esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Equivalent Flexural Stiffness of Steel-Concrete Composite Beams in Frame Structures
## 6 Conclusions  
Based on the concept of equivalent flexural stiffness, a simplified design method is proposed to calculate the vertical deflection of a composite frame beam. To identify the influence of the rotation restraint stiffness at the beam ends and the cracked section in the negative moment regions, a theoretical beam model is established. By conducting an extensive parameter analysis of different frame beams, a fitting formula for evaluating the equivalent flexural stiffness as well as a simplified design formula for calculating the vertical deflection are proposed. The main conclusions are summarized as follows:  
1. For a composite frame beam under uniform vertical loading, the rotation restraint stiffness at the two beam ends has a significant effect on its vertical deflection and internal force distribution, which should be elaborately considered when calculating its equivalent flexural stiffness;
2. By considering the boundary constraint conditions at the beam ends, the proposed formula for calculating the equivalent flexural stiffness can reasonably describe the deformation mechanism of a composite frame beam, presenting a higher calculation accuracy than those of the recommended formulas in ANSI/AICS and by [PERSON] et al.;
3. By comparing with the non-linear FE analysis results of multiple entire frame structures, the accuracy and reliability of the proposed formula is further validated. The vertical deflections in the service stage calculated using the proposed formula agree well with those obtained from the entire process load-displacement curves in the FE analysis;
4. Utilising the internal force distribution of a frame beam by elastic analysis, a simplified, but effective design method is proposed to calculate the vertical deflection. The design procedure is based on the deflection correction of the non-cracking analysis results, which is demonstrated to capture the influence of the complex boundary conditions and can be easily used in the practical design of composite frame beams.  
Conceptualization, M.-X.T. and L.-Y.X.; methodology, M.-X.T.; software, Z.-A.L. and Q.-L.Z.; validation, L.-Y.X.; investigation, M.-X.T. and L.-Y.X.; data curation, Z.-A.L. and Q.-L.Z.; writing--original draft preparation, Z.-A.L.; writing--review and editing, M.-X.T. and L.-Y.X.; visualization, L.-Y.X.; supervision, M.-X.T.; funding acquisition, M.-X.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grants No. 51878378 and No. 51878379.
## 4 Discussion
### Method Application  
Data presented in Table 4 revealed that absorbance values obtained for sheep milk were higher than those obtained for cow milk and a possible explanation could be the difference in milk fatty-acid profiles between the two ruminant species, with the unsaturated fatty-acid content being higher in the sheep milk fat structure composition than in cow milk [23]. In addition, after 24 h of storage an even higher degradation of sheep milk compared to cow milk was observed for each absorption wavelength (450, 495, and 532 nm) and MDA concentration considered, calculated considering the absorbance values obtained at 532 nm. As it can be observed in Figure 2, the 24 h storage time led to a higher degradation indicator value for both PUFAs and MUFAs oxidation. Sheep milk contains higher conjugated linoleic acid (CLA) concentrations, compared with cow milk [23], leading to a higher concentration of total PUFAs and a higher risk for lipid peroxidation, explaining the higher values of MDA concentrations for the sheep milk sample and the characteristic absorbance at 532 nm. The absorption values recorded at 450 nm and 495 nm (wavelengths specific for MUFAs degradation products) indicated higher MUFAs concentrations for sheep milk compared to cow milk, an observation also sustained by another study [24]. Sheep milk, with important concentrations of unsaturated fatty acids, is assumed to be healthier than cow milk [3], but the susceptibility to peroxidation has to be considered.  
The Pearson correlation showed a strong positive relationship between mono- and poly-unsaturated fatty acids and the absorbance values which characterized their oxidation during storage. The results of correlation matrix described the pattern of fatty acids degradation: higher concentrations of MUFAs led to higher values of absorbances recorded at 450 and 495 nm. The same trend was confirmed for the PUFAs degradation process which led to higher MDA concentrations. Negative correlations were registered between SFAs and degradation parameters, because milk samples with lower concentrations of SFAs are more stable to the oxidation process. None of the considered degradation parameters characterize the oxidation of SFAs, so the results of the correlation matrix describe a consequence of the degradation pattern of unsaturated fatty acids.
# Improving Threat Detection in Wazuh Using Machine Learning Techniques
## 3 Materials and Methods
### Data Collection, Preprocessing, and Feature Engineering
Source IP and Destination IP (categorical or numerical): Source and destination IP addresses encoded, with potential inclusion of reputation scores in subsequent steps;
5. Destination TCP/UDP Port (numerical): Network port at the destination of the event;
6. Event Severity (numerical): Severity level assigned by Wazuh, normalized to [0,1]. Given the absence of ordinal variables in the dataset--i.e., variables with a ranked or hierarchical relationship--the classification of categorical variables was handled with techniques that preserve their discrete and non-relational nature. For categorical features without inherent order, such as rule IDs or countries, integer encoding would introduce misleading relationships. Thus, one-hot encoding was selected to maintain categorical integrity, representing each class as an independent binary vector. All numerical features were normalized using the MinMaxScaler from the sklearn.preprocessing module in Scikit-learn 1.4.1, which scales values into the [0,1] range based on the observed minimum and maximum values of each feature. This normalization ensures that all features contribute proportionately during model training, particularly for algorithms sensitive to feature magnitude, such as Support Vector Machines (SVM) and K-Nearest Neighbors (KNN). For high-cardinality nominal variables such as Agent ID, label encoding was applied to assign unique integer values to each category. This approach avoids the creation of large, sparse matrices associated with one-hot encoding, thereby reducing model complexity and memory usage during training and inference. Finally, feature importance analysis was conducted using a Random Forest classifier to quantify the predictive relevance of each feature, guiding the final feature selection process. Table 2 summarizes the features, their data types, and the applied engineering techniques.
# Dynamic UAV Inspection Boosted by Vehicle Collaboration Under Harsh Conditions in the IoT Realm
## 5 Simulation
### Experimental Setup  
#### 5.1.1 Evaluation Metrics  
1. Time Cost Time cost refers to the total time taken for the system to execute tasks, starting from the moment when multiple UAVs and vehicles receive the routes from the central console and depart from starting point \(A_{start}\) and ending when the last task point is completed and the vehicles, carrying the UAVs, return to starting point \(A_{start}\). For the entire dynamic process, each dynamic planning step aims to optimize time cost, enabling the UAVs to work collaboratively and complete the data collection from all task points as quickly as possible, which is of great significance for practical production and life. Therefore, time cost was the primary optimization objective and an important performance evaluation metric in this study.
2. Vehicle Travel Distance Section 3 provided a detailed description of the vehicle's travel route \(r_{0}\) and its length \(l_{r_{0}}\), as referenced in Equation (2). The length calculated by this formula represents the vehicle's travel route length in a static scenario, but in the case of dynamic task arrivals and handling, deviations from this theoretical value may occur. In dynamic scenarios, multiple dynamic route planning steps are performed, requiring route replanning after each fluctuation. Therefore, in simulation experiments, vehicle state values are set to track and update the vehicle's position information at intervals of unit time \(\tau\), with the final accumulated sum representing the actual travel distance of the vehicle. vehicle travel distance is a secondary metric for evaluating algorithm performance.
3. UAV Flight Distance This study utilized \(M_{uav}\) UAVs to collaboratively collect data points, with their total flight distance representing the sum of all UAV routes \(L_{uav}\). Due to multiple route replanning steps in the dynamic scenario presented in this paper, UAV state values were also set in simulation experiments to track and update UAV position information at intervals of unit time \(\tau\). The final accumulated sum represented the actual flight distance of all UAVs. Accordingly, UAV flight distance served as a secondary performance metric for algorithm evaluation.
# A Compound Approach for Monitoring the Variation in Wind Turbine Power Performance with SCADA Data
## 4 Application Example Analysis  
### Case 1: Blade Damage Fault Diagnosis  
A 2.5 MW wind turbine located in Eastern China, which was grid-connected and operational in June 2018, was found to have a blade tip broken on 13 January 2021 by the on-site operation and maintenance engineers. The affected wind turbine is numbered 24# in the wind farm. The operational data of turbine 24# are collected, and the operation data from June 2018 to December 2019 are utilized as the training set. The KPCA-AdaBoost method is applied to develop a regression prediction model for wind turbine active power, which enables us to predict the theoretical active power from January 2020 to January 2021. Subsequently, the prediction results undergo secondary processing, utilizing Equation (12), leading to the derivation of the trend in power generation performance changes, as depicted in Figure 7.  
Following the application of a \(\pm\)5% threshold, the power generation performance index \(K_{T}\) of the wind turbine experienced a decline greater than 5% on 25 November 2020, and, subsequently, another drop exceeding 10% on 17 December 2021. Preliminary observations indicate that the emergence of significant blade damage became apparent on turbine 24# on 25 November 2020. The analysis of this deterioration trajectory suggests that critical cracking damage likely occurred on 17 December 2020, which had a significant impact on the power performance of the wind turbine. The validation of the findings demonstrates that the proposed approach is capable of diagnosing blade damage several weeks prior to the critical event, thereby providing a window of opportunity for proactive intervention to mitigate further degradation.
## 1 Introduction
RF energy harvesting can be combined with other sources, such as solar or vibration, to create hybrid energy harvesting systems allowing IoT and WSN devices to leverage multiple energy sources, increasing the overall energy harvesting efficiency and ensuring continuous operation even in challenging environments. Developing better methods to gain more efficient and reliable RF energy harvesting capabilities seems to be a path toward ensuring the future sustainable development of means designed to overcome future difficulties related to energy consumption. Consequently, RF energy harvesting systems with high conversion efficiency need to be developed to power IoT devices. As mentioned earlier, the conversion efficiency depends on the incident RF energy of the antenna elements. We hypothesize that, in an enclosed space (such as the cabin of a car), where there are sources of EMFs (such as a mobile phone, infotainment, etc. ), enough RF power is generated to be suitable for capture and conversion into usable electrical energy for powering IoT devices.
# Multiple Observations for Secret-Key Binding with SRAM PUFs
## 6 The Soft-Decision Helper Data Scheme
### Achievable Performance  
We are interested in the maximum achievable secret-key rate of the SD scheme. As for the MO scheme (Section 4.2) we should first show that the SD scheme is secure, i.e., the uniformity condition of Definition 1 is satisfied since the key is generated uniformly at random. Furthermore, the leakage condition is satisfied as \(I(S;W^{n},\Psi^{n})=0\) as is shown in Appendix A.2. Now, we can derive the achievable rate over the channel from the encoder to the decoder. The channel is described by the conditional distribution \(\Pr(R=r,\Psi=\psi|C=c)\), and it follows from the channel coding theorem (see, e.g., [20], Chapter 3) that the maximum achievable rate is given by the mutual information maximized over the input distribution. Furthermore, \(C\) is uniform here, since we are using a linear code. We therefore evaluate this mutual information next  
\[R^{*}_{\text{SD},\infty}\stackrel{{\Delta}}{{=}}I(C;R,\Psi) \stackrel{{(a)}}{{=}}I(C;R|\Psi)\stackrel{{(b)}}{{=} }1-\int_{0}^{1/2}h_{2}(\psi)p_{\Psi}(\psi)\,d\psi. \tag{33}\]  
In \((a)\) we used the fact that the key is generated independently from the SRAM-PUF observations and thus \(I(C;\Psi)=0\). In \((b)\) we used the fact that \(r=c\oplus(u\oplus y)\) so the channel from encoder to decoder can be modeled as a binary symmetric channel with cross-over probability \(\psi\) (the probability that \(u\
eq y\)). Please note that \(R^{*}_{\text{SD},\infty}=C_{\infty}\) (for  
Figure 7: The Soft-Decision helper data scheme, with public information the helper data sequence \(w^{n}\) and error probabilities \(\psi^{n}\).  
symmetric SRAM-PUF) this rate is equal to the limit (for \(t\) to infinity) of the secret-key capacity for multiple enrollment observations, see (16). Therefore, the SD helper data scheme is optimal, and furthermore the MO helper data scheme approaches the same key rate as the SD helper data scheme when sufficient enrollment observations are used.
# Research Progress Regarding the Precision of Dosing and Distribution Devices for Fertilizers
## 2 Current Status of Research on Precision Fertilizer Dischargers
### Research Status of Groove Wheel Fertilizer Discharge
Bench testing confirmed the reliability of the simulation with a deviation of only 0.91%. Field trials concluded that the coefficient of variation of fertilization uniformity was 10.13%, meeting the agronomic requirements for deep-side fertilization in tea gardens. Figure 12.4 Different cross-sectional shapes of spiral sheaves: (**a**) circular arc, (**b**) spoon shape, (**c**) trapezoid. Figure 12.5 Structural diagram of opposed double-spiral sheave wheels: (**a**) top view; (**b**) left view. Note: \(D\) is the diameter of the sheave and the maximum effective working length; \(r\) is the radius of the concave section. In addition, in order to make breakthroughs in the precise fertilizer discharge dosing from the groove wheel fertilizer discharger, Chinese researchers initiated studies on new designs for groove wheel fertilizer discharges. [PERSON] et al. [34] enhanced the design of a staggered gear fertilizer discharger based on spur-tooth fertilizer discharge gears, illustrated in Figure 12.6 Based on theoretical analysis of the fertilizer discharge amount, they utilized the discrete element analysis method to simulate the discharge process of urea granules. Single-factor orthogonal simulation experiments were conducted to analyze the effects of parameters on the uniformity of fertilizer discharge. Results from simulations indicated that the number of staggered gear has a more significant impact on the discharge uniformity than the wheel clearance. Combining integrating simulation tests and bench tests, the best performance was attained by employing three staggered gear plates, with a wheel clearance set at 5 mm and a rotation speed of 60 rpm. This configuration yielded a coefficient of variation for the fertilizer discharge uniformity of no more than 4.8%, closely aligning with theoretical projections. The accuracy deviation of fertilizer amount was measured at 3.1%. The optimized fertilizer discharger exhibits excellent fertilizer uniformity and enables precise control of the fertilizer discharge. The research above primarily focused on optimizing parameters such as groove wheel radius, arc center distance, number of grooves, groove cross-section radius, groove depth, effective working length, spiral angle, cross-sectional shape, rotation speed, angle of the lower fertilizer discharge tongue opening, number of staggered gears, and fertilizer wheel clearance through discrete element simulation, bench tests, field trials, and other research methodologies. It offers crucial theoretical and practical backing for achieving precise fertilization with groove wheel fertilizer dischargers and enhancing the stability and uniformity of fertilizer dosing.
## 3 Results
### Perimeter of Knee  
To evaluate inflammation of the knee, the knee's perimeter was assessed (Figure 5). Statistical analysis showed the edema in the ipsilateral knee varied differently throughout the experimental period depending on the experimental group (ANOVA\({}_{\text{two-way}}\): Interaction: F(8,54) = 1.694, \(p\) = 0.01210; Time: F(2,54) = 51.98, \(p\) < 0.0001; Group: F(4,54) = 4.383, \(p\) = 0.0038). Post hoc tests showed OA animals displayed increased knee perimeter when compared to shams during weeks one to four. Moreover, both extract treatments partially reverted the knee swelling when compared to non-treated OA animals. Treatment with the commercial non-steroidal anti-inflammatory drug also partially reverted knee oedema when compared to non-treated OA animals, but only in the fourth week.
# Stochastic Modeling of Smartphones GNSS Observations Using LS-VCE and Application to Samsung S20
###### Abstract  
In recent years, numerous smartphones have been equipped with global navigation satellite system (GNSS) technology, enabling individuals to utilize their own devices for positioning and navigation purposes. In 2016, with the launch of a mobile app by Google, namely GnssLogger, smartphone users with Android version 7 or higher were able to record raw GNSS measurements (i.e., pseudorange, carrier phase, Doppler, and carrier-to-noise density ratio (C/N0)). Since then, enhancing the accuracy and efficiency of smartphone positioning has become an interesting area of research. Precise point positioning (PPP) is a powerful method providing precise real-time positioning of a single receiver, and it can be applied to smartphone observations as well. Achieving high-precision PPP requires selecting appropriate functional and stochastic models. In this study, we investigate the development of more reliable stochastic models for smartphone GNSS observations. The least-square variance component estimation (LS-VCE) method is applied to double-difference (DD) pseudorange and carrier phase observations from two Samsung S20s to obtain appropriate variances for GPS and GLONASS. According to the results, there is no significant correlation between the pseudorange and carrier phase observations of GPS and GLONASS on the L1 frequency. Furthermore, the quality of GLONASS carrier phase observations is comparable to that of GPS. The model's performance is then assessed with respect to single-frequency precise point positioning (SF-PPP) using a dataset collected in kinematic mode from a Samsung S20 smartphone. A significant improvement of 25.1% and 32.7% on the root-mean-square (RMS) of horizontal positioning and the 50 th percentile error, respectively, was achieved when employing the obtained stochastic model.  
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## 3 Results and Discussion
### The Distillation Experiments of REMES and the Phase Changes of REEs
#### 3.2.2 The Distillation Experiments on the Temperature for the Phase Changes of REMES  
The possible phase changes of rare earth fluorides were investigated through the distillation experiments of REMES at different temperatures. According to the TG-DSC results, the distillation temperature varied from 1173 K to 1573 K. The other conditions were a distillation time of 6 h and an absolute pressure of 0.1 Pa. The results are shown in Figure 5.  
Figure 4: The consequences of the experiments at different distillation times: (**a**) the XRD patterns of the distillation residue; (**b**) the changes in the recovery efficiencies of the REEs and Li with time.  
Figure 5a illustrates that the diffraction peaks of NdF\({}_{3}\) gradually decreased, with the disappearance of NdF\({}_{3}\) observed at 1573 K, while the rise in the diffraction peaks of NdOF started at 1173 K, reaching their highest points at 1473 K. According to Figure 5b, at 1173 K, only LiF could evaporate. When the temperature reached 1373 K, a high recovery efficiency of Li could be obtained, and the recovery efficiency of Li then increased slightly. As for the distillation of REEs, the evaporation of rare earth fluorides could not occur until 1473 K, with 37.36% of the total REEs recovered, and the rare earth recovery efficiency improved slightly at 1573 K.  
The results were consistent with the results of the distillation time experiments. Additionally, the generation of NdOF might start before 1173 K, and this reaction notably affected the recovery of rare earth fluorides.