esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Fabrication of a Novel Window-Type Convection Device
## 3 Discussion and Validation of Results
### Effects of Airfoil Thickness and Outlet Slit Size on Air Entrainment Performance  
As illustrated in Figure 12a, increasing the airfoil thickness exerted negligible effects on the mass flow rate on Plane S, with a relative camber of 4%, a maximum camber located 40% from the leading edge, an increase in the maximum thickness from 12% to 24%, and a lengthening of the slit size from 0.3 to 1.5 mm. Furthermore, the mass flow rate on Plane S of the NACA4424 airfoil demonstrated less noticeable improvement in relation to the slit size than those of the NACA4412 and NACA4418 airfoils did. Next, Figure 12b depicts the effects of the airfoil thickness on the mass flow rate on Plane M1. The highest mass flow rate on Plane M1 of the NACA4424 airfoil occurred with a slit size of 0.6-0.9 mm; however, increasing the airfoil thickness decreased the volume of the runner, thereby reducing the optimal size of the slit. Figure 12c shows the mass flow rate at the outlet of Plane M2 in relation to the airfoil thickness, which was the approximate sum of the mass flow rate at the inlet of Plane S and that on Plane M1; this finding concurs with that illustrated in Figure 11c. In addition, as the slit size increased, the mass flow rate at the inlet of Plane S rose, whereas that at the outlet of Plane M2 declined (Figure 12a,c), indicating that increasing the slit size impeded air entrainment. Finally, Figure 12d outlines the effects of airfoil thickness on the mass flow rate on Plane M3. Notably, the mass flow rate on Plane M3 was higher than that on Plane M2, suggesting that airflows passing Plane M2 induced indoor air entrainment. This phenomenon coincides with the results shown in Figure 11d. Moreover, analyzing the effects of airfoil thickness on the mass flow rate under the relative camber of 1% to 4% revealed similar mass flow rate changes to those presented in Figure 12.  
To examine the effects of the geometric structure of the window-type convection device on its air entrainment performance, the mass flow rates at the outlet of Plane M3 and that at the inlet of Plane S were proportionally calculated to determine the ratio of which the flow rate was magnified. Analysis of a range of geometric parameters revealed that the NACA4424 airfoil with a 1.2 mm outlet slit size attained the optimal magnification ratio of 11.59. Therefore, a convection device was constructed on the basis of this parameter to measure the actual flow rate of the device and validate the aforementioned simulation results. Section 3.3 details this validation process.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Slit (mm)**} & \multicolumn{2}{c}{**NACA1412**} & \multicolumn{2}{c}{**NACA2412**} & \multicolumn{2}{c}{**NACA4412**} \\  & \(\mathbf{V_{M1}}\) **(m/s)** & \(\mathbf{P_{M1}}\) **(Pa)** & \(\mathbf{V_{M1}}\) **(m/s)** & \(\mathbf{P_{M1}}\) **(Pa)** & \(\mathbf{V_{M1}}\) **(m/s)** & \(\mathbf{P_{M1}}\) **(Pa)** \\ \hline
0.3 & 1.98 & \(-\)2.74 & 2
# Breaking Alert Fatigue: AI-Assisted SIEM Framework for Effective Incident Response
## 5 Experiments
### Experiment II  
The IDS alerts collected in the NSOC2022 dataset comprise a vast amount of data, with 7.3 million lines of alert logs extracted from only two appliances between 18 June 2021 and 30 November 2022. We employed ROV to evaluate the effectiveness of the proposed SIEM scheme in a real-world SOC context. To present the results clearly, we only report the outcome for IWSVM in this experiment due to its exceptional performance.  
Table 4 summarizes the training and testing dataset used to evaluate model accuracy in terms of ROV. In the first configuration, we trained the model using alert data collected between June 2021 and June 2022, and we tested it on data from July 2022 to November 2022. For the subsequent four configurations, we gradually removed the earliest month's data from the testing set and added these data to the training dataset. This allowed us to observe the changes in prediction accuracy when the training and testing datasets had different time intervals.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Category** & **Algorithm** & **Accuracy (\%)** & **Recall (\%)** & **Precision (\%)** & **FPR (\%)** & **TNR (\%)** & **F1 (\%)** & **BCR (\%)** \\ \hline Unsupervised & IF [40] & 90.705 & 100.000 & 0.551 & 9.300 & 90.700 & 1.097 & 95.350 \\ learning & IF + DC [41] & 94.141 & 95.876 & 0.837 & 5.860 & 94.140 & 1.659 & 95.008 \\ \hline Supervised & KNN & 99.955 & 98.749 & 89.046 & 0.041 & 99.959 & 93.647 & 99.354 \\ learning & NB & 89.741 & 93.390 & 2.950 & 10.271 & 89.729 & 5.719 & 91.559 \\  & LDA & 99.983 & **99.732** & 95.387 & 0.016 & 99.984 & 97.511 & 99.858 \\  & DT & 99.916 & 99.285 & 80.137 & 0.082 & 99.918 & 88.689 & 99.602 \\  & AdaBoost & 99.997 & **99.732** & 99.466 & 0.002 & 99.998 & 99.599 & **99.865** \\  & SVM & 99.992 & 97.901 & 99.682 & **0.001** & **99.999** & 98.783 & 98.950 \\ \hline Over- & RF-SMOTE [70] & 99.914 & 99.048 & 95.632 & 0.072 & 99.928 & 97.310 & 99.488 \\ sampling & DT-SMOTE [70] & 99.573 & 99.286 & 78.977 & 0.422 & 99.578 & 87.975 & 99.432 \\  & RF-SVMSMOTE [70] & 99.925 & 98.571 & 96.729 & 0.053 & 99.947 & 97.642 & 99.259 \\  & DT-SVMSMOTE [70] & 99.562 & 99.286 & 78.531 & 0.443 & 99.567 & 87.697 & 99.426 \\  & XGBoost-MAS-OSS [59] & 99.875 & 99.519 & 93.034 & 0.119 & 99.881 & 96.167 & 99.700 \\ \hline Cost-sensitive & WSVM & **99.998** & 99.598 & **99.687** & **0.001** & **99.999** & 99.643 & 99.799 \\ learning & IWSVM & **99.998** & 99.643 & **99.687** & **0.001** & **99.999** & **99.665** & 99.821 \\ \hline \hline \end{tabular} Bold numbers signify the utmost values attained on the particular evaluation criteria.  
\end{table}
Table 3: Performance comparison on NSOC2017 dataset.
## 7 Simulation Results Discussion: Analysis and Evaluation
### Evaluating RSU Availability and Overnight Connectivity  
The analysis explored different scenarios of RSU availability and overnight connectivity percentages, from 0% (non-existent) to 100% (all vehicles have up-to-date Pre-Signatures at the start of day). These factors were assessed at every second of the simulation. This granular monitoring of parameters at each one-second interval allowed us to gain detailed insights into the dynamics of vehicular communications across different RSU density scenarios.
## 2 Simulation Environment and Studied Buildings  
### Climate Zones in China  
According to the Code for Design of Civil Buildings (GB 50352-2005), there are five different climate zones in China, namely the Severe Cold Region, Cold Region, Hot-Summer Cold-Winter Region, Hot-Summer Warm-Winter Region, and Temperate Region. The standard also provides for further categorization into A, B, C, and D sub-regions according to the specific geographical location. Buildings in different climate zones have to follow the local regulations which define the structural criteria, the insulation properties for opaque walls floors and roofs, and the thermal and optical performance of windows and skylights. In order to evaluate the energy saving comparison between RC and CLT buildings, five major cities in China are selected as the building simulation environment, each located in different climate zones (Figure 2). The basic climatic data for these cities is presented in Table 1.  
Figure 2: Location of the five case study cities.
## 6 Structural Performances
### Vibration-Resistant Systems  
Similarly, the nonstructural components of the IMU in the PFISB might be damaged during the transit, lifting, and handling [130]. It has been shown that screws and screw heads are highly influential, but the damage does not affect the capacity. The mechanical performance during the road transportation revealed that the screw connections are sensitive to the vehicle's vibration [131]. However, the trailer's front chassis always seems susceptible to excitations and necessitates attention [132]. So to withstand the PFISBs IMUs' lifting forces, neighboring members and their connections must be strengthened [133].
# A Comparison of Deep Transfer Learning Methods for Land Use and Land Cover Classification
## 5 Conclusions  
In this article, we leveraged Tensorflow's pre-trained models to develop 39 deep transfer learning models and compare their accuracy and training time. The EuroSAT was divided into three subgroups (80% for training, 10% for validation, and 10% for testing). ResNet50, EfficientNetV2B0, and ResNet152 models were the best three in terms of kappa and accuracy scores among our 39 deep transfer learning models. ResNet152 took about three times as long to train as EfficientNetV2B0 on our test computer, while ResNet50 took roughly twice as long. On the test set, ResNet50 obtained an overall f1-score of 0.967, with the lowest score in the Highway class and the greatest in the Sea Lake class. In the Annual Crop, Highway, Permanent Crop, and River classes, EfficientNetV2B0 had the highest f1-score, while ResNet50 had the best f1-score in the Forest, Herbaceous Vegetation, Industrial, Pasture, and Sea Lake classes. ResNet152 had undoubtedly the strongest f1-score in the Residential class. The Highway class was the most confusing for ResNet50 in the test set, while Sea Lake was the least confusing. EfficientNetV2B0 performed best in the Permanent Crop class, whereas ResNet152 performed best in the Herbaceous Vegetation, Highway, and Residential classes. ResNet50 excelled in the Annual Crop, Forest, Industrial, Pasture, and River categories. Based on these results, we suggest that EfficientNetV2 is the optimal choice for transfer learning-based land cover and land use image classification.  
Conceptualization, H.D. and Q.K.H.; methodology, H.D.; software, H.D.; validation, H.D., Q.K.H.; formal analysis, H.D.; investigation, H.D.; resources, Q.K.H.; data curation, H.D.; writing--original draft preparation, H.D.; writing--review and editing, H.D.; visualization, H.D.; supervision, Q.K.H.; project administration, Q.K.H.; funding acquisition, Q.K.H. All authors have read and agreed to the published version of the manuscript.  
This study was partially funded by a Discovery Grant from the Natural Sciences and Engineering Research Council of Canada to Q.K.H.  
Not applicable.
## 6 Conclusions  
We used double logistic functions to fit the curve for GIMMS3g NDVI and selected five commonly employed methods to extract the phenological parameters above 30\({}^{\circ}\)N during 1982-2015. We found that the spatial distribution and trends of the SOS and EOS under the chosen extraction methods had both similarities and differences. Furthermore, we found that through the verification of flux data, the SOS extracted using Method 3 from the GIMMS3g dataset was the closest to the ground data, while the EOS extracted using Method 4 was the closest to the ground data.  
Overall, regardless of the method employed, the average changes in phenological parameters were consistent during the study period, with the SOS showing Asia > whole > North America > Europe, while the EOS showed North America > whole > Europe > Asia. However, the SOS under Methods 1, 3 and 5 was significantly earlier than that under Methods 2 and 4, while the EOS under methods 1 and 3 was notably later than that under Methods 2 and 4.  
Regarding phenological parameters, the SOS trends under the five methods mostly showed an advance, except for the SOS trends from 45\({}^{\circ}\)N to 60\({}^{\circ}\)N in Asia and Europe. The EOS trends, on the other hand, showed a clear lag under Methods 1 to 4 from 30\({}^{\circ}\)N to 60\({}^{\circ}\)N and an advance above 60\({}^{\circ}\)N. In addition to the above dissimilarities, vegetation type was also shown to be a notable factor influencing the extraction of phenological parameters above 30\({}^{\circ}\)N.  
Conceptualization, X.L. and Y.C.; methodology, Y.C.; validation, X.L., Y.L. and Z.L.; writing--original draft preparation, X.L.; writing--review and editing, X.L., Y.L. and Y.C.; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program, 2019 YFA0606902.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 5 Conclusions  
This work proposes a novel sensor-system-based network by integrating multi-hop communication with swarm intelligence to achieve low-power communication and data transmission. In this technique, clustering is combined with data transfer via location-based routing and low-power communication in two phases to calculate the ideal cluster heads (CHs). Initially, the nodes are clustered and calculated to determine the optimal CHs. Then, the data transmission is performed using the adaptive clustering-based locative data transmission (ACLDT) algorithm, where the sink node and base station locations are precisely known. A CH will seek the nearest CH and utilize it as the next hop, and then a path to the base station is formed through the development of CH chains. The experimental results show that this technique can attain a network lifetime of 96%, average delay of 53 ms, coverage rate (CR) of 83%, throughput of 97%, and an energy efficiency of 95%. In future works, this low power consumption model with efficient data transmission can be implemented and simulated in real-time applications, such as VANET integrated with cloud architecture to enhance network security.  
Conceptualization, V.M.; supervision, K.C.J.; original draft and review and editing, S.V.N.S. and M.R.; validation, M.M.; proposition of the new method or methodology, A.H.G. All authors have read and agreed to the published version of the manuscript.  
Figure 5: Comparative analysis of throughput.  
Figure 6: Comparative analysis of energy efficiency.  
**Funding:** No fund has been received for the completion of this research work.  
**Institutional Review Board Statement** Not applicable.  
**Informed Consent Statement** Not applicable.  
**Data Availability Statement** Data will be shared for review based on the editorial reviewer's request.  
**Conflicts of Interest** The authors declare no conflict of interest.
## 1 Introduction
employed a high-speed schlieren system to observe the leader channel near the tip of the electrode with a gap distance of 2.2 m during the positive discharge process [16]. It was found that, before the restrike occurred and during the discharge relaxation process with a discharge current of I = 0, the insulation characteristics near the electrode tip were restored for a certain period of time, and the original leader channel was no longer connected to the electrode. In recent years, researchers have conducted in-depth experimental studies on the influence of humidity on the discontinuous development of leaders, the pulse characteristics of restrike current, the charge amount, the leader development speed, etc. [7; 8; 17; 18]. Despite these advances, the restrike phenomenon and its physical process during the discontinuous development of leaders have been overlooked. In the study presented in this paper, by constructing a synchronous observation experimental platform for long-air-gap discharge, a positive switching impulse with a wavefront time of 1 ms was applied to a 6 m rod-plate gap. The \"steep\"-type restrike during the discharge process was observed, and the luminescence phenomenon of the leader channel during the relaxation process before the occurrence of restrike was studied and analyzed. Lastly, based on the experimental results, the physical mechanism of the luminescence phenomenon generated by the leader channel during the relaxation process was preliminarily explored.
# Electrochemical Diagnostics of Sprayed Fiber-Reinforced Concrete Corrosion  
[PERSON]  
1 Faculty of Civil Engineering and Architecture, Kielce University of Technology, Al. of FRC is especially useful in the repairs of structures which are in use, since it allows work to be performed without disusing them [3; 6; 14]. For dispersed reinforcement, various types of fibers can be used, such as steel, synthetic (polypropylene, polyester, polyacrylonitrile), glass, carbon, basalt, or even organic. A summary of the basic parameters for selected types of fibers is provided in Table 1[18]. The addition of fibers does not affect the method of application and curing of the concrete mix. The only difficulty might be the reduced workability of the concrete mix if steel fibers are used. In such cases, workability can be improved by adding appropriate plasticizers and super plasticizers, and using fine-fraction aggregates with the particle diameter not exceeding 1/2 of the fiber length for hooked-end steel fibers, or 1/3 of the length of smooth fibers [3; 6; 8; 9; 14]. Usually, a 8/16 mm aggregate is used. The water/cement ratio should not be higher than 0.45. Steel fibers are among the most frequently used for concrete reinforcement, owing their popularity to both efficiency and relatively low cost. Steel fibers for concrete reinforcement are supplied in various shapes and sizes (Figure 1). The fiber content in a concrete mix usually ranges from 0.25-2% vol. per 1 m\({}^{3}\), although some authors have provided a broader range up to 3.0% [10; 13; 18]. Adding less than 0.25% of reinforcement is insufficient (studies indicate that it will not improve the concrete parameters), while adding more than 2.0% of fibers considerably reduces the workability of the concrete mix (even if super plasticizers are used). The authors' own research indicates that adding 1.5% of fibers is enough to significantly affect the workability of the mix; due to the formation of so-called \"nests,\" i.e., bundles of fibers, the mix loses uniformity and the concrete parameters are worse than those of a mix with a 1.0% fiber content [19]. Adding steel fibers improves tensile strength, fatigue resistance, impact strength, fracture resistance, and abrasion resistance [10; 11; 12; 15; 20]. Concrete with added fibers is more cohesive, as the fibers \"clamp\" the concrete matrix and prevent the formation of large pores in the concrete mix, and also reduce the formation and spreading of shrinkage cracks [6; 13] during the setting and hardening of the concrete (Figure 2). The fibers also improve the freeze-thaw durability of the concrete [19]. Unlike synthetic fibers, which prevent cracks only in fresh concrete, steel fibers also reduce the cracking resulting from the drying of hardened concrete, as well as cracks caused by mechanical loads [10; 11; 12; 13; 15]. Since steel fibers \"seal\" the concrete cover [7; 17], they might also indirectly inhibit the corrosion of the primary reinforcement in reinforced concrete structures, thus improving the rebar's durability.
## 4 Cantilever Beam System Design  
The system uses a step function with a final value of zero as the reference input, indicating the desired steady-state position where no residual vibration remains. The system is subjected to two types of disturbances: an impulse disturbance and a random disturbance modeled by a Gaussian distribution.  
The impulse disturbance is applied at t = 0, representing a sudden force that excites the beam, inducing immediate vibrations that propagate through the entire system, affecting its dynamics from the onset. The random disturbance, following a Gaussian distribution, simulates continuous and unpredictable environmental forces affecting the system over time. Both disturbances introduce significant forces, causing high levels of vibration in the cantilever beam. The system's response is observed through the voltage generated by the piezoelectric sensor, which captures the beam's oscillations and serves as the output signal for control and analysis.  
A low-pass filter is integrated into the system to enhance stability by filtering out high-frequency noise and disturbances while preserving critical low-frequency signals. The block diagram of the open-loop system with a low-pass filter under disturbance is shown in Figure 4. The cutoff frequency, \(f_{c}\) = 11 Hz, was selected based on system output analysis to eliminate irrelevant signals while retaining essential information. This frequency results in a time constant \(T\), which determines the rate at which the filter responds to changes in input. The time constant \(T\) is calculated using the formula:  
\[T=\frac{1}{2\pi f_{c}}\]  
where \(f_{c}\) is the chosen cutoff frequency. Substituting \(f_{c}\) = 11 Hz yields a time constant of approximately 0.0144 s.  
The filter's behavior is described by the transfer function:  
\[H(\mathbf{s})=\frac{1}{1+\mathbf{s}\mathbf{T}}\]  
where \(H(\mathbf{s})\) represents the filter's output-to-input ratio as a function of the complex frequency variable \(\mathbf{s}\), and \(T\) is the time constant. This transfer function ensures that frequencies higher than the cutoff are attenuated while lower frequencies pass through with minimal distortion. Incorporating the low-pass filter improves system performance by ensuring smoother responses, reducing noise, and maintaining signal fidelity, which is essential for effective vibration control under both impulse and random disturbance conditions.
## 3 Results
### Individual Tree Crown Approach  
_ITCsegment_ used variable window sizes when searching for local maxima, which avoided omitting small palms when the window was large, or over-segmenting large palms when the window was small (Figure 7a). Heights obtained from the individual tree crown segmentation (H\({}_{\text{ITC}}\)) included the upward pointing fronds of palms. The relationship between stem height and H\({}_{\text{ITC}}\) (Figure 7b) was:  
\[\text{Hc}=0.61\text{H}_{\text{ITC}}^{1.02} \tag{11}\]  
Figure 5: Relationship between aboveground carbon density (ACD) and (a) top-of-canopy-height (TCH), and (b) canopy cover (CC) at 3 m height per plot for 8-year-old and 14-year-old plantations.  
Figure 6: Relationship between aboveground carbon density (ACD) estimated from field data and (a) ACD estimated from the leave-one-out cross validated datasets using LiDAR-derived top of canopy height (HTC), (b) canopy cover (CC) at 3 m, and (c) ACD estimated from TCH and residual CC at 3 m height per plot for 8-year-old and 14-year-old plantations. The 1:1 line is given for reference in both panels (dashed black lines).  
This equation was used to predict the tree stem height (Hc) of all trees in the plots, from their segmented heights.  
A comparison of the number of delineated trees with the tree density obtained from the Pleiades optical imagery was used to estimate the accuracy of the ITC delineation for all the 1-ha plots (Figure 8a). ACD of individual oil palm trees was summed to estimate plot ACD at the 1-ha plot level (Figure 8b). The ITC approach gave a percentage RMSE of 25.9% and was biased (\(-11.6\%\)), showing a slight trend from over-estimation of lower carbon density values and under-estimation of higher carbon density values.  
Figure 8: Relationship between (**a**) the number of trees in the 1-ha plots estimated from Pleiades optical imagery and number of trees obtained from the LiDAR-derived individual tree crown (ITC) segmentation approach, and (**b**) aboveground carbon density predicted from the ITC approach and ACD estimated from field data for 8-year-old and 14-year-old plantations. The 1:1 line is given for reference in both panels (dashed black lines).  
Figure 7: (**a**) Detection of individual tree crowns in a 1-ha plot LiDAR imagery using _itcSegment_([PERSON] and [PERSON], 2016) for oil palm plantations; (**b**) The relationship between tree stem height obtained from field data and tree height obtained from the individual tree crown segmentation (H\({}_{\text{ITC}}\)). The regression line fitted to all 27 plots is shown as a red line and the 1:1 line as a black dashed line.