esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Performance Metrics and Augmentation Methods in Lung Nodule Classification
## 5 Results and Discussion
### Augmentation Method Analysis
The second-best combination, involving shift and rotate, underscores the importance of rotational and translational invariance in medical imaging analysis. Rotation mimics the axial changes that can occur if a patient is not perfectly aligned in the scanner, while shifts account for lateral movements. These types of augmentations are particularly useful for enhancing the model's ability to generalize from the training data to new, unseen images that may not be perfectly aligned or scaled. The variations in augmentation performance also suggest that while rotation alone is useful--evidenced by its third-place ranking--its combination with other techniques, particularly noise, further refines the model's accuracy. This is likely because noise introduces a level of difficulty that forces the model to learn more robust features rather than overfitting to noise-free images in the training set. Lastly, the combination of scale and rotate points to the significance of handling both size and orientation variability together. Each augmentation method does not work in isolation but rather complements others to cover a broader range of real-world variations in lung nodule appearance. These results highlight the critical role of data augmentation in preparing robust models capable of high accuracy and generalization in medical imaging tasks, particularly for applications as sensitive and variable as lung nodule classification. The gap in performance among the different combinations provides valuable insights into the complex interplay of factors that affect image recognition and classification in a clinical context. Figure 6: Top 5 augmentation combinations. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Idx** & \(F_{5}\) & **Flip** & **Shift** & **Scale** & **Rotate** & **Noise** \\ \hline
1 & **0.8428** & \(\surd\) & \(\surd\) & \(\surd\) & & \(\surd\) \\ \hline
2 & **0.841** & & \(\surd\) & & \(\surd\) & \\ \hline
3 & **0.8383** & & & & \(\surd\) & \\ \hline
4 & **0.8366** & & & & \(\surd\) & \(\surd\) \\ \hline
5 & **0.8357** & & \(\surd\) & \(\surd\) & \\ \hline \hline \end{tabular} Note: The table is ranked based on the bold values.  
\end{table} 
Table 9: The rank of augmentation combinations based on \(F_{5}\) score.
# Design of a Land Area Measuring Instrument Based on an STM32 and a BeiDou Positioning Chip
## 2 Principle of Operation
### Area Measurement Principle
#### 2.2.3 Polygon Area Synthesis  
The area calculation formula is obtained by summing up the results of all side integrals as  
\[S\ =\ \frac{1}{2}\sum\limits_{i\ =\ 1}^{n}\left(x_{i}y_{i+1}-x_{i+1}y_{i}\right) \tag{8}\]  
The user steps the point sampling through the handheld measuring instrument, and the satellite positioning module can collect the latitude and longitude coordinates of each point in the target area. Therefore, the area of any irregular area can be calculated based on [PERSON]'s formula.
###### Abstract  
Data security technology is of great significance for the effective use of high-resolution remote sensing (HRRS) images in GIS field. Integrity authentication technology is an important technology to ensure the security of HRRS images. Traditional authentication technologies perform binary level authentication of the data and cannot meet the authentication requirements for HRRS images, while perceptual hashing can achieve perceptual content-based authentication. Compared with traditional algorithms, the existing edge-feature-based perceptual hash algorithms have already achieved high tampering authentication accuracy for the authentication of HRRS images. However, because of the traditional feature extraction methods they adopt, they lack autonomous learning ability, and their robustness still exists and needs to be improved. In this paper, we propose an improved perceptual hash scheme based on deep learning (DL) for the authentication of HRRS images. The proposed method consists of a modified U-net model to extract robust feature and a principal component analysis (PCA)-based encoder to generate perceptual hash values for HRRS images. In the training stage, a training sample generation method combining artificial processing and Canny operator is proposed to generate robust edge features samples. Moreover, to improve the performance of the network, exponential linear unit (ELU) and batch normalization (BN) are applied to extract more robust and accurate edge feature. The experiments have shown that the proposed algorithm has almost 100% robustness to format conversion between TIFF and BMP, LSB watermark embedding and lossless compression. Compared with the existing algorithms, the robustness of the proposed algorithm to lossy compression has been improved, with an average increase of 10%. What is more, the algorithm has good sensitivity to detect local subtle tampering to meet the high-accuracy requirements of authentication for HRRS images.  
high-resolution remote sensing image; deep learning; perceptual hash; integrity authentication; U-net +
Footnote †: journal: Applied Soft 2019, 9, 2972; doi:10.3390/app9152972
## 6 Simulation Results and Discussions
### Ser  
Figure 8 illustrates the SER performance of the LU and the Eve in two positions with various power allocation factors for the proposed multiple antenna arrays DM schemes and the conventional single antenna array DM schemes, where the baseband modulation modes are all set as QPSK, respectively.  
Several conclusions can be summarized from Figure the results in Figure 8a: (1) For the LU, when the signal power allocation factor is set as \(\beta_{1}\)= 0.9, to achieve the same SER level (e.g., \(SER=10^{-4}\)), the SNR per bit needed is approximately 1 dB less than that of the standard QSPK modulation, which indicates that the inserted AN will cause some degree of SNR loss. (2) Given a fixed SNR per bit (e.g., \(10\log_{10}(E_{b}/N_{0})=10\) dB), the SERs for the LU increase as the signal power allocation factors decrease. (3) The SER performance of the single antenna array DM schemes is the same as the multi-carrier multiple antenna arrays DM scheme, while the SER performance for the single-carrier multiple antenna arrays scheme is slightly worse than other two DM schemes.  
From the results in Figure 8b, the SERs of three symbol streams for the Eve in Location 1 are approximate to the worst theoretical value, which indicates that the transmission security can be guaranteed when the Eve is not located in any desired directions.  
From Figure the results in Figure 8c, the SERs of the symbol stream 1 and 2 for the Eve are as worse as the idea value, while SERs of the symbol stream 3 are as good as that of the LU, because the Eve is in the desired direction of the third transmit array. However, the transmission security can also be guaranteed because the confidential information received by the Eve is not intact.  
Figure 7: The locations of the LU, the potential Eve, and three transmit antenna arrays.  
The SER performance versus azimuth angle for the two proposed DM schemes is shown in Figure 9. In this scenario, the baseband modulation is also set as QPSK, the SNR per bit is 10 dB, and the signal power splitting factor is set as \(\beta_{1}\)= 0.9.  
As we can see, the information beam-width (\(SER=10^{-4}\)) of the two proposed schemes for each symbol stream is almost the same. Meanwhile, the information beam-width (\(SER=10^{-4}\)) for different symbol streams is also the same because each transmitter can be regarded as a single antenna array DM transmitter. Obviously, only the receiver located at the intersection of three desired directions can recover the full confidential information. By contrast, the receiver located in other positions will be interfered by AN and the mixed signals. Therefore, the security of the proposed DM schemes can be hold even if part of the useful information is intercepted.  
Figure 8: SER performance versus SNR per bit (dB) with various power allocation factors for the proposed DM schemes and the conventional DM scheme: (**a**) the LU; (**b**) the Eve in Location 1; and (**c**) the Eve in Location 2.
# Experimental Study of Imbibition Characteristics of Silica Sol in Coal-Measure Mudstone Matrix
## 4 Test Analysis of Key Factors Affecting Imbibition Effect
### Diffusion Difficulty  
The grout viscosity is the key index to reflect diffusion difficulty. Viscosity test results are shown in Figure 6, obtained using a NDJ-5S rotary viscosity meter. The experimental data was fitted by Origin software (Table 3).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Liquid Index** & **Contact Angle (\({}^{\circ}\))** & **Surface Tension (mN/m)** & **Affinity (mN/m)** \\ \hline Silica sol & 49.26 \(\pm\) 0.06 & 73.2 & 47.77 \\ Water & 39.15 \(\pm\) 0.04 & 73.5 & 57.00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Driving force of different liquids.  
Figure 5: Contact angle between liquid and sample. (**a**) Contact angle between water and sample; (**b**) contact angle between silica sol and sample; water has a slightly better spreadability on the sample.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ratios between Silica Sol and Accelerator** & **Fitting Formula** & **Related Coefficient \(R^{2}\)** \\ \hline
4:1 & \(\mu=55.267e^{\frac{i}{200\pi\text{m}}}-824.41615\) & 0.9335 \\ \hline
6:1 & \(\mu=0.00254e^{\frac{i}{200\pi\text{m}}}-66.05023\) & 0.9668 \\ \hline
9:1 & \(\mu=5.83191\times 10^{-6}e^{\frac{i}{200\pi\text{m}}}-77.03688\) & 0.9962 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fitting formula of time-varying curves of viscosity with different ratios.  
The experimental data can be expressed by the first-order decay exponential function. Dynamic viscosity of silica sol has maintained at a low level below 10 mPa\(\cdot\)s before gelling.  
The initial viscosity of MP364 is 250-450 mPa\(\cdot\)s [6]. Before the water cement ratio reaches 0.8, the initial viscosity of Portland cement decreases sharply as the water cement ratio increases. When the ratio of the water cement exceeds 0.8, the initial viscosity attenuation rate dramatically decreases and it is basically in a stable stage. Considering the strength characteristics, the water cement ratio of cement type grout is 0.7 generally on the field, and the initial viscosity is about 10 mPa\(\cdot\)s. Hence, silica sol has a relatively stronger diffusivity.
## 2 Literature Review
### Zigbee  
Zigbee is open standard wireless communication technology suitable for low data-rate IoT applications, it consumes low power, and is a low-cost solution. Zigbee is based on the Institute of Electrical and Electronics Engineers (IEEE) 802.15.4 wireless standard, operating in three different bands 868 MHz, 915 MHz, and 2.4 GHz [30].  
Zigbee has three specifications namely, Zigbee RF4 CE, Zigbee PRO, and Zigbee IP. Zigbee RF4 CE is developed for simplex and duplex communication-based applications. Zigbee Pro devices are usually self-powered devices that do not need any external power supply or batteries. They are designed for IoT applications with low-cost and highly reliable communication networks for node-to-node communication. Zigbee IP is a better option for iVv6-based full wireless mesh network applications with internet connectivity to control costs, as well as low-power devices. To establish a Zigbee communication network, three types of nodes are essential, namely: coordinators; routers; and end-users, each of them have a different role to play. The role of the coordinator is to store the network's information and security keys, routers can transmit and receive data, and the end-devices are usually battery-operated low power devices that can communicate to either the router or coordinator but cannot relay data from other nodes [31]. In (IEEE 802.15.4 Standard 2003), the coordinator and router are called Full Function Devices (FFD), which are capable of implementing all of the functionalities of the IEEE 802.15.4 protocol for the management of the network and ensuring synchronization. The end-user can also be an FFD but it is usually called a Reduced Function Device (RFD), meant for operating with a minimal implementation of the IEEE 802.15.4 protocol [32].
# Artificial Intelligence in Agriculture: Benefits, Challenges, and Trends
## 4 Artificial Intelligence in Agriculture (RQ2)
### Artificial Intelligence Technologies

Table 14: AI technologies in agriculture. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Technology** & **Crop** & **Water** & **Soil** & **Fertirigation** & **Disease** & **Crop** & **Crop** \\ \hline Robotics and automation & [8,14,19] & [14] & & & & & \\ \hline Drones and Unmanned Aerial Vehicles & [14,25] & [14] & & & & & \\ \hline Machine learning & [24,25] & [21] & [24] & & & [5,23] \\ \hline Artificial neural networks & [19] & [15] & & & & \\ \hline Deep Learning: Convolutional neural network & & [17] & & [6,16,17,18] & [13,26] \\ \hline Genetic algorithm & & & [20] & & \\ \hline Computer vision & [19] & & & [6] & [13] \\ \hline Digital Twins & [22] & & & & \\ \hline Internet of Things & [24] & [15] & [24] & [20] & \\ \hline Cloud computing & [25] & & & & \\ \hline Data analytics and big data & [8] & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Identified AI technologies in the papers selected. Finding 2: In the reviewed literature, we identified seven main applications: crop management, water management, soil management, fertigation, crop prediction, crop classification, disease, and pests. And twenty-four different artificial intelligence technics, including more big data, IoT, and cloud computation, were identified. Applications that were more frequent included crop management, water management, diseases and pests. The technics used the most were machine learning, robotics, deep learning, and the Internet of Things.
## 6 Propagation Time for a Signal, Emitted and Intercepted by Satellites on a Space-Oriented Orbit
### Finding the Weierstrass Function \(\rho(z_{1})\) and the Invariant \(g_{2}\)  
Equation (293), further, will play an important role, since it allows expressing the invariant \(g_{2}\) as a function of the Weierstrass function \(\rho(z_{1})\). More importantly, it shows that for a given function \(\rho(z_{1})\), there is only one value of \(g_{2}\). Yet, Equation (293) is not sufficient for finding both the Weierstrass invariant \(g_{2}\) and the Weierstrass function \(\rho(z_{1})\). We need one more equation, and for the purpose, we will use the defining equality for \(m\) (277) \(m:=\frac{\widetilde{A}_{1}-\widetilde{B}_{1}}{\widetilde{A}_{1}+\widetilde{B }_{1}}\), where \(\widetilde{A}_{1}\) and \(\widetilde{B}_{1}\) were previously defined by the formulaes (262)  
\[\widetilde{A}_{1}:=\sqrt[3]{F(q)+\frac{1}{4}\sqrt{\widetilde{\Delta}}}, \widetilde{B}_{1}:=\sqrt[3]{F(q)-\frac{1}{4}\sqrt{\widetilde{\Delta}}}.\]  
Thus, it can easily be obtained that \(\frac{\widetilde{A}_{1}}{\widetilde{B}_{1}}=\frac{1+m}{1-m}\). Taking into account (262), it can be derived that  
\[\frac{F(q)+\frac{1}{4}\sqrt{\widetilde{\Delta}}}{F(q)-\frac{1}{4}\sqrt{ \widetilde{\Delta}}}=\frac{(1+m)^{3}}{(1-m)^{3}}, \tag{301}\]  
where \(\widetilde{\Delta}\) has been defined by the equality (261)  
\[\widetilde{\Delta}:=\frac{1}{4}\bigg{(}\frac{\widetilde{\xi}_{3}}{\widetilde{ \xi}_{2}}\bigg{)}^{\frac{81}{28}}(9K(q))^{\frac{25}{86}}-\frac{1}{4.27}\xi_{2}.\]  
Denoting the first term in the above equality as \(\widetilde{\Delta}_{1}:=\frac{1}{4}\bigg{(}\frac{\widetilde{\xi}_{3}}{\widetilde {\xi}_{2}}\bigg{)}^{\frac{81}{28}}(9K(q))^{\frac{25}{86}}\) and remembering the defining equality (260)  
\[F(q):=\frac{1}{8}\bigg{(}\frac{\widetilde{\xi}_{3}}{\widetilde{\xi}_{2}} \bigg{)}^{\frac{81}{28}}(9K(q))^{\frac{25}{86}}=\frac{1}{8}N(q),\]  
one can obtain the simple expression  
\[\frac{\widetilde{\Delta}_{1}}{F(q)}=2\bigg{(}\frac{\widetilde{\xi}_{3}}{ \widetilde{\xi}_{2}}\bigg{)}^{\frac{81}{86}}(9K(q))^{\frac{25}{86}}:=16F(q). \tag{302}\]  
Consequently, the discriminant \(\widetilde{\Delta}\) (261) can be represented as  
\[\widetilde{\Delta}:=\widetilde{\Delta}_{1}-\frac{1}{4.27}\xi_{2}=16F^{2}(q)- \frac{1}{4.27}\xi_{2}. \tag{303}\]  
Making use of (301), one can find an expression for \(\sqrt{\widetilde{\Delta}}\):  
\[\sqrt{\widetilde{\Delta}}=4F(q)\frac{\big{[}(1+m)^{3}-(1-m)^{3}\big{]}}{[(1+m) ^{3}+(1-m)^{3}\big{]}}. \tag{304}\]  
Transforming this equality and using (303), one obtains the final formulae for \(\widetilde{\Delta}\), from where \(g_{2}\) can be expressed and \(m^{2}\) can be expressed in two different ways:  
\[\widetilde{\Delta}=16F^{2}(q)-\frac{1}{4.27}\xi_{2}=\frac{16F^{2}(q)m^{2}(3+m ^{2})^{2}}{(1+3m^{2})^{2}}. \tag{305}\]In this formulae, we substitute \(m^{2}\), expressed in (291) through the Weierstrass invariant \(g_{2}\) and higher powers of the Weierstrass function \(\rho(z)\).
# Time-Varying Preference Bandits for Robot Behavior Personalization
## 8 User Studies
### Simulation User Study  
We also conducted a simulation user study to validate the adaptability of DPB in time-varying scenarios, especially regarding robot behavior adaptation over repeated interactions. We believe that human preferences may evolve to repeated interactions with robot behaviors over time. The simulation user study proceeded with two major modifications from the real-world user study. First, algorithms were deployed in _Tosser_ with 10 participants. Furthermore, 30 iterations with five batches for each algorithm were executed to induce participants' robot behavior adaptation over repeated interactions. Response times were measured at every iteration without the participants' awareness. Anticipating that participants would gradually adapt to the robot's behavior, we hypothesized that theirdecision-making process to give feedback accelerates and thereby response times would exhibit a decrease.  
Time-Varying Scenario with Robot Behavior Adaptation over Repeated Interactions  
Figure 6 demonstrates the result of the simulation user study in the time-varying scenario with robot behavior adaptation over repeated interactions. Figure 5(a) illustrates the outcomes of **Q1** and **Q2**, indicating that DPB exhibits better performance over the greedy algorithm in adapting to time-varying scenarios involving robot behavior adaptation \((p<0.001)\) and in the ability to generate easy queries to respond \((p<0.001)\), supporting **H1** and **H2**. Moreover, Figure 5(c) shows that response time decreases as the iteration proceeds, implying that participants tend to adapt to robot behaviors. The simulation user study results validate the practical adaptability of DPB in time-varying scenarios with robot behavior adaptation over repeated interactions. Additionally, we also conducted a Bonferroni correction in simulation user study to the results of **Q1** and **Q2**. The adjusted p-values, which remained highly significant at \((p<0.001)\), indicate strong support for the effectiveness of the proposed approach.
## 3 Results and Discussion
### Discussion
#### 3.6.2 Comparison with Deep Learning Models  
Contrary to the common expectation that deep learning models like CNNs and BiLSTMs would yield superior performance, our XGBoost-based method consistently outperformed these architectures. Several factors may explain this observation. First, deep learning models typically require extensive datasets to learn complex patterns effectively, and our benchmark dataset, though well-curated, is of limited size. In such data-scarce scenarios, ensemble methods like XGBoost are often more robust and less prone to overfitting. Second, the strong performance of PLM-FRP is heavily reliant on its highly discriminative, engineered feature set (DDE + ESM2). While deep learning models can learn features end-to-end, the explicit provision of these potent, pre-extracted features to XGBoost proved more effective for this specific task. Finally, despite systematic hyperparameter optimization via grid search for the CNN and BiLSTM models, the inherent stability and powerful gradient boosting framework of XGBoost demonstrated superior performance with our optimized multimodal features.
## 2 Materials and Methods
### Reliability and Analytical Effectiveness of the Method  
The analytical procedure for quantifying AFM1 in milk was validated in accordance with the guidelines established by the International Council for Harmonization. This validation considered the following parameters: linearity, limit of detection (LOD), limit of quantification (LOQ), selectivity, precision, and recovery [41]. Linearity was evaluated by constructing a calibration curve from standard solutions of AFM1 in acetonitrile, which spanned a concentration range of 0.01 to 0.5 \(\upmu\)g/L. Duplicate calibration points were prepared, and the determination coefficient (R\({}^{2}\)) to assess the linear response was used. The LOD and LOQ were computed based on the standard deviation of the intercept point (_Sy_) and the slope (_b_) of the calibration curve, utilizing the following equations:  
\[\text{LOQ}=3.3\times Sy/b \tag{1}\]  
\[\text{LOQ}=10\times Sy/b \tag{2}\]  
Selectivity was evaluated by analyzing blank CM samples subjected to the identical liquid-liquid extraction (LLE) procedure outlined in Section 2.4 to ensure the absence of retention time interference peaks associated with AFM1. Accuracy was ascertained through the calculation of the relative standard deviation from three repeated injections of CM. Recovery was assessed by supplementing a previously analyzed CM sample with a quantifiable baseline concentration of AFM1 and a predetermined amount of standard solution (1.0 \(\upmu\)g/L). The spiked CM underwent triplicate processing employing uniform extraction and quantification methods. To compute the recovery of the introduced standard, the native concentration was deducted from the total measured value. Subsequently, the recovery rate was established utilizing the following equations:  
\[\text{Theoretical total concentration = }C_{\text{native}}+C_{\text{added}} \tag{3}\]  
\[\text{Recovered standard = }C_{\text{measured}}-C_{\text{native}} \tag{4}\]  
\[\text{Recovery (\%) = }(C_{\text{measured}}-C_{\text{native}}/C_{\text{added}}) \times 100 \tag{5}\]  
where \(C_{\text{native}}\) = initial concentration of AFM1 in the unfortified CM; \(C_{\text{added}}\) = known concentration of the standard spiked; and \(C_{\text{measured}}\) = final concentration determined after spiking.
# Modified Smith Input-Shaper Crane-Controller for Position Control and Sway Reduction
## 4 Experimental and Simulation Results
### Experimental Verification of the Proposed Control Structure
According to Case 2, the sway reduction increased where the proposed control structure was introduced. The sway reduction is 87% for IAE, 89% for ITAE, 98% for ISE, and 99% for ITSE. Concerning Case 2, the increase in the sway reduction is up to 30%. According to IAE, ITAE, ISE, and ITSE, the positioning quality decreases by 3%, 9%, 8%, and 13%, respectively. This decrease is slight, considering there is no position feedback in this proposed control structure in Case 3. However, position estimation is performed using the mathematical model for cart positioning. An error controller was introduced in Case 4 to address the error in describing the load position dynamics. This controller aims to eliminate the estimation error. With the implementation of the error controller, improvements in position quality are observed compared to Case 3: an 8% enhancement according to IAE, a 14% improvement according to ITAE, a 10% increase according to ISE, and a 15% increase according to ITSE. Additionally, introducing the error controller did not negatively impact sway prevention; it resulted in a positive reduction in sway, showing an improvement of 2% for both IAE and ITAE.