esa-sceva/satcom-chunk-collection huggingface dataset:
# Beamforming Techniques for Resilient Navigation with Small Antenna Arrays +
## 1 Introduction
Two representative antenna array distributions are considered: four-antenna uniform rectangular and two-antenna uniform linear array distributions, henceforth denoted as URA4 and ULA2, respectively.
# Deployment Protection for Interference of 5G Base Stations with Aeronautical Radio Altimeters
## 4 Results and Discussion
### Interference Mitigation Methods in Prohibited Zone
#### 4.4.1 Tx Power Control  
In Figure 20a,b, the results of horizontal prohibited zones for both omnidirectional and beamforming antennas are shown for a vertical scan angle of \(\theta=0^{\circ}\) and an aircraft pitch/roll angle of \(\alpha\backslash\beta=0^{\circ}\). The results show that for omnidirectional antennas, the base stations require up to a 40 dB attenuation of the Tx power, and 10.8% of the base stations require a 1 dB attenuation, which is the highest percentage. For beamforming antennas, the maximum required attenuation is 15 dB, and 27% of the base stations require a 2 dB attenuation, which is the highest percentage. This implies that Tx power reduction is more effective for beamforming antennas. Furthermore, there is a significant difference in results between the use of omnidirectional and beamforming antennas due to the directional attenuation produced by beamforming antennas. Figure 21 shows a comparison of attenuation within a range of 25 dB for four different scenarios. The power attenuation ratio for the base stations varies significantly for different scenarios.  
Figure 21: Tx power control in horizontal prohibited zone for different scenarios.  
The results of vertical prohibited zones for both omnidirectional and beamforming antennas are shown in Figure 22a,b for a vertical scan angle of \(\theta=0^{\circ}\) and an aircraft pitch and roll angle of \(\alpha\backslash\beta=0^{\circ}\). For the vertical prohibited zone, analysis of the 40 dB attenuation range can cover most of the base stations. The proportion of base stations that require 1 dB attenuation ranges between 5.8% and 8.1%, which is the highest. The proportion of base stations in the four scenarios within the 25 dB attenuation range is compared in Figure 23, and as the attenuation level increases, the percentage of corresponding base stations shows a downward trend overall.
## 4 Factors Affecting the Performance of AC
### \(pH\)  
The pH of the liquid or aqueous solution is one of the key factors that determine the surface charge density as well as the cationic or anionic nature of the surface. The solution with high pH will attract cations and that with low pH values will attract anions. In other words, the acidic carbons will be more suitable for retaining cations, and basic carbons more effective for anion removal. Hence, by proper adjustment of the pH of the solution, the uptake of the cationic and anionic groups can be controlled. When pH increases, the removal of contaminants decreases, which means that the adsorption rate will be less. Most organic compounds are adsorbed at low pH, and they are less soluble [48]. A thumb rule for effective adsorption is that for every pH unit > 7.0, there must be an increase in the size of the carbon bed by 25%.
## 5 Experiment
### Ablation Study
#### 5.3.4 Effect on SPARQL Difficulty  
We categorized the test dataset from LC-QuAD 2.0, QALD-9 plus, and QALD-10 based on the number of triples (hops) in the corresponding SPARQL queries into four classifications: \(\text{hop}=1,2,3\) and \(\geq 4\). Table 7 presents the performance of the TSET model in comparison to the baseline T5 model on questions of varying complexities. The results demonstrate that the TSET model consistently outperforms the T5 model across almost all subsets. This performance enhancement validates the effectiveness of the methods introduced in our study.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Type** & **Example** \\ \hline Query & SELECT (COUNT (?vr0) AS?vr1)[wd:Q18813 wdt:P1574?vr0.] \\ E \& R & New Testament wd:Q18813; exemplar of wdt:P1574 \\ \hline w. ST & SELECT (COUNT (?vr0) AS?vr1)[wd:New Testament wdt texexemplar of?vr0.] \\ \hline TOC & SELECT (COUNT (?vr0) AS?vr1)[yr0 wdt:texemplar of wd:New Testament.] \\ SOC & (AS?vr1 COUNT (?vr0))[wd:New Testament SELECT wdttexemplar of?vr0.] \\ TSC & SELECT (COUNT (?vr0) AS?vr1)[wd:New Testament?vr0 wdt texemplar of.] \\ \hline \hline \end{tabular}
\end{table}
Table 6: Illustration of pretraining input text variants with Semantic Transformation (w. ST) and structure permutation for TOC, SOC, and TSC objectives. “w. ST” is short for “with Semantic Transformation”. E \& R denote entities and relations inside the query.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Dataset** & **Hop** & **1** & **2** & **3** & \(\geq\)**4** \\ \hline \multirow{6}{*}{LC-QuAD 2.0} & \# example & 1548 & 2346 & 1472 & 680 \\ \cline{2-6}  & T5-small & 85.4 & 91.51 & 92.79 & 92.5 \\ \cline{2-6}  & T5-base & 87.01 & 92.07 & 93.47 & 94.26 \\ \cline{2-6}  & TSET-small & 87.72 (+2.32) & 93.90 (+2.39) & 92.93 (+0.14) & 93.52 (+1.02) \\ \cline{2-6}  & TSET-base & 89.92 (+2.91) & 94.37 (+2.30) & 94.29 (+0.82) & 93.82 (\(-0.44\)) \\ \hline \multirow{6}{*}{QALD-9 plus} & \# example & 67 & 32 & 24 & 13 \\ \cline{2-6}  & T5-small & 73.13 & 37.5 & 37.5 & 7.69 \\ \cline{1-1} \cline{2-6}  & T5-base & 76.11 & 40.62 & 41.66 & 7.69 \\ \cline{1-1} \cline{2-6}  & TSET-small & 85.07 (+11.94) & 43.75 (+6.25) & 37.5 & 0 (\(-7.69\)) \\ \cline{1-1}  & TSET-base & 85.07 (+8.96) & 50.00 (+9.38) & 46.87 (+5.21) & 0 (\(-7.69\)) \\ \hline \multirow{6}{*}{QALD-10} & \# example & 182 & 93 & 45 & 72 \\ \cline{1-1} \cline{2-6}  & T5-small & 58.24 & 13.97 & 4.44 & 0 \\ \cline{1-1} \cline{2-6}  & T5-base & 64.83 & 18.27 & 4.44 & 0 \\ \cline{1-1} \cline{2-6}  & TSET-small & 68.13 (+9.89) & 22.58 (+8.61) & 4.44 & 0 \\ \cline{1-1} \cline{2-6}  & TSET-base & 70.32 (+5.49) & 32.25 (+13.98) & 4.44 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The performance on questions of varying difficulty levels (across different hops).
# A Comprehensive Review of Multimodal Analysis in Education
## 3 Methodologies in Multimodal Analysis
### Feature Extraction and Preprocessing
#### 3.2.2 Visual Data Processing  
Processing of visual data in MMLA involves a range of techniques designed to extract meaningful features from videos, images, and spatial representations of learners' behaviours. Several studies utilised deep neural networks (DNNs) and convolutional models to extract high-level features from facial expressions, head pose, and posture information. In some cases, feature extraction was performed through VGG16 and ResNet-101 for facial and pose estimation, respectively, while Mediapipe provided landmark detection [35]. To ensure clarity and robustness, images can be cropped, resized, and normalised to reduce background interference and enhance classifier accuracy [72].  
Some approaches focused on classroom video analysis using pyramid-based architectures and context-sensitive prediction modules [73]. Low-level features were extracted using Inception-ResNet-V2 to detect students' engagement with high precision. In other frameworks, facial features were processed frame-by-frame using OpenFace and OpenPose, yielding detailed facial action unit intensities and 3D keypoints to represent body pose in collaborative scenarios [74]. Standardisation of visual features within cross-validation splits helped mitigate bias across training and testing data [1].  
Visual features were also analysed through blend shape coefficients from iPhone depth cameras to measure movement in key facial areas, such as eyelids and mouth, enabling proxies for cognitive or affective states [49]. Motion capture systems employing infrared cameras and Smart Tracker software enabled precise detection of movement features by translating marker coordinates into behavioural indicators [75]. To support visual emotion detection, video frames were preprocessed using tools such as DeepFace, with additional transformation to gray-scale and resizing before analysis [76].  
In some cases, spatial data extracted from videos were synchronised with interaction metrics and normalised to preserve behavioural fidelity across different sessions [65]. Posture analysis was facilitated through skeletal heat-maps and salient video frames fused in a common embedding space, providing robust feature representations for model training [15]. Additionally, optical flow algorithms such as TV-L1 were employed to analyse motion in physical education contexts, particularly when evaluating embodied learning [42]. These diverse visual data processing strategies contribute significantly to capturing the complexity of learner behaviours in multimodal educational environments.
## 2 Preliminary Knowledge: The Inverter Model and Voltage Source Inverter (VSI) Controller  
### The Inverter Model  
In recent years, there have been more and more topological structures of inverter circuits designed for characterizing distributed generations (DGs) [29]. A typical three-phase, two-stage conversion circuit of inverters [29] (as Figure 2 shows) was taken as the controlled object for model building. The mathematical model of the inverter in this figure will be used as the controlled object to participate in offline tuning of the control parameters.  
According to the Kirchhoff laws, the mathematical models of the circuit on the DC side can be expressed as the following:  
\[\frac{\mathrm{d}u_{pv}}{dt}=\frac{i_{pv}}{C_{1}}-\frac{i_{L1}}{C_{1}}, \tag{1}\]  
\[\frac{\mathrm{d}i_{L1}}{dt}=\frac{u_{pv}}{L_{1}}-\frac{s_{k}u_{dc}}{L_{1}}. \tag{2}\]  
The mathematical models of the conversion circuit on the AC side can be expressed as the following:  
\[U_{a}=i_{a}R+L_{2}\frac{di_{a}}{dt}+e_{a}, \tag{3}\]  
\[U_{b}=i_{a}R+L_{2}\frac{di_{b}}{dt}+e_{b}, \tag{4}\]  
\[U_{c}=i_{c}R+L_{2}\frac{di_{c}}{dt}+e_{c}, \tag{5}\]  
\[C_{3}\frac{du_{dc}}{dt}=i_{ES}-(i_{a}S_{a}+i_{b}S_{b}+i_{c}S_{c}). \tag{6}\]  
In the above models, \(u_{pv}\) is the output voltage of the inverter; \(\mathrm{I_{PV}}\) is the input current at the DC side of the inverter; \(u_{dc}\) is the voltage between two ends of the capacitor \(\mathrm{C_{2}}\); \(\mathrm{S_{k}}\) represents the duty cycle of the thyristor at the DC side of the main conversion circuit; \(\mathrm{S_{a}}\), \(\mathrm{S_{b}}\), and \(\mathrm{S_{c}}\) represents the duty cycle of  
Figure 2: Traditional two-stage type and three-phase inverter circuit.  
the thyristor at the AC side of the main conversion circuit; and the parameter \(\mathrm{i_{ES}}\) can be expressed as the following:  
\[i_{ES}=S_{K1}i_{L1}-C_{2}\frac{du_{dc}}{dt}. \tag{7}\]
## 2 System Definition
### The Experimental ATENEA Microgrid  
The Renewable Energy Grid Integration Department in CENER (National Renewable Energy Centre of Spain) has developed and deployed a microgrid (ATENEA) placed in the Rocaforte industrial area (town of Sanguesa, Navarra, Spain) according to the interest in an industrial test scenario and environment. The generation equipment of the facility can be seen in Figure 1.  
It consists of an AC architecture with total installed power of about 120 kW that can supply part of the Wind Turbine Test Laboratory (LEA), electric loads and Rocaforte industrial lighting area. It also can be used as a test-bench for different generation and storage technologies and control strategies. The generation equipment available in the facility can be seen in Figure 1.  
The ATENEA microgrid structure is based on an AC low voltage three-phase, four-wire bus (400 V, 50 Hz) connected to all of the equipment. This experimental microgrid has two configurations: grid-connected and grid-disconnected. Its main objective is to manage generation and demand in order to obtain high ratios of energy self-efficiency. In grid-connected mode, the microgrid is connected to the network and the V/f performance is fixed by its own network. In the grid-disconnected scenario, one of the converters linked with an energy storage system (flow battery, Valve-Regulated Lead-Acid (VRLA) battery, Li-ion battery) or a diesel generator is configured to form the grid, mastering the V/f. In this way, the ATENEA microgrid adopts a master-slave control in the grid-isolated configuration and the slaves work under a PQ control, P and Q being active and reactive power, respectively.
## 3 Problem Formulation
### Consumer Behavior Modeling  
The PEMD reflects the consumer response to a DR program which is represented by the values of self and cross elasticities, as well as their distribution in the PEMD, where different DR policies and consumer response patterns to the price change can impact the estimation of the PEMD. Figure 5 represents the two types of PEMD modeling a day-head DR policy \((a)\) and an hour-ahead DR policy \((b)\)&\((c)\). In day-ahead DR polices, prices are announced one day earlier or at the start of the day, where consumers can re-schedule their demand for each interval \(i\) of the day, hence all elements in the PEMD can be non-zero. Whereas in hour-ahead DR, consumers can only reschedule the price of the next hour, where they only have information on the current price and hour ahead price, hence it is unlikely that they can reschedule their demands ahead of time, making all cross-elasticities above the diagonal zero [23]. Some researchers considered all off-diagonals to be zero in this case [24].  
The PEMD estimation is also correlated to how consumers in different markets reschedule their demands to different hours of the day, represented by the cross elasticities' distribution in the PEMD. Figure 6 shows the different types of consumer rescheduling, which can be assumed into five different categorical behaviors [46]:  
The categories of behaviors for day-ahead demand response are (1), (2), and (5), where the last two are the most probable, as it is unlikely that consumers are extremely optimized to fully reschedule their demand to hours of least price. The difference between flexible and inflexible consumers is the time horizon to where demand at a certain hour is rescheduled to other hours of the day, depicted by the arrow lengths in Figure 6. The selection of this horizon is based on a detailed analysis of the types of loads that exist, where water heating,  
Figure 5: PEMD under different policies: (a) day-ahead DR, (b,c) hour-ahead DR.  
Figure 6: Different consumer rescheduling behaviors in DR markets.  
for example, can be shifted to a larger time horizon prior to periods of high demand than other appliances such as lighting [47].
# A Survey on Diabetic Retinopathy Lesion Detection and Segmentation
## 2 Related Works  
The DR studies reviewed in this article have been classified into two categories which include traditional-based approaches and DL-based studies.  
Different traditional methods are being used for retinal blood vessel segmentation as well as for lesion detection from fundus images. Detection and counting of MA were done by the authors in [5] by using morphological operations, histogram equalization, and intensity transformation functions. HM segmentation was performed by the authors of [6] using histogram equalization and morphological operations on the green channel image of fundus images.  
Several traditional methods have been used for EX detection like thresholding, morphology, edge detection, mixture modeling, and region growing. Grayscale morphology was used along with active contour-based segmentation and region-wise classification by the authors of [7] for automatic EX detection. The authors used a blend of region growing and edge detection in [8]. They used the Luv color space to get the color difference image of objects. A mean squared Wiener filter in two dimensions was used to remove noise which was trailed by edge detection and an enhancement of the region growing method. Gray level thresholding was used by the authors in [9] since EX have high local and gray levels. The local properties of EX were exploited and a blend of global as well as local histogram thresholding was used by them. [PERSON] et al. [10] used an EX detection method built using mixture models for obtaining image histograms. This helped them get a dynamic threshold for every image. Some machine learning approaches have also been used in the literature. [PERSON] et al. [11] used the k-nearest neighbor classifier for detecting red lesions from fundus images. A ranking SVM-boosted convolutional neural network (CNN) was utilized for EX detection by the authors of [12]. Damaged or falsely detected OD and HM were also detected by them.  
Retinal blood vessel segmentation using a top-hat morphological transform followed by hybrid median filtering was performed by the authors in [13]. [PERSON] and [PERSON] [14] performed a contrast adjustment of the green channel of the image trailed by curvelet transform and morphological reconstruction for retinal blood vessel segmentation. A mathematical morphology-based fuzzy algorithm method was used for OD elimination followed by retinal blood vessel extraction by the authors of [15].  
The DL-based DR studies reviewed in this study have been categorized into two types which include segmentation-based studies and lesion detection-based studies. Figure 3 captures these two distinct types of DR studies.  
Figure 3: Categorisation of Diabetic Retinopathy studies reviewed.
## 5 Challenges and Perspectives
Yet, challenges persist in integrating ML into clinical systems. A significant hurdle for data-driven ML models, such as DNNs, is their generalization capability, relying on the quality of the training dataset. The standardization and integration of data from various imaging modalities into a cohesive and interoperable framework to feed ML models remains a formidable task. Different healthcare institutions and imaging modalities often use diverse formats and standards for data storage, challenging the creation of unified datasets and ML models that work seamlessly across institutions. Compiling a comprehensive dataset from clinical data proves intricate due to a wide data distribution, result dependency on various machines, and ethical restrictions on accessing medical data. Biased training data can lead to biased ML models, causing disparities in diagnosis and treatment. Ensuring fairness and mitigating bias in training datasets is crucial in healthcare applications. Developing weakly supervised DNN models that require fewer labeled training data emerges as a plausible solution. Uptake of ML-assisted innovations by clinicians necessitates an emphasis on explainable AI approaches, including measures such as simplifying models, incorporating interpretability tools into solutions (e.g., attention maps or feature importance maps), leveraging referenceable data for training, and promoting human-in-the-loop decision making. The lack of robust criteria for recommending optimal settings and architectures for ML models, particularly for ANNs, underscores the need for a systematic approach in proposing optimized models based on given datasets. In essence, the collaborative synergy between SWIR techniques and ML is poised to redefine the landscape of biomedical research and clinical applications. As these technologies continue to evolve, interdisciplinary efforts of researchers, engineers, and clinicians will be instrumental in realizing their full potential. The challenges outlined, while formidable, are surmountable through concerted research endeavors, opening the way for a future where SWIR and ML contribute seamlessly to the advancement of personalized medicine and transformative healthcare outcomes.
## 4 Experiments
### Description of the Datasets  
#### 4.1.1 NSL-KDD Datasets  
The University of New Brunswick made the NSL-KDD dataset public. The NSL-KDD dataset is an upgrade of the KDDCup'99 dataset, which has inherent flaws, as shown by numerous analyses. NSL-KDD comprises the core records of the entire KDD dataset and is one of the most used datasets for analyzing network intrusion detection systems that can be applied as an effective benchmark to compare different intrusion detection methods, along with UNSW-NB15 and CICIDS-2017 [43]. The elimination of redundant  
Figure 3: An example of a CNN architecture.  
records, the availability of records in the training and testing datasets, and the inverse relationship between the number of selected records from each difficulty group and the percentage of records in the original KDD dataset are just a few of how NSL-KDD differs from its predecessor. The dataset contains 41 features, categorized into four groups, as listed in Table 1. Nine elements make up the first group (basic), which includes essential details, including the protocol, service, and length. Thirteen features are represented by the second category (content), which includes details about the content, including login activities. Nine time-based elements are contained in the third group (time), including the number of connections that are connected to the same host in a two-second window. Ten host-based elements are included in the fourth (host) section, and they offer details about the connection to the host, including the frequency of connections with the same destination port number attempting to be accessed by other hosts. The NSL-KDD dataset contains the KDDTrain+dataset as the training set and the KDDTest+ and KDDTest-21 datasets as the testing set. It contains normal traffic and four different attack types, namely denial of service (DoS), root to local (R2L), user to root (U2R), and probing attacks (Probe), as shown in Table 2.
## 5 Numerical Results
### Evaluation Setup  
We assess the efficiency of the proposed architecture by employing mobile traffic statistics collected from ten discrete Mobile Edge Computing (MEC) servers spanning one month. There are multiple factors that can impact the precision of predictions, including the validity of data sources, techniques used for prediction, and experimental conditions. Therefore, evaluating the forecasting model mandates the appropriate selection of indicators. Accuracy functions are the essential key performance indicator (KPI) that measures the forecasting model's quality directly. The study employs Normalized Root Mean Square Error (NRMSE) to evaluate the forecasting model, which is defined as  
\[NRMSE=\frac{1}{y}\sqrt{\frac{1}{N}{\sum_{i=1}^{n}(y_{i}-\widetilde{y_{i}})^{2}}} \tag{9}\]  
where \(y_{i}\) denotes the actual value, while \(\widetilde{y_{i}}\) symbolizes the predicted value of \(y_{i}\), with \(N\) representing the total number of testing samples.  
Python served as the programming language of choice for conducting the simulation, with the TensorFlow and Keras backends employed. Table 1 features the hyperparameters selected for this study. One of the essential hyperparameters that must be defined is the number of hidden layers, which can influence the trade-off between time and accuracy required for training the network. For this study, we have fixed the number of hidden layers to 5. The amount of data used to train the network is a crucial determinant of the prediction accuracy. Adding more layers may help to enhance the prediction accuracy but may also increase the training time. To ensure a reasonable balance between training time and accuracy, the total number of epochs was set to 100. The model undergoes training and validation using three weeks of data, and the outcomes summarized for the last week. Adam optimization guides the iterative update of network weights using training data.