esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Orthogonal Least Squares Identification Method for a Class of Fractional Hammerstein Models
## 3 Identification Algorithm
### Determination of Sparsity Level  
To determine the sparsity level, various information-theoretic criteria (ITCs) can be employed, including the Akaike information criterion (AIC) [29], the Bayesian information criterion (BIC) [29], and the Mallows Cp criterion (Cp) [30]. For the purposes of this paper, our focus will be on the BIC. Typically, the BIC is expressed as  
\[\text{BIC}(k)=m\ln(\frac{\|\mathbf{r}_{k}\|^{2}}{m})+k\ln(m), \tag{35}\]  
where \(\mathbf{r}_{k}:=\mathbf{Y}-\mathbf{\Phi}_{k}\hat{\mathbf{\theta}}_{k}\) denotes the residual vector. The sparsity level can then be determined as  
\[\hat{K}=\arg\min_{\hat{k}=1:\hat{K}}\text{BIC}(k), \tag{36}\]  
where \(\hat{K}\) denotes the maximum allowed level of sparsity. However, the upper bound \(\hat{K}\) is hard to determine. In this paper, we propose a new stopping rule based on the BIC criterion,  
\[R(k)=\frac{\text{BIC}(k)-\text{BIC}(k-1)}{\text{BIC}(k-1)-\text{BIC}(k-2)}. \tag{37}\]  
Given a small threshold \(\varepsilon_{0}\)[31], when \(R(k)<\varepsilon_{0}\), the iteration stops. Then, we have  
\[\hat{K}=k-1. \tag{38}\]By adopting this stopping criterion, the computational complexity is reduced, enabling the model to converge rapidly to an accurate sparse structure. Once the sparsity level \(K\) has been established, the following subsection explores methods for determining the model order and separating the mixed parameters based on the obtained estimates \(\hat{\mathbf{\theta}}\).
# OPMSS: Optimal Producer Mobility Support Solution for Named Data Networking
## 2 Related Works
### Broadcasting Method  
The broadcasting of interest packets is a practical aspect of NDN; therefore, much of the research has used the broadcasting method in different sub-fields of networking to enhance the performance of NDN. Vehicle-to-NDN [40], vehicle-to-vehicle NDN [41; 42], and wireless NDN [43] utilize the broadcasting method for data and interest communication to support moving contents, although some additional methods, techniques, or mechanisms have been added to mitigate broadcasting storms. [PERSON] in [44] proposed a popularity-aware intra-domain content mobility management solution that uses broadcasting and a name resolution service. The model utilizes a name resolution system (NRS) to update the network about the new location of the mobile producer when there is high popularity content and low mobility, while broadcasting of the location queries is used when the content popularity is low and there is high mobility [44]. The solution providesmobility support and significantly reduces signaling overhead costs. [PERSON] and [PERSON] [45] proposed a location-aware on-demand multipath protocol that uses a broadcast transmission mechanism to transmit data and interest packets. The packets are transmitted via multiple routes to the new location of the mobile producer. In addition, a relay node is used to control high signaling and to prevent intermittent connectivity and packet flooding that negatively affects network performance.  
[PERSON], [PERSON], and [PERSON] [46] showed how broadcast-based self-learning is applied to solve the trust and name-prefix granularity problem in NDN networks. The broadcast-based self-learning mechanism is used to find the delivery path of packets when content is moving within the network [46]. Therefore, in this paper, a broadcasting strategy is designed to utilize _MI_ packets to update intermediate routers when the producer relocates to a new point of attachment (PoA). The _MI_ packets are similar in function to the traced and tracing interest in [25] the binding update or forwarding hint in [47], and the mobility management packet in [39]. In addition, a network domain is restricted as in [11] to control broadcasting storms and to prevent high signaling.
## 6 Analysis of Power Loss and Efficiency
With the application of the proposed controller, each switch records almost identical power loss compared to varying power loss which is observed when the conventional controller is used. In other words, uniform distribution of power loss among the switches can be successfully achieved with the use of the proposed controller.
# Convolutional Sparse Modular Fusion Algorithm for Non-Rigid Registration of Visible-Infrared Images
## 4 Modular Training Strategy and Loss Functions  
The proposed ICSF adopts a modular training strategy that allows for flexible adjustment of parameter quantities according to specific tasks or processes while avoiding coupling between modules. The modular training strategy is divided into three stages, as shown in Figure 5.  
In the first stage, Codec Training Stage, the codec (encoder-decoder) is trained through a reconstruction loss.  
In the second stage, Feature Filtering Training Stage, the feature filtering network is trained using feature-guided and entropy loss.  
The third stage is divided into the fusion and registration stages, which can be trained simultaneously. The fusion module is trained using a fusion loss in the fusion stage. In the registration stage, the registration matrix prediction module is trained using both self-supervised registration loss and fully supervised prediction loss.
# A Study on ML-Based Sleep Score Model Using Lifelog Data
## 2 Materials and Methods
### Second Step: Intermediate Sleep Score
#### 2.3.2 Modeling: Multi-Stacking Ensemble Models Based on Machine Learning and Deep Learning  
Figure 3 shows in summary form the machine learning and deep learning-based stacking ensemble model construction and design used in this study.  
Machine learning algorithms used for prediction are XGBoost [46], LightGBM [47], CatBoost [48], and the TabNet neural network model (a deep learning model) [49]. Meta-models used are linear regression, Bayesian Ridge Regressor [50], ElasticNet Regressor [51], and Ridge Regressor [52]. The stacking ensemble design method consists of three steps, presented in Figures 4-6. Figure 7 shows the operating process based on cross-validation within each individual model.  
Figure 3: Summary graph for stacking technique: ML and DL indicate machine learning and deep learning, respectively.  
Figure 4: Summary graph for the first step of the stacking method: training dataset of metamodels is generated by machine learning models and a deep learning model in this step.  
Figure 5: Summary graph for the second step of the stacking method: training dataset of final model is generated by metamodels in this step.  
Figure 6: Summary graph for the final stage of the stacking method: in this step, a sleep habit score is calculated for the intermediate sleep state.  
In summary, in the first step, data is predicted using ML and DL models (LightGBM, XGBoost, CatBoost, TabNet) for good/bad sleep state data (feature) and sleep habit score (target). Stacking the output data by ML and DL models composes the data for the metadata (Figure 4). In the second step, three metamodels, linear regression, Bayesian Ridge Regressor, and Elastic-Net Regressor, are trained on the data constructed in the first step. Stacking the predicted data by metamodels composes the data for the final model. (Figure 5). In the last third step, the final prediction model, the Ridge Regressor algorithm, is used to predict the intermediate sleep habit score, and the performance error is measured by the mean squared error [53] (Figure 6). Specifically, in the first step, XGBoost, LightGBM, and CatBoost models derive optimal hyperparameters using the Optuna hyperparameter tuning framework [54]. The hyperparameters for each model are summarized in Table 13.  
In Figure 7, to improve overfitting that may occur in the process shown in Figures 4-6, each model generates stacking data for metamodel training and testing through cross-validation. Based on the generated data, the metamodel then yields the training and prediction performance.
# Photodynamic Therapy--An Up-to-Date Review
## 4 Nanomaterials for Photodynamic Therapy
Different architectures, ranging from linear and branched to crosslinked polymers, have been designed as delivery systems for the photo-based treatment of cancers [24]. An example of a convenient polymer for PDT use is polyacrylamide (PAA). PAA nanoparticles can be functionalized with amine or carboxyl groups, with a targeting moiety and/or with polyethylene glycol, and filled with actuating molecules, making them suitable for tumor-selective PDT [52]. PAA nanoparticles are highly soluble in water, being excellent delivery vehicles for PSs, such as methylene blue [96; 97; 98], Photofrin [98], 5,10,15,20-tetrakis(1-methyl 4-pyridinio) porphyrin tetra(p-toluenesulfonate) (TMPyP) [52], and Temoporfin (m-THPC) [52; 99]. Poly (D,L-lactide-co-glycolide) (PLGA) has also attracted attention for PSs encapsulation due to its biodegradability and ease of formulation. PLGA nanoparticles loaded with PSs have shown a higher photoactivity than free PDT drugs against cells from mammary tumors and ovarian cancer cells [52]. Due to their highly branched structure and monodispersity, dendrimers have been adopted in PDT for PS-delivery [52; 100]. Dendrimers-based delivery vehicles also benefit from enhanced permeability, sustained drug-release, high solubilization potential, high loading capacity, and improved colloidal, biological, and shelf-stability [101]. Particularly, dendrimers were used as carrier platforms for 5-aminolevulinic acid (ALA), an FDA-approved precursor of a PS employed in topical PDT [52]. Natural polymers, such as proteins and polysaccharides, can be turned into PDT-useful nanoparticles [52]. Human serum albumin, bovine serum albumin, xanthan gum,alginate, chitosan, and gelatin are several examples of such materials investigated for PDT [52,100]. Lipid-based nanostructures can also serve well in PDT [102]. Liposomes may be a promising targeted delivery nanosystem, as they can encapsulate unstable PSs and facilitate their permeation to and through the cell membrane [7,23,46]. Liposome-based PS delivery was shown efficient against metastatic melanoma cells, breast cancer cells, skin cancer cells, and tumor-derived angiogenic vascular endothelial cells [100]. Another option is to combine PSs with low-density lipoproteins (LDL), especially due to many LDL receptors present on tumor cells' surface. Studies have proved that a PS-bonded non-covalently to LDL before administration results in improved PDT efficiency compared to free PS delivery [7]. Organic-inorganic hybrid materials, such as nanoscale metal-organic frameworks (MOFs), have also emerged as favorable delivery platforms in PDT. What recommends them for such application are their versatile functionalities owed to their chemical composition, the well-defined crystalline structures, large surface-to-volume ratio, high porosity, low-density, regular channel, adjustable aperture, and diverse topology and tailoring [8,14]. MOFs can be successfully applied in the treatment of various malignancies [103, 104, 105].
## 5 Limitations of the Study  
The results of this study should be understood with caution due to certain limitations. The variables measured in the lower extremities of swimmers have not been compared with those of the lower extremities of athletes in other disciplines, so we must not attribute the findings observed in this study exclusively to swimming. However, this is the first study to report foot characteristics in elite swimmers. Our research has been based exclusively on analyzing competitive swimmers and comparing our results with those obtained by other authors in previous research.  
A longitudinal study would provide more detailed information on the progression of biomechanical adaptations in swimmers from their early stages to high-performance levels, allowing for causal relationships to be established between training load and changes in foot structure and function. Future research should consider long-term follow-up to examine the influence of continuous training on foot biomechanics and its possible relationship with the onset of muscular discomfort.  
We have not been able to explain the differences found between the left and right feet of the swimmers with the methods used for the analysis without being able to guarantee whether the difference in length of the lower limbs represents a risk of injury or whether, on the contrary, they are a protection mechanism against injury.  
Another limitation concerns the decision to analyze each lower limb independently. Although this approach allows for a more detailed assessment of biomechanical differences between limbs, it may be considered controversial, as both extremities belong to the same individual. However, in recent years, several studies have employed a similar methodology, including feet or lower extremities, instead of individuals in their samples [38, 39, 40, 41]. This analytical strategy is based on the premise that functional and structural asymmetries between limbs may be clinically relevant and deserve to be evaluated separately.  
While this study provides relevant information on the biomechanical characteristics of the foot and ankle in competitive swimmers, specific variables related to training intensity or specialization in swimming styles were not included, factors that could influence musculoskeletal adaptations. Future research could explore these aspects in greater depth through a more detailed analysis of training load type and its impact on swimmer biomechanics.  
In future research, it would be interesting to take measurements before and after sports activity in order to observe anatomical changes.
# Design of a Compact Planar Magnetic Levitation System with Wrench-Current Decoupling Enhancement
## 6 Discussion
### Compact Planar Maglev Design  
The proposed compact maglev system had a planar square coil array to achieve both fine and long-range motion controls. The round-corner square coil was initially designed by the Fabry factor as it resembled the cylindrical coil. With the magnetic flux density comparison, the coil height was thereby optimized to achieve a high levitation force with acceptable power efficiency. Even though the zigzag array was better, the straight array might be more suitable to build as a compact module for an expanded work area. The design of the magnet mover will be important for efficiency in levitation.  
For the development of an expandable planar maglev system, two main components must be considered: system communication and motion-tracking system. The analog signal command in this work might not be suitable for a large area. The control and current driver components with automation protocols will provide more robust communication. The vision system is flexible for scalable areas. However, the larger the tracking area, the worse the motion-tracking quality.
## 3 Design and Analysis
### Field Stress Distribution in Bended Segment of FGIL  
In order to analyze the practicability of the proposed scheme for bent segments, simulation regarding electrostatic stress distribution for FGIL in case of bend was performed in comparison to its equivalent straight model. For the said purpose, 20-m-long straight and bent FGIL models were developed using Autodesk Inventor(r), and further field distribution and FUF for the two models were analyzed using COMSOL Multiphysics(r). Line bending was performed as per the permissible longitudinal minimum bending radius (LMBR) of reinforced polyvinyl chloride (RPVC), and field distribution as well as FUF analysis for the respective FGIL models was performed regarding axial and radial cross sections. Critical appraisal of bent and straight FGIL models regrading axial cross section revealed a trivial deviation of 0.7% and 0.8% in electric field intensity and FUF, respectively. Moreover, detailed comparison of two FGIL models regrading radial cross section showed a slight deviation of 0.4% and 0.5% in electric field intensity and FUF, respectively. Minimal deviation in the compared models was observed because longitudinal bending as per LMBR limits resulted in negligible circumferential deformation as well as gradual bending. Thus, field magnitude and stress distribution for the bent line segment were nearly the same as those for the straight line segment.
# Comparative Analytical Study of SCMA Detection Methods for PA Nonlinearity Mitigation
## 5 Conclusions  
In this paper, a low-complexity detector, the Bussgang-based MPA, was derived, and its BER performance was quantified. The proposed detector was found to present a BER floor comparable to that of existing RFF-based approaches. The BER floor was quantified analytically relative to the optimal RFF-based MPA without specific assumptions about the nature of the PA nonlinearity or the fading distribution. Additionally, the analytical results were validated by computer simulations considering different channel distributions. The detector is attractive despite its error floor due to its simplicity and suitability for hardware-limited IIoT systems, wherein achieving a certain level of QoS with low computational cost outweighs the requirement of obtaining a universally optimal BER performance.  
Figure 5: BER vs. SNR validation for the Bussgang-based detector for a Rayleigh channel.  
Figure 6: BER vs. SNR validation for the Bussgang-based detector for a Nakagami-\(m\) channel with \(m=0.5\).  
**Author Contributions:** The contributions to this paper are classified as follows: E.S.: writing-original draft preparation, conceptualization, software and visualization. R.M.: writing--Review and editing, supervision, formal analysis and validation. G.K.: writing--Review and editing, validation, methodology and project administration. V.B.: Review and editing. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the [PERSON] (RJM) research chair, and by the grant from the National Natural Sciences and Engineering Research Council of Canada (NSERC).  
**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.
# A Constrained Louvain Algorithm with a Novel Modularity
## 3 Materials and Methods
### Data  
In this paper, we use two kinds of networks. One kind is the ground truth networks with the recognized real community structures. Another kind is the \(LFR\) benchmark net works generated by the computer [43]. In the ground truth networks, the DBLP and amazon networks give top 5000 overlapping communities with highest quality. We deal with each overlapping node to the community with more edges to the community with the node and delete the communities with the size less than and equal to 3 nodes. The structure parameters and the communities' number of these networks are all listed in Table 1. Their detailed description is shown below. (i) Karate Network: The [PERSON]'s karate club network is a friendship network in a karate club in an American university [44]. After a dispute between the coach and the treasurer, the club splits into two. (ii) Dolphins Network: The dolphins network contains 62 dolphins living in Doubtful Sound, New Zealand, as reported by [PERSON] [45]. Two dolphins are connected by an edge if they are observed together more often than expected by chance during the years from 1994 to 2001. (iii) Football Network. The football network is the network of American football games between Division IA colleges during the regular season of fall 2000 [6]. The nodes denote the 115 teams and the edges represent 613 games played in the course of the year. The teams are divided into 12 conferences containing around 8-12 teams each. The games are more frequent between members of the same conferences than those between members of different conferences. (iv) DBLP Network: The DBLP network is a co-authorship network in computer science, where two authors are connected if they publish at least one paper together. Authors who published in a certain journal or conference form a community. (v) Amazon Network: The Amazon network was collected by crawling the Amazon website. It is based on the _Customers Who Bought This Item Also Bought_ feature of the Amazon website. If a product \(i\) is frequently co-purchased with product \(j\), the graph contains an undirected edge from \(i\) to \(j\). Each product category provided by Amazon is a ground-truth community. (vi) \(LFR\)s networks: Different from the networks above, \(LFR\) networks are classical benchmark networks generated by the computer [43]. For \(LFR\) networks with the number \(N\) of nodes and the average degree of \(\langle k\rangle\), degrees of nodes are distributed according to the power law with exponent \(2<\gamma<3\), and the sizes of communities also obey the power law distribution with exponent \(1<\beta<2\). Moreover, the community size \(s\) and node degree \(k\) satisfy the constraint \(s_{min}>k_{min}\) and \(s_{max}>k_{max}\). A mixing parameter \(\mu\) represents the ratio between the external degree of a node with respect to its community and the total degree of the node. When the value of \(\mu\) becomes large, the community structure of the network becomes ambiguous.
# Exploring Multi-Agent Debate for Zero-Shot Stance Detection: A Novel Approach
## 5 Discussion
### Error Analysis  
Based on the experimental results, we conducted a detailed error analysis of the ZSMD framework. Figure 4 summarizes the distribution of classification errors made by DeepSeek-v3 on the SemEval-2016 Task 6 dataset, categorized by underlying causes. We categorized the primary sources of errors into five types, as follows:  
(1) Insufficient or biased background knowledge supplementation: Due to the reliance on GPT-4o for data augmentation, the supplemented background knowledge may not always be entirely accurate or sufficiently comprehensive. This can lead to agents debating based on inadequate information, thereby affecting the final stance determination.  
(2) Complex sentence structures and implicit stance expressions: Certain tweets express stances in subtle ways, such as through sarcasm, double negatives, or metaphors, making it challenging for agents to accurately identify the stance.  
(3) Interference from language style and social media noise: Tweets may contain spelling errors, slang, abbreviations, or internet memes, which hinder the agents' ability to precisely interpret the true meaning of the text.  
(4) Ambiguity in stance judgment (subjectivity): Some tweets may lack a clear stance, or their interpretation may vary depending on the reader, introducing subjectivity that complicates stance detection.  
(5) Insufficient logical reasoning capability: During reasoning, the model may struggle to form a coherent and robust chain, leading to weak arguments. Supporting and opposing agents might list facts or opinions independently without logical progression, limiting the referee agent's ability to determine an accurate stance.  
Figure 3: Comparison of different numbers of debate rounds in our proposed ZSMD approach using DeepSeek-v3.  
To further illustrate the identified error types, we present two representative misclassified examples in Table 4. These error categories highlight the challenges faced by the ZSMD framework and provide insights into areas for potential improvement.