esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Assessment and Repair Method of Runway
## 2 The Depth of CBU Penetration
### Calculation of Penetration Depth  
Even though the concrete's compressive strength is relatively stronger than its tensile strength, its tensile strength is approximately 1/10 of its compressive strength, which necessitates the use of rebar to minimize the tensile fracture or destruction from missile impacts. However, runway pavements are constructed with unsupported concrete without any rebar, and to reinforce its weak tensile strength, much stronger flexural strength is applied to its construction. CBU penetration depth onto concrete is experimentally found to be inversely proportional to the concrete's compressive strength \(\left(f_{c}\right)^{0.5}\), and as the aggregate size increases, penetration depth decreases [8]. CBU penetration depth can be represented as a function of a missile's impact speed, its angle of impact, the mass of the projectile, and the shape of the projectile. United States Army and Ballistics Research Laboratory conducted numerous impact experiments on concrete and developed the CONWEP equation from the collected data. In 1946, the National Defense Research Committee (NDRC) conducted additional experiments based on above mentioned equation and developed an NDRC formula. This formula was made with quantitative measurement of impact response and concrete penetration ability, based on a projectile's shape, such as warhead shape factor, missile shape, density, and speed. The revised NDRC equation, shown as Equation (1) below, includes the relationship between concrete's compressive strength and penetrability which they obtained from additional experiments [7]. Among many empirical formulas, this research used the NDRC formula in Equation (1) with the CBU warhead specification in Table 1 to calculate the penetration depth, with the assumption that the intrusion was 33 cm into the pavement surface.  
\[G=\frac{180 NW}{d\sqrt{f_{c}}}\Big{(}\frac{v}{1000d}\Big{)}^{1.8}\,\ \frac{x}{d}=G+0.9395\ for\ G\ \geq\ 1.0605 \tag{1}\]  
where  
\(x\) = Penetration depth;  
\(N\) = Nose shape factor;  
\(W\) = Projectile weight;  
\(d\) = Projectile diameter;  
\(f_{c}\) = Compressive strength of concrete; \(v\) = Velocity of projectile.
## 2 Data
### Crop Yield Statistics  
The reference data for the crop yield forecasting model consisted of official yield statistics provided by Statistics Poland at NUTS-2 and LAU levels (Figure 2). The NUTS-2 data included winter wheat, winter rapeseed, and maize yields expressed in decatons [dt] for the period 1997-2019. At the LAU level, the length of the time series was shorter, and also inconsistent among administrative units (Figure 3).  
The yield statistics for each NUTS-2 and LAU region were transformed into temporal yield residuals (Figure 4) from the Theil-Sen monotonic trend [30] in annual yields estimates covering the period 1997-2019 (Figure 5). These yield residuals were used as response variables in crop yield forecasting. The final absolute yield forecast consisted of a sum of the monotonic trend and a forecasted yield residual for a particular year.  
Figure 1: Fractional arable land [%] in Poland.  
Figure 4: Temporal yield residuals from Theil-Sen monotonic trend used as a response variable in crop yield forecasting for NUTS-2. Please mind different limits of Y-axes.  
Figure 3: Time span of official crop yield statistics at the LAU level. Black signifies no data.  
Figure 2: Administrative divisions of Poland for which crop yields were predicted: Nomenclature des Unités Territoriales Statistiques level 2 (NUTS-2)—red lines, and Local Administrative Units (LAU)—gray lines.
## 1 Introduction
Four types of antennas and an RF-energy-harvesting evaluation board are included in this research. Experiments have shown that power losses in air and antennas have a stronger effect on the total efficiency of the system than the losses in the RF energy harvester. The RF-energy-harvesting system can be improved using antenna arrays, particularly for supplying power to low-power sensors and IoT devices. Most previous studies have used an individual energy-harvesting source to supply power to low-power sensors and IoT devices. However, the available environmental energies affect the input power of the RF transmitter (Powercast Corporation) at a frequency of 2.4 GHz. Sensors and IoT devices are always installed on the ceiling of a room. For uniform RF transmission and increasing communication distance, this study employed a TX91501 RF transmitter from Powercast Corporation [18], which provides an output power of 3 W at an ISM band of 915 MHz, with a maximum transmission distance of 12-15 m. Moreover, a 1-dBi bipolar antenna was used as a receiving antenna to design an RF-energy-harvesting chip. Figure 1 shows the proposed RF-energy-harvesting system, which includes a Powercast transmitter, receiving antenna, RF-energy-harvesting IC, and power source. The following section elucidates the circuit topology of the designed RF-energy-harvesting IC. Sections 3 and 4 present the simulated and measured results and conclusions, respectively.
## 3 Types of Channel Hole Defect
### Channel Hole Not-Open  
In 3D NAND flash memory, it is natural that the diameter of the channel hole becomes narrower as the layer goes down because both vertical and lateral etching rates are not uniform but vary depending on the vertical locations of stacked layers. Unfortunately, some channel holes cannot successfully reach the substrate during the etching process,  
Figure 6: Schematic diagram of various abnormalities in the channel hole profile.  
and we call this type of abnormality Hard Not-Open. As shown in the left channel hole of Figure 6b, the not-opened channel holes are not fully punched-through, so they fail to connect to the substrate physically. Because the not-opened channel holes are isolated from the substrate, the electrical voltage for NAND operations cannot be applied to flash cells in a block. Therefore, flash cells along the not-opened channel hole become electrically disabled, making it impossible to perform flash operations correctly. Since the Hard Not-Open defect causes malfunctions of the NAND device (i.e., we cannot complete the erase, program, or read operations.), it can be easily screened out by a simple test pattern during post-manufacturing testing procedures (more details in Section 4).  
There can be another type of not-open abnormality. The right channel hole in Figure 6b shows another type of channel hole that is not open. In this case, the end of the channel hole succeeds in reaching the substrate. However, its connection is relatively weaker compared to the normal channel holes (i.e., the channel hole diameter of the bottom layer is much narrower than that of a normal channel hole), called Soft Not-Open. This type of channel hole works well at first and shows no difference from other normal channel holes in performing NAND operations. However, physically weak connections between the substrate and channel hole can be easily broken by even a small amount of electrical stress (i.e., repetitive program and erase operations, P/E cycles), which eventually leads to the Hard Not-Open channel hole defect. Compared to the Hard Not-Open, the Soft Not-Open channel hole defect in flash memory products causes more severe problems for two reasons. First, it cannot be detected by conventional standard test procedures, which consist of function checks based on various test patterns. Second, since it can be easily turned into device failure even when the user has utilized the flash product only for a short time, the initial quality of the flash product is considerably worsened (i.e., infant mortality failure is dramatically raised). Therefore, it is crucial to apply different approaches to detect and screen out the Hard Not-Open and Soft Not-Open channel hole defects.
# A Method of Detections' Fusion for GNSS Anti-Spoofing
## 3 Performance Evaluation
### Simulation Results
#### 3.2.2 Test Results on TEXBAT
In Scenario 3, the spoofer's power advantage is weaker than the case of Scenario 2, but it still can be detected by the monitoring of correlator outputs. Besides, it can be observed in Figure 10 that the fused result exceeds the threshold at about 160 seconds, even though the second detector does not raise the alarm. This shows the robustness of the fusion method. It can be observed from the above results that decisions after the fusion are more distinguishable than before the fusion. It should be noted that stable tracking conditions are crucial to obtain the test statistics; however, the stability of tracking cannot be guaranteed under the spoofing attack. Actually, in the above tests, tracking conditions are stable for most of the time, except sometimes in the tests of Scenarios 3 and 4. In test of Scenario 3, the receiver failed to track and lock the signal for about fifteen seconds at about 160 s in the time history. In this period, the receiver attempted to track and lock the signal; the test statistics based on the outputs of tracking loop cannot reflect the real conditions. However, failure to lock the signal indicates the anomalies of the signal. It is reasonable that detectors issue alarms to declare the unavailability of the signal in this case. The above simulations illustrate the process and effect of the detections' fusion. It can be observed on them that the fusion of multiple detections reduces the false alarms and makes the results more reliable. Only two detectors are fused in this section. However, a complete anti-spoofing method should employ more detectors and fuse them together. Figure 10: Detections’ fusion of the Scenario 4 dataset. Figure 9: Detections’ fusion of the Scenario 3 dataset.
# Streaming-Based Anomaly Detection in ITS Messages
## 4 Discussion
### Limitations  
The first drawback of ELSCP is the extraction of adjacent data points forming the local region of the test instance using a distance metric applied to the kNN ball tree algorithm. This strategy has two challenges:  
1. It takes much time to find the test instance's nearest neighbours;
2. When many features or attributes are irrelevant, performance in a multidimensional space can be degraded.  
To remedy this problem, the local-region definition can be solved by the use of fast approximate methods [56] or by prototyping [57]. Since these strategies do not require all data points, they can greatly minimise the time necessary to build up a local domain. The second drawback is that we used a basic maximisation approach to generate the pseudo-ground truth. This might be enhanced by considering exact techniques, such as active base detector pruning [58].
## 6 Conclusions  
The traditional AE methods failed to identify anomalies in the liquid level in mold time series data. The proposed method, FEG-AE, is inspired by RAE and decomposes a time series of liquid level in a mold into a normal time sequence and an anomaly sequence. A forecasting network to reconstruct the normal sequence using the previous sequence's features. An error extraction network to extract error from the free forecasting network from anomalies' pollution improves precision compared to the traditional AE. A dynamic threshold method is proposed to identify the anomaly with higher precision. Compared to a fixed threshold, the proposed method significantly increases precision but slightly reduces recall. FEG-AE achieves the highest precision of 0.895 and an F1 score of 0.872 compared to baseline methods. The dynamic threshold method can significantly improve precision and F1 but has a minor recall reduction due to higher thresholds in some windows, resulting in more FNs.  
Future work will investigate a more robust way to balance the forecasting network and error extraction network to reduce the effects of the hyperparameter \(s_{W_{p}}\). Also, a more robust dynamic threshold method is under investigation to improve recall.  
Conceptualization, X.W.; data curation, X.W., H.K., Q.G. and J.M.; formal analysis, X.W. and S.Y.; funding acquisition, Q.G.; investigation, W.J.; methodology, H.K.; project administration, S.Y; resources, Q.G. and J.M.; software, H.K. and W.J.; validation, X.W., H.K. and S.Y.; visualization, X.W.; writing-original draft, H.K.; writing-review and editing, X.W., S.Y., Q.G. and J.M. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Key Research and Development Project of Shaanxi Province under Grant 2021 ZDLGY10-01 and the Self-Supporting Science and Technology Research and Development Project of SINOMACH-HE under Grant 2021 ZLKY-02.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
The authors gratefully acknowledge the financial support offered by the Key Research and Development Project of Shaanxi Province under Grant 2021 ZDLGY10-01 and the Self-Supporting Science and Technology Research and Development Project of SINOMACH-HE under Grant 2021 ZLKY-02.  
The authors declare no conflict of interest.
# Analysis of Fault Classifiers to Detect the Faults and Node Failures in a Wireless Sensor Network
## 5 Simulations and Results
### Datasets  
As mentioned in the system, two multi-hop sensors are used for data sensing and communication. These sensors are placed on the outside of the data gathered. Temperature and humidity are sensed by the sensors. The data are collected at time instant of \(t_{0}\), \(t_{1}\) and \(t_{2}\) consecutively. At every instant of time, the sensors measure the temperature and humidity twice at every instance (\(T_{1}\), \(T_{2}\) and \(H_{1}\), \(H_{2}\)). After the measurement of temperature and humidity, the various faults (Offset, Gain, Stuck-at, Out of Bounds, Spike, and Data loss) are induced randomly at the predefined rates (10%, 20%, 30%, 40% and 50%). The classified datasets are tabulated as normal data (error-free) and marked as one, whereas faulty datasets are denoted as abnormal with \(-1\).
# Structural Design and Parameter Optimization of Bionic Exhaust Tailpipe of Tractors
## 3 Results and Discussion
### Aeroacoustics and Transmission Loss Analysis
To research the size parameter influence on transmission loss, bionic tailpipes with differences in the top angle \(\theta\), height h, and number of circumferential columns n were analyzed. The research suggested that the effect of the top angle on the transmission loss was not obvious. Because of the bionic triangular convex texture of the direction along the tailpipe acoustic wave propagation and airflow direction, the top angle \(\theta\) did not greatly change the tailpipe acoustic wave propagation. The texture height and the number of circumferential columns played a more significant role in transmission loss. As the texture height was low, the inner surface of the pipe was not much different from the smooth wall surface of the prototype, so the effect of the texture height on the transmission loss was relatively low. As the texture height was high enough, the texture decreased the airflow velocity and reduced the acoustic impedance, so the transmission loss was reduced. Therefore, the texture height should be kept moderate. Meanwhile, the number of circumferential columns of the texture should also be kept moderate. The transmission loss increased as the number of circumferential columns increased. The main reason was that the increase in texture hindered the propagation of sound waves, which increased the acoustic impedance inside the pipe and increased the transmission loss. However, when the number of textures reached a certain level, the transmission loss decreased. The main reason was that the airflow velocity near the pipe wall gradually decreased as the number of circumferential columns n gradually increased, which reduced the acoustic impedance of the bionic exhaust tailpipes and reduced the transmission loss.
### Contribution of the Proposed Algorithm  
The proposed algorithm performs better than those mentioned in the literature due to its speed in obtaining the threshold values for setting the overcurrent and overvoltage relay. Additionally, fault signals are accompanied by noise. Therefore, using the wavelet transform to determine the threshold current and voltage helps denoise the signal to attain stability in the system. It also serves as a fast gateway for instantaneous relay settings for optimal protection of transmission and distribution line fault detection and isolation with the use of a circuit breaker.
# Fault Coupling Analysis and Reliability Assessment of Actuation System Based on Bond Graph Model
## 2 Bond Graph Model of Actuation System
### Signal Transmission Model  
For the flight control computer, the role of the controller is primarily to complete the energy distribution. Since the onboard energy output is fixed, but different actuators require different inputs, the controller can complete the energy distribution here. At the same time, the controller can also switch between actuators, with HA being the primary actuator. When the HA fails to complete the effective output to the control surface, the controller switches the energy supply from the HA to the EHA, thereby switching between actuators.  
The HA and EHA bond graph models are connected to the controller representing the flight control computer to form a complete bond graph model of the actuation system. Figure 3 shows the actuator bonding graph model for a single surface, which is composed of two parts: the upper part is the main actuation HA, the lower part is the backup actuation EHA, the left part is the voltage source, the hydraulic source, and the controller, and the right part represents the hydraulic rod of the rudder surface. The composition of the actuation bond graph model is described in detail in Section 2.1. Figure 4 depicts the nodal graph for the entire actuation system. Here, each meta-component of HA and EHA is  
Figure 2: Architecture block diagram of HA and EHA.  
treated as a node, the actuation system is composed of seven rudder surfaces, and onboard energy is presented in the form of nodes.
# A Novel Method of Emotion Recognition from Multi-Band EEG Topology Maps Based on ERENet
## 2 Materials and Methods
### An Emotion Recognition Method from Multi-Band EEG Topology Maps Using ERENet  
The proposed emotion recognition framework based on multi-band EEG topology maps is shown in Figure 1. Firstly, the multi-channel EEG signal sequences are acquired and preprocessed mainly through removing the baseline signals and augmenting the data. Secondly, the spectral power of the theta, alpha, beta, and gamma bands is extracted using FFT, and a 2D topology map corresponding to each band is created. Then, the corresponding four EEG topology maps are used as the input of ERENet to learn the emotion generation modes and emotion evaluation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Label**} & \multirow{2}{*}{**Threshold**} & \multicolumn{2}{c}{**Data Quantity**} \\ \cline{3-4}  & & **Instances** & **Total** \\ \hline LV 1 & \textless{}5 & 84,708 & \\ HV 2 & \textgreater{}5 & 65,052 & 149,760 \\ \hline \hline \end{tabular} \({}^{\dagger}\) LV means low valence. 2 HV means high valence.  
\end{table}
Table 1: Sample distribution on the valence classification task.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Label**} & \multirow{2}{*}{**Threshold**} & \multicolumn{2}{c}{**Data Quantity**} \\ \cline{3-4}  & & **Instances** & **Total** \\ \hline LA 1 & \textless{}5 & 88,218 & 149,760 \\ HA 2 & \textgreater{}5 & 61,542 & 149,760 \\ \hline \hline \end{tabular} \({}^{\dagger}\) LA means low arousal. 2 HA means high arousal.  
\end{table}
Table 2: Sample distribution on the arousal classification task.  
In this subsection, the process of how to preprocess the multi-channel EEG sequences is firstly described, then the multi-band EEG topology maps and their construction process are introduced, and finally the proposed emotion recognition network ERENet is described in detail.
# Engineering Properties and Environmental Impact of Soil Mixing with Steel Slag Applied in Subgrade
## 3 Results and Discussion
### Determination of Optimum Proportion (SL/S) between SL and Intact Soil
#### 3.1.2 Liquid-Plastic Limit Combined Measurement Test  
Table 2 shows the results of the Atterberg limits test for two kinds of soil samples (i.e., S1 and S2) mixed with SL. It was observed that with the addition of SL, the liquid-plastic limit of the test soil specimen decreased. Due to the greater drop in the liquid limit, the plasticity index also decreased. This phenomenon can also be attributed to the reduction of the clay particle content. Furthermore, SL can contain a small number of active components, and the potential chemical reaction can also lead to a change in the Atterberg limit value; however, this was ignored because the SL used in this study had low activity.  
Figure 6: The compaction test results: (**a**) compaction curves of specimens from S1; (**b**) compaction curves of specimens from S2.  
In this study, S1 was silty clay and S2 contained high liquid limit clay. At 0% SL content, the liquid limit was 43.7%-59.6%, and the plasticity index 12.2% and 31.9%. The high plasticity index means that the water sensitivity was low, but the water stability of soil with a higher liquid limit was poor, which could also lead to poor compactness of subgrade filler. Compaction during the engineering construction could be difficult, resulting in some problems with later shrinkage and deformation. Therefore, the addition of SL had an obvious improvement effect on the liquid and plastic limits of S1 and S2. With the increase in the amount of SL, the plasticity index of the test soils S1 and S2 decreased, as shown in Figure 7.
## 2 Related Work
### Optimization Strategies for FL in Heterogeneous IoT Environments  
To address the challenges of resource heterogeneity and limited device capabilities, researchers have explored various optimization strategies, including model compression [35] and adaptive aggregation [36]. Model compression techniques reduce the size of model updates, alleviating communication costs but potentially impacting model fidelity. Adaptive aggregation methods, such as FedProx [36] and FedNova [37], aim to account for device variability by adjusting the aggregation weights based on device-specific factors. However, these methods often assume synchronous updates, which are susceptible to delays caused by stragglers in heterogeneous IoT networks.  
Asynchronous FL techniques [2; 32] have been proposed to mitigate the impact of stragglers by allowing updates from faster devices to proceed without waiting for slower ones. While effective in reducing latency, these approaches often lack mechanisms to balance contributions from heterogeneous devices, which can lead to suboptimal model convergence [38]. Existing methods also fail to fully leverage hierarchical architectures to further enhance scalability and resource utilization.  
Federated-learning frameworks specifically designed for IoT scenarios have also been explored to address challenges unique to these environments. Techniques like LightFed [39] introduce lightweight protocols to improve communication efficiency, while methods such as EdgeFL [40] leverage edge servers to manage dynamic device participation. Despite these advancements, existing IoT-focused FL frameworks often fall short in addressing the combined challenges of device heterogeneity, dynamic participation, and privacy.