esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Adsorption Mechanism
#### 3.5.3 XPS Study  
The XPS spectra of MZP-PM before and after the NH\({}_{4}\)\({}^{+}\)-N and TP adsorption at pH 7.0 were investigated. The XPS full-range spectra of MZP-PM before and after the adsorption are given in Figure 9. The presence of a distinct centered peak at 835.8 eV indicated the intercalation of La-oxides onto the zeolite powder. Relatively, a distinct P 2p binding energy peak at 131.7 eV after the adsorption of phosphate onto MZP-PM, demonstrating the feasible adsorption. The standard P 2p spectrum originated from KH\({}_{2}\)PO\({}_{4}\) purified sample, is appeared at 134.0 eV and the obvious shifts of 0.5 eV to lower energy level demonstrated the strong affinities between phosphate and MZP-PM. As shown in Figure 9b, c, the representative satellite peaks of La 3d\({}_{5/2}\) for original MZP-PM were located at 834.9 eV and 838.3 eV, respectively. The phosphate adsorption led to the apparent transformation of the proportion onto La 3d\({}_{5/2}\) double peaks and a visible peak shift to higher binding energy, indicating the new La-complex formation [37]. The large shifts of 0.6 and 1.1 eV indicated the existence of strong affinities by metal oxide surface through the formations of potentially mono and bi dentate inner-sphere surface complexes [38]. The atomic fraction of Na (1070.12 eV) decreased from 9.5% to 4.5% upon adsorption, implying that NH\({}_{4}\)\({}^{+}\)-N was most probably eliminated by ion-exchange with Na\({}^{+}\), which was in accordance with the results of the ammonium speciation experiment.  
Figure 8: FTIR spectra of MZP-PM before and after reaction with NH\({}_{4}\)\({}^{+}\)-N and TP.  
The concentration of NH\({}_{4}\)+-N and TP in the current study were investigated by the single factor experiment approach. Usually, numerous factors could influence the elimination efficiency of the PAP system simultaneously with an MZP-PM filter column in practical road projects. For instance, adsorption and removal efficiencies are affected by rainfall intensity, temperature, and pollution levels in different seasons. Therefore, in terms of practical application, further research about the effect of weather conditions, the conformation of stormwater, and loading levels on the elimination of other contaminants such as TSS, COD, and heavy metals are recommended.
# A Systematic Review of Cybersecurity Risks in Higher Education
## 8 Vulnerabilities in HE
### Technical Vulnerabilities in HE
#### 8.2.2 Data Acquisition, Storage, Processing, and Transfer  
FireEye found that one of the primary problems is that each academic department or unit is often responsible for storing, processing, and securing its data [5]. This finding is also supported in the UNIT report, which addressed that the personnel had low knowledge of security policies, especially regarding secure research data storage [6]. 5% also knew about data leaks caused by improper data storage in the survey at the Norwegian university [58]. Technical controls for data storage and communication was also missing at the Turkish universities [4]. There are at least two dimensions to this problem: The policies and guidelines must adequately describe proper data management for both administrative tasks and research. Furthermore, the technical systems must be user-friendly and scaled adequately secure to handle sensitive data and be described in the policy. Given the problem described by FireEye on university network security, leaving the employees to create their own data management systems, for example, for research projects, allows for a lot of variance in security measures and is a major vulnerability.
# ADDAEIL: Anomaly Detection with Drift-Aware Ensemble-Based Incremental Learning
## 2 Related Work
### Concept Drift Detection Strategies  
As discussed in the previous section, concept drift has a large effect on anomaly detection performance in streaming applications. Although some detection approaches attempt to account for changing data distributions, their effectiveness is generally limited by the quality of the underlying drift detection method. In this section, we review the approaches used to detect concept drift, and then group methods into distributional, performance, and hybrid categories [3; 23].  
Distribution-based methods observe changes in the input data stream. Most distribution-based methods statistically model changes in data distributions across sliding windows. The KS test [24], the Kullback-Leibler (KL) divergence [25], and the MWU test [26] are commonly used for drift detection due to their simplicity, statistical foundation, and lack of dependence on labeled data [14; 27]. Thus, they are often applied in unsupervised settings. However, these methods are vulnerable due to their sensitivity to even small distributional changes that have little impact on model performance. As a result, they often trigger unnecessary model updates and reduce stability in deployment.  
Performance-based methods detect drift by observing changes in a model's behavior, such as changes in prediction accuracy, error rates, or output scores. In supervised learning, these changes are usually tracked using labeled data over time [23; 28]. However, in anomaly detection, which is often unsupervised, labeled data is usually unavailable in real-time. As such, the application of traditional performance-based methods is limited in practice.  
To address the limitations of single-source drift detection, recent works have considered hybrid methods combining distributional and performance-based signals. For example, the AEADF framework [15] adopted a hybrid strategy: using Hellinger distance to measure distributional change and comparing the output of a local model (trained on the current window) with the output of the initial model to detect performance degradation.  
Although this represents an important step towards practical hybrid drift detection, AEADF still suffers from several major limitations. Firstly, Hellinger distance is a histogram-based measure that works well on discrete distributions but is sensitive to the choice of bin size on continuous data. Poor choices of bin size may lead to unstable or even misleading results. At the same time, comparing the output of a local model (trained on the current window) with the output of the initial model brings additional computational overhead: a new local model needs to be trained for every incoming window. This may increase computational resource consumption and limit scalability in edge or streaming applications.  
Therefore, although AEADF introduces the hybrid detection concept, its design suffers from both statistical instability and computational inefficiency. These limitations motivate the need for a more robust hybrid drift detection strategy.
# Hybrid Precoding Design for Subarray-Structure-Enabled mmWave URLLC System
## 2 System Model  
Consider a point-to-point mmWave MISO URLLC communication system, as shown in Figure 1[13]. The base station is equipped with \(N_{\rm t}\) antennas and \(N_{\rm RF}\) RF chains, while the user is equipped with one antenna. The data stream of a single user model is \(N_{\rm P}=1\).
## 4 Enhancement of Robot Position Control  
As outlined in the previous section, the force acting upon the object increases as the network delay grows. Therefore, it is necessary to consider different ratios of two haptic interface device's position vectors when calculating the robot's position vector and to reduce the applied force when the network delay is large. In this section, we enhance the robot position control by taking account of network delays to reduce the applied force.
## 2 Test Setup and Model Preparation  
### Shaking Table Facility  
The test was performed in the shaking table laboratory of the geotechnical experiment building of Chongqing University, China. The size of the shaking table was 1.2 m \(\times\) 1.2 m (length \(\times\) width), and the maximum horizontal acceleration was 2.0 g. The specific parameters of the shaking table are shown in Table 1. Based on previous research [29], a laminar shear box was used to reduce the boundary effect in this experiment. The internal dimensions of the laminar shear box were 950 mm \(\times\) 828 mm \(\times\) 650 mm (length \(\times\) width \(\times\) height).  
In 1 g shake table tests on soils, the stress and strain conditions at scale are not representative of stress and strain conditions in the field. Therefore, based on previous studies [30; 31], the Buckingham \(\pi\) theorem and the similitude laws recommended by [PERSON] [32] were employed in this study to design the scale model. Based on the size of the shaking table, the geometric similarity ratio was set to \(S_{\text{L}}\) = 0.05. The specific dimensions of the bridge foundation after reduction by the similar ratio is shown in Figure 1. Since the test was conducted in an environment with a gravitational acceleration of 1 g, the acceleration similarity ratio was \(S_{\text{a}}\) = 1.0, and the sand (coral sand) used in actual engineering was used in this test, so the density similarity ratio was \(S_{\text{\tiny{p}}}\) = 1.0. The similarity ratio of other parameters could be calculated according to the Buckingham \(\pi\) theorem, as shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Figure** \\ \hline Platform size & 1.2 m \(\times\) 1.2 m \\ Maximum load & 1000 kg \\ Maximum horizontal acceleration & 2.0 g \\ Frequency range & 0-50 Hz \\ Maximum horizontal speed & 0.5 m/s \\ Maximum horizontal displacement & 100 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the shaking table system.
## 7 Tests and Results  
The system has been realized and tested in the field for some months to verify the overall functionality under variable real-life conditions. The measurements allowed us to calculate energy and cost savings compared to other older technologies.
# Polarization Effect on the Performance of On-Chip Wireless Optical Point-to-Point Links
## 2 Design of the Dielectric Vivaldi Antenna
### Vivaldi Antenna Radiation Analysis
However, the on-chip wireless communication occurs in a multilayer structure, which is typical of photonic integrated circuits. As will be discussed in the following, when the propagation occurs in a multilayer medium, the power budget of the point-to-point link is found to be different for the two polarizations, even if the antenna gain is the same. This difference can be ascribed to the radiated wave polarization and to the interaction of the radiated field with the multilayer medium. Figure 7: Maximum gain as a function of the antenna length L\({}_{\text{a}}\) for the TE (solid curve) and the TM (dashed curve) modes, in the case of configuration B (w = 900 nm) with slot gap g = 300 nm and slot rail width s = 300 nm. The wavelength is \(\lambda\) = 1.55 \(\upmu\)m.
## 1 Introduction  
Solid-phase microextraction (SPME) is a gas-phase, absorptive extraction method used for the isolation of volatile organic compounds with detection limits in the range of parts per billion by volume through static headspace sampling and GC-MS analyses. SPME has been widely used for plant [1], fruit [2, 3], beverage [4, 5, 6], and meat [7, 8] volatile analyses because it is simple, automatable, highly sensitive, and rapid.  
Alfalfa (_Medicago sativa_ L.) is a perennial flowering legume grown in many parts of the world that serves as a premium forage used to feed animals. Previous studies have demonstrated that alfalfa varieties with resistance to potato leafhopper (PLH) have a high density of glandular trichomes on their stems [9, 10]. Plant glandular trichomes are epidermal appendages with a stalk and secretary head (Figure 1). These organs are known to synthesize, accumulate, and secrete volatile metabolites to protect themselves against pathogens and herbivores [11; 12]. A previous report suggested that volatile metabolites released by glandular trichomes in alfalfa might play important roles as repellents, deterrent, or fumigants to PLH [13; 14]. However, there is minimal information on the vast array of volatiles produced by alfalfa glandular trichomes. Volatile metabolites in leaves and flowers of alfalfa were analyzed using dynamic headspace analyses with Tenax trapping, and a modest number volatile metabolites were detected (16 leaf and 33 floral) using a vacuum steam distillation method [15; 16].  
Volatiles emitted from fruits and plants are typically very diverse, including alkanes, alcohols, aldehydes, ketones, terpenoids, and esters. The absorption efficiency of these compounds depends upon the SPME fiber type, extraction temperature, and time. Here, we report the optimization of a SPME extraction procedure using alfalfa leaves as samples. The effects of three different fibers, extraction temperature, and extraction time on the volatile extraction efficiency were investigated and optimized. These specific parameters were investigated because they critically affect the equilibrium of volatiles between the fiber, headspace, and sample [17; 18; 19; 20; 21]. The optimized method was then applied to the large-scale, non-targeted profiling of volatile metabolites emitted from glandular trichomes isolated from stems, stems free of trichomes (i.e., stem after isolation of trichomes), and leaves of alfalfa. A substantial number of volatile metabolites were detected and identified, and the efficiency of the optimized SPME-GC-MS method was validated using multivariate analyses including hierarchical cluster analysis (HCA) and principal component analysis (PCA).
## 3 Underwater Wireless Optical Communication System
### UWOC Light Modulation Technique  
Figure 9 shows a UWOC environment in which noise occurs during data communication due to several factors, such as ambient light, sunlight, the turbidity of the water, and scattering and absorption of light [26]. Since noise distorts signals, the receiver uses a low-pass filter (LPF) to remove noises after receiving the signal.  
Figure 8: LED light transmission and reception system.  
Figure 7: UWOC system for AUV.  
Figure 9: Noise of visible light communication.  
However, because the LPF also distorts the original signal, a modulation-based restoration process is required. Figure 10 shows the state of the ideally received data signal. Passing the signal through the LPF filter without modulation results in a waveform that does not clearly exhibit the high signal and low signal, as seen in Figure 11, which increases the error rate during data communication. Therefore, the data signal must be modulated before being passed through the filter using modulation techniques such as OOK and RZ.  
Figure 12 shows the waveform of the data signal passed through the LPF using the RZ modulation method. In the RZ modulation method, the received high signal is maintained and immediately returned to the low signal. The low signal is then maintained for a certain period between bit pulses before being transmitted to the next signal. The RZ modulation method allows high-speed communication of visible light underwater and reduces the signal error rate since it requires a smaller bandwidth than the OOK modulation method and uses high-output light to transmit light far underwater or a photodiode sensor that detects weak light with a lower bandwidth [27]. Figure 13 presents a comparison of the real data before and after the application of RZ modulation. The magnitude of the received data (Rx Data) nearly doubles after applying RZ modulation, significantly enhancing signal clarity and making it distinctly separable from noise.  
Figure 11: Signal after LPF.  
Figure 12: Signal after LPF and RZ modulation.  
Figure 10: Signal before LPF.
# Semantic Web and Knowledge Graphs for Industry 4.0
## 5 Semantic Web and Ontologies for Industry 4.0
### Ontologies for Industry 4.0 Predictive Maintenance  
Process in the manufacturing production line is the sequence of actions performed on materials and energy to transform it into a finished product. These processes may be affected by the faults and failures in the machines. The early detection of these failures can make sure the availability, efficiency, and high productivity of manufacturing processes. Usually, the abnormality is detected from analyzing data generated by the sensors placed on machine modules and other areas of the manufacturing production line.  
CPS take an advantage of predictive maintenance. The communication between the production entities within a CPS is performed intelligently and autonomously which helps manufacturers to augment the production process.  
[PERSON], [PERSON] et al. proposed the Industrial MAintenance Management Ontology (IMAMO) to provide interoperability and create new knowledge that encourage making decision to be carried out in the process of maintenance [26]. [PERSON], [PERSON] et al. reported the problem of insufficient data of historical events regarding the factory maintenance [79]. The process and inspection data need to be correlated to enhance the prognostics and diagnostics to make the maintenance decision better. In another work, an ontology model has been developed to capture the context of flexible manufacturing system that are utilized for making a real-time decision to optimize the key performance indicators [80].  
[PERSON] et al. developed a manufacturing predictive maintenance ontology (MPMO) to formally describe the chronicic concepts and their relationships [81]. They proposed an algorithm to create semantic web rule language (SWRL) logic rules from historical events providing formalized predictive outcomes. The semantics and chronicic mining allows temporal constraints reasoning to forecast future malfunction of machinery. There are two main issues in the proposed approach that are (1). the partition methodology of numerical values and (2). the advancement of ontology and rule base. Thus, it is hard for this method to predict critical failure.  
In I4.0 application, the monitoring of critical components is a complex job and needs to be resolved in real-time. The fuzzy-logic based approach decomposes any real-time complex system into a simple weighted sum of linear subsystems and is very actively used in several fields such as energy management [82; 83; 84]. The authors in [85] utilized a Fuzzy C-Means (FCM) based approach to tackle with the uncertainties and classify the critical failures. Failure events are obtained from the industrial raw data by applying Sequential Pattern Mining (SPM). After that FCM is applied to the failure events along with its temporal information. A survey paper explored eMaintenance ontologies related to several fields [86]. However, the focus is more on the data problem used in maintenance, standards, and eMaintenance tools.
# Examining the Efficiency of Electric-Assisted Mountain Biking across Different Types of Terrain
## 4 Discussion
In addition, the weight of the bike itself may have an impact. Lighter bikes may offer an advantage on descents because they are easier to ride and adapt more quickly to changes in terrain. There are also some minor changes in bike geometry of both types of mountain bikes, such as frame design and suspension adjustment to a lighter conventional mountain bike, which could help minimize physiological differences between riders. These factors can affect the bike's performance on rough and technical terrain and potentially level the competition. Another important aspect is the electrical assistance provided by the bike's battery. The motor stops assisting once the rider exceeds a certain speed, which is usually around 25 km/h. This means that on descents, where the speed may exceed this limit, the importance of the electric assistance decreases. Consequently, the rider with a heavier and less maneuverable bike must rely more on his own pedaling power. Thus, riding a heavy mountain bike with electric assist through technically challenging terrain, such as a rocky single track with numerous roots and rocks, can negate any advantages or even become a disadvantage. There are also some limitations to this study. Firstly, the small sample size of only six participants and the use of a single cross-country mountain bike course present challenges in terms of generalizing the results despite the variety of terrain of the course. For future research, it is recommended that multiple trials be conducted on different cross-country courses of varying durations. Another limitation of the present study may be the difference in duration of the rides because of the faster cycling with the electric-assisted mountain bike compared to conventional mountain bikes. As riders completed their rides in different time durations, muscular fatigue could influence the physiological response and power output values. A useful strategy might involve conducting studies on different off-road terrains at same speeds both with and without electric assistance. It should be noted, however, that maintaining equal speeds on off-road terrain can be quite challenging due to the unpredictable nature of the terrain. It is also important to acknowledge that mountain bikes with electric assistance tend to be heavier than their conventional counterparts, which means that riding an e-bike with the power off is not directly comparable to riding a conventional bike. For example, it may be assumed that riding with a heavyweight electric-assisted mountain bike through a very technically demanding terrain (e.g., a rocky single track with many roots and stones) would no longer be an advantage or could become a disadvantage. Potentially using the two similar bikes, one could consider that the two bicycles should have had the same weight optimally. This could have been achieved by adding extra weight to the conventional mountain bike, but on the other hand, heavier electrically assisted mountain bikes are still a reality.
## 6 Simulation Results
### SQ-Based CSI Feedback  
The sum-rate curves of IA with perfect CSI and SQ-based CSI feedback with different quantization bits in \((2\times 2,1)3\), \((3\times 2,1)4\) and \((3\times 3,1)5\) are separately compared in Figures 7-9, and the corresponding approximation errors are calculated in Figure 10. The theoretical sum-rate curves are based on (22), and the other curves also result from simulation. In each snapshot, the channel matrices were quantized using SQ, and the decoders were also perfectly known to the receivers, similar to what is described in the previous subsection.  
Figure 6: Theoretical sum-rate comparison of the proposed method and other rate-loss-based analysis methods with RVQ-based CSI feedback in \((2\times 2,1)3\).  
Figure 7: Performance comparison of theoretical and simulation results with SQ-based CSI feedback with different quantization bits in \((2\times 2,1)3\).  
Figure 8: Performance comparison of theoretical and simulation results with SQ-based CSI feedback with different quantization bits in \((3\times 2,1)4\).  
Figure 9: Performance comparison of theoretical and simulation results with SQ-based CSI feedback with different quantization bits in \((3\times 3,1)5\).  
With the increase in quantization bits, the theoretical sum-rates gradually transition from being below the simulation results to being above them, especially when the SNR is higher than 20 dB, but the normalized approximation errors are still less than 5%. To guarantee the DoF performance, increasing the quantization bits is feasible in SQ since the implementation complexity of SQ is much lower than that of RVQ. Meanwhile, the total feedback bits of SQ are much more than these of RVQ to maintain the same quantization accuracy level. Therefore, SQ is more applicable to high SNR regimes, larger-sized matrices and real-time implementations of IA.