esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Biomarkers Analysis
#### 3.3.5 LDL-Cholesterol Levels  
Estimated marginal means of low-density lipoprotein cholesterol (LDL-C) at baseline (t1), 1 h (t2), 2 h (t3), and 4 h (t4) are summarized in Table 8 and Figure 6. Base \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **HDL-C** & **HDL-C** & **HDL-C** & **HDL-C** & \multicolumn{3}{c}{**95\% Confidence Interval**} & \\  & **(Mean \(\pm\) SE)** & **(Mean \(\pm\) SE)** & **(Mean \(\pm\) SE)** & **(Mean \(\pm\) SE)** & **Time** & **Lower** & **Upper** & **(Group \(\times\) Time)** \\  & **at Baseline (t1)** & **at 1 h (t2)** & **at 2 h (t3)** & **at 4 h (t4)** & **Time** & **Bound** & **Upper** & **(Group \(\times\) Time)** \\ \hline CTRL & 67.875 \(\pm\) 3.464 & 67.458 \(\pm\) 3.482 & 65.625 \(\pm\) 3.491 & 68.375 \(\pm\) 3.487 & t1 & 60.709 & 72.428 & \\  & & & & t2 & 60.255 & 74.662 & \\  & & & & t3 & 58.404 & 72.846 & \\  & & & & t4 & 61.162 & 75.588 & \\ \hline OLT & 66.208 \(\pm\) 3.436 & 66.292 \(\pm\) 3.509 & 64.458 \(\pm\) 3.438 & 66.542 \(\pm\) 3.340 & t1 & 59.010 & 73.317 & \\  & & & & t2 & 59.034 & 73.550 & \\  & & & & t3 & 57.346 & 71.571 & \\  & & & & t4 & 59.632 & 73.451 & \\ \hline OLB & 66.792 \(\pm\) 2.725 & 66.583 \(\pm\) 2.957 & 64.417 \(\pm\) 2.866 & 65.750 \(\pm\) 2.891 & t1 & 60.155 & 72.428 & \\  & & & & t2 & 60.466 & 72.701 & \\  & & & & t3 & 58.488 & 70.345 & \\  & & & & t4 & 59.770 & 71.730 & \\ \hline \end{tabular}
\end{table} 
Table 7: Postprandial changes in serum HDL-C levels. Figure 5: Postprandial high-density lipoprotein concentrations (Mean \(\pm\) SE) over time (baseline, 1 h, 2 h, 4 h) in control (CTRL), olive leaf tea (OLT), and olive leaf biscuit (OLB) groups. line LDL-C values were similar across groups: CTRL (93.463 \(\pm\) 5.566 mg/dL), OLT (95.379 \(\pm\) 5.360 mg/dL), and OLB (94.458 \(\pm\) 4.973 mg/dL). LDL-C levels showed minor variations over time within groups. In the CTRL group, concentrations ranged from 93.463 \(\pm\) 5.566 mg/dL at baseline to 95.152 \(\pm\) 5.482 mg/dL at t4. Similarly, OLT and OLB groups demonstrated fluctuations within comparable ranges, with OLB presenting a slight, non-significant increase at t3 (95.540 \(\pm\) 5.096 mg/dL). No significant group-by-time interaction was observed (\(p\) = 0.325), indicating that changes in LDL-C over the 4 h period were not statistically different between groups. The 95% confidence intervals overlapped substantially across time points and groups (e.g., t4: CTRL [83.812, 106.49], OLT [83.879, 104.95], OLB [86.218, 107.35]). Overall, LDL-C concentrations remained relatively stable throughout the study, with no statistically significant temporal or treatment-related effects.
## 4 PSIFTIS Imaging Experiment Results and Data Processing
### Polarization Spectral Reconstruction
The polarization interference intensity sequence is addressed, baseline-corrected, and apodized, and then subjected to two-dimensional discrete Fourier transform to obtain the spectral information \(B_{\theta}(x,y,\
u)\) of each polarization channel. The reconstruction spectra of each polarization channel and Stokes vector spectral lines can be obtained by Equation (4), as shown in Figure 11.  
Figure 11: Reconstructed polarization spectra: (**a**) the reconstructed spectra corresponding to each polarization channel; (**b**) the reconstructed spectra corresponding to each Stokes vector. Figure 10: Polarization interference intensity sequences of pixel points at the central wavelength coordinate (10,6) at 3970 nm: (**a**) 0\({}^{\circ}\) polarization; (**b**) 45\({}^{\circ}\) polarization; (**c**) 90\({}^{\circ}\) polarization; (**d**) 135\({}^{\circ}\) polarization. The reconstructed spectra show that in the system with a 3970 nm filter, the wavenumber errors of the maximum spectral peak corresponding to the four polarization channels are 1.82 cm\({}^{-1}\), 0.12 cm\({}^{-1}\), 1.67 cm\({}^{-1}\), and 0.03 cm\({}^{-1}\) respectively, and the reconstructed spectra are well consistent with the theoretical spectral lines. The maximum spectral peak corresponding to the four polarization channels are 37.377, 75.140, 56.767, and 23.388, respectively. The Stokes vector peaks are 94.145, 19.491, and 53.184, respectively. According to Equation (6), the polarization direction of incident light can be calculated as 38.704\({}^{\circ}\), which differs from the actual incident polarization angle of 38\({}^{\circ}\) by 0.704\({}^{\circ}\), with an error of less than 1\({}^{\circ}\). In summary, the reconstructed polarization spectra have good accuracy.
# Event-Based Probabilistic Embedding for POI Recommendation
## 4 Experiments
### Evaluation Methods and Metrics  
As for the performance comparison with baselines, we apply two widely used metrics, i.e., _Accuracy@k_ and _MAP@k_, where accuracy measures the number of correct recommendation in the result, while MAP considers the rank of the recommendation.  
Particularly, for each test case \((u,v,l_{v},W_{v},t_{q})\), we use Gaussian function to generate a coordinate \(l_{q}\) within the circle of radius \(d\) centered at \(l_{v}\) to represent the current standing point of user \(u\). Thus, a query \(q=(u,l_{q},t_{q})\) is formed for the test case. The set of all queries is represented as \(S_{test}\).  
For each query \((u,l_{q},t_{q})\) in \(S_{test}\), we calculate the user preferences for POIs and generate a ranked list based on their preference scores. Let \(p\) denote the rank of the test item \(v\) within this list. Finally, we obtain a top-\(k\) recommendation list. If \(p<k\), we have a hit (i.e., the test item \(v\) is recommended to the user).  
The computation of _Accuracy@k_ proceeds as follows. For a single test, we define _hit@k_ as 1 when \(v\) appears in the top-\(k\) recommendation list; otherwise, _hit@k_ is 0. The overall _Accuracy@k_ is defined by averaging all test cases:  
\[Accuracy@k=\frac{\#hit@k}{|S_{test}|} \tag{27}\]  
where \(\#hit@k\) denotes the number of hits in the test set, and \(|S_{test}|\) is the number of all test cases.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Dataset** & **\#User** & **\#POI** & **\#Check-In** & **Sparsity** \\ \hline
**Gowalla** & 19,017 & 11,681 & 643,620 & 99.72\% \\ \hline
**Foursquare** & 7961 & 10,629 & 459,221 & 99.56\% \\ \hline
**Yelp** & 17,624 & 15,040 & 602,445 & 99.78\% \\ \hline \end{tabular}
\end{table}
Table 3: The statistics of the used data sets.  
_MAP@k_ is highly relative to the position of \(v\) in the list (i.e., \(p\)). When we have a hit, we focus on the value of \(p\). _MAP@k_ is defined as follows:  
\[MAP\text{@k}=\frac{\sum_{i=1}^{|S_{\text{test}}|}\frac{ht\text{@k}_{i}}{p_{i}}}{ |S_{\text{test}}|} \tag{28}\]
## 5 Case Studies
### Case Study Two: Coursera
#### 5.2.3 Application of DFDLOF in Coursera  
Using the Dynamic Feedback-Driven Learning Optimization Framework (DFDLOF) in Coursera represents a major step in personalized e-education. The DFDLOF integration into the context of Coursera's diverse course offerings constitutes a sophisticated customization of learning experience oriented toward adult learners and professionals. This stage starts with a deep analysis of learner data that include previous course interactions, learning styles, and performance metrics. Data from the above processes are fed into machine learning algorithms within DFDLOF to develop dynamic and bespoke learning paths that meet learners' needs and goals.  
The adaptive course structure is a vital quality of DFDLOF, as it applies to Coursera. Coursera offers more scheduled and structured courses, similar to those of traditional higher education, compared to those of Khan Academy, which focuses on K-12 education. However, DFDLOF solves this problem by providing flexibility in such structures, permitting them to personalize the contents of courses and determine their schedule and assessment methods. For example, those who have difficulty understanding some topics should be provided with additional resources or alternative explanations, and learners who belong to advanced groups should work with more challenging materials.  
DFDLOF improves Coursera's current recommendation system, making it more powerful and catered to the subtleties of each learner's experience. This leads to better course recommendations, increasing learner engagement and happiness. Moreover, its real-time  
\begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Feature/Aspect** & **Khan Academy** & **Coursera** \\ \hline Target Audience & K-12 students, focusing on fundamental subjects & Adult learners, higher education, and professional development \\ \hline Learning Approach & Mastery-based learning, self-paced & Structured, time-bound courses emulating traditional higher education \\ \hline Personalization Strategy & Personalized content based on individual learner’s performance and progression & Customized course offerings and paths tailored to adult learners’ professional and academic goals \\ \hline Adaptation Mechanism & Real-time content and teaching method adjustments based on learner analytics & Flexibility in course structure and assessment methods, adapting to learners’ needs in a more structured environment \\ \hline Feedback and Assessment & Dynamic, personalized feedback and adaptive assessments & Real-time feedback mechanisms tailored to individual learning progress \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative analysis of DFDLOF application in Khan Academy vs. Coursera.  
feedback mechanisms make the learning process even more individual-specific, as learners can instantly see how they are performing.
# MEASegNet: 3D U-Net with Multiple Efficient Attention for Segmentation of Brain Tumor Images
## 5 Ablation Experiments  
### Ablation Study of Each Module in MEASegNet  
In the MEASegNet network, we incorporated three distinct attention modules: the PCSAB within the encoder, the CRRASPP in the bottleneck layer, and the SLRFB in the decoder. An ablation experiment entails \"removing\" or \"eliminating\" certain components of the model to assess how their removal affects the overall effect. To systematically assess their individual contributions, we performed ablation experiments (Expt) on these modules.  
As illustrated in Table 7 and Figures 9 and 10, the average (AVG) Dice score improved by 0.75% (Experiment B), 0.87% (Experiment C), and 0.65% (Experiment D) when SLRFB, CRRASPP, and PCSAB were individually integrated into the baseline network, respectively. Except for Experiment D, the average HD95 values were also optimized. To assess the synergistic effects of the developed attention mechanisms, we performed experiments with various combinations of SLRFB, CRRASPP, and PCSAB. The results demonstrate that the pairwise combinations of these attention modules consistently enhanced both the average Dice score and HD95 values compared to the baseline network. When all three attention mechanisms were applied concurrently, the optimal performance was achieved. Specifically, the Dice values for WT, TC, ET, and the average reached 92.50%, 87.  
The Studies of Different Convolutional Kernels in SLRFB in the Context of Multiscale Feature Extraction  
The efficacy of the SLRFB attention module compared to the baseline network was established, with the results reported in Table 7. However, the capacity to capture multiscale features can differ significantly depending on the combination of the convolutional kernels employed. To explore how different convolutional kernels influence the MEASegNet network, this study conducted three sets of experiments. These experiments tested sequential kernel sizes of \(3\times 3\times 3\) followed by \(3\times 3\times 3\) (SLRFB33), \(3\times 3\times 3\) followed by \(5\times 5\times 5\) (SLRFB35), \(5\times 5\times 5\) followed by \(5\times 5\times 5\) (SLRFB55), and \(5\times 5\times 5\) followed by \(7\times 7\times 7\) (SLRFB57).  
As indicated in Table 8, the SLRFB35 experiment demonstrated superior performance, achieving Dice values of 92.50% for WT, 87.49% for TC, 84.16% for ET, and an average of 88.05%. The corresponding HD95 values were 4.18 for WT, 7.96 for TC, 14.40 for ET, and an average of 8.85. Figures 11 and 12 further reveal that larger convolutional kernels did not always result in improved performance. In fact, it is the judicious selection of kernel sizes that leads to optimal outcomes, particularly within the decoder.
# LDPC Decoder Design Using Compensation Scheme of Group Comparison for 5G Communication Systems
## 5 VLSI Implementation of Dual-Mode LDPC Decoder for 5G NR Systems
### CNU Using Compensation Scheme of Group Comparison  
Figure 8a illustrates the architecture of the CNU and Figure 8b illustrates the architecture of the min and index finder, and the scaler, in the proposed INP MSA. First, the prior message is stored in the D-flip-flop for the pipeline after receiving prior messages from the PMU. The prior message is then transmitted to a sign bit and absolute value. The individual sign bit for each row weight is then generated by the \"XOR sign\" module after which the \(Index_{min}\), \(min^{1 st}\), and \(min^{2 nd}\) are generated by the Min & Index Finder. In the scaler, the \(min^{1 st}\) and \(min^{2 nd}\) are multiplied by the scaling factor (\(\gamma\) = 0.75) and improved factor (\(\gamma\)\(\kappa\) = \(\gamma\)\(\beta\) = 0.375), respectively. Finally, this data is combined with an extrinsic message and transferred to the VNU. Additionally, Table 2 listed in [10] reveals that the scaler in the INP MSA is simpler and faster than that in the rExMin by approximately one comparator. It demonstrated that the hardware implementation result of the CNU used 36 inputs and divided them into four groups: the NPMSA, the INPMSA, and the rExMin algorithm. The INP MSA has a critical path that is increased by approximately 0.3 ns due to the addition of a compensation scheme. Compared to the rExMin, the INPMSA is approximately 0.08 ns  
Figure 6: Overall architecture of the proposed dual-mode LDPC Decoder.  
Figure 7: Bit assignment of the extrinsic message of the proposed dual-mode LDPC decoder for the 5G NR BGs 1 and 2.  
faster. By comparing the comprehensive decoding ability and hardware operation speed, we found that the INPMSA can maintain the same decoding capability as the rExMin, and has more advantages during hardware implementation.
## 2 Related Works
The proposed SC-FDO improved on the original FDO by obtaining a better exploit-explore trade-off while also attaining a quicker convergence speed. In [PERSON] et al. [52], a hybrid cuckoo search and simulated annealing (CSA) approach was proposed. The optimization operators of the simulated annealing technique are included in the cuckoo search algorithm. They introduced discrete CSA (DCSA), a discrete version of CSA for solving the JSSP, in this study. DCSA introduces four changes to CSA. First, in the initialization stage, it employs the opposition-based learning approach to generate a varied set of candidate solutions. Second, it combines variable neighborhood search (VNS) and Levy flight techniques to improve search space exploration. Third, it jumps out of local optima using the elite opposition-based learning approach prior to the abandonment stage of CSA. Finally, the lowest position value is used. In this paper, we introduce a hyper-heuristic for combining the behavior of two independent heuristic algorithms. The combination of the BA [53] and the KH [6] is provided as a case study. Using 12 benchmark optimization challenges, the efficiency of the hyper-heuristic is compared to that of stand-alone BA and KH.
# Three-Dimensional Imaging via Time-Correlated Single-Photon Counting
## 3 Experimental Setup and Results  
To verify the 3D imaging scheme based on TCSPC proposed in this paper, three groups of experiments are performed to confirm the de-noise ability, high-resolution imaging ability, 3D imaging based on the photometric stereo algorithm. We use a pulsed laser with a wavelength of 532 nm as light source, which generated 30 ps duration pulses at a repetition rate of 10 MHz. The SPD module is the PDM series manufactured by Micron Photon Devices. The dead time of SPD is 80 ns and the quantum efficiency at 532 nm is about 45%. Besides, HydraHarp 400, whose time resolution down to 1 ps and maximum count rate of up to 12 Mcps per channel, is used as the TCSPC module and a field-programmable gate array (FPGA) is used as the main controller.
# X-GANet: An Explainable Graph-Based Framework for Robust Network Intrusion Detection
###### Abstract  
Modern networks are increasingly targeted by sophisticated cyber threats, making effective intrusion detection a critical challenge. Traditional intrusion detection systems (IDSs) often struggle with capturing the complex relationships within network traffic and suffer from a lack of explainability in their decision-making processes. To address these limitations, we propose X-GANet, Explainable Graph Anomaly Network, a novel graph-based intrusion detection framework that models network traffic as a graph and leverages deep representation learning for precise threat identification. The model extracts both flow-based and structural features, aligns multi-view representations through contrastive learning, and employs a transformer-based embedding module for enhanced feature extraction. An adaptive graph detection fusion mechanism ensures effective graph-level representation and detection techniques for robust attack classification. Experimental results obtained from benchmark intrusion detection datasets demonstrate that X-GANet significantly improves detection performance while maintaining interpretability, making it a promising solution for real-world cybersecurity applications.  
intrusion detection; graph neural network; deep learning; flow-based characteristic
## 3 The Antenna Backscattering Method  
The antenna backscattering method is a wireless sensing approach that relies on the interaction of an interrogation signal with a sensor's resonant frequency. The interrogator antenna transmits frequency sweeps to the patch antenna sensor, which is designed to resonate at a specific frequency. Because of the low impedance at resonance, effective energy transfer occurs when the interrogation frequency closely matches the sensor's resonant frequency. This coupling generates surface charges on the conducting plates, leading to the generation of electric fields between conductors and magnetic fields from current flow in  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Patch Shapes** & **Parameters** & \begin{tabular}{c} **Theoretical** \\ **Value (mm)** \\ \end{tabular} & \begin{tabular}{c} **Wavelength** \\ **Value (\(\lambda_{\text{eff}}\))** \\ \end{tabular} & \begin{tabular}{c} **Optimized** \\ **Value (mm)** \\ \end{tabular} & \begin{tabular}{c} **Wavelength** \\ **Value (\(\lambda_{\text{eff}}\))** \\ \end{tabular} \\ \hline Rectangular & \
\begin{tabular}{c} W \\ L \\ \end{tabular} & 26.89 & 0.62 & 25.00 & 0.58 \\  & L & 19.45 & 0.45 & 18.38 & 0.43 \\ \hline Circular & R & 11.99 & 0.28 & 11.10 & 0.26 \\ \hline Equilateral &
\begin{tabular}{c} S \\ \end{tabular} & 24.98 & 0.58 & 25.59 & 0.60 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Theoretical and Optimized Values of Sensor Antenna Dimensions.  
Figure 1: The three patch shapes of sensor antenna designs: (**a**) simulated 3D model; (**b**) manufacturing result of sensor antenna.  
the patch and ground plane. These electromagnetic fields trap energy within the sensor, enabling it to function as a transmission line model [8].  
Upon receiving coupling energy from the interrogator antenna, the sensor antenna re-radiates a backscattered signal carrying information about temperature variations as illustrated in Figure 2. These variations manifest as a frequency shift relative to the initial resonant frequency measured at room temperature. The shift is primarily influenced by two temperature-dependent factors: the dielectric constant variation, commonly quantified by the Temperature Coefficient of Frequency (TCF), and the substrate expansion or contraction, governed by the Coefficient of Thermal Expansion (CTE). Since both parameters are highly temperature-dependent, any change in temperature directly affects the frequency shift in the backscattered signal. This relationship is mathematically described by Equation (10) [12].  
\[\frac{\delta f_{nm}}{f_{nm}}=-\left(\frac{1}{2}\alpha_{\varepsilon}+\alpha_{T }\right)\delta_{T}=k_{T}\delta_{T} \tag{10}\]  
where \(\frac{\delta f_{nm}}{f_{nm}}\) represents the normalized frequency shift of the fundamental mode. \(\alpha_{\varepsilon}\) and \(\alpha_{T}\) are the TCF and CTE, respectively, and \(k_{T}\) denotes the temperature sensitivity.
## 5 Illustrative Example
### Vessel Scenarios  
To demonstrate the impact of the life-safety category and the spill consequence category, five different vessels were selected: A bulk carrier, an oil tanker, a cruise ship, a fishing vessel, and an autonomous ship. The vessel ice class, POB, associated life-safety category, and spill consequence value for each vessel is provided in Table 11.  
The bulk carrier, with a crew of 55, had a moderate life-safety category (S2) and a moderate spill consequence (SC = 2). The oil tanker had a high spill consequence category (SC = 3) and a moderate life-safety category (S2). The cruise ship, with the highest number of passengers at 2500, received a high life-safety category (S1), but a low spill consequence (SC = 1). The fishing vessel had both a low  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ice Regime** & **Ice Type** & **Concentration** \\ \hline \multirow{3}{*}{Ice Regime 1} & Thin First Year & 5/10 th \\  & Grey Ice & 4/10 th \\  & Open Water & 1/10 th \\ \hline \multirow{3}{*}{Ice Regime 2} & Thick First Year & 8/10 th \\  & Thin First Year & 1/10 th \\ \cline{1-1}  & Open Water & 1/10 th \\ \hline \hline \end{tabular}
\end{table}
Table 10: Ice regimes for illustrative example.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Vessel** & **Ice** & **OOB** & \
\begin{tabular}{c} **Life-Safety** \\ **Category** \\ \end{tabular} &
\begin{tabular}{c} **Spill Consequence** \\ **Category** \\ \end{tabular} \\ \hline bulk carrier & PC5 & 55 & S2 & SC = 2 \\ oil tanker & PC5 & 75 & S2 & SC = 3 \\ cruise ship & PC5 & 2500 & S1 & SC = 1 \\ fishing vessel & PC5 & 12 & S3 & SC = 1 \\ autonomous & PC5 & 0 & S4 & SC = 1 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Vessel details for illustrative example.  
life-safety category (S3) and a low spill consequence (SC = 1). The autonomous ship had an unmanned life-safety category (S4) and a low spill consequence (SC = 1). All vessels had an assigned ice class of PC5.
## 2 Case Study and Data  
### Natl60  
The nature run (NR) used in this work corresponds to the NATL60 configuration [8] of the NEMO (Nucleus for European Modeling of the Ocean) model. It is one of the most advanced state-of-the-art basin-scale high-resolution (\(1/60^{\circ}\)) simulations available today, whose surface field effective resolution is about 7 km.  
In this work, two specific \(10^{\circ}\times 10^{\circ}\) GULFSTREAM and \(8^{\circ}\times 10^{\circ}\) OSMOSIS (Ocean Surface Mixing, Ocean Sub-mesoscale Interaction Study) domains were chosen (see Figure 1) to assess the performances of the data-driven interpolation methods. Over those regions, for the sea surface height (SSH), the resolution of the nature run was downgraded to \(1/20^{\circ}\), which is enough to capture both the GULFSTREAM mesoscale dynamical regime and the OSMOSIS small scales, while avoiding an unnecessary heavy computational time.  
The NATL60 nature run was then used as the reference ground truth (GT) in the observing system simulation experiments (OSSE). The pseudo-altimetric nadir and SWOT observational datasets were generated by realistic sub-sampling of satellite constellations.