esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Final Model Evaluation
The XGBoost model had a moderate recall, about 79.81%, and theLSTMmodel had a low recall, about 37.60%, indicating difficulties in detecting all anomalous patterns. * F1-score: The F1-score of the Random Forest model was high, about 95.84%, reflecting a good balance between precision and recall. The XGBoost model obtained a moderate F1-score of 87.02%, while the LSTM model showed a low F1-score of 53.04%, indicating a less effective performance in anomaly classification. All models were trained using one million instances (1,000,000) of network traffic as the training set and 250,000 test data. Of the 250,000 data, 13,643 corresponded to benign traffic data and the rest corresponded to malicious traffic. The confusion matrices for each algorithm, presented in Figure 8, provide a detailed view of their classification behavior. The analysis of the confusion matrices reveals that the Random Forest model achieved the most effective classification performance, exhibiting a high proportion of true positives and true negatives, along with minimal false positive and false negative rates. Although the XGBoost model also demonstrated satisfactory results, it showed a noticeable increase in misclassification errors, particularly false positives, which suggests a propensity to incorrectly identify benign traffic as malicious. In contrast, the LSTM model reported the weakest performance, with a substantial number of false negatives, indicating a limited ability to detect anomalous patterns in network traffic. These findings are consistent with the evaluation metrics presented in Table 5, further highlighting the robustness of ensemble-based approaches for intrusion detection tasks. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline Random Forest & 99.96 & 92.02 & 95.84 \\ XGBoost & 95.80 & 79.81 & 87.02 \\ LSTM & 89.81 & 37.60 & 53.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance metrics of the evaluated models.
## 4 Performance Evaluation
### Simulation Results and Discussion  
To critically compare and analyse our proposed SISO-NOMA system in TI, we observe and measure KPIs, such as Bit Error Rate (BER), achievable sum rate and outage probability. The fixed PA coefficient pairs are used to calculate the trend of the mentioned KPIs. Furthermore, to infer meaningful insights, an analysis of performance comparisons for BER and achievable sum rates is made between SISO-NOMA and OMA. In addition, the effect of fair PA on certain users is also considered whilst being compared against the fixed PA to see the outage probability and achievable sum rate trends of the users. Moreover, a comparative latency analysis is conducted for SISO-NOMA and OMA with fixed and fair PAs. Finally, the performance comparison concerning achievable sum rate and latency is analysed between \(4\times 4\) MIMO-NOMA and SISO-NOMA. The simulation results reported in this paper showed steady-state behaviour with a relative statistical error of \(\leq\)5% at a 95% confidence level. Table 1 lists the simulation parameters used in the proposed SISO-NOMA-based TI system.
## 7 Limitations  
Although real data were used in this study, the results of this study were derived through the simulation of a controlled environment. All simulations have inherent constraints when compared to real-world behaviour. In a real-world environment, there might be other factors and conditions, such as network infrastructure parameter values, upload and download speed fluctuations and dynamic IoT devices, that are not replicable or have not been considered under the simulation conditions of this study, leading to different outcomes and limiting the generalisation of the results.  
The small number of smart IoT devices used in this study, such as CCTV devices, is a limitation and limits the generalisation of the results. Meanwhile, in a real-world environment, smart IoT device numbers are much higher, which will make the results very different from the simulation in this study.  
In addition, the efficiency benefits of Fog computing are variable and are dependent on the type of smart IoT device; the generated quantity of data, such as for CCTV data, is greater than CO\({}_{2}\) and light sensor data. This indicates that the results might vary across various smart home network architecture environments and cannot be generalised.  
Only the IoT device implementation was considered, whereas the other general computer network traffic was outside of the scope of this study. The simulation used a single floor plan that is specific and directly impacts where smart IoT devices are placed in a fixed scenario, making it unique, while a generalised result analysis is available in the scalability section.  
Due to the large-scale nature and geographical location of the smart IoT devices and the Cloud and Fog computing network infrastructure, it was impractical to measure accurately and in detail from the smart IoT devices through to the Fog and Cloud layer measurements.
# Discrete-Time First-Order Plus Dead-Time Model-Reference Trade-off PID Control Design
## 4 Proportional-Integral-Derivative Parameter Design  
In order to obtain a universal design method for arbitrary FOPDT models, first, in Section 4.1, the plant model and the PID control law are normalized. Second, in Section 4.2, optimal PID parameters are obtained subject to the pre-established stability margin. Third, in Section 4.3, the parameter tuning rule is derived based on the optimal parameters calculated in Section 4.2. Finally, the algorithm for the proposed design method is summarized in Section 4.4.
## 2 Materials and Methods
### Data Acquisition  
Based on the grassland climatic conditions, vegetation growth cycles, sunlight intensity, and sun altitude variations, data were collected between 22 July and 31 July 2022, from 10:00 am to 2:00 pm. The weather was clear with no cloud cover, and the wind speed was less than level 3. The illumination type was natural light. The acquisition of single hyperspectral images was: 775 lines \(\times\) 696 samples \(\times\) 256 bands. Standard whiteboard corrections were performed before and after the UAV flight to prevent the overexposure or underexposure of the hyperspectral camera owing to changes in light intensity. During the experiment, a DJI Phantom 3 Pro UAV was used to acquire high-definition image data of the study area to provide an overview.  
The experimental area covered 2.5 hectares. Given the low vegetation and sparse growth in the desert grassland, flying the UAV at an excessively high altitude would have resulted in a lower spatial resolution of the captured images, whereas an excessively low altitude would have limited the efficiency of the experiment. After the experimental investigation, the flight height of the UAV was set to 30 m and the spatial resolution of the image was 2.3 cm/pixel, which ensured experimental accuracy and simultaneously achieved the most suitable experimental effect. A total of 65 sampling plots were established with a sample size of 1 m \(\times\) 1 m. Among them, there were 40 pure samples, 20 each of Stipa brevilora and Artemisia frigida, and 25 mixed samples with a size of 2 m \(\times\) 2 m. For the  
Figure 1: The UAV hyperspectral remote sensing system.  
Figure 2: Experimental area.  
hyperspectral image data acquisition, the type of plant taxa in the vegetation within each sample was recorded. The samples were marked with mats and small flags. The mixed samples were arranged according to the principle of uniform distribution. To ensure data reliability, each sample was photographed at least thrice.
# Multi-Granularity User Anomalous Behavior Detection
## 5 Experiments
### Experimental Settings
#### 5.1.2 Compared Baselines  
For CERT R4.2, we select the following baseline models:  
\begin{table}
\begin{tabular}{l r r} \hline \hline
**Number of** & **Normal** & **Anomalous** \\ \hline users & 930 & 70 \\ behaviors & 32,762,949 & 7273 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistics of CERT R4.2.  
S-LSTM [6]: S-LSTM is based on the integration of sampling approach, which is the generation of synthetic samples to balance the two classes of learning by the SMOTE technique and LSTM algorithm for identify anomalous behavior. * ITDBERT [10]: ITDBERT embeds temporal information into behavior and catches the fused semantic representation via pretrained language models. ITDBERT also leverages attention-based Bi-LSTM to provide behavior-level detection results. * SPYRAPTOR [24]: A stream-based smart query system for real-time threat hunting within an enterprise. It is mainly composed of a graph construction subsystem, query synthesis subsystem, and query execution subsystem. * SeqA-ITD [25]: This model focuses on user behavior sequences and proposes an Augmentation framework to boost the performance on Insider Threat Detection (SeqA-ITD). SeqA-ITD first embeds temporal information into user behavior sequences and then captures malicious user behavior's temporal and sequential patterns to generate discrete temporal sequences. A multi-granular enhanced Long Short-Term Memory (LSTM) model learns the original and generated temporal sequences with distinct temporal granularities to detect anomalous ones. * RAP-Net [26]: A Resource Access Pattern Network (RAP-Net), which applies a reinforcement-learning-based Generative Adversarial Network, Word2 Vec, Convolutional Neural Network, Recurrent Neural Network, and Attention Mechanism to insider threat detection. RAP-Net extracts user resource access pattern sequences from audit log files, and then performs data augmentation on the minority class sequences. * LSTM-Autoencoder [27]: An LSTM-Autoencoder framework that has been proposed and tested on the CMU CERT r4.2 dataset and has shown to achieve high accuracy and low false alarm rates in detecting insider threats
# Dual-Polarization Observations of Slowly Varying Solar Emissions from a Mobile X-Band Radar
## Radio Emission Properties of the Sun
### Accurate Measurements of the Solar Flux at the S-Band: The DRAO Reference  
The solar flux is continuously monitored at the Dominion Radio Astrophysical Observatory (DRAO) at a 10.7 cm wavelength, that is inside the so-called S-band. The observatory has been located near Penticton in British Columbia, Canada, since 1990 and characterized by low interference levels at the decimeter and centimeter wavelengths; the quality of the environment is maintained by means of local, provincial and federal protection [17]. Details regarding the measurements and the hardware (antenna, receiver, reference noise source,... ) can be found in Section 4 in [17]. DRAO measurements are more accurate than the values retrieved using weather radar observations. It is worth noting that: (1) they last one hour (instead of a minute); (2) the antenna half power beam width is much larger than the solar disk; (3) DRAO measurement scheme waits at an estimated position for the sun to pass by, while Sun-tracking, which is scheduled every 30 min (see Section 3.1) may be affected by variable ground noise effect.
## 2 Methods
### Instruments
#### 2.3.2 Gait  
Gait was assessed using the APDM mobility LabTM (APDM Inc., Portland, OR, USA) during a 2-min walk around a 6 \(\times\) 1-m oval track, a protocol most often used in literature when machine learning is used to identify mood states using gait parameters [39; 40]. The APDM mobility LabTM system consists of a set of wireless, body-worn OpalTM inertial sensors, each with a docking station, an access point for wireless data transmission and sub-millisecond synchronization of the independent sensors. The OpalTM inertial sensors contain a tri-axial accelerometer, a trial axial gyroscope, and a tri-axial magnetometer and have a range of 6 m from the docking station. The accelerometers measure linear acceleration, gyroscopes measure angular velocity, and the magnetometers measure heading with respect to the earth's magnetic field. For this study, seven OpalsTM were attached to the body using VelcroTM straps: lumbar region (5 th lumbar vertebra), sternum (body of sternum superior to the xyphoid process), forehead (middle of the frontal bone, approximately 2.5 cm above the nasal bone), right and left foot (on the metataralsals, directly superior to the metatarasophalangeal joint), and the right and left wrist (immediately superior to the radio-ulnar joint). Participants were asked to walk to a cone that was placed 6 m from the starting point, make a 180\({}^{\circ}\) left turn around the cone and then come back to the starting cone. The cones were placed 6 m apart and participants were asked to walk back and forth for 2-min at whatever speed they felt most comfortable. This method of gait assessment using the APDM OpalTM monitors has been validated and proven reliable [41; 42; 43], and is the most commonly used gait assessment method for identifying subjective parameters using walking gait [39; 40; 44].
## 4 Tactile Internet Application Characteristics
### Discussion on Application Characteristics
Some other characteristics, like bandwidth required, cost of the overall system, complexity in building, deploying, and maintaining the applications, synchronization among different devices, standardization for interoperability and scalability, environmental considerations, user experience, ethical issues, and impact on society, can also be considered.
# Remote and Proximal Sensing Techniques for Site-Specific Irrigation Management in the Olive Orchard
## 4 Discussion
### Comparing Proximal and Remote Sensing Indices against Tree Performance
Interestingly, under deficit irrigation, both tree (NDVI) and soil (ECa\({}_{1}\) and ECa\({}_{2}\)) parameters were identified as factors contributing to the measured yield parameters. In a similar study in an irrigated vineyard, [PERSON] et al. [68] also reported high correlations between NDVI and soil ECa, but such a relationship was strongly dependent on the growing season and the irrigation regime. Therefore, the role of soil spatial variability was much more important as water deficit became more severe, whereas in cases when water availability was high due to rains or irrigation, the variability of soil characteristics played a less important role on vegetative growth. [PERSON] et al. [69] showed that the effects of soil features on grape and wine peculiarities were much more evident during dry years, whereas rainy summers tended to hide this variability in a rainfed vineyard.
# Characterization and Optimization of PLA Stereocomplexed Hydrogels for Local Gene Delivery Systems
## Materials and Methods
### Stereocomplexed Hydrogel Preparation  
The hydrogel formation procedure is similar to that reported for the standard PLA-PEG-PLA associative micelle network [29] and 3 LM-hydrogel network [32; 33] utilizing the PLA stereocomplexation. Controlled hydrogel formation was achieved by blending 3 LM and counter-gelling PDLA-PEG-PDLA micelle solutions. The separately-prepared micelle solutions with matched concentration (for example, both solutions can be 20 wt \(\%\) to obtain 20 wt \(\%\) hydrogel) were mixed at adjusted volumes which were calculated in order to contain 1:1 ratio of PLLA-PEG-PLLA in 3 LM and PDLA-PEG-PDLA in D-micelle with ultrasonic wave applied at 4 \({}^{\circ}\)C in a scintillation vial. The mixture was allowed to sonicate at 4 \({}^{\circ}\)C for 30 min or until a homogeneous solution is obtained. The vial is then transferred to a temperature-controlled circulating water bath and the temperature of the bath is gradually increased with 1 h hold at each temperature interval. The vial tilting method was used to determine sol to gel transition behavior, if the mixture flowed then it was reported as a solution and if it did not flow for at least 8 s it was reported as a gel [24; 34].
# NOMA-Based VLC Systems: A Comprehensive Review
## 3 Integration of NOMA-Based VLC with Emerging Technologies
### MISO/MIMO Techniques in NOMA-Based VLC Systems
The authors of these studies demonstrate that a certain type of NOMA power allocation--normalized gain difference power allocation (NGDPA)--greatly improves the achievable sum rate. The BER performance of OQAM-OFDM-aided MIMO-NOMA over VLC has been evaluated as a function of the power allocation ratio between users [75]. Researchers point out that a MIMO-NOMA transmission has not thus far been considered in the articles on multiuser MIMO VLC systems described in the studies in order to mitigate the performance loss of multiuser precoding techniques occurred by the strong correlation between channel gain vectors of various users. VLC systems and MIMO-VLC systems [71; 76] have explored PD-NOMA as well. While power domain NOMA has the potential to improve performance, it highly depends on power allocation algorithms and the user pairing/grouping techniques that are used [65], making it challenging to attain optimal performance in real-world cost-efficient MIMO-VLC systems.
## 3 Proposed Methodology
### Feature Optimization Using Dual Adaptive Selector  
The dataset was balanced, and a new Dual Adaptive Selector (DAS) was introduced to improve feature optimisation efficiency while maintaining predictive power [33]. A hybrid framework is created by combining a Conditional Relevance Assessor (CRA) with an Iterative Dependency Filter (IDF). The chosen features are informative and non-redundant, improving multi-label DDoS attack detection performance using CRA for relevance scoring and IDF for redundancy filtering. The Conditional Relevance Assessor examines each feature's contribution based on its conditional connection with target labels. For a feature, \(z_{q}\), and target class, \(t_{r}\), the relevance score, \(\mathcal{R}_{q,r}\), is calculated using the conditional relevance function in Equation (7):  
\[\mathcal{R}_{q,r}=\frac{\text{Cov}(z_{q},t_{r})^{2}}{\text{Var}(z_{q})\cdot \text{Var}(t_{r})} \tag{7}\]  
In this context, \(\text{Cov}(z_{q},t_{r})\) symbolises the covariance between feature \(z_{q}\) and target \(t_{r}\), whereas \(\text{Var}(z_{q})\) and \(\text{Var}(t_{r})\) represent their variances. Higher scores indicate target class relevance. A global relevance rating for each feature is calculated from all target label relevance scores. The Iterative Dependency Filter (IDF) uses a similarity threshold, \(\vartheta\), to remove associated features and reduce duplication among high-ranked features. The dependence coefficient, \(\mathcal{C}_{q,p}\), is calculated for two features, \(z_{q}\) and \(z_{p}\), as illustrated in Equation (8).  
\[\mathcal{C}_{q,p}=\frac{\text{Cov}(z_{q},z_{p})}{\sqrt{\text{Var}(z_{q})\cdot \text{Var}(z_{p})}} \tag{8}\]  
Features with lower relevance rank are excluded from selection if \(\mathcal{C}_{q,p}\geq\vartheta\). The threshold \(\vartheta\) adjusts dynamically depending on feature space size to maintain the least associated features.  
DAS is hybrid because CRA detects essential characteristics, and IDF ensures non-redundancy. Equation (9) defines the final set of chosen features, \(\mathcal{S}_{f}\):\[\mathcal{S}_{f}=\{z_{q}\in\mathcal{Z}:\mathcal{R}_{q,r}>\lambda\text{ and }\mathcal{C}_{q,p}<\theta,\forall z_{p}\
eq z_{q}\} \tag{9}\]  
where \(\mathcal{Z}\) represents the initial feature set, \(\lambda\) represents the minimal relevance threshold, and \(\theta\) represents the dependence threshold. Features are chosen to maximise relevance and minimise redundancy.  
DAS constantly adapts to the dataset, making feature optimisation efficient in settings with many input variables. The dual strategy improves model predictive power and computational efficiency for multi-label DDoS attack detection.
# Demystifying Quantum Gate Fidelity for Electronics Engineers
## 5 Brief Case Studies  
To better grasp the relevance of Equation (42) as an RFIC designer, let us consider the brief case studies of the one-qubit Pauli X and two-qubit CNOT quantum gates implemented in the spin of electrons confined in quantum wells [22]. The quantum gate is implemented by irradiating the qubit, via a micro-antenna, with a microwave pulse at the resonance Larmor frequency and for a suitable time duration \(\Delta t\); this microwave irradiation induces the following rotation angle \(\theta\) of the state vector:  
\[\theta=\frac{g\mu_{B}}{2\hbar}B_{1}\Delta t \tag{43}\]  
where \(g\) is the electron spin g-factor, \(\mu_{B}\) is the Bohr magneton, \(\hbar\) is the reduced Planck constant, and \(B_{1}\) is the magnitude of the magnetic component of the microwave electromagnetic pulse applied to the qubit. The magnitude \(B_{1}\) depends on the amplitude of the microwave signal \(s_{RF}\), generated by the RFIC, which excites the micro-antenna used to irradiate the qubit.
## 1 Introduction
The last heat treatment step (partitioning) is performed at relatively low temperatures, for example at 250 \({}^{\circ}\)C in our own experiments, see Section 2.1. Therefore, joining of Q&P steels without influencing the microstructure is a big challenge. On the one hand, a low joining temperature is necessary in order not to influence the partitioned microstructure; on the other hand, high joint strengths are required. A new way of meeting these two contradictory requirements is joining with nanoparticles, which has been the subject of intensive research in recent years. Joining with nanoparticles exploits the effect that nanoparticles exhibit lower melting and sintering temperatures compared to the corresponding bulk material because of the large surface-to-volume ratio. This enables joining at lower temperatures. The main advantage is that after melting and sintering of the particles, i.e., after the joining process, the material possesses the properties of the bulk material so that high-strength and temperature-resistant joints can be produced at low temperatures. In addition, hybrid joints, such as metal-ceramic joints, can be produced in this way, which is not feasible with fusion welding processes. Until now, joining with nanoparticles has mostly been investigated as an alternative for electronic packaging, i.e., for components subjected to low mechanical stresses [18; 19; 20; 21; 22; 23]. However, our own recent studies show that joining with nanoparticles is also suitable for components subjected to high mechanical loads if suitable process parameters are used [24; 25; 26]. In summary, joining with nanoparticles represents an interesting alternative to perform joining processes at low temperatures, which allows to maintain the specific microstructure and mechanical properties of Q&P steels. In the present study, joining of Q&P steels using Ag nanoparticles is investigated. As a novelty, additional activating materials (SnAg and Sn) were used at the interface to achieve an improved bonding to the steel substrates.
## 3 Numerical Results and Discussions
### Simulation for a Distribution Network
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{7}{c}{**Fixed POD**} \\ \hline \multirow{2}{*}{**SNR**} & \multicolumn{4}{c}{**AWGN**} & \multicolumn{4}{c}{**IMPN**} \\ \cline{2-7}  & **Sag** & **Swell** & **Har.** & **Sag** & **Swell** & **Har.** \\ \hline \multirow{3}{*}{35 dB} & MV & 100/5.8 & 100/3.0 & 100/1.9 & 100/0.0 & 100/0.0 & 100/0.0 \\  & RMS & 100/3.8 & 100/2.8 & 100/1.8 & 100/0.0 & 100/0.0 & 100/0.0 \\  & WC-Det. & 100/20.6 & 100/25.2 & 57.6/52.7 & 100/99.6 & 100/99.8 & 42.4/94.0 \\ \hline \multirow{3}{*}{30 dB} & MV & 100/6.4 & 100/5.6 & 99.5/1.8 & 100/0.0 & 100/0.0 & 100/0.0+ \\  & RMS & 100/5.8 & 100/5.0 & 100/1.2 & 100/0.4 & 100/0.0 & 100/0.0 \\ \cline{1-1}  & WC-Det. & 100/11.4 & 100/15.4 & 53.8/54.6 & 99.6/100 & 96.2/100 & 40.6/94.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Double-edge-based detection performance (DR/FER (%)) of the proposed algorithm, for which a sag signal with 78% \(pu\), a swell signal with 111% \(pu\) generated from the implemented network model of Figure 7a, and a harmonics signal with 10% \(pu\) generated from the implemented network model of Figure 7b are generated over AWGN channels with SNR = 30–35 dB or IMPN channels with SNR = 30–35 dB, \(\mu\) = 400, and \(\psi\) = 0.02%.