}\) was a variable, which was determined by Equation (5). Therefore, we adopted the power function form for the maximum strain of the pipeline, as shown in the following equation:\[\varepsilon_{\text{max}}=k^{\prime}\overline{R}^{-\alpha^{\prime}} \tag{9}\]  
where \(k^{\prime}\) and \(\alpha^{\prime}\) are parameter of the comprehensive reaction charge conditions, pipe properties, field character, and other factors. \(\alpha^{\prime}\) is the attenuation index. Its rationality was verified by the experimental data.
## 4 Experiment Overview
### Experiment Methodology  
Our methodology, for each experiment, can be summarized by the following steps:  
1. A set of \(N\) TelosB sensors was used, with all the nodes configured to use one of the 16 communication channel defined by the IEEE 802.15.4 standard.
2. The sensors were linearly deployed, at ground level or on a support at a height of 57 cm above ground, with a fixed distance \(d\) between two consecutive nodes.
3. To evaluate the impact of the message size, each node sent \(M\) messages for each application payload from 2 bytes to 100 bytes.
4. All messages were sent in broadcast mode, at a frequency of two messages per second.
5. All nodes used the communication protocol described in Section 4.2.
6. When a node received a message, it stored in its external flash memory the message sequence number and the RSSI. The message sequence number was used to calculate the PRR.
7. The experiment ended when every node in the deployment had sent all its messages.  
In the following sections, we give, for each experiment we discuss, the number of sensors \(N\), the number of messages \(M\) and the distance \(d\) between two consecutive nodes.
## 5 Summary of the Interoperability Tests
### Use of IFC 2x3 Structural Analysis View (SAV)  
In this part of the study, IFC 2x3 CV 2.0 was again tested, but the attention was focused on the exchange of structural analysis information. The benchmark model was changed according to the information to be tested, i.e., the analytical model including data about constraints and loads (a frame load of 50 kN/m and a point load of 25 kN), which were defined directly in the sender CAE software.  
The study was limited to only the sending software SAP2000 and RFEM that could export/import in the IFC 2x3 SAV format. ModeSt had the possibility to export/import in this format, but the trial version used in the current study was limited to the import task only. Table 7 reports the results of the IFC 2x3 SAV benchmark.  
In summary, the analytical models with all boundary conditions were generated in RFEM and SAP2000 (Figure 6) and then exported into ModeSt, RFEM, and SAP2000. The self-tests worked well for both RFEM and SAP2000. ModeSt was unable to import restraints and loads from both RFEM and SAP2000, while only the loads were lost in RFEM from the model generated in SAP2000. No problems were found for RFEM to SAP2000.  
Figure 6: Analytical models generated in SAP2000 (**a,b**) and RFEM (**c**).
# A Machine Learning Predictive Model for Ship Fuel Consumption
## 2 Materials and Methods
### Data Processing
#### 2.3.2 Transformation of Categorical Variables  
Transforming categorical (nominal) variables into numeric variables allows for the application of learning algorithms to the data used for model development [43]. For better machine interpretation and improved performance, nominal values should be converted into numeric (binary) format.  
Using the Coding Method, also known as the One-Hot Encoding Method [44], each categorical attribute (e.g., Type of Vessel and Hull Material) was transformed into new attribute columns. For each value in these categorical attributes, as listed in Table 3, a corresponding column was created, where a value of 1 was assigned to indicate the presence of that attribute in the record, while all other columns were set to 0. This method provides  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline  & **VT\({}^{1}\)** & **HM** & **LEN** & **BRH** & **DFT** & **DPH** & **NT** & **NG** & **MP** & **TV** & **LD** & **CS** & **TGS** & **PC** & **CC** & **NCM** & **FOC\({}^{2}\)** \\ \hline
1 & Speedboat & Aluminum & 17 & 3.45 & 1.2 & 1.9 & 215 & 22 & 1 & 35 & 7 & 0 & 100 & 0 & 3 & 0.45 & 70 \\
2 & Ferry Boat & Marine steel & 32 & 8 & 5 & 6 & 450 & 19 & 1 & 528 & 98 & 2 & 99 & 670 & 8 & 36 & 3000 \\
3 & Speedboat & Marine steel & 256 & 6.3 & 1.8 & 2.2 & 315 & 18 & 1 & 21 & 6 & 2 & 90 & 0 & 2 & 24 & 2500 \\
4 & Speedboat & Marine steel & 24.89 & 6.3 & 1.4 & 2.3 & 550 & 18 & 1 & 21 & 6 & 2 & 90 & 0 & 2 & 24 & 2500 \\
5 & Passenger/ & Wood & 19 & 5.3 & 1.8 & 2.8 & 200 & 10 & 2 & 109 & 21 & 2 & 65 & 25 & 3 & 7 & 300 \\ General Cargo & & & & & & & & & & & & & & & & & & & & \\
6 & Passenger/ & Wood & 24 & 6 & 2.15 & 2.8 & 315 & 15 & 3 & 109 & 21 & 2 & 88 & 34 & 3 & 8 & 400 \\
7 & Passenger/ & Wood & 32 & 8 & 2.4 & 3 & 400 & 20 & 3 & 109 & 21 & 1 & 126 & 288 & 7 & 40 & 2300 \\
8 & Passenger/ & Wood & 28 & 7.4 & 1.5 & 1.75 & 367 & 14 & 2 & 109 & 21 & 2 & 130 & 90 & 5 & 144 & 5000 \\
9 & Passenger/ & Wood & 17.6 & 4.2 & 1.8 & 1.8 & 612 & 15 & 1 & 109 & 21 & 1 & 60 & 19 & 2 & 6 & 180 \\
10 & Speedboat & Aluminum & 12 & 2.4 & 1 & 1.8 & 481 & 20 & 1 & 35 & 7 & 0 & 35 & 0 & 1 & 2 & 87 \\ \hline \hline \end{tabular}  
* TV—Vessel Type; HM—Hull Material; LEN—Length; BRH—Breadth; DFT—Draft; DPH—Depth; NT—Number of Thrusters; NC—Number of Generators; MP—Motor Power; TV—Travel Time; LD—Light Displacement; CS—Cruising Speed; TGS—IonRange Gross Shipping; PC—Passenger Capacity; CC—Cargo Capacity; NCM—Number of Crew Members. \({}^{2}\) FOC—Fuel Consumption, regarded as the target column.  
\end{table}
Table 2: Model input parameters after applying the filter method.  
a numerical representation of nominal characteristics [45; 46], ultimately increasing the total number of attributes to 24.
# Enhancing the Iterative Smoothed Particle Hydrodynamics Method
## 1 Introduction  
Mesh-free methods are promising approaches emerging in recent years as valid computational alternatives to grid-based ones. Grid-based methods adopt a computational frame which is made up of nodes, where the field variables are evaluated, related to each other through a predefined nodal connectivity. Mesh-free methods are independent of a mesh and are well suited for problems with complex geometries or problems which require highly adaptive discretizations maintaining a suitable computational effort [1, 2, 3]. Smoothed Particle Hydrodynamics is a popular kernel based approach introduced in astrophysics [4, 5, 6, 7, 8, 9] and it is being increasingly used [3, 10, 11, 12, 13, 14]. The method provides a spatial approximation via a discrete kernel convolution. In [15] the standard procedure has been improved by means of an iterative approach refining the residuals. An approximant is generated by employing strictly definite positive kernel functions and the method, in convergence, improves the results without requiring evenly spaced data distribution. If the linear approximation order is ensured [16, 17], the iterative procedure performs better. In a recent paper [18] some results on this improved approach have been presented, however, the algorithm can become prohibitive for the number of iterations. In this work, starting with the linear approximation order, a formulation which guarantees accuracy with a lower number of iterations is proposed and discussed. The paper is organized as follows. In Section 2 the main ideas of the method are presented. In Section 3 the iterative process and the fast formulation are described. In Section 4 numerical validations are reported choosing the linear approximation order as starting iteration estimate. In Section 5, remarks and ideas on future work are outlined.
## 3 Results and Discussion
### ATR-FTIR Spectra  
The ATR-FTIR analyses were made directly on samples of R4, as obtained by the polymerization reaction and on a sample of R4 achieved after drying a sample of R4 swollen at its maximum swelling capability. Spectra were acquired in triplicate and those reported in Figure 3 are representative images.  
As expected, the ATR-FTIR spectrum of R4 showed a weak band of CH stretching of methylene groups (2926 cm\({}^{-1}\)) deriving from the monomer M4, and a strong band at 1614 cm\({}^{-1}\), typical of dimethyl acrylamide derivatives, and in this case belonging both to the co-monomer and the crosslinkers, thus assessing the presence of all three main ingredients in the structure of R4. No bands in the range 900-911 cm\({}^{-1}\) were observable, thus establishing the absence of residual monomers. The pink spectrum given by dried R4 obtained by the experiment of weight loss described in Section 2.5 appeared to be identical to that given by pristine R4, thus establishing that the interactions that occurred between water and R4 during swelling are fully reversible.
## 2 Mathematical Model  
Let a body (pressure hull) perform its descent along its axis of revolution and in the direction of positive \(z\). Therewith, the equation of the motion of the body takes the form  
\[(M+m^{\prime})\ddot{z}(t)=Mg-\rho(z)gV(t)-C_{D}\frac{\rho(z)}{2}S_{D}\dot{z}(t )|\dot{z}(z)|. \tag{1}\]  
Equation (1) comprises the following notations: \(M\) is the mass of the body; \(m^{\prime}\) is the added mass of water in the direction of motion; \(g\) is the gravitational acceleration; \(\rho(z)\) is the water density at depth \(z\); \(V(t)\) is the variable volume of the pressure hull; \(C_{D}\) is the hydrodynamic drag coefficient of the body in axial flow with reference to the maximum area of its lateral cross-section \(S_{D};z(t)\) and \(\dot{z}(t)\) are the submersion and speed of the body correspondingly.  
Note that Equation (1) implies the quadratic law of hydrodynamic drag. Representing the volume of the pressure hull as \(V(t)=V_{0}-\Delta V_{c}(z)+\Delta V_{b}(t)\), where \(V_{0}\) is the volume of the pressure hull in the state of neutral buoyancy prior to submersion (\(\rho_{0}V_{0}=M\)), \(\Delta V_{c}(z)\) is the variation in the volume of the body due to its compression under the action of pressure at depth and \(\Delta V_{b}(t)\) is the variation in buoyancy, one can reduce Equation (1) to  
\[\ddot{z}(t)=\frac{1}{1+\lambda}\{g[1-\frac{\rho(z)}{\rho_{0}}[1-\frac{\Delta V _{c}(z)}{V_{0}}\mp\frac{\Delta V_{b}(t)}{V_{0}}]]-C_{D}\frac{\rho(z)}{2\rho_{ 0}}\frac{S_{D}}{V_{0}}\dot{z}(t)|\dot{z}(t)|\}, \tag{2}\]  
where \(\lambda=m^{\prime}/M\), and \(\rho_{0}\) is the density of water near the surface. Introducing the relative density \(\overline{\rho}(z)=\rho(z)/\rho_{0}\), relative compression of the body \(\eta_{c}(z)=\Delta V_{c}(z)/V_{0}\) and relative maximal volume of replacement \(\eta=\Delta V_{0}/V_{0}\), re-write Equation (2) as follows  
\[\ddot{z}(t)=\frac{1}{1+\lambda}\{g[1-\overline{\rho}(z)(1-\eta_{c}(z)\mp\eta \cdot f(t))]-C_{D}\frac{\overline{\rho}(z)}{2l^{*}}\dot{z}(t)|\dot{z}(t)|\}, \tag{3}\]where \(f(t)\) is the normalized function of time defined for the period of the buoyancy change; \(l^{*}\) is the ratio of the body volume to its maximal lateral cross-sectional area (\(l^{*}=V_{0}/S_{D}\)). The solution of Equation (3) with appropriate initial conditions and control function \(f(t)\) allows finding parameters of the vehicle's submersion accounting for the regimes of acceleration, steady motion and deceleration motion, depth-wise variation in physical characteristics of water, configuration and dimensions of the body, as well as compression of the body under the action of pressure.
## 3 Spatial Enhanced Detection Algorithm
### Local Uncertainty Measurement
\(M_{ij}^{k}\) is the component consistency confidence value assigned to each block in the window structure centered on \((i,j)\). The confidence assignment function can be expressed as  
\[M_{ij}^{k}=\frac{LC_{ij}^{k}}{\sum\left(LC_{ij}\right)} \tag{35}\]  
In this way, the confidence of each block in the window structure is processed so that the sum is one. The Local Uncertainty Measurement results are scaled to the same scale to ensure that the uncertainty results measured at different sliding window positions can be compared. Figure 5 shows the distribution curve of the variation entropy function used in the case of binary information sources. Compared with the information entropy curve, the variation entropy used changes more sharply at both ends and more gently in the middle. In a scenario based on local component consistency as a reliability distribution function, if the estimates of local component consistency in the sample region are close, the variation entropy function will give a lower entropy value, reflecting that the elements in the region have a lower uncertainty. Conversely, if the estimates of local component consistency differ significantly within the sampling region, the value of the variation entropy function is higher, indicating significant variation and high uncertainty in the region. The entropy operator is not sensitive to the composition difference in a small range but shows a high sensitivity to the difference in a large range. Regions with significant differences in local component consistency usually appear at the target's location and around areas with extreme changes in background edges and corners. The high sensitivity of variation entropy means that it is able to ignore those small changes in composition and focus more on features with significant differences. This property can effectively suppress background noise and improve the overall performance of the algorithm. Just as the information entropy follows the maximum entropy principle, the proposed mutation entropy operator follows the minimum entropy theorem, and the minimum value is obtained when \(M_{ij}^{k}=1/(K+1)\):  
\[Entropy_{min}=-\log\left(1-\left(\frac{1}{K+1}\right)^{2}\right) \tag{36}\]  
Based on the above, to suppress the background to a greater extent, the measured component uncertainty is modified into the following form:  
\[LUM_{ij}=U_{ij}-Entropy_{min} \tag{37}\]  
When the signal component consistency is higher in the local region, the uncertainty is lower. The smaller the value of the variation entropy operator is, the closer the performance of the modified uncertainty operator is to 0.
# Enhancing Sign Language Recognition Performance Through Coverage-Based Dynamic Clip Generation
## 3 Methods  
We propose a dynamic clip generation method for sign language recognition that adaptively determines the number of clips based on the input video's length, using a novel metric called coverage. The concept of coverage is summarized as follows: a coverage value of 1 implies that every frame in the sign language video is used exactly once during clip generation. When the coverage exceeds 1, it indicates that certain frames are reused across multiple clips. Hence, higher coverage values reflect a greater degree of frame duplication within the generated clips. Traditional approaches generate a fixed number of clips regardless of video length, which can lead to inefficient use of resources and potential information loss, ultimately limiting recognition performance. In contrast, our method generates more clips for longer videos, thereby improving the model's ability to capture contextual information. For shorter videos, fewer clips are generated, which enhances inference speed without compromising performance. The following sections describe the key components of the proposed dynamic clip generation method, including the clip generation process, the coverage calculation, and the overall dynamic clip generation method.
## 4 Results
### Monitoring of the RCA and Calibration Bias
#### 4.1.1 Well-Calibrated Radar  
The RCA of the GNG radar did not change during the entire period. The mean RCA of GNG radar for P1 was \(-\)0.14 dB with a STD of 0.16 dB. The \(\varepsilon_{\text{SC}}\) was close to 0.0 dB, which indicates that the GNG radar was very well calibrated. In other words, the GNG radar is a good reference for calculating the mean bias between two radars.