esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Discriminatory Analyses
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Metabolite**} & \multicolumn{3}{c}{**Nonparametric ANOVA**} & \multicolumn{3}{c}{**Mann–Whitney U-test**,} \\  & \multicolumn{3}{c}{**(Kruskal–Wallis)**} & \multicolumn{3}{c}{**Only Significant Changes (\(p<0.05\)) are Listed**,} \\  & \multicolumn{3}{c}{**Arrows Indicate the Direction of Change**} \\ \hline  & **chi.** & \multirow{2}{*}{\(p\)**-Value**} & **FDR \(p\)-Value** & **Group A** & **Group B** & **Group A** \\  & **Squared** & & **Adjusted** & **Against Controls** & **Against Controls** & **Against GroupB** \\ \hline ketoisooleucine & 19 & 0.0042 & 0.0059 & Day1\(\uparrow\), Day3\(\downarrow\), & \\  & & & & Day7\(\downarrow\) & \\ ketovaline & 17 & 0.0094 & 0.012 & Day1\(\uparrow\), Day3\(\downarrow\), & \\  & & & & Day7\(\downarrow\) & \\ creatine & 64 & \(8.5\times 10^{-12}\) & \(2.0\times 10^{-11}\) & Day3\(\uparrow\), Day7\(\uparrow\) & Day3\(\uparrow\), Day7\(\downarrow\) & \\ alanine & 49 & \(8.5\times 10^{-9}\) & \(1.8\times 10^{-8}\) & Day1\(\downarrow\) & Day1\(\downarrow\), Day3\(\downarrow\), & Day3, Day7, A \\ glutamine & 29 & \(6.1\times 10^{-5}\) & \(9.8\times 10^{-5}\) & Day1\(\downarrow\) & Day1\(\downarrow\), Day3\(\downarrow\) & \\ phenylalanine & 120 & \(1.2\times 10^{-23}\) & \(8.5\times 10^{-23}\) & Day1\(\uparrow\), Day3\(\uparrow\), & Day1\(\uparrow\), Day3\(\uparrow\), \\  & & & & Day7\(\uparrow\) & Day7\(\uparrow\) & \\ Phe/Tyr ratio & 85 & \(3.3\times 10^{-16}\) & \(1.4\times 10^{-15}\) & Day1\(\uparrow\), Day3\(\uparrow\), & Day1\(\uparrow\), Day3\(\uparrow\), & \\  & & & & Day7\(\uparrow\) & Day7\(\uparrow\) & \\ lipoproteins & 150 & \(7.8\times 10^{-31}\) & \(1.6\times 10^{-29}\) & Day1\(\downarrow\), Day3\(\downarrow\), & Day1\(\downarrow\), Day3\(\downarrow\), & \\  & & & & Day7\(\downarrow\) & Day7\(\uparrow\) & \\ acetate & 100. & \(2.1\times 10^{-19}\) & \(1.1\times 10^{-18}\) & Day1\(\downarrow\), Day3\(\downarrow\), & Day1\(\downarrow\), Day3\(\downarrow\), & \\  & & & & Day7\(\downarrow\) & Day7\(\uparrow\) & \\ lysine & 79 & \(6.6\times 10^{-15}\) & \(2.0\times 10^{-14}\) & Day1\(\uparrow\), Day3\(\uparrow\), & Day7\(\uparrow\) & \\  & & & & Day7\(\uparrow\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._It was of interest to see whether there are any metabolites in blood plasma that could serve as potential predictors of disease progress/outcome. We ran RF discrimination for binary systems of patients' groups on collection days. Here the performance was weaker, with AUC values of 0.67 on Day 1 and 0.78 on Day 3 for common, permuting variables: Phe/Tyr ratio, alanine, lysine, glutamine, leucine, and phenylalanine. A further increase in the number of variables did not improve the performance of the discrimination analysis. For the data set of Group A versus Group B on Day 7, the system did not show any discriminatory potential, with an AUC value of 0.503, in other words, the classification was not relevant. Based on this, the biochemical changes observed were rather indicative, not defining unambiguous biomarkers for patient outcome.
# Fatigue Performance Test and Numerical Analysis of Composite Girders with CSW-CFST Truss Chords
## 3 Analysis of Fatigue Performance Results  
### Extrapolation Methods for Hot-Spot Stress  
The methods for calculating hot-spot stress mainly include the one-point representation method [24], two-point extrapolation method [25], and three-point extrapolation method [26]. The one-point representation method directly assesses the stress measured at a point as the hot-spot stress. The two-point extrapolation method obtains the hot-spot stress by linear extrapolation to the weld end position. The three-point extrapolation method derives the hot-spot stress parabolically by extrapolation to the weld end position.  
For brevity, the hot-spot stress at point XL3 with the maximum tensile stress measurement point was taken as an illustrative example. The results obtained by different calculation methods are shown in Figure 8, where the abscissa indicates the distance of the unidirectional strain gauge on the gradient strain gauge substrate from the welding toe. The hot-spot stress obtained by the one-point representation method was the smallest, and the hot-spot stress obtained by the two-point extrapolation method was slightly larger than that obtained by the three-point extrapolation method, with a difference of no more  
Figure 7: Gradient strain gauge (unit: mm).  
than 3.6%. The maximum peak obtained by the three-point extrapolation method was at a distance of about 2 mm from the weld instead of at the weld, which is unreasonable. Considering structural safety and convenience, in a design situation and for evaluating the results of fatigue testing, it is advisable to use the two-point extrapolation method and one-point representative method to calculate the hot-spot stress in composite girders with CSW-CFST truss chords, respectively.
## 4 Experimental Results
### Dataset and Experimental Setup
#### 4.1.2 Experimental Setup  
The experiment uses the Adam optimizer with an initial learning rate of 0.001 and a weight decay of 0.00001 to prevent overfitting. The model was trained for 100 epochs with a batch size of 4, balancing performance and computational costs. For hyperparameter optimization, we employed a combination of grid search and cross-validation to fine-tune the learning rate, batch size, and weight decay values.  
This process helped us identify the optimal combination for achieving the best model performance. We also conducted experiments with different learning rates and weight decay values, evaluating the model's accuracy and stability, and we selected the values that yielded the best results. The hardware platform includes an Intel(R) Xeon(R) Platinum 8362 CPU (2.80 GHz), an NVIDIA GeForce RTX3090 GPU with 24 GB of memory, and 50 GB of RAM. The operating system is Ubuntu 22.04, and the development environment consists of Python 3.8.10, PyTorch 1.11.0, and CUDA 11.3. The experimental code implementation and debugging were performed in the Visual Studio Code development environment.
## 2 Materials and Methods
### EVI Calculation  
EVI has shown a strong correlation with canopy structural attributes, such as leaf area index and chlorophyll content, making it a prevalent indicator of green vegetation [32]. EVI time series have been employed extensively to effectively identify crop phenological traits, reflecting various growth stages, such as time of planting and harvesting. Notably,  
Figure 5: Cropland map of Shaanxi Province and accuracy at city levels. (**a**) Cropland map of Shaanxi derived from the WorldCover 2021 product. (**b**) Comparison between cropland area estimated from the map and data acquired from the Third National Land Survey (TNLS) results for cities in Shaanxi Province.  
different cropping intensities exhibit distinct EVI patterns within one year [11]. Therefore, EVI has been used in several studies for crop intensity mapping [11; 12]. The EVI calculation formula is as follows:  
\[EVI=2.5\times\frac{NIR-Red}{NIR+6\times Red-7.5\times Blue+1} \tag{1}\]  
where _NIR_, _Red_, and _Blue_ represent the surface reflectances in the near-infrared, red, and blue bands, respectively.
# Mapless Collaborative Navigation for a Multi-Robot System Based on the Deep Reinforcement Learning
## 3 Methods
### Single-Robot End-to-End Navigation
#### 3.2.3 Reward Shaping  
The reward function plays an important role in reinforcement learning: it greatly influences the navigation of the robot system and serves as the truth holder of the learning process. For the single robot mapless navigation task, the reward function shown in Equation (7) consists of three different parts: the arrival award, the collision penalty and the approaching award. If the distance value between the robot and the target point is less than the arrival threshold, the robot can get a positive reward \(r_{arrive}\) if the robot collides with the obstacles during navigation. In other words, one of the lidar distance observations is less than the safety threshold. The robot will get a negative reward \(r_{collision}\). To enable the intuition of approaching the target, we add the approaching award to the robot, where the reward is noted as \(k(\rho_{t-1}-\rho_{t})\). The \(k\) term is a constant to adjust the amplitude of the approaching reward. The \(\rho\) term represents the distance between the target and the mobile robot at timestep \(t\). To unify the range of different parameters, they will be normalized before utilizing:  
\[r(s,a)=\begin{cases}r_{arrive}&(\rho<d_{goal})\\ r_{collision}&(\min(d_{1},d_{2},...,d_{180})<d_{collision})\\ k(\rho_{t-1}-\rho_{t})&(\text{{At each time step }t}).\end{cases} \tag{7}\]
## 3 Materials and Methods
### Evaluation of Created Models  
#### 3.4.1 Accuracy Evaluation of Each Classifier  
To evaluate the accuracy of the classifiers created by transfer learning, the initial classifier and the patches satisfying the respective classification probabilities (0.80, 0.85, 0.90, 0.95, and 1.00), the recall, precision, overall accuracy, area under the curve (AUC), and F1 score were calculated using the test data in Table 5. The test data in Table 5 were used to evaluate the accuracy of the classifiers. Each index was calculated as follows: true positive (TP), which predicts a positive class for those in the positive class; false positive (FP), which predicts a positive class for those in the negative class; true negative (TN), which predicts a negative class for those in the negative class; and true negative (TN), which predicts a negative class for those in the positive class; and false negative (FN), which predicts a negative class for those in the positive class. The definitions and formulas for each are given below.  
(1) Recall is an index that indicates the ratio of data that can be judged as positive by artificial intelligence to the total number of actual positive cases (TP + FN). The higher the recall value, the better the performance, as it indicates fewer missed classifications.  
\[Recall=\frac{TP}{TP+FN}\] (2) Precision is an index that indicates the percentage of data that are actually positive among the data (TP + FP) judged positive by artificial intelligence. The higher the value, the better the performance of the model, as it indicates fewer misclassifications.  
\[Precision=\frac{TP}{TP+FP}\]  
Figure 2: An example of a classifier created by transfer learning using patch image training with a classification probability greater than 0.80.  
Overall Accuracy  
This index indicates the percentage of correctly classified TPs and TNs in the data classified by artificial intelligence. The higher the value, the better the performance is.  
\[Overall\ Accuracy=\frac{TP+TN}{TP+FP+TN+FN}\]  
(4) Area Under the Curve (AUC)  
The AUC is the area under the ROC curve, which is a curve drawn with the true positive rate (TPR) on the vertical axis and the false positive rate (FPR) on the horizontal axis. The AUC takes values from 0 to 1. The closer the value is to 1, the higher the discriminative power and the more accurate the model is.  
(5) F1 score  
The F1 score is the harmonic mean of the two values, with values between 0 and 1.  
\[F1\ score=\frac{2\times Recall\times Precision}{Recall+Precision}\]
# Comprehensive Study of IoT Vulnerabilities and Countermeasures
## 3 IoT Vulnerability Layers
### Network Vulnerabilities
#### 3.3.3 Cloud Based Networks
Session hijacking exploits weak authentication, such as unsecured API keys or stolen credentials, letting attackers impersonate devices and issue rogue commands--perhaps unlocking a smart door remotely. Resource exhaustion attacks target cloud endpoints, flooding them with false requests to overwhelm processing capacity, distinct from bandwidth-focused DDoS,  
Figure 15: Phantom-delay attack. This image has been completely re-modified; however, the idea of the image was inspired by [103]. targeting server-side logic [103]. Then there is traffic manipulation, where an attacker intercepts and alters data in transit--imagine tweaking a smart meter's usage report to inflate bills--all while the cloud assumes legitimacy [103]. To counteract data injection threats, implementing cryptographic message authentication codes (MACs) ensures that only legitimate IoT devices can submit data to the cloud. Secure firmware updates with digital signatures help prevent unauthorized modifications that might enable spoofed sensor readings. In mitigating session hijacking, enforcing multi-factor authentication (MFA) for cloud access and regularly rotating API keys reduces exposure to credential theft. Additionally, role-based access control (RBAC) minimizes the impact of compromised accounts by restricting device privileges. For resource exhaustion and traffic manipulation attacks, rate limiting on API endpoints prevents abuse by restricting excessive requests from any single source. Encryption protocols such as TLS prevent in-transit data tampering, ensuring the integrity of communications. Advanced intrusion detection systems (IDS) equipped with behavioral analysis can identify unusual traffic patterns indicative of an ongoing attack, enabling swift countermeasures. These vulnerabilities take advantage of the cloud's reliance on continuous IoT interactions. Using readily available tools such as WiFi sniffers, Kali Linux suites, or a Raspberry Pi configured for ARP spoofing, adversaries can easily launch attacks. The very feature that makes the cloud powerful--its ability to integrate diverse devices--also makes it susceptible, magnifying the consequences of any breach. As IoT and cloud technologies become increasingly interconnected, these risks highlight a crucial reality as follows: greater connectivity necessitates equally strong security measures to prevent the system's own architecture from becoming its greatest vulnerability.
# Modeling and Optimizing the System Reliability Using Bounded Geometric Programming Approach
## 4 Computational Study
#### Model B
\begin{array}{ll}\underline{F}=\text{Minimize}&\left(\frac{0.5}{y_{01}} \right)^{y_{01}}\left(\frac{1.1}{y_{02}}\right)^{y_{02}}\left(\frac{8.4}{ \tilde{B}_{1}}\right)^{y_{11}}\left(\frac{8.4}{\tilde{B}_{2}}\right)^{y_{11}} \left(\frac{0.5y_{20}}{y_{21}}\right)^{y_{21}}\left(\frac{y_{20}}{y_{22}} \right)^{y_{22}}\left(\frac{1.6y_{20}}{y_{23}}\right)^{y_{23}}\\ \text{s. x.}&\\ &y_{01}+y_{02}=1,\\ &2y_{01}-y_{02}+y_{11}+y_{22}=0,\\ &y_{01}-y_{02}-y_{11}+y_{21}=0,\\ &-y_{02}-y_{11}+y_{21}+y_{23}=0,\\ &y_{01}-y_{11}-y_{22}+y_{23}=0,\\ &y_{01}+y_{11}-y_{22}=0,\\ &y_{01},y_{02},y_{11},y_{21},y_{22},y_{23}\geq 0\\ &4\leq B_{1}\leq 4.2,\ \ 0\leq B_{2}\leq 1,\ \ \forall\ \ i=1,2,\ldots,m.\end{array}  \tag{31}\]  
where \(y_{21}+y_{22}+y_{23}=y_{20}\) and the upper bound can be stated as follows (32):  
**Model B**  
\[ \begin{array}{ll}\overline{F}=\text{Maximize}&\left(\frac{0.5}{y_{01}} \right)^{y_{01}}\left(\frac{1.1}{y_{02}}\right)^{y_{02}}\left(\frac{8.4}{ \tilde{B}_{1}}\right)^{y_{11}}\left(\frac{8.4}{\tilde{B}_{2}}\right)^{y_{11}} \left(\frac{0.5y_{20}}{y_{21}}\right)^{y_{21}}\left(\frac{y_{20}}{y_{22}} \right)^{y_{22}}\left(\frac{1.6y_{20}}{y_{23}}\right)^{y_{23}}\\ \text{s. x.}&\\ &y_{01}+y_{02}=1,\\ &2y_{01}-y_{02}+y_{11}+y_{22}=0,\\ &y_{01}-y_{02}-y_{11}+y_{21}=0,\\ &-y_{02}-y_{11}+y_{21}+y_{23}=0,\\ &y_{01}-y_{11}-y_{22}+y_{23}=0,\\ &y_{01}+y_{11}-y_{22}=0,\\ &y_{01}.y_{02},y_{11},y_{21},y_{22},y_{23}\geq 0\\ &4\leq\tilde{B}_{1}\leq 4.2,\ \ 0\leq\tilde{B}_{2}\leq 1,\ \ \forall\ \ i=1,2,\ldots,m.\end{array}  \tag{32}\]  
where \(y_{21}+y_{22}+y_{23}=y_{20}\). The above Problems (31) and (32) provide the required upper and lower bound for the problem (30). Both of these problems are concave programming problems with linear constraints. Upon solving the problem at zero degree of difficulty, the upper and lower bounds for the objective functions are obtained as \(\overline{F}=4.314\) and \(\underline{F}=3.045\), respectively. However, the objective values at \(\tilde{B}_{1}=4.2\) and \(\tilde{B}_{2}=1\) are found to be \(\bar{F}=3.561\). Therefore, the obtain objective function lies between the range of the upper and lower bounds, which shows that it is justified to reduce the objective values at the maximum RHS under variations.
# A Comparative Study of Indoor Accuracies Between SLAM and Static Scanners
## 3 Results
### Accuracies of Clouds Transformed to Spherical Targets  
The accuracies of entire point clouds acquired after the initial transformation to the four spherical targets (see Section 2.6.3) are presented in Table 2. The Leica RTC 360 performed the best (RMSE of 1.2 mm), while the worst performance was detected for the old-generation SLAM scanner ZEB Horizon (36.2 mm).
## 2 Principle
### The Principle of RBF Integration Method  
The weighted combination of analytical derivatives of the RBF integration method is used to represent the test surface in Equation (2), where \(\Phi\) is the Wendland [21] radial basis function and can be written as Equation (3).  
\[\mathrm{W}(\mathrm{x},\mathrm{y})=\sum_{i}\alpha_{i}\frac{\partial\Phi(x-x_{i},y-y_{i})}{\partial x}+\beta_{i}\frac{\partial\Phi(x-x_{i},y-y_{i})}{\partial y } \tag{2}\]  
\[\Phi(x,y)=\left\{\begin{array}{ll}\frac{1}{3}\bigg{(}1-\frac{\sqrt{x^{2}+y^{ 2}}}{\rho}\bigg{)}^{6}\bigg{(}35\frac{x^{2}+y^{2}}{\rho^{2}}+18\frac{\sqrt{x^{2 }+y^{2}}}{\rho}+3\bigg{)},&\rho>0,\rho\geq\sqrt{x^{2}+y^{2}}\\ 0&\text{otherwise}\end{array}\right. \tag{3}\]  
In Equation (3), \(\rho\) is a scaling factor that can be adjusted to make the reconstruction have high quality [22]. The relation between the analytical derivatives of \(\mathrm{W}(\mathrm{x},\mathrm{y})\) and the measured slope values \(\tan\theta_{x}\) and \(\tan\theta_{y}\) based on PMD (\(1\leq i\leq\mathrm{N},\ 1\leq j\leq\mathrm{N}\)) can be established in Equation (4).  
\[\left[\frac{\frac{\partial\mathrm{W}}{\partial x}\big{(}x_{j},y_{j}\big{)}}{ \frac{\partial\mathrm{W}}{\partial y}\big{(}x_{j},y_{j}\big{)}}\right]=\left[ \frac{\frac{\partial^{2}\Phi}{\partial x^{2}}\big{(}x_{j}-x_{i},y_{j}-y_{i} \big{)}}{\frac{\partial^{2}\Phi}{\partial x^{2}}\big{(}x_{j}-x_{i},y_{j}-y_{i }\big{)}}\right]\begin{pmatrix}\alpha_{i}\\ \beta_{i}\end{pmatrix}=\left[\begin{array}{c}\tan\theta_{x}\big{(}x_{j},y_{ j}\big{)}\\ \tan\theta_{y}\big{(}x_{j},y_{j}\big{)}\end{array}\right] \tag{4}\]  
After expressing Equation (4) as a matrix form and using the least squares method to obtain \(\alpha_{i}\) and \(\beta_{i}\), the surface \(\mathrm{W}\) can be obtained by taking the resulting coefficients \(\alpha_{i}\) and \(\beta_{i}\) back into Equation (2). The Wendland radial basis function has a high-order continuity property to meet the integrability condition, and Hermite interpolation is applicable to discrete data [23], which makes it feasible to deal with the quadrilateral geometry issue.
# Spitzer Resurrector Mission: Advantages for Space Weather Research and Operations
## 2 Mission Objectives  
The Spitzer-Resurrector Mission (SRM, hereafter) is a space mission concept which recently underwent a Phase I study funded by the U.S. Space Force (USSF) under the Orbital Prime [4] call. SRM is 'two missions in one', with goals of demonstrating spacecraft recovery and control in deep space, as well as demonstrating novel SWx research and operations capabilities. The spacecraft recovery is accomplished by launching a small satellite to rendezvous with the Spitzer Space Telescope [5] (currently in hibernation) in 2030, positioning itself around it, and serving as a relay for recommissioning and science operations is discussed in Section 3.  
The on-orbit servicing objectives of SRM have long-term benefits (lower risk, higher science return) for space missions in the future. A sample of science goals for Spitzer is described in Section 2.1. However, the focus of this paper is on the unique opportunity offered by SRM to demonstrate novel SWx research and operations capabilities, highlighted in Section 3.2.2.
# Narrowing Linewidth of Wavelength-Swept Active Mode Locking Laser Based on Cross Gain Modulation
###### Abstract  
We demonstrate a novel narrow-linewidth configuration of a wavelength-swept active mode locking (AML) fiber laser. The frequency response of the modulation depth of a semiconductor optical amplifier can be improved in a higher modulation frequency region by adapting the cross gain modulation (XGM) configuration, compared to that of the conventional direct gain modulation (DGM) configuration. As a sufficient modulation depth is implemented for an AML at higher modulation frequencies at around gigahertz order, it results in a narrower linewidth of lasing output. For the same modulation frequency of 1361.25 MHz, the linewidth of 0.25 nm with DGM becomes narrower up to 0.113 nm with XGM, which corresponds to an improved point spread function with 1.41 mm of 6-dB roll-off.  
Keywords:cross gain modulation; wavelength-swept laser; tunable laser; fiber laser; active mode locking +
Footnote †: journal: Applied
# Improving Conceptual Modeling with Object-Process Methodology Stereotypes
## 4 OPM Computational Objects: Stereotype-Related Extensions
### Permissible Value Ranges and Their Validation  
In OPM, a value is defined as a state of an attribute. Since an attribute is an object, an attribute value corresponds to an object state. Computational objects can have different values, including numerical and textual ones, but almost invariably, their range is limited. For example, an adult person's weight in kg can be defined to have a range from 40 to 140. Employing features beyond what is defined in ISO 19450:2015, OPM enables expressing and validating such constraints.  
Since an attribute is a stateful object, a permissible or legal attribute value is a member of the set of permissible states of that stateful object. The set of permissible states of an OPM computational object (or values of a computational attribute) can be an enumerated list of numbers or character strings, or a set of one or more ranges of numbers or character strings. These constraints on the permissible values of an object class are defined in design time at either the computational object class level or at the stereotype level. Similarly, during runtime, i.e., when the OPM model is executed, the value assigned to a computational object instance of that object class can be validated for compliance with the value ranges defined for that object at the class or stereotype level. The latter level is preferred, as it enforces the same definitions and validations for objects linked to the same stereotype across the entire model, and they cannot be overridden.  
Validation requires some software tool in any case, be it OPCloud or any other modelling tool. This is true not only for OPM but also for UML, SysML and any other modelling language. Stereotypes impose additional validations on top of those imposed by the syntax of OPM as a language. In theory, one could do the validations dictated by a stereotype definition manually, but for stereotypes to be of practical use, automated validation by some tool is required.