esa-sceva/satcom-chunk-collection huggingface dataset:
# Force Fight and Its Elimination in a Prototype of a Redundant Direct-Drive Avionic Actuator
## 7 Experimental Validation
### Validation of the Monitoring Mode and Master/Slave Method  
This method uses one position information feedback for the different motor lanes as shown in Figure 17. In this case, if an offset appears on the master sensor or slave sensors, no force fight will be created between the motor lanes. This is because of the monitoring block, which detects the fault on the position sensor of the motor lane 4 and isolates it. In this case, the position sensor of motor lane 4 is provided using the master/slave method, which is based on a voting algorithm implemented in the flight control computer.  
Figure 16: Torque responses in faulty operation mode.  
Figure 17: Master/slave method.  
In this method, the measured position to provide to the ECU of each motor lane is computed from the measured position of the three motor lanes. As they are similar, one among the three provides the same measured position to all the control loops as a master while the others, considered as slaves, are used to compare the master position with the slave positions.  
By implementing this method on the test bench and estimating the position feedback of the faulty motor lane from the other sensors, we notice that the position response (Figure 18) of the designed actuator is reached with better performance compared to the same response given in Figure 14. The absolute error is given between the two curves. Due to the type of the corrector (PI), the error is important during transient period. In addition, as shown in Figure 19, the force fight phenomenon between the motor lanes is eliminated. Indeed, all the motor lanes operate in motor mode to provide a quarter of the total motor torque and to compensate the load torque.  
Finally, for the other faults creating the force fight phenomenon, the master/slave method can be also used to provide the same Iq command or the same position command to all the motor lanes. In this case, a new voting algorithm will be developed to deal with each fault in order to isolate the false current or position command, providing a master value to the other motor lanes.  
In addition, other faults were created on the same test bench in order to validate the robustness of the designed control in faulty operation mode:  
1. Loss of one machine (BLAC lane),
2. Short circuit to the ground of a machine phase of a six-phase stator,
3. Loss of an inverter arm of an electronic power converter,  
Figure 19: Response torques using master/slave method.  
Figure 18: Position response using master/slave method.  
Loss of power (DC bus voltage) of an electronic power converter.  
These faults were selected according to their rate of occurrence. The experimental results show, for each fault, that the position and torque responses match the required specification in faulty mode and confirmed that the designed control is robust.
## 2 Materials and Methods  
### LIDC-IDRI Dataset  
Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) [28; 29; 30] is a well-known public dataset of thoracic CT scans for lung cancer. This dataset is comprised of 1018 exams, and each exam contains sequential, two-dimensional (2D) CT slices in DICOM format. Each slice has a \(512\times 512\) pixels dimension, and the number of slices per exam varied from 65 to 764. Moreover, each exam in the dataset contained an XML file that annotated the diagnosis results. These results are accurate because four experienced thoracic radiologists confirmed them through a two-phased reading process.  
In our proposed nodule detection, we applied 888 exams of LIDC-IDRI because some exams contained missing and inconsistent slices; thus, we excluded such exams from our experiments. We applied the annotation files as the ground truths to train and evaluate our method. Instead of detecting the pulmonary nodules directly from the 2D scans, we reconstructed three-dimensional volumetric images by stacking all the 2D slices in a single exam, i.e., each exam gave a volume image, and it was used as an input for our nodule detection.
# A Review on Flavonoids as Anti-_Helicobacter pylori_ Agents
## 3 Results  
### Review on H. pylori Infection and Treatment  
#### 3.1.1 Infection  
For _H. pylori_ to be able to infect its host, it must adhere to the stomach host cells in acidic pH. The bacterial strategy is to produce a cytoplasmatic urease enzyme. The presence of nickel ions helps the bacteria to metabolise molecules and survive in the stomach's acidic pH. It has been verified that urease is also associated with reactive oxygen species (ROS), which help colonise the stomach mucosa [25]. It has been observed that the pro-inflammatory activity of urease promotes neuroinflammation and tau phosphorylation, which may be linked to AD [26]. On the other hand, if the urease is reduced, then colonisation would be difficult, which might be a strategy for treatment [2].  
Adhesin proteins such as BabA, SabA, and HopQ (conserved outer membrane adhesin) are crucial for bacterial colonisation and survival, as they bind to mucins and receptors in mucosa cells.
# Enhancing Discoverability: A Metadata Framework for Empirical Research in Theses
## 2 Related Work
### Data Documentation Initiative (DDI)  
The DDI standard was developed to describe quantitative research data in the social sciences [18,19]. Written in XML, DDI includes rich metadata about study design, sampling, data collection, and variable-level information. Despite its power, DDI is too complex for widespread use by students documenting theses, especially those involving qualitative or mixed methods.  
The Data Documentation Initiative (DDI) provides rich metadata structures for quantitative research, but its complexity creates significant adoption barriers. DDI's XML-based architecture and 300+ element tags overwhelm non-technical users.  
While DDI offers comprehensive coverage for quantitative studies, its practical limitations are well-documented:* **Technical Complexity**: The standard's reliance on XML schemas and nested hierarchies (<dataDscr>, <var> tags) demands programming knowledge [19].
* **Training Requirements**: Average onboarding requires 40+ h for competent use [18]. [PERSON] and [PERSON] [12] noted that while many higher education institutions (HEIs) are aware of rich metadata models such as DDI, implementation is limited by technical barriers, lack of expertise, and insufficient institutional support. Their systematic review found no consistent framework in HEIs for documenting research metadata, nor widespread use of standards suited to non-specialist users--particularly for preserving, discovering, and reusing student-generated research data.  
Recent initiatives have acknowledged these limitations. The Qualitative Metadata Working Group [20] proposed best practice guidelines aimed at harmonizing qualitative data documentation across European social science repositories. These emphasize capturing contextual details such as interview settings, ethical considerations, and data processing workflows--areas typically underserved by Dublin Core and only partially addressed by DDI. Similarly, Nvivo, a widely used qualitative data analysis tool, offers internal metadata tagging for attributes like interview dates, participant demographics, and case classifications; however, these remain confined to proprietary environments and do not enable open, interoperable documentation [21]. While proposals such as the Qualitative Data Model for DDI [22] suggest promising extensions to existing standards, widespread adoption has yet to materialize.  
Together with the findings of [PERSON] and [PERSON] [12], these developments underscore a shared conclusion: existing standards either lack the specificity (e.g., Dublin Core) or accessibility (e.g., DDI) needed to document empirical student research effectively. This motivates the development of a lightweight, hybrid metadata framework that accommodates diverse methodological approaches while remaining usable by students and institutional staff.
# Normalized Weighting Schemes for Image Interpolation Algorithms
## 2 Materials and Methods
### Virtual Pixel Length-based Normalized Weighting Schemes
#### 2.3.2 Virtual Pixel Length for Hypotenuse-based Radius  
Figure 7 shows the virtual pixel length (A), the base (B), and the hypotenuse (C). In this case, the hypotenuse (C) is used as the radius to find the area of a circle, using Equation (6).  
Referring to the example, shown in Figure 7, the Equation (6)-based area replaces the pixel weights, in Equation (4). Furthermore, given that the four circular areas do not sum up to one, there is a need for weight normalization, using Equation (3). Apart from that, it is important to note that this is inspired by one of the works presented in [28], in which the Pythagorean theorem equation was preliminary used to tackle the image interpolation algorithmic efficiency.  
Figure 8 illustrates the simplified or generalized flowchart of the procedure of how the image interpolation generally works for upscaling/zooming-in purposes by TB, MD, HR, AT, and AC. Specifically, Figure 8 shows at which step interpolation weighting schemes are needed. Proposed image interpolation methods are different from traditional methods in terms of, or at the level of, the weighting schemes. Note that traditional nearest neighbor interpolation (TN) and traditional bicubic interpolation (TC) also follow the same procedure (as shown in Figure 8) but use different weighting schemes [10; 23]. In addition, note that,  
Figure 6: Example showing the virtual pixel length-based height for the area of the triangle weighting scheme—this is the AT weighting scheme.  
Figure 7: Example showing the virtual pixel length for hypotenuse-based radius for the area of the circle weighting scheme – this is the AC weighting scheme.  
for image upscaling or zoom-in purposes, the value of (\(x\)) in Figure 8 must be at least equal to one.
## 4 Optimization Model Solution
### SA Algorithm  
The simulated annealing (SA) algorithm [35] has the ability to make probabilistic jumps in search processes, which can effectively prevent the search process from falling into the local minimum solution. The simulated annealing algorithm not only accepts good solutions in the annealing process but also accepts poor solutions with certain probabilities. At the same time, this probability is controlled by temperature, and its size decreases with a decrease in temperature. At the beginning of the iteration, an initial temperature \(T_{0}\) thus needs to be set, and an initial state is generated in the search space as the initial solution. When the new solution is accepted at each iteration, the temperature is reduced using the cooling coefficient. The formula for calculating the temperature is as follows:  
\[T_{n}=\left\{\begin{array}{l}T_{0}&n=1\\ kT_{n-1}&n>1\end{array}\right. \tag{27}\]  
where \(T_{n}\) is the system temperature at the nth iteration, \(T_{0}\) is the initial temperature, and \(k\) is the temperature attenuation coefficient.  
For the new solution and the old solution generated in the process of optimizing the parameters of PSO, a decision is made for whether the new solution is taken as the global optimal solution under the Metropolis criterion or the old solution of a certain probability \(P\) is taken as the global optimal solution. The formula for calculating probability \(P\) is as follows:  
\[p=\frac{\exp(-(f(p_{i})-f(p_{g}))/T_{n})}{\sum\limits_{i=1}^{N}\exp(-(f(p_{i} )-f(p_{g}))/T_{n})} \tag{28}\]
## 4 Establishment of the Model
### Computational Model
#### 4.2.2 Loss Calculation Model  
Although it is desirable to reduce the volume of the power equipment by increasing the operating frequency, a higher operating frequency and a smaller volume leads to a smaller surface area through which heat can be dissipated; this also leads to a rapid increase in the high-frequency loss of the core and winding, resulting in low power transmission efficiency [21; 22]. Therefore, it is necessary to calculate and analyze the loss.  
There are two main methods for calculating the core loss under sinusoidal excitation: namely, the iron loss separation method and the Steinmetz empirical formula. According to the different mechanisms of magnetic core loss, the Bertotti iron loss separation method decomposes the magnetic core loss into hysteresis loss, eddy current loss, and residual loss. The formula of the iron loss separation method under sinusoidal excitation is as follows [23]:  
\[P_{V}=P_{h}+P_{e}+P_{c}=k_{h}B_{m}^{a}f+k_{e}B_{m}^{2}f^{2}+k_{e}B_{m}^{1.5}f^ {1.5} \tag{7}\]  
where \(P_{V}\) is the total loss, \(k_{h}\) is the hysteresis loss coefficient, \(k_{e}\) is the eddy current loss coefficient, \(k_{c}\) is the residual loss coefficient, and \(\alpha\) is the magnetic density coefficient.  
Generally, as long as the magnetic core loss experimental data measured under sinusoidal excitation are fitted, the magnetic core loss coefficient and the magnetic flux density index value can be obtained, following which, the analytical formula of the iron loss separation method can be obtained.  
As the degree of saturation increases, a local hysteresis loop will be produced, which will increase the hysteresis loss. If the coefficient \(\alpha\) is fitted with only one constant, the error between the fitting value of the hysteresis loss and the measured value will increase when the peak magnetic induction intensity, Bm, is higher. Moreover, the hysteresis loss, Ph, is the energy loss caused by the magnetic domain overcoming the field force during the rotation process. Its size depends on the intensity, Bm, of the magnetic flux density, and is independent of the frequency, \(f\), of the external excitation [24]. Therefore, we express \(\alpha\) in the form of a Bm polynomial [25], namely  
\[\alpha(B_{m})=\alpha_{3}B_{m}^{3}+\alpha_{2}B_{m}^{2}+\alpha_{1}B_{m}+\alpha_ {0} \tag{8}\]  
In the above formula, \(\alpha_{i}\) (i = 0, 1, 2, 3) is the coefficient of the i-th power of \(B_{m}\), and \(\alpha_{i}\) is obtained by fitting the measured values.  
The hysteresis loss formula can be obtained as follows:  
\[P_{h}=k_{h}B_{m}^{(\alpha_{3}B_{m}^{3}+\alpha_{2}B_{m}^{2}+\alpha_{1}B_{m}+ \alpha_{0})}f \tag{9}\]  
The coefficient of the hysteresis loss of the 0.10 mm UTGO silicon steel and the FBA alloy can be obtained using the least square method, as shown in Table 2.  
At this time, the total loss \(P_{V}\) is  
\[P_{V}=k_{h}B_{m}^{(\alpha_{3}B_{m}^{3}+\alpha_{2}B_{m}^{2}+\alpha_{1}B_{m}+\alpha _{0})}f+k_{e}B_{m}^{2}f^{2}+k_{e}B_{m}^{1.5}f^{1.5} \tag{10}\]
# Methodology for Simulating 5G and GNSS High-Accuracy Positioning
## Appendix A CRLB on mmWave Channel Parameters  
In this section, the derivation of the Fisher Information matrix related to the estimation of the channel parameters \(\mathbf{\eta}=[\tau_{0},\theta_{0},\phi_{0},h_{0}]\) for the LoS channel is provided. To begin with, let the matrix \(\mathbf{J}\) denote the FIM given by  
\[\mathbf{J}=\frac{1}{\sigma^{2}}\mathbb{E}_{\mathbf{a},\omega}\left\{\Re\left\{ \
abla^{\mathrm{H}}y_{n,m}\
abla y_{n,m}\right\}\right\}, \tag{10}\]  
where \(y_{n,m}\) is the received signal at the \(m\)-th receiving chain at the \(n\)-th subcarrier and \(\mathbb{E}_{\mathbf{a},\omega}\) indicates the expectation of a random variable with respect to the distribution of \(a\) (pilot symbol) and \(\omega\) (frequency). To compute the gradient \(\
abla y_{n,m}\), the following partial derivatives are used  
\[\frac{\partial}{\partial\tau_{0}}y_{n,m} =-j\frac{\sqrt{P_{c}}}{\sigma}2\pi\Delta_{f}h_{0}ne^{-j2\pi n \Delta_{f}\tau_{0}}\mathbf{w}_{m}^{\mathrm{H}}\mathbf{H}_{0}\mathbf{F}\mathbf{ a}_{n}, \tag{11}\] \[\frac{\partial}{\partial\theta_{0}}y_{n,m} =\frac{\sqrt{P_{c}}}{\sigma}h_{0}e^{-j2\pi n\Delta_{f}\tau_{0}} \mathbf{w}_{m}^{\mathrm{H}}\mathbf{D}_{r}(\theta_{0})\mathbf{a}_{R}(\theta_{ 0})\mathbf{a}_{T}^{\mathrm{H}}(\phi_{0})\mathbf{F}\mathbf{a}_{n}\] (12) \[\frac{\partial}{\partial\phi_{0}}y_{n,m} =\frac{\sqrt{P_{c}}}{\sigma}h_{0}e^{-j2\pi n\Delta_{f}\tau_{0}} \mathbf{w}_{m}^{\mathrm{H}}\mathbf{a}_{R}(\theta_{0})\mathbf{a}_{T}^{\mathrm{ H}}(\phi_{0})\mathbf{D}_{I}^{\mathrm{H}}(\phi_{0})\mathbf{F}\mathbf{a}_{n},\] (13) \[\frac{\partial}{\partial h_{0}}y_{n,m} =\frac{\sqrt{P_{c}}}{\sigma}e^{-j2\pi n\Delta_{f}\tau_{0}} \mathbf{w}_{m}^{\mathrm{H}}\mathbf{H}_{0}\mathbf{F}\mathbf{a}_{n},\] (14) \[\frac{\partial}{\partial h_{Q0}}y_{n,m} =j\frac{\sqrt{P_{c}}}{\sigma}e^{-j2\pi n\Delta_{f}\tau_{0}} \mathbf{w}_{m}^{\mathrm{H}}\mathbf{H}_{0}\mathbf{F}\mathbf{a}_{n}, \tag{15}\]  
where \(P_{c}\) and \(\sigma^{2}\) is transmit power and noise per subcarrier and \(h_{0I}\) and \(h_{0Q}\) are the in-phase and quadrature term of the complex number \(h\).
# Scaling of Rain Attenuation Models: A Survey
## 6 Conclusions  
This survey reviewed more than 17 rain attenuation scaling models. This review included the well-known and related literature on the scaling of rain attenuation models. Existing scaling models were categorized according to the model development and framework. The comparative table included different performance parameters such as the applicable frequency range and the long-term or short-term applicability of these scaling models. In the scaling of rain attenuation models, the rain rate distribution and the radio link usually follow a uniform distribution. In such cases, in the frequency scaling method, the SAM model is used to determine the spatial rainfall distribution, but it is a simple technique. Consequently, a sophisticated spatial rainfall distribution model that can represent the rain cell structures along the radio link is necessary. Furthermore, as mentioned earlier, beyond the rainfall rate, some other factors (fog, cloud, visibility) play a significant role in deteriorating the attenuation in GHz-range radio links. However, the proper scaling mechanism of these environmental elements has not been developed yet. Massive microwave links are projected to be implemented in 5G and future wireless networks where the operating frequency is within a frequency range susceptible for rain attenuation. In this situation, the scaling approaches can assist in making fast estimation of attenuation either in rainy or nonrainy conditions to manage the link budget.  
**Author Contributions:** [PERSON] reviewed the related literature and evaluated and outlined this survey. [PERSON] played a significant role in coordinating the research. [PERSON] drafted the initial manuscript, and it was subsequently modified and justified by D.[PERSON]. Both authors have read and agreed to the published version of the manuscript.  
**Funding:** The BrainKorea21 Four Program supported this research through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (4299990114316). Additionally, this research was also partially supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2019R1F1A1058128).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Learnable Nonlocal Contrastive Network for Single Image Super-Resolution
## 4 Experiment
### Ablation Study of Different Modules
#### 4.4.1 Learnable Nonlocal Contrastive Attention (LNLCA)  
By testing our model on the Set5 dataset, we verified the importance of the LNLCA module. Specifically, the established baseline network contained 20 LSRAG modules, each of which contained 10 simplified residual blocks, resulting in a network architecture with hundreds of layers, and we added skip connections to each base module. Table 2 shows the performance of the model after \(8.9\times 10^{5}\) training iterations on the Set5 dataset with a downsampling factor of 4, where the baseline model achieves PSNR = 32.642 dB and SSIM = 0.9003. The result for \(R_{a}\) verifies the effectiveness of the learnable nonlocal contrastive attention (LNLCA) module, as the PSNR value increases from the original 32.642 dB to 32.692 dB and the SSIM value increases from the original 0.9003 to 0.9007 compared with the baseline network. Specifically, we combined the LSRAG module in the baseline and our proposed LNLCA module to form a deep feature fusion attention group (DFFAG). The results for \(R_{a}\) are predictable because, compared with the traditional nonlocal attention module adopted by the baseline, we applied the proposed learnable nonlocal contrastive attention to sparsely aggregate the globally relevant information of the image with linear computational complexity, which significantly increased the discriminative learning ability of the network. These experimental results fully prove the superiority of using the LNLCA module.
# Predicting Football Match Results Using a Poisson Regression Model
## 1 Introduction  
The concept of producing predictions for football scores has been widely studied and researched during the past few decades, using the first statistical modeling approaches and insights. Current research has shown that the number of goals scored by a team in a football match can be modeled using a Poisson distribution. Specifically, the double Poisson model, which was initially developed in the 1980s, still remains a popular choice in such football scores' predictions.  
Therefore, the most notable attempts [1; 2; 3] use the Poisson distribution and, secondly, the Negative Binomial Distribution to model and calculate the probability of the number of goals scored in a football match by each team, based on previous scores. Additionally, these methods can also be used to estimate the outcome of the match based on the goals scored.  
The purpose of this paper is to assess whether it is possible to predict the result of a football match, by estimating the number of goals scored by each team, using historical data of past matches' results and Poisson distribution-based predictive models. A Poisson regression model is also utilized using a number of independent variables as predictors in order to estimate the expected number of goals scored for each team. Our methodology was applied to the Premier League's results from the 2022-2023 season. We have chosen to work with the Premier League, which is the top tier of English football, because it is one of the most popular and competitive leagues globally. Renowned for its high-paced, intense matches and attracting top talent from around the world, the Premier League garners a massive international fan base and lucrative broadcasting deals, solidifying its position as a premier destination for football excellence.  
The basic contribution of this work is the development of an algorithm of practical use, which, by using Poisson distributions along with football teams' historical performance asstated above, will be able to predict future football games' results. Although this algorithm has been developed based on the Premier League's 2022-2023 season, it can be applied to any football league. More specifically, the algorithm estimates the number of goals scored by each team with high accuracy. The vast majority of the differences between the observed number of goals and those estimated by the algorithm are \(\pm 1\) goal. Larger deviations have been observed in the results, which are considered as surprises, where a strong favorite side was not able to score against a weaker team.  
The rest of this paper is structured as follows: Section 2 discusses the related work, while Section 3 provides the theoretical background. The analysis of the results is described in detail in Section 4. Conclusions and future work are discussed in Section 5.