esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Fuzzy and Pattern-Based Approaches for Class Imbalance Problems
## 2 General Background
### Class Imbalance Problem  
As we previously stated, the class imbalance problem is a recurrent issue in real-world data, and it occurs when the objects are not distributed equally among the problem's classes. The objects of the minority class can be described as safe, borderline, rare, and outliers [30], making the classification process even harder for classifiers [31]. In the following list, there is a description for each of them:  
* Safe: Data located in the homogeneous regions from one class only (majority or minority).
* Borderline: Data located in nearby decision boundaries between classes. In this scenario, the classifiers need to decide the class of the objects when they are in the decision boundary, which, due to the bias, result in favor of the majority class.
* Rare: Data located inside the majority class is often seen as overlapping. The classifier tends to classify the minority class as part of the majority class. The effect of this has been discussed in different works [32].
* Outliers: Data located far away from the sample space. The minority objects could be treated as noise by the classifier; on the other hand, noise could be treated as minority objects [33]. This happens when there are outlier objects in the database and the data should not be removed because it could be a representation of a minority class.  
The presence of small disjuncts and the lack of density in the training data also affect classic classifier performance. These characteristics tend to affect outlier data with a greater impact [19].
## 3 Results
### Spatiotemporal Parameters  
The results of the spatiotemporal parameters according to both groups are summarized in Table 5. A significant difference with a large effect was found between load and no-load conditions (\(p<0.05\), \(\eta\)2p \(>0.4\)) in the following variables: single- and double-leg support, stride length and walking speed, which thereby indicated that with load, the single-leg support decreased, whereas the double-leg support time increased, the stride length was shorter, and the walking speed decreased. No difference between conditions was found in the other variables measured or between groups, and no interaction was found between load conditions and research groups. No interaction between weight carrying and dominant leg or between weight carrying and leg side (left or right) was found, without a difference between groups (\(p>0.05\)).  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline  & \multicolumn{2}{c}{**PFPS Group**} & \multicolumn{2}{c}{**Control Group**} & \multicolumn{2}{c}{**\multirow{2}{*}{**\(p\) Value (Interaction Group X Load)**}} \\  & \multicolumn{2}{c}{**(N = 10)**} & \multicolumn{2}{c}{**(N = 13)**} & \multicolumn{2}{c}{**\(p\) Value (between Load vs. No Load)**} & \multicolumn{2}{c}{\(p\) value (between Load vs. No Load)**} \\  & \multicolumn{2}{c}{**Without Load**} & \multicolumn{2}{c}{**With Load Load (\(\overline{\textbf{X}}\) (\(\pm\)SD)**} & \multicolumn{2}{c}{**With Load (\(\pm\)SD)**} & \multicolumn{2}{c}{**(Partial Era (\(\pm\)SD) Square \(\eta\)2p)**} & \multicolumn{2}{c}{**(Partial Era (\(\pm\)2p) Square \(\eta\)2p)**} \\ \hline
**Hip adduction** & 0.85 (0.28) & 1.01 (0.34) & 0.75 (0.28) & 1.18 (0.51) & 0.005 * & 0.735 & 0.155 \\
**Hip internal rotation** & 0.17 (0.05) & 0.21 (0.77) & 0.16 (0.05) & 0.21 (0.10) & 0.001 * & 0.970 & 0.791 \\
**Knee flexion** & 0.89 (0.35) & 0.99 (0.48) & 0.75 (0.39) & 0.86 (0.48) & 0.089 & 0.451 & 0.857 \\
**Knee valgus** & 0.76 (0.29) & 1.00 (0.50) & 0.66 (0.32) & 1.08 (0.64) & 0.002 * & 0.965 & 0.356 \\
**Knee internal rotation** & 0.15 (0.06) & 0.18 (0.08) & 0.16 (0.06) & 0.24 (0.12) & 0.006 * & 0.234 & 0.197 \\
**Foot** & 0.07 (0.06) & 0.10 (0.09) & 0.04 (0.03) & 0.05 (0.05) & 0.119 & 0.115 & 0.423 \\
**pronation** & & & & & (0.112) & (0.114) & (0.031) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mean peak moments with and without load according to research groups and load condition (right leg, in Nm/BW).  
Figure 5: Maximal knee joint valgus moment in both groups.
# GDPR and Large Language Models: Technical and Legal Obstacles
## 1 Introduction
### The European Union's Artificial Intelligence Act (AI Act)  
The EU's Artificial Intelligence Act (AI Act), officially adopted on 13 June 2024, introduces a harmonized regulatory framework for AI systems across the European Union [58]. In addition to the current regulation, earlier studies have provided an AI security perspective on the proposed requirements. For instance, [PERSON] et al. have examined the applicability of the AI Act's technical requirements to high-risk systems, highlighting the gap between the regulatory mandates and available AI security countermeasures [59]. Their work reinforces the need for an integrated framework to evaluate AI system security within the context of the new regulatory landscape.  
Key provisions relevant to Large Language Models (LLMs) include several requirements. First, AI systems operating in high-risk areas must meet stringent data quality standards, ensuring that training datasets are accurate, representative, and free of bias. This aligns closely with GDPR principles by ensuring data used by AI systems is both relevant and fair. In addition, transparency obligations require that AI systems interacting with users disclose their AI nature, making it clear when individuals are engaging with an automated system. Organizations must document and share detailed information about the functioning of their AI systems, thus enhancing traceability and explainability. Furthermore, the Act mandates the establishment of compliance frameworks: providers must maintain detailed technical documentation, register high-risk AI systems in an EU database, and conduct ongoing monitoring and reporting to mitigate risks related to data accuracy, bias, and safety.  
For LLMs, these provisions have significant implications. Providers must clearly disclose when users are interacting with an AI system and ensure that risk management practices, including comprehensive risk assessments, are in place. Data governance is also critical: training data must meet high standards of quality and fairness to avoid perpetuating biases, and human oversight is required especially in critical decision-making contexts such as recruitment or credit assessments. Organizations need to navigate the overlaps between the AI Act and GDPR, ensuring compliance with both sets of regulations. This involves maintaining detailed technical documentation for each AI system and implementing robust risk management and human oversight measures to ensure that AI systems are deployed in a safe, transparent, and ethically sound manner.  
In summary, the AI Act represents a significant step toward making AI systems, including LLMs, safer and more transparent by mandating robust risk management, human oversight, and detailed documentation. Organizations must proactively align their AI development and deployment practices with these new regulations to ensure full compliance and to foster ethical and fair AI practices within the EU [58].
###### Abstract  
Interactive evolutionary algorithms (IEAs) coupled with a data-driven user surrogate model (USM) have recently been proposed for enhancing personalized recommendation performance. Since the USM relies on only one model to describe the full range of user preferences, existing USM-based IEAs have not investigated how knowledge migrates between preference models to improve the diversity and novelty of recommendations. Motivated by this, an interactive multifactorial evolutionary optimization algorithm with multidimensional preference user surrogate models is proposed here to perform a multi-view optimization for personalized recommendation. Firstly, multidimensional preference user surrogate models (MPUSMs), partial-MPUSMs, and probability models of MPUSMs are constructed to approximate the different perceptions of preferences and serve for population evolution. Next, a modified multifactorial evolutionary algorithm is used for the first time in the IEAs domain to recommend diverse and novel items for multiple preferences. It includes initialization and diversification management of a population with skill factors, recommendation lists of preference grading and interactive model management of inheriting previous information. Comprehensive comparison studies in the Amazon dataset show that the proposed models and algorithm facilitate the mining of knowledge between preferences. Eventually, at the cost of losing only about 5% of the Hit Ratio and Average Precision, the Individual Diversity is improved by 54.02%, the Self-system Diversity by 3.7%, the Surprise Degree by 2.69%, and the Preference Mining Degree by 16.05%.  
evolutionary optimization; multitask optimization; interactive; surrogate model; personalized recommendation Article
# Learning-Based Traffic Scheduling in Non-Stationary Multipath 5G Non-Terrestrial Networks
## 4 Discussion  
We now discuss in detail the simulation results presented above and provide a proof of concept for how our AC agent works and how it can handle a non-stationary channel model by retraining its model either after moderate changes in the elevation angle or after abrupt changes (e.g., due to handoffs). We do not wish to elaborate on how the satellite sequences were selected to perform the handoffs. This could instead be the subject of future studies to determine an optimal strategy for the handoffs that moderates the abrupt changes and shortens the learning periods. We evaluated the performance of our AC agent in terms of the following aspects: retraining performance, path selection, E2E loss reduction and bandwidth utilization. We also compared the performance of our agent with the optimal  
Figure 8: Bandwidth utilization.  
Figure 7: E2E Loss rates.  
policy and an RR system that transmitted with only a single path in a round-robin fashion with no regard to the path conditions.
# Study on Corrosion Monitoring of Reinforced Concrete Based on Longitudinal Guided Ultrasonic Waves
## 4 Testing of Corroded Reinforcement
### Test Results for Bare Reinforcement  
#### 4.2.1 Mass Loss Rate of Reinforcement  
Reinforcement with different corrosion degrees is produced by controlling the corrosion time through corrosion tests, and the mass loss of reinforcement is shown in Table 2. It can be seen that the reinforcement mass loss rate is linearly related to the corrosion time and that the mass loss rate observed in the tests is in general agreement with the theoretical value derived from [PERSON]'s law. Therefore, the mass loss at any time can be estimated according to [PERSON]'s law. In order to ensure the unity of the full text and facilitate the comparison between the finite element and the test, the theoretical mass loss rate will be used as the actual corrosion degree to analyze the results.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Corrosion Time T (h)** & **0** & **24** & **48** & **72** & **96** & **120** & **144** \\ \hline \(\eta_{test}\) (\%) & 0 & 5.85 & 10.67 & 18.25 & 22.49 & 32.06 & 36.78 \\ \(\eta_{throw}\) (\%) & 0 & 6.27 & 12.54 & 18.81 & 25.08 & 31.35 & 37.62 \\ \(\Delta\eta\) (\%) & 0 & \(-6.70\) & \(-14.91\) & \(-2.98\) & \(-10.33\) & 2.26 & \(-2.23\) \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of test and theoretical loss rate for reinforcement.  
Figure 10: Time domain waveform and frequency spectrum of the excitation signal.  
Figure 9: Test setup.
## 7 Joint Spectrum
Therefore, the factorable condition can be given by  
\[\frac{1}{\sigma_{p}^{2}}+\frac{r\tau_{s}\tau_{i}}{2}=\frac{1}{\sigma_{p}^{2}} +\frac{rL^{2}}{2}\left(\frac{1}{v_{p}}-\frac{1}{v_{i}}\right)\left(\frac{1}{v _{p}}-\frac{1}{v_{s}}\right)=0. \tag{26}\]  
There are two conditions for factorizing photon-pair state. If, for the broad pump width, \(\sigma_{p}\gg\tau_{j}\), either \(v_{p}=v_{s}\) or \(v_{p}=v_{i}\) is satisfied, generated photons are factorable. But, since the pump wavelength is very close to the wavelength of either signal or idler photon, it is difficult to suppress the pump noise. The second condition is that the two terms \(1/\sigma_{p}^{2}\) and \(r\tau_{s}\tau_{i}/2\) are canceled each other. In this case, group delays \(\tau_{s}\) and \(\tau_{i}\) have different signs and the phase-matching function exhibits a positive slope. The most conventional way to reduce the correlation between the signal and idler photons is to use spectral filters. By choosing narrowband signal and idler photons, they consist of a single spectral mode that makes them uncorrelated. However, using a filter with narrow bandwidth brings about a reduction in the probability that the photon transmits through it, and reduces the efficiency of generation. Figure 17 illustrates the possibility of generating experimentally realizable uncorrelated photon-pairs without employing spectral filters. Here, for 1164 nm pump, a 5 cm-long MNF with 810 nm-diameter can generate uncorrelated photon-pairs with 825 nm and 1.55 \(\upmu\)m. In this condition, idler photons at 1.55 \(\upmu\)m are compatible with the telecom-band fiber-based system, while the signal photons at 825 nm have high detection efficiency in a Si-based single photon detector that makes it suitable to build heralded single photon sources in telecom-band with high efficiency. Also, the Raman noise can be significantly suppressed due to the large detuning between pump and signal/idler photons. As a result, in Figure 17c, uncorrelation between photon-pairs is not completely observed due to the sinc-shaped phase-matching function. The secondary lobes along the vertical axis always cause a small amount of correlation between signal and idler, although the main peak is not correlated. In this case, we are able to suppress the secondary peaks of the sinc function by using a spectral filter. Because of the side lobes which are less than 10% of the total intensity, the photon flux suffers from a little reduction in a high peak signal. It is on the contrary to the correlated main peak cases in Figures 15 and 16.
## 4 Results and Discussion
Figure 6 presents the dynamics of \(\rho_{a}^{(1)}\) and \(\rho_{a}^{(2)}\) at the fixed points \(x=0\) m and \(x=0.02\) m with and without dynamical factors to estimate the influence of parameters \(\gamma^{(i)}\), \(\omega^{(i)}\). We can see some non-monotonic changes in \(\rho_{a}^{(1)}\). From Figure 6a we can conclude that when dynamic factors are not taken into account over time, the capacity of active zone for each component \(\rho_{a0}^{(i)}\) is completely saturated with deposition at the point \(x=0\). In the case when dynamical factors are taken into account at the point \(x=0\) over time first \(\rho_{a}^{(i)}\) increases, then decreases, and finally the dynamical equilibrium occurs before capacity of active zone is completely saturated with deposition. Due to increasing of depositions this leads to decrease of porosity, and by increasing the pressure gradient intensity the detachment of deposited particles also increases. From Figure 6b at the point \(x=0.02\) dynamical equilibrium is not occurred in studied time. Including dynamic factors in kinetic equation leads to increase of concentration of deposition in active zone. This is more noticeable in dynamics of \(\rho_{a}^{(1)}\) (Figure 6b). Figure 7 presents the dynamics of \(\rho_{p}^{(i)}\) and \(\rho_{a}^{(i)}\) at the fixed point \(x=0.01\) m with different values of kinetic coefficients. It can be seen that increasing kinetic parameters leads to increase of deposition concentration in both zones. Increasing parameters \(\beta_{p0}^{(i)}\) leads increases intensity of deposition formation in passive zone, respectively increasing parameters \(\beta_{a}^{(i)}\), leads increases intensity of deposition formation in active zone. It is significant in small values of time. Figure 5: Dynamics of \(\rho_{p}^{(1)}\) and \(\rho_{p}^{(2)}\) at the points \(x=0\) m (**a**) and \(x=0.04\) m (**b**), at different values of \(\rho_{p1}^{i}\).
# Lightweight Frequency Recalibration Network for Diabetic Retinopathy Multi-Lesion Segmentation
## 3 Results and Discussion
### Training Parameter and Evaluation  
The experiments are conducted on Keras and optimized using the Adam (adaptive moment estimation) optimizer. Considering the stability of the model training process and the speed of the training, the initial learning rate is set at 0.001. According to the sizes, complexities, and need to reduce the risk of model overfitting of the IDRID, DDR, and ultra-wide-field fundus image datasets, the training epochs for the three datasets are 100, 150, and 100, respectively. Considering the scales of the datasets, as well as the convergence speed of the model and hardware memory usage, the batch sizes for training the threedatasets are 8, 8, and 16, respectively. During the training process, our model with the least weight loss in the validation set is retained for testing.  
In this paper, quantitative evaluation metrics include accuracy, recall, specificity, precision, and F1, and ROC (receiver operating characteristic) and PR (precision-recall) curves are plotted. For fundus image segmentation, we primarily focus on the area under the ROC and PR curves. The ROC curve has the false-positive rate (FPR) on the x axis and the true-positive rate (TPR) on the y axis. The larger the area under the curve (AUC-ROC), the better the model's performance at different thresholds. The PR curve has recall on the horizontal axis and precision on the vertical axis. The larger the area under the curve (mAP), the better the model's performance in predicting the positive class.
## 1 Introduction  
Nowadays, microwave communication technologies have reached the upper limit of data throughput. As a result, the interest in laser technology in optical wireless communication (OWC) has been renewed. From the practical point of view, this technology applies to devices operating in the range of several atmosphere transmission windows, which are characterized by lower propagation losses. These windows are located around 0.8 um and 1.55 um in near-infrared (NIR), from 3.0 to 5.2 um in mid-infrared (MWIR), and from 8 to 12 um in long-wavelength infrared (LWIR) [1]. Commercially available OWC systems most often operate in the NIR range in which well-developed optoelectronic technologies exist.  
MWIR is a highly transparent optical transmission window in the atmosphere, mainly characterized by a \"lack\" of water vapor absorption. There is a lower beam distortion due to beam scattering, beam wandering, loss of spatial coherence, or scintillation [2, 3]. However, developing such technology requires the MWIR radiation sources with high power, eye-safe, small size and cost, and room temperature (RT) operation. Quantum Cascade Laser (QC laser) and Interband Cascade Laser (IC laser) have great potential as components of novel OWC systems. Some experimental results of using QC lasers to transmit data with the rates from tens of Mb/s to a few Gb/s at wavelengths from 3 um to 4.8 um and 77 K or 300 K temperatures are discussed in the literature [4, 5, 6, 7, 8, 9, 10]. However, most of them concern OWC laboratory setups with QC laser structures working at very short distances.  
The very good operating parameters of ICLs, such as low threshold currents resulting in low power consumption [11], relatively high output power [12], wide tunability of the emission wavelength [13; 14], have found numerous applications in medical diagnostics [15] and environmental protection (monitoring of, e.g., formaldehyde [16]). But only a few tests of the optical link have been described. For example, the laser operated at T = 77 K with an output power of \(\sim\) ten mW and a wavelength of 3.0 \(\upmu\)m was used to obtain a data rate of 70 Mb/s [8]. A construction of two-mm long lasers, mounted onto an oxygen-free copper holder with temperature-controlled cold finger of the optical cryostat and wire bonded to 50 \(\Omega\) RF microstrip line, was also described in [4]. The results were obtained using unique designed lasing structures operated at 77 K with a pulse power of 10 mW.  
Although the IC laser technology is still developing, the research area of its application in wireless communications is still unexplored. The main goal of the performed works was to define the state-of-the-art of this technology enabling transmission of data signals. It should also be emphasized that, to the best of the acquired knowledge, this is the first demonstration of the data link using the \"on-shelf\" IC laser operated at room temperature at the wavelength of \(\sim\)4 \(\upmu\)m. The main virtues of this link compared with some OWC setups described in the literature are listed in Table 1.
# Advancing Phantom Fabrication: Exploring 3D-Printed Solutions for Abdominal Imaging Research
## 2 Materials and Methods
### Printing of the Abdominal Mold  
This research employed a custom-designed 3D printer to fabricate the foundation of the abdominal phantom. The printer utilized a fused deposition modeling (FDM) technique, offering a balance between affordability, resolution, and material compatibility. We used the custom 3D printer Signage 3D S1 with a build volume of 1200 \(\times\) 800 \(\times\) 250 to build the mold shown in Figure 3.  
The design of the 3D model for the abdominal mold was generated using DICOM images from the CT scans of a reference anatomy. Segmentation techniques were employed to isolate individual organs and structures within the abdominal cavity using 3D Slicer, a free open-source software application; 3D Slicer's versatile image processing capabilities enabled the precise delineation of abdominal organs from CT scan data. The segmented volumes were subsequently exported as STL files for additional optimization, ensuring the accurate representation of the target anatomical structures.  
These segmented regions were then converted into 3D models using Meshmixer software Version 3.5, which allowed for precise plane cuts to optimize model orientation for subsequent 3D printing. This software enabled the repair of any imperfections, such as holes and inconsistencies, ensuring optimal printability. Additionally, Meshmixer was used to manipulate model geometry, including scaling and combining components, to create the desired phantom configuration. The software's slicing capabilities were instrumental in generating support structures, enhancing the overall print quality.  
The final 3D model incorporated features that facilitated the subsequent casting process, such as registration points for alignment and channels for introducing the casting materials.  
Figure 3: The procedure of manufacturing an anthropomorphic phantom. (**A**) Three-dimensionally printed mold from DICOM images; (**B**) volume rendering reconstruction after scanning on CT; (**C**) axial images of phantom mold after scanning on CT.