esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion and Conclusions
As a matter of fact, rather than comparing results, we believe it is more appropriate to summarize the results obtained (see Table 6), in order to provide an overview of the literature and the parameters for analyzing the robustness of the characteristics. The main focus of our work was on the development of a reliable system in terms of robust radiomic features according to the quantization settings and segmentation approaches. The adopted methods are not computationally expensive since traditional statistical analyses (mostly based on the ICC) are applied after the radiomic feature extraction. This assessment allowed us to propose a lightweight system based on classic machine learning techniques. Moreover, the type of analysis performed disregards variations in the data that could affect the stability of the system. In fact, the analysis performed on the features is computed from the segmented ROI and by quantifying the correlation (_via_ ICC) as the quantization level and segmentation approach change. Presently, clinical decision support systems (CDSSs) are becoming increasingly prevalent in clinical routines, and are able to assist the work of clinicians [34; 35]. In the near future, radiomics will certainly represent a tool that clinicians will rely on. An analysis and quantification approach, such as the one proposed here, inserted upstream in the analysis and modeling pipeline will enable even more reliable radiomics tools. Our study can represent an improvement over a traditional radiomic analysis pipeline that might be affected by parameter choices. In fact, the used techniques, inserted upstream of the modeling pipeline, allows for the definition of predictive models based only on features (or feature categories) that are robust against levels of quantization and segmentation methods. Certainly, considering that clinical scenarios are among the most critical, any tool that takes advantage of ICT must be carefully validated before being integrated into clinical practice. Furthermore, as future developments, it would be very interesting to validate the insights gained from this study on a more extensive dataset in order to assess the repeatability of the analysis made and its ability to generalize. A pre-analysis and feature calibration phase--such as the one proposed in this study--is absolutely essential to have an initial set of non-redundant and robust features. In fact, every radiomic study consists of several phases [3]: reducing the uncertainty on the input features allows to improve the repeatability and robustness of the study.
# Feedback Approach for the Relay Channel with Noisy Feedback and Its Security Analysis
## Appendix B Proof of Theorem 2
### Message Mapping Method  
In the proposed SK-Type FBL scheme for the MISO FRC with noisy feedback, we extend the one-dimensional real number axis mapping in the GRC with noisy feedback to the two-dimensional complex plane mapping. To be specific, for given coding blocklength \(N\) and decoding error probability \(\varepsilon\), let \(|\mathcal{M}|=2^{NR(N,\varepsilon)}\), where \(M\) is the transmitted message uniformly distributed over the set \(\mathcal{M}=\{1,2,\ldots,2^{NR(N,\varepsilon)}\}\). We divide a complex plane region \([\pm 0.5,\pm 0.5]\) into \(2^{NR(N,\varepsilon)}\) equally spaced sub-complex plane regions with both the length and width of each sub-complex plane region being \(2^{-NR(N,\varepsilon)/2}\). The message \(M\) is one-to-one mapped to the center point of each sub-complex plane region, denoted as \(\Theta=\theta_{R}+j\theta_{I}\), where \(\theta_{R}\) and \(\theta_{I}\), respectively, represent the real and imaginary parts of the complex element \(\Theta,j\) is the imaginary unit. Since \(M\) is equi-probably distributed over the set \(\mathcal{M}\), \(\theta_{R}\) and \(\theta_{I}\) are both approximately uniformly distributed over the interval \([-0.5,0.5]\) (i.e., \(\theta_{R},\theta_{I}\sim\text{Unif}[-0.5,0.5]\)), and their variances approximately equal \(1/12\), i.e., \(E[\theta_{R}^{2}]=E[\theta_{I}^{2}]=1/12\), so  
\[\text{Var}(\Theta)=E\Big{[}\Theta\Theta^{\mathcal{H}}\Big{]}=E\Big{[}\theta_{ R}^{2}\Big{]}+E\Big{[}\theta_{I}^{2}\Big{]}=\frac{1}{6}.\] (A28)
# Frequency Domain Full Waveform Inversion Method of Acquiring Rock Wave Velocity in Front of Tunnels
## 3 Numerical Simulation
### Geological Interface Model
On the whole, the inversion wave speed is close to the real wave speed, and it reflects the changing trend of wave speed from high to low, which has a good guiding role for construction.
# Estimating Maize Yield from 2001 to 2019 in the North China Plain Using a Satellite-Based Method
## 2 Materials and Methodology  
### Study Area  
This work focuses on the main summer maize producing region in China, the NCP, which includes most parts of Hebei (HB), Henan (HN), Shandong (SD), and the north of Jiangsu (JS) and Anhui (AH) provinces. This area of mid-eastern China, which stretches from 110\({}^{\circ}\)E to 122\({}^{\circ}\)E and from 28\({}^{\circ}\)N to 40\({}^{\circ}\)N (Figure 1), has a semi-humid monsoon climate with an annual rainfall of 400 to 800 mm (mostly falling from July to August, concentrated in the maize-growing season), and average annual temperatures that range from 10 \({}^{\circ}\)C in the northern part to 15 \({}^{\circ}\)C in the southern part of the region. The plain, where the growing season for maize runs from June to September, is one of the important grain baskets in China, producing one-third of China's maize. Winter wheat and summer corn are rotated as the primary agricultural practice.
# Autonomous Navigation of Robots: Optimization with DQN
## 5 Design and Execution Strategy for the Control System: A Comprehensive Overview
### ROS System  
For the proposed implementation of the case study, a four-node ROS system was developed, as shown in Figure 11; by employing a reinforcement learning system utilizing Deep Q-Network (DQN), it becomes possible to control the simulated Turtlebot3 Burger mobile robot. The communication between nodes in this system relies on the exchange of messages through publishing and subscribing to various topics. This mechanism facilitates the seamless flow of information and enables effective coordination and control of the robot's actions within the simulated environment. In order for the neural network designed in the controller node to receive the necessary information from the environment and be able to send the speed commands to the robot, the ROS API describes how the messages will be published and read by the agent, which is the core of the control system; however, the ROS master is initialized by the simulated environment.  
In addition to executing the DQN neural network node with its respective hyperparameters, it transmits the robot's speed commands to another node, which contains the simulated environment; the latter also receives information from two more nodes about the combination of movements for the moving obstacles, and sends the parameters of odometry and scanning of the Lidar sensor to the network.  
The ROS rat graph tool is used to obtain a diagram of the state of communication in real time between the nodes of the system and how they send and receive information about the topics through the messages; it allows one to graphically observe the flow of information. Figure 12 shows the graph obtained as a result of the system proposed as a case study.  
The Turtlebot3 Burger receives orders through the topic /cmd_vel, and according to its operation, it is made up of two vectors: one of linear velocity and the other of angular, where one indicates how fast it should move forward and the other how fast it should turn.  
Figure 11: ROS architecture.
## 4 Sensitive Analysis of Rotor Parameters
### Sensitivity Analysis  
The above analysis shows that each parameter has a different degree of influence on motor performance. Sensitivity analysis is an effective way to evaluate the impact of individual parameters on optimization objectives [24]. In this optimization, the Pearson correlation coefficient is chosen to define sensitivity [25]. As shown in (1):  
\[\rho_{X,Y}=\frac{\sum_{i=1}^{n}\left(x_{i}-\overline{x}\right)\left(y_{i}- \overline{y}\right)}{\sum_{i=1}^{n}\left(x_{i}-\overline{x}\right)^{2}\sum_{i= 1}^{n}\left(y_{i}-\overline{y}\right)^{2}} \tag{1}\]  
where \(y_{i}\) is the value of the optimization objective corresponding to the optimization variable \(x_{i}\), \(\overline{y}\) is the average of the optimization objective when the parameters take different values. \(n\) is the sample size.  
The histogram in Figure 13 shows the sensitivity coefficient of this optimization objective. A positive value means that the optimization objective increases with the increase in the optimization variable [26]. From the analysis results of Figure 13, it can be seen that different rotor optimization parameters have different sensitivities to motor characteristics. For example, PM thickness has a significant influence on the efficiency and rotor core loss of the motor. The stiffener thickness has a relatively large influence on rotor stress. In contrast, the influence of rib width on motor performance is modest.
## 4 Proposed Robust Capon IOVP Beamforming  
In this section, we present a robust beamforming method based on the robust capon beamforming (RCB) technique reported in [4] and the IOVP detailed in the previous section for robust beamforming applications with large sensor arrays. The proposed technique, denoted RCB-IOVP, gathers the robustness of the RCB approach [4] against uncertainties and the low complexity of IOVP techniques. Assuming that the DoA mismatch is within a spherical uncertainty set, the proposed RCB-IOVP technique solves the following optimization problem:\[\min_{\mathbf{a}_{d},\mathbf{s}_{d}} \mathbf{a}_{d}^{H}\mathbf{S}_{D}^{H}\mathbf{R}^{-1}\mathbf{S}_{D}\mathbf{a}_{d},\] (31) subject to \[\left\|\mathbf{S}_{D}^{H}\mathbf{a}_{d}-\mathbf{S}_{D}^{H}\bar{\mathbf{a}}\right\|^{2}=\epsilon\]  
where \(\bar{\mathbf{a}}\) is the assumed steering vector and \(\mathbf{a}_{d}\) is the updated steering vector for each iteration. The constant \(\epsilon\) is related to the radius of the uncertainty sphere. The Lagrangian of the RCB-IVOP constrained optimization problem is expressed by:  
\[f_{\text{RCB}}(\mathbf{a}_{d},\mathbf{s}_{d}) =\left(\sum_{d=1}^{D}\mathbf{q}_{d}\mathbf{s}_{d}^{H}\mathbf{a}_{d}\right)^{H} \mathbf{R}_{D}^{-1}\left(\sum_{d=1}^{D}\mathbf{q}_{d}\mathbf{s}_{d}^{H}\mathbf{a}_{d}\right)+ \tag{32}\] \[\lambda_{\text{RCB}}\left(\left\|\sum_{d=1}^{D}\mathbf{q}_{d}\mathbf{s}_ {d}^{H}\mathbf{a}_{d}-\sum_{d=1}^{D}\mathbf{q}_{d}\mathbf{s}_{d}^{H}\bar{\mathbf{a}}\right\|^{ 2}-\epsilon\right)\]  
where \(\mathbf{R}_{D}^{-1}=\mathbf{S}_{D}^{H}\mathbf{R}^{-1}\mathbf{S}_{D}\) is the reduced rank covariance matrix. From the above Lagrangian, we will devise efficient adaptive beamforming algorithms in what follows.
# An Optimal Authentication Scheme through Dual Signature for the Internet of Medical Things
## 6 Informal Security Analysis
### Replay Attack  
Suppose an intruder eavesdrops on a communication channel, intercepts the message, and then replays the same message to gain access to communication. We use fresh nonce in our scheme to thwart such an attack. According to our approach, whenever data is transmitted by IoMT devices, a nonce \(\chi\) which is equal to \(\mathcal{Q}.\mathcal{D}\) is transmitted along with it. Due to use of \(\mathcal{Q}\), which is the hash of (\(m\)), an intruder cannot create a fresh nonce by themselves. Hence, in this way, it effectively counters replay attacks by satisfying the message's freshness requirement.
# Temporal Instability Analysis of Injury Severities for Angle and Non-Angle Crashes at Roundabouts
## 6 Results and Discussion
### Analysis of the Characteristics' Effects
#### 6.1.2 Environmental Characteristics
Discrepancies between roundabouts and other road segments may explain the inconsistent finding. For basic road segments, drivers are usually in a relatively relaxed driving state and keep a higher speed, making it easy to cause serious injuries on rainy days. In contrast, the roundabout can catch more driver attention and elevate their alertness because of the special geometry and complex traffic [58]. Hence, drivers are more likely to slow down and adopt more conservative driving behavior when entering the roundabout on rainy days. This could explain why injuries in roundabouts are less severe on rainy days.
## 4 Case study  
In this section, the framework and relative guidelines are exemplified with a case study. An ultralight aircraft part called Propeller Pitch Controller (PPC) has been selected, and it is represented in Figure 7. Given the interest in parts obtainable by metal deposition, which are collected in the industrial field nowadays, a metal component has been selected to demonstrate the proposed design workflow. This product is assembled on the variable pitch aircraft propellers with three blades and it is connected through three connecting rods to them (Figure 7b). It allows the pitch of the blades to change thanks to a translation movement, thus regulating the provided engine thrust.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Equation** & **Description** \\ \hline BuyToFly index & \(Cr=\frac{Vtot}{Vbb}\) & Where \(Vtot\) is the product volume and \(Vbb\) is the volume of the bounding box. \\ \hline Functional & \(Cf=\frac{\sum Sf}{Stot}\) & Where \(\sum Sf\) is the sum of functional surface and \(Stot\) is the product surface. \\ \hline Supported & \(Cs=\frac{\sum Scs}{Stot}\) & Where \(\sum S\)s is the sum of the surfaces that need supports and \(Stot\) is the product surface. \\ \hline Hole index & \(Ch=\frac{\sum n^{c}Ha}{Htot}\) & Where \(\sum n^{c}Ha\) is the sum of the holes that do not have the axis parallel to the production direction and \(Htot\) is the number of the total holes. \\ \hline Massive & \(Cn=\frac{\sum Vm}{Vtot}\) & Where \(\sum Vm\) is the sum of the massive volumes and \(Vtot\) is the overall product volume. \\ \hline Height ratio index & \(Cl=\frac{Hp}{Lmax}\) & Where \(Hp\) is the product height and \(Lmax\) is the maximum length of the part in one of its three principal directions. \\ \hline Small holes index & \(Cd=\frac{\sum n^{c}Hd}{Htot}\) & Where \(\sum n^{c}Hd\) is the sum of the holes number with the diameter below a critical value that depends on the specific type of technology and \(Htot\) is the number of the total holes. \\ \hline Thickness & \(Cd=\frac{\sum Vtn}{Vtot}\) & Where \(\sum Sfn\) is the sum of the walls volume with thickness below a critical value that depends on the specific type of technology and \(Vtot\) is the product volume. \\ \hline Surface & \(Ce=\frac{Sp}{Stot}\) & Where \(Sp\) is the surface parallel to the build direction and \(Stot\) is the product surface. \\ \hline Layer variation index & \(Cl=\frac{\sum_{i=1}^{N}(Sli-Sl(i-1))}{Slot}\) & Where \(Sli\) is the surface of the \(i\)-th layer and \(Sl(I-1)\) is surface of the \((i-1)\)-th layer. \\ \hline \hline \end{tabular}
\end{table}
Table 6: Indices contributing to the Printability Index.  
Figure 6: Final assessment of cost and time-to-market.  
Analog design procedures could be applied in case studies employing plastic materials and technologies such as Fused Deposition Modelling or Stereolithography. Some considerations, requirements and data reported in the papers should be modified accordingly to the different application contexts and specificities.
## 4 Relay Scheme based on FAHP  
The proposed protocol uses a fuzzy analytical hierarchy process (FAHP) to select the best relay node among several alternatives. These alternatives are selected based on three input parameters: number of hops, number of neighbors, and distance to the gateway.
# Identification of Rice Sheath Blight through Spectral Responses Using Hyperspectral Images
## 3 Results  
### Spectral Response of Leaf Blade Fractions with ShB Symptoms  
Figure 2 showed the original and different transformed spectral curves of healthy and diseased tissue fractions. Their differences were obvious in the original spectrum, which were mainly reflected in the blue edge, green peak, yellow edge, red edge and near-infrared band. Compared with the spectrum of healthy leaves, the spectrum of diseased leaves showed no absorption valleys in the blue edge and no peak in the green band but higher reflectance in the yellow edge. Furthermore, there was a weaker absorption valley, higher reflectance and a smaller \"steep slope\" in the red edge, and lower reflectance in the near-infrared band. Meanwhile, some specific wavelengths were found at 490, 508, 530, 542, 560, 568, 573, 638, 671, 700, 736 and 749 nm from ratio and derivative spectrums. This provides us with the possibility of selecting appropriate wavelengths to detect the disease.
# Removal of Methylene Blue from Aqueous Solution by Bone Char
## 4 Conclusions  
In summary, the adsorption behaviors and mechanisms of methylene blue on bone char were studied. Results showed that the removal rate of methylene blue is highly rapid in the first ten minutes with eighty percent of adsorption capacity, which mainly relates to the external specific surface area of bone char. Then, the removal rate slows down and increasingly reaches equilibrium. It was proven that the adsorption process is internally diffusion-controlled. The removal rates of methylene blue decrease with an increase in temperature, which suggests that the adsorption process is exothermic.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(T\) (K) & \(K_{d}\) (L mol\({}^{-1}\)) & \(\Delta G^{0}\) (kJ mol\({}^{-1}\)) & \(\Delta H^{0}\) (kJ mol\({}^{-1}\)) & \(\Delta S^{0}\) (J mol\({}^{-1}\) K\({}^{-1}\)) \\ \hline
273 & 194.58 & \(-11.96\) & & \\
293 & 134.36 & \(-11.94\) & \(-13.45\) & \(-5.36\) \\
313 & 91.09 & \(-11.74\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Thermodynamic parameters of the removal of methylene blue by bone char.  
Figure 4: (**a**) The reusability result of bone char for the removal of methylene blue, (**b**) FT-IR spectra of poorly crystalline bone char sample (before adsorption and after desorption of methylene blue).  
The negative \(\Delta G^{0}\) values and the \(R^{2}\) closing to 1 shown in Figure 2\(f\) demonstrate that the adsorption process is spontaneous and obeys the pseudo-second-order kinetic model. The adsorption data fit the Freundlich model very well, showing that the adsorption is single-layer and prone to occurrence. The adsorption energy value 13.29 kJ mol\({}^{-1}\) and reusability data of bone char states that the adsorption of methylene blue on bone char is a reversible ion-exchange process. This research suggests that bone char can be developed as a novel, effective, and regenerable adsorbent for supporting photocatalysts to adsorb methylene blue.  
Methodology, investigation, writing--original draft preparation, and funding acquisition, P.J.; formal analysis and data curation, H.T.; writing--review and editing, W.G.; supervision, W.G. and K.L.  
This research was funded by the China Scholarship Council.  
The authors appreciate the support from the China Scholarship Council, the assistance of the Advanced and Nano-Materials Research Group, and the technicians in the Department of Chemical & Materials Engineering, The University of Auckland.  
The authors declare no conflicts of interest.
## 3 Remote Sensing Challenges and Solutions to Increase Agricultural Productivity
### Remote Sensing Solutions and Future Applications
#### 3.2.1 New RS Techniques and Data to Increase the Accuracy of Crop Area Estimation
In their research, spatio-temporal fusion was considered to fuse S-2 and S-3 to generate nearly daily S-2 images which may be useful for monitoring highly dynamic environmental, agricultural or ecological phenomena. This approach may be suitable for forecasting crop yield in ASA regions in the future.
## 3 Problem Description and Modeling
### Computational Model  
For task \(t_{i}\in T\), the required CPU cycles for processing \(t_{i}\) is \(\lambda_{i}\mu_{i}\). Thus, the computational latency of \(t_{i}\) on offloaded satellite \(s\) is  
\[\tau_{i,s}^{comp}=\frac{\lambda_{i}\mu_{i}}{C_{s}} \tag{4}\]  
In this paper, we consider a sequential computation model [14] in Figure 2, i.e., each satellite can process only one task at a time, and preemption is not allowed. Hence, when multiple tasks are offloaded to a specific satellite, there will be queue delays that must be taken into account for task computation.  
Then we let \(\theta=\{\theta_{1},\)\(\theta_{2},\)\(\ldots,\)\(\theta_{m}\}\) be a task offloading assignment, where \(\theta_{s}\), \(\forall s\in S\), represents the task sequence on satellite \(s\). Several time parameters of task \(t_{i}\) are defined as follows:  
1. \(\tau_{i}^{A}\): the time that offloaded satellite \(s\) is available for task \(t_{i}\);
2. \(\tau_{i}^{D}\): the time that all required data of \(t_{i}\) are received by \(s\);
3. \(\tau_{i}^{S}\): the time at which \(t_{i}\) is performed;
4. \(\tau_{i}^{F}\): the finish time of task \(t_{i}\).  
First, we let \(t_{\rho(i)}\in T\) be the preceding task of \(t_{i}\) in task sequence \(\theta_{s}\). If \(t_{i}\) is the first task of \(\theta_{s}\), we set \(t_{\rho(i)}=\emptyset\); otherwise, \(t_{i}\) can be executed by \(s\) only after task \(t_{\rho(i)}\) is completed. Thus, we have  
\[\tau_{i}^{A}=\left\{\begin{aligned} 0,&\text{ if }t_{\rho(i)}=\emptyset\\ \tau_{\rho(i)}^{F},&\text{ otherwise}\end{aligned}\right. \tag{5}\]  
Furthermore, task \(t_{i}\) can be performed only after \(s\) receives the data from \(t_{i}\). We assume that all data begin transmitting simultaneously (i.e., time 0), and we have  
\[\tau_{i}^{D}=0+\tau_{i,s}^{comm} \tag{6}\]  
Then, the start time of task \(t_{i}\) can be calculated by  
\[\tau_{i}^{S}=\max\left\{\tau_{i}^{A},\tau_{i}^{D}\right\} \tag{7}\]  
where the queue delay of \(t_{i}\) is defined as \(\tau_{i}^{queue}=\tau_{i}^{S}-\tau_{i}^{D}\).  
Finally, the finish time of \(t_{i}\) is given by  
\[\tau_{i}^{F}=\tau_{i}^{S}+\tau_{i,s}^{comp} \tag{8}\]  
Based on above analysis, given an offloading assignment \(\theta\), we can calculate \(\tau_{i}^{A}\), \(\tau_{i}^{D}\), \(\tau_{i}^{S}\), and \(\tau_{i}^{F}\) for each \(t_{i}\in T\), sequentially, using (5), (6), (7), and (8), respectively.
## 2 Results
### Method Validation and Data Acquisition
Selectivity is an important quality to enable component extraction in the data-processing phase [10]. LOD concentrations were below 0.2 ng/L for each standard and LOQ were 2.5 ng/L (Supplementary Materials Table S1), indicating a high sensitivity in the method for detecting low-abundance components. Even though these parameters cannot be used to quantify other metabolites in untargeted methods [34], this method still provides an overview of metabolites' chromatographic behavior. Interestingly, p-Cymene and 2-Undecanone could be used as an internal control for SPME fiber's life span. As a sign of fiber deterioration, p-Cymene splits in two chromatographic peaks and 2-Undecanone abundances greatly decrease (data not shown). An advantage of using a wine-spiked pool as matrix for method validation was that 74 compounds were identified, allowing the calculation of their extracted-area reproducibility \(\leq\) 15.0% RSD (Supplementary Materials Table S2). Figure 1a shows a typical total ion chromatogram (TIC) of wine components; however, components present in most abundant chromatographic peaks could not be identified because of ion saturation and/or ion peak aberrancy. Thus, a method with split desorption must be performed to identify these metabolites. Since our interest resides in the low-abundance (Figure 1b and Table 1) metabolites present in wines, and the method was able to separate and extract them (Figure 1c,d), we decided to work with a splitless method to analyze samples. Consequently, method validation demonstrated that the extracted components' area, RT and match factor were reproducible and unaffected by the concentration required for successful data processing/mining, data identification and data interpretation/analysis.