esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Sensor Node Operation with an Energy Harvesting Scheme as a Power Supply and a Battery as a Backup  
In the first scenario, our system utilizes the dual-frequency-vibration-based energy harvester's reference design, which was comprehensively characterized in [38]. This harvester is employed to power our sensor node, integrated with the same conditioning circuitry and power management scheme (DC2151A provided by Analog Devices Inc.(r), Wilmington, MA, USA) used for the harvester's characterization. The power management board can connect a 3.6 V coin-cell Lithium-Ion battery, serving both as a storage unit and a backup power supply. This board, equipped with internal circuitry and a prioritizer, allows seamless switching between different power sources, in our case, the energy harvester and the Lithium-Ion battery. However, as shown in Table 1, the dual-frequency-vibration-based energy harvester alone cannot supply the high current levels required to initiate the system. To overcome this limitation, we used the battery to provide the necessary high current spikes during the active mode while storing harvested energy during the sleep mode phase in the battery. To conduct these tests, we used the same test bench and vibration equipment described in [36; 37; 38]. During the experimental characterization, we subjected the energy harvester to harmonic base-acceleration with a value of a = 0.5 g. We also tested the entire sensing system, incorporating the three MCU platforms, at various broadcast frequencies and input voltage levels. For the sake of simplification, we assumed a 65% charge level for the LIR2032 coin-cell battery, providing a nominal voltage of 3.6 V and a capacity of 45 mAh, which operates within the linear regime. The system characterization involved monitoring the battery's voltage and current levels over a duration of 30 min. Negative current values indicate discharge behavior, signifying that despite the harvester's presence, the system will eventually fail after a certain period of time. Detailed long-term experimental test data are shown in Figure 9 and Table 4.  
Figure 8: Quantitative power consumption comparison of the different sensor nodes measured using a DC power supply vs. broadcast interval (**a**) and broadcast frequency (**b**). The power management board includes an \"EH-ON\" pin that generates a logical signal. This signal is in a high state when the system is powered by the harvesting source, and it transitions to a low state when the system draws power from the battery. Figure 10 provides a visual representation of the current consumption signal during a single broadcast, overlaid with the EH-ON signal. This illustrates that the system primarily relies on the battery to deliver the high current spikes required during the active broadcast phase. The presence of a harvesting source prompts the system to swiftly recover and switch back to utilizing the harvesting source for power. This behavior is evident in the EH-ON signal, which intermittently flickers but does not consistently drop to a low state during the active region's power demands.
# A Hybrid Deep Learning Model Based on FFT-STL Decomposition for Ocean Wave Height Prediction
## 3 Experimental Results and Analysis
### Prediction About Data  
Wave conditions for January 2024 are predicted using observational data from 46047 station in 2022 and 2023. FFT was applied to the dataset to identify the dominant frequency components. Subsequently, the data underwent STL, resulting in the separation of the time  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **MSE** & **RMSE** & **MAE** & **Runtime** \\ \hline Hybrid-STL model & 0.0087 & 0.0935 & 0.0783 & 24 s \\ BiLSTM & 0.0554 & 0.2353 & 0.1478 & 16.1 s \\
1D-CNN & 0.0292 & 0.1709 & 0.1534 & 4.2 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Prediction of each model based on the site 42040 dataset.  
series into trend, seasonal, and residual components, as illustrated in Figure 13. The data were forecasted using the proposed predictive model, and the resulting predictions are illustrated in Figure 14.  
Pearson correlation coefficient and dynamic time warping distance quantitatively show a highly significant positive correlation, which effectively verifies the model's ability to capture the overall trend of wave height changes. In the period of stable fluctuations, taking January 1 from 16:00 to 19:00 as an example, the correlation coefficient between the predicted curve and the actual data reaches 0.89, indicating that the model has a high accuracy in trend fitting during this period. From the perspective of error evaluation, the MSE of the model is 0.1123, and RMSE is 0.3351. According to the established error evaluation criteria, the error level is within an acceptable range, indicating that the prediction results of the model are reasonable and reliable.
# Intrusion Detection Method Based on Preprocessing of Highly Correlated and Imbalanced Data
## 3 Results
### Feature Engineering  
Data Balancing Procedure  
Firstly, the original data are imbalanced and have the following class distribution, as shown in Table 1.  
To balance the classes, the Undersampling, Oversampling, and their combination (Over + Undersampling) algorithms were used. The results of this study are presented in Figure 1. The Random Forest algorithm was used as the base classifier. The SMOTEENN algorithm achieved the best result, as it combines the SMOTE (Synthetic Minority Oversampling Technique) and Edited Nearest Neighbor (ENN). This combination generates instances of the minority class while removing noisy data [31]. Using the SMOTEENN algorithm for class balancing improved the model's performance (F1 score) by up to 31% compared to imbalanced data.
# Performance Study of Black Shale Modified Soil for Road Use Based on Eshelby-Mori-Tanaka Theory
###### Abstract
Therefore, clay and cement can be mixed into black shale gravel particles to enhance the strength and water stability of the fill material. The particle composite material composed of black shale crushed stone and cement-soil has extremely uneven stress and strain fields, and it is extremely difficult to describe the mechanical properties of such materials using traditional homogeneous elastoplastic mechanics. In material mechanics, the effective mechanical properties of multiphase composite materials are usually evaluated using the continuous medium micromechanical equation derived from the Eshelby solution based on ellipsoidal inclusion problems. From the perspective of micromechanics, it can better describe the macroscopic mechanical properties of two-phase particle composite materials and predict the effective elastic modulus of materials [19; 20; 21]. In the field of geotechnical engineering, many scholars have adopted this theory to study the mechanical properties of geotechnical materials and achieved very good results [22; 23]. [PERSON] et al. used this method to study the mechanical properties of geotechnical materials and established a constitutive equation in the elastic state, which can be used to predict the effective Young's modulus [24]. By considering the cement-clay mixture as the matrix and black shale gravel as the inclusion, predicting the influence of black shale content on fill strength through the average Eshelby tensor solution of the two-phase inclusion material is a promising avenue of research. In summary, scholars have varying conclusions regarding whether easily disintegrating and softening shale can be directly used as roadbed fill material. Additionally, there is limited research on the modification of black shale aggregate. In this paper, black shale mixed with cement-soil (clay and cement mixture) was modified as road base materials. Through indoor experiments, the mechanical properties were measured to provide reference for addressing similar engineering problems in the future.
# Simultaneous Detection of Nine Key Bacterial Respiratory Pathogens Using Luminex xTAG(r) Technology
## 4 Discussion  
_S. pneumoniae, M. catarrhalis, S. aureus, S. pyogenes, H. influenzae, M. pneumoniae, Legionella_ spp., _P._ aeruginosa, and _K. pneumoniae_ are the most commonly reported causative respiratory pathogens in LRTI worldwide [2; 3; 4; 5; 6]. All these bacteria have similar clinical features, and may cause multiple infections, which may increase their morbidity. Etiologic diagnosis of the causative pathogen for LRTI can pose problems, with causative organisms identified in approximately 50% of community-acquired pneumonia (CAP) cases [39; 40; 41]. Indeed for LRTI, the proportion of identified organisms is even lower. This is mainly due to difficulties in generating efficient associated methods that have both high sensitivity and good specificity for detection of these pathogens. The Luminex xTAG(r) platform is a rapid, accurate and high-throughput nucleic acid detection method which permits multiplex PCR analysis, therefore it is both attractive and reproducible. Furthermore, this method can be expanded to include more pathogens, further increasing the potential ability of the diagnostic platform. Recently, several commercial kits have been developed based on this technology. For example, the Luminex xTAG(r) Gastrointestinal Pathogen Panel (xTAG(r) GPP) can detect the 15 most common gastrointestinal pathogens and toxins in a single assay [42]. The Luminex xTAG(r) Cerebrospinal Fluid Pathogens Bacterial Panel (xTAG(r) CSF Bacteria) and the Luminex xTAG(r) Cerebrospinal Fluid Pathogens Viral Panel (xTAG(r) CSF Virus) can be used to detect common bacterial and viral pathogens from cerebrospinal fluid [30; 31]. The Luminex xTAG(r) Bloodstream Pathogen Panel (xTAG(r) BPP) can be used to detect 23 pathogens commonly identified in bloodstream infections [43]. Previously, the Luminex xTAG(r) technology has been successfully applied to simultaneously detect 18 respiratory viral pathogens (xTAG(r) RVP FAST v2) [44]. However, there are still no commercially-available kits to detect respiratory bacterial pathogens. Recently, a Luminex assay for the detection of pathogens in acute respiratory tract infections has been built [45], however the target respiratory bacterial pathogens for this assay are limited. In addition to _S. pneumoniae, S. aureus, M. pneumoniae, P. aeruginosa, and K. pneumoniae_ mentioned in this assay, we also involved _M. catarrhalis, S. pyogenes, Legionella_ spp., _H. influenzae_ as our target respiratory bacterial pathogens. Now, we have
# Spoofing Detection Algorithm Based on Pseudorange Differences
## 3 Simulation Tests  
To verify the feasibility and effectiveness of the algorithm, different kinds of test datasets, including the university test dataset, the Beidou Open Laboratory test dataset and the Texas spoofing test battery (TEXBAT), are used in the simulation. We generated the first two datasets at our university and Beidou Open Laboratory, respectively, and the TEXBAT comprised the only public spoofing test datasets published by The University of Texas at Austin [26; 27; 28; 29]. The data generation of these datasets is described in the following subsections. The performance of the algorithm is also verified in the dynamic whole-time duration position spoofing scenario. In all spoofing scenarios, all satellites are spoofed, and there is only one spoofer. Other characteristics of different scenarios are summarized in Table 1. The false alarm rate and the missed detection rate in each scenario are calculated. The false alarm rate indicates the probability that the algorithm misjudges the real signal as the spoofing one. The missed detection rate indicates the probability that the algorithm misjudges the spoofing signal as the real one.
## 3 Experimental Results
### Comparison Experiments  
Comparison experiments of the system before and after optimization were performed. The two-dimensional moving stages driving the slope sensor moved along the X or Y direction on the rotating paraboloid with a step size of 50 \(\upmu\)m. The relative displacement of the slope sensor was monitored by the laser interferometer. The experimental results are shown in Figures 10 and 11.  
It can be seen from the error curve that the error of the measurement system before optimization was within \(\pm\)2.0 \(\upmu\)m, however, the deviation of the optimized system was no more than \(\pm\)0.4 \(\upmu\)m within the range of the measurement of 1500 \(\upmu\)m.
## 4 Results and Discussion  
### Comparison of SNN Edges with Sobel and Canny Edge Detectors  
The SNN edge detector presented in Section 3 is used to generate edges of three example images from the Berkeley Segmentation Dataset and Benchmark collection, BSDS500 [23]. Figure 4 compares these example images and their edge map based on the SNN detector with the results from the Sobel edge and Canny edge detectors. As can be seen in Figure 5, the SNN edge detector generates thicker edges that are brighter and offer higher contrast. In addition, the spatial extent of the edge features generated by the SNN edge detector is qualitatively higher compared to the edge features of Sobel edge and qualitatively lower compared to the edge features of the Canny edge. We also find that the shape of the objects is more distinctive and recognizable in the SNN edge map than in the other two edge maps.  
Figure 3: Flow chart showing the computational steps for the implementation of the Spiking Neural Network (SNN) edge detector in MATLAB with I as the input image of \(m\times n\) pixels.  
In order to test the potential of our SNN edge detector for applications in neuromorphic computing, especially processors such as IBM-TrueNorth, we first simulated low-exposure image frames with exposure times in the range of 1 to 50 ms using a Non-linear Subtraction Range (NSR) low-exposure frame generation algorithm [24]. Then, we generated the SNN edges of the low exposure frames. Figure 5 shows the SNN edges generated for an example image from the MIT Sun Database [25] at various low exposure times in the range of 1 to 50 ms for the image. To compare the quantity of pixels that are part of the edges in the image at different exposure times, we define Edge  
Figure 4: Examples images along with the comparison of edges generated by SNN, Canny and Sobel edge detectors.  
Figure 5: Comparison of edge detection methods for low exposure frames, \(t=1-50\) ms with an example image from MIT Sun Database.  
Pixel Metric (EPM):  
\[EPM=\sum_{i,j}\delta(I^{\prime}(i,j)-1), \tag{19}\]  
where \(I^{\prime}\) is the edge map of the image generated by any of the three edge detectors and \(\delta\) is the Dirac delta function. Figure 6 shows the exposure time dependence of the edge pixel metric, EPM, for an example image from the MIT Sun Database. As can be seen in the plot, the metric for SNN edge is significantly higher compared to the respective metric values for both the Sobel and Canny edge detectors. The higher metric for the SNN edge comes from the broader edge lines as a result of the unique architecture used for the edge detector.
## 3 Simulation Analysis for the Designed ISTN Protocol Stack
### Test of the ISTN Protocol Stack in the Software Platform
Most of the time, the link variation is induced by the predicted satellite orbital movement, and there us no need to flood those variations;
2. Rarely, the link variation is unexpectedly caused by nodes out of work, and the variations must be flooded between nodes to update the network topology. As mentioned before, we periodically update the OSPF LSDB via the SLPC. Thus, the predicted OSPF flooding is restrained, while the unexpected link variation is also under link monitoring, as depicted in Figure 13.  
Figure 11: The principle of uni-directional OSPF protocol. Figure 12: Simulation in the software platform with 16 simulated nodes under the circumstances of uni-directional link. (**a**) The status of a neighbor’s ports and explicit constraint routing path for data under QoS requirements in node vx_simulator_01 (SCID 1). (**b**) The status of a neighbor’s ports in node vx_simulator_04 (SCID 4). Figure 13: Flooding restraint and flooding procedure for the two scenarios of satellites link variation.
## Appendix A Implementation of the Proposed Scheme
### Performances of the Scheme  
First, we combine the \(A\) dataset (30 data) and the \(B\) dataset (20 data), and then we calculate the parameters of the linear regression model. The average computational times (in seconds) for each case executed 10 times are shown in Table 14:  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & \(m=50\) & \(m=75\) & \(m=100\) \\ \hline n = 10,000 & 0.133 s & 0.284 s & 0.511 s \\ \hline n = 15,000 & 0.188 s & 0.411 s & 0.815 s \\ \hline n = 20,000 & 0.300 s & 0.766 s & 1.467 s \\ \hline \hline \end{tabular}
\end{table}
Table 14: The computational times (in seconds). The two datasets are first combined.  
Then, we first compute the system parameters on the datasets of \(A\) and \(B\) separately and then consolidate the results. The time required to perform the calculations is listed below.  
As can be seen from the above two tables, there is no obvious difference in the computational time based on either implementing our proposed scheme or combining the datasets first and then calculating the solutions. This is also consistent with our analysis that our scheme incurs no additional computational cost.  
The above experiments do not include the communication time. The communication cost in our scheme is much lower because only \((m+1)(m+2)\) aggregated data needs to be exchanged, while the communication cost of those schemes that do not use aggregated data require the exchange of \(n(m+2)\) data whether the data are encrypted or not.
# Leveraging Machine-Learning for D2D Communications in 5G/Beyond 5G Networks
## 4 Applications of ML in D2D Communications
### ML for NDS  
Single and multi-hop neighbor probing is a severe problem in D2D communications that can be efficiently solved using ML. In [16], the authors proposed a DL based peer discovery technique that applies information about social network relationships to reject ill-disposed devices. In addition, the authors of [17,18], formulated the problem of mmWave D2D NDS as a stochastic MAB to gain maximum long term throughput. In [4, 19], a multiplayer MAB algorithms were leveraged for surrounding gateway UAV selection by access UAVs in a disaster area scenario. This results in not only reducing the probability of encountering malicious devices but also enhancing the efficiency of peer discovery.
## 4 Discussion
### Effect of Presence in Non-Green Vegetation Canopy on FAPAR Estimation  
FAPAR\({}_{\rm LAI}\) uses the Beer-Lambert's law, a simple form of radiative transfer, to calculate FAPAR directly, whereas FAPAR\({}_{\rm MOD}\) calculates FAPAR based on the more complex stochastic radiative transfer (SRT) model [54; 55; 56]. However, both FAPAR calculations are based on the [PERSON]'s law. Since the vegetation canopy in most biome types cannot satisfy the homogeneous turbid medium assumption of [PERSON]'s law at the pixel (sample site) scale, and the presence of non-green vegetation canopies also causes errors in FAPAR\({}_{\rm MOD}\) and FAPAR\({}_{\rm LAI}\), and FAPAR\({}_{\rm FVC}\) obtains a more accurate FAPAR by introducing a more accurate model of the FVC.  
For cases where there is clear phenological variation in the vegetation canopy or where non-vegetation canopy is present, green leaves are concentrated in areas where green canopy is distributed. Because the [PERSON]'s law uses a homogeneous turbid medium assumption, green leaves are also assumed to be distributed in non-green canopy distribution areas, potentially underestimating the shading effect between leaves, which in turn exaggerates the absorption of solar radiation by vegetation, leading to an overestimation of FAPAR. Compared to the FAPAR\({}_{\rm LAI}\), under different LAI conditions, FAPAR\({}_{\rm FVC}\) gradually decreased with decreasing FVC (Figure 7).  
As a result, the improvement magnitude in FAPAR\({}_{\rm FVC}\) accuracy increased with the decrease in FVC compared to FAPAR\({}_{\rm LAI}\) (Table 4). For the FAPAR\({}_{\rm MOD}\) overestimated biome types, the average FVC was clearly lower than the biome types underestimated at FAPAR\({}_{\rm MOD}\) (Table 5), and thus the FAPAR overestimation was more clearly corrected by the FAPAR for the biome types with lower FVC (Figure 3). For most biome types, due to the obvious phenological changes in vegetation, both FAPAR\({}_{\rm MOD}\) and FAPAR\({}_{\rm LAI}\) showed overestimation as vegetation entered the growth period; with the gradual increase in the FVC, the GPP overestimation gradually decreased, during the peak vegetation growth period FAPAR overestimation eased, and then after the peak period GPP overestimation increased with the decrease in FVC (Figure 6). Since FAPAR\({}_{\rm FVC}\) takes into account the effect of FVC, relative to FAPAR\({}_{\rm LAI}\), FAPAR accuracy improved and overestimation reduced over the off-peak vegetation growth period, and accuracy did not change much over the peak vegetation growth period (Table 3).  
For boreal forest and tropical broadleaf evergreen forest, the increased underestimation of FAPAR\({}_{\rm FVC}\) is mainly related to the already existing underestimation of FAPAR\({}_{\rm LAI}\). With FAPAR\({}_{\rm LAI}\) itself underestimated, FAPAR\({}_{\rm FVC}\) will be less than FAPAR\({}_{\rm LAI}\) after accounting for the effect of FVC, thus increasing the underestimation of FAPAR.