esa-sceva/satcom-chunk-collection huggingface dataset:
# Classification of Parkinson's Disease Using Machine Learning with MoCA Response Dynamics
###### Abstract  
Neurodegenerative diseases (NDs), including Parkinson's and Alzheimer's disease, pose a significant challenge to global health, and early detection tools are crucial for effective intervention. The adaptation of online screening forms and machine learning methods can lead to better and wider diagnosis, potentially altering the progression of NDs. Therefore, this study examines the diagnostic efficiency of machine learning models using Montreal Cognitive Assessment test results (MoCA) to classify scores of people with Parkinson's disease (PD) and healthy subjects. For data analysis, we implemented both rule-based modeling using rough set theory (RST) and classic machine learning (ML) techniques such as logistic regression, support vector machines, and random forests. Importantly, the diagnostic accuracy of the best performing model (RST) increased from 80.0% to 93.4% and diagnostic specificity increased from 57.2% to 93.4% when the MoCA score was combined with temporal metrics such as IRT--instrumental reaction time and TTS--submission time. This highlights that online platforms are able to detect subtle signs of bradykinesia (a hallmark symptom of [PERSON]'s disease) and use this as a biomarker to provide more precise and specific diagnosis. Despite the constrained number of participants (15 [PERSON]'s disease patients and 16 healthy controls), the results suggest that incorporating time-based metrics into cognitive screening algorithms may significantly improve their diagnostic capabilities. Therefore, these findings recommend the inclusion of temporal dynamics in MoCA assessments, which may potentially improve the early detection of NDs.  
machine learning (ML); Montreal Cognitive Assessment (MoCA); diagnostic accuracy; Parkinson's disease (PD); rough set theory (RST); time-based measurements; web-based cognitive testing; IRT; TTS +
Footnote †: journal:  
The result of this neuronal damage is the manifestation of neurological symptoms. Neurological symptoms of [PERSON]'s disease primarily include tremors, muscle stiffness, slowed movement (bradykinesia), and impaired balance and coordination. Cognitive changes, such as difficulties with memory and problem solving, can also occur as the disease progresses.  
Despite the fact that this damage is significant, it often goes unnoticed at first. This is partly because the brain has mechanisms to compensate for the damage. Keeping in mind that neuronal degeneration is a normal part of aging, in NDs, this process occurs much more rapidly and is initially masked by the brain's compensatory responses. That makes it difficult to identify specific biomarkers for early detection, diagnosis, and disease progression [7].  
Therefore, the lack of a cure for [PERSON]'s disease can be attributed to several factors and challenges in hidden manifestations across various domains (including genetic, clinical, and pathophysiological).
## 3 Routing
### Routing in CRSN: Rising Issues
#### 3.2.6 Cross-Layering Approach  
The success of designing efficient protocols for CRSNs lies in adopting the cross-layer approach. Even in the realms of WSNs, some solutions have adopted this approach for routing [21, 22] to improve the routing performance. Thus, because of the benefits of adopting this design approach, more researchers are adopting it as a practice in various areas. However, the cross-layer approach usually adopted in these schemes [21, 22] involves a one-way directional flow of information due to the traditional layered communication approach that is usually employed in protocol design.  
However, in reality, the interrelationship/interdependency of the layers in the CRSNs is bi-directional. As shown in Figure 1, both the spectrum mobility and management functions affect all communication layers. For example, the latency introduced by spectrum handoff adversely affects both the routing protocols and the transport layer protocols.  
Additionally, the application layer can stipulate its channel condition requirements or can request a spectrum handoff, just as the physical and MAC layers can also state their available conditions to the application layer, and adjust appropriately. Likewise, [23] demonstrated the interdependency between local contention (MAC layer) and end-to-end congestion (transport layer). Finally, routing must fully consider spectrum handoff and medium contention (MAC) alongside the action of routing [23] to meet specific QoS demands. The manner in which variations in channel properties affect routing has also been explored by [21, 22].  
The concept of cross-layering in the design of protocols does not seek to change the traditional information flow in the established layered communication model. Rather, all information needed from each layer (depending on the design) is usually stored in a dedicated buffer for individual layers to access before processing any data in their domain. Thus, the standard communication paradigm still holds. This concept of cross-layering alongside the CR at the physical layer actually adds cognition to the entire layer. This effect will produce better network performance in all scenarios.
## 2 Materials and Methods
### Immunoblotting  
Electroporated H9c2 cells were cultured in 6-well plates in complete medium and left to attach for 4 and 24 h in incubator. Next, cells were lysed with Pierce(tm) RIPA buffer (#89901, Thermo Fisher Scientific, Waltham, MA, USA), and their protein concentration was determined using the Pierce BCA Protein Assay Kit (#23225 and 23227, Thermo Fisher Scientific, Waltham, MA, USA), following the manufacturer's guidelines. Each sample within every replication was adjusted to the identical concentration prior to separation. The proteins were separated using Bolt(tm) 4-12%, Bis-Tris, 1.0 mm, Mini Protein Gels (#NW04125 BOX, Thermo Fisher Scientific, Waltham, MA, USA), and Mini Gel Tank (#A25977, Thermo Fisher Scientific, Waltham, MA, USA) and then transferred onto PVDF membranes using Power Blotter Select Transfer Stacks (mini, #P85210, Thermo Fisher Scientific, Waltham, MA, USA) and Power Blotter-Semi-dry Transfer System (Thermo Fisher Scientific, Waltham, MA, USA), following the manufacturer's guidelines. Ponceau S (0.1% (\(w/v\)) in 5% (\(v/v\)) acetic acid) staining was used to assess loading and transfer efficiency. After transfer, membranes were blocked with 7.5% (\(w/v\)) dry skimmed milk in Tris-buffered saline with Tween (TBST: 20 mM Tris, 150 mM NaCl, 0.02% (\(v/v\)) Tween 20, pH 7.5) for 1 h at room temperature. Following this, the membranes were incubated overnight at 4 \({}^{\circ}\)C with different primary antibodies in a solution containing 20 mM Tris, 150 mM NaCl, pH 7.5, 0.1% (\(w/v\)) BSA, and 0.1% (\(w/v\)) NaN\({}_{3}\). Additional information regarding primary antibodies used in this study can be found in Table 1.  
Following several washing steps in TBST solution, membranes were incubated with a secondary antibody horseradish peroxidase conjugate in TBST with 5% (\(w/v\)) dry skimmed milk for 1 h at room temperature. After additional washing steps in TBST, membranes were incubated with Pierce Enhanced Chemiluminescence reagent (#32106, Thermo Fisher Scientific, Waltham, MA, USA) for 1 min, after which bands were visualized and quantified using Uvitec Alliance Q9 (Uvitec, Cambridge, UK) and Quantity One 1-D Analysis Software 4.6.8. (Bio-Rad, Hercules, CA, USA).  
Figure 2: Comparison of mRNA expression levels of different sarco/endoplasmic reticulum Ca\({}^{2+}\)-ATPase (SERCA) isoforms, Na\({}^{+}\),K\({}^{+}\)-ATPase (NKA) subunits, and their regulators phospholaman (PIn) and phospholemman (FXYD1), respectively, in H9c2 cells. qPCR was used to evaluate basal levels of gene expression for SERCA1-3 (_Atp2a1-3_), NKA\(\alpha\)1-3 (_Atp1a1-3_), NKA\(\beta\)1-3 (_Atp1b1-3_), and their regulators phospholaman (_PIn_), and phospholemman (_Fxyd1_), respectively. qPCR was performed using rat-specific gene expression assays, normalized to geometric mean of 18S and PPIA endogenous controls. Results are means \(\pm\) SD (n = 7).
## 4 Results
### GPP Estimation Using the Light Use Efficiency Model
#### 4.1.2 Acquisition of Fitting Coefficients and Estimation of GPP  
Table 3 presents the calculated fitting coefficients specifically for the Huoditang forest area, which serves as a representative example of the broader forest area, from June to September for the years 2003 to 2010. The data shows an increasing trend in the fitting coefficients at this site during this period.  
Similarly, after obtaining the monthly average fitting coefficients for GPP and SIF for each grid in the Qinba Mountain forest area from June to September during 2003-2010, the corresponding monthly average fitting coefficients were applied to the SIF data for each grid from June to September 2011-2018 to estimate GPP. The GPP data product from the GPP dataset for June to September 2011-2018 was then used to validate the GPP estimates derived from SIF, resulting in the scatter plot shown in Figure 5. The estimated GPP demonstrated relatively high accuracy, with an R\({}^{2}\) value of 0.771.  
Since the fitting coefficient in the SIF-GPP relationship is the sole parameter in Equation (7) and is directly multiplied with the SIF data, a 1% perturbation of the fitting coefficient results in only a 1% change in the fitted GPP values.  
Figure 4: Monthly average trends in fitting coefficients (2003–2010). Note: The dots of different colors represent the values of the fitting coefficient S for each year between 2003 and 2010 in different months, while the black line indicates the average value of the fitting coefficient S for each month from 2003 to 2010.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Time** & **2003** & **2004** & **2005** & **2006** & **2007** & **2008** & **2009** & **2010** & **Average** \\ \hline Jun. & 174.13 & 211.01 & 166.01 & 137.14 & 201.24 & 149.57 & 133.88 & 211.58 & 173.07 \(\pm\) 26.71 \\ Jul. & 249.02 & 187.00 & 410.01 & 171.48 & 284.82 & 185.82 & 185.82 & 286.33 & 254.55 \(\pm\) 68.53 \\ Aug. & 366.90 & 354.62 & 424.29 & 344.05 & 288.43 & 270.15 & 270.15 & 326.92 & 337.60 \(\pm\) 45.06 \\ Sept. & 471.14 & 527.97 & 530.80 & 478.05 & 471.06 & 430.94 & 430.94 & 416.74 & 473.43 \(\pm\) 36.20 \\ \hline \end{tabular} Note: The horizontal title reflects the situation of the fitting coefficient S for each year from 2003 to 2010 as well as the multi-year average, while the vertical title reflects the situation of the fitting coefficient S for different months.  
\end{table}
Table 3: Fitting coefficient analysis of GPP and SIF during the growing season in Huoditang.
# Applications of Magnesium and Its Alloys: A Review
## 5 Conclusions  
Magnesium's availability and unique blend of properties has made it very attractive for engineering and biomedical applications. Furthermore, alloying magnesium can modify or enhance its existing properties to make it suitable for even more applications. As an engineering material, magnesium's notable characteristics include its lightweight, specific high strength to weight ratio, and superior machinability and castability. These factors enabled magnesium to find its footing in aerospace and automotive applications, where the industry is actively seeking lighter-weight alternatives to harness additional environmental and economic benefits. As a biomaterial, magnesium's notable characteristics include its biocompatibility, biodegradability, and bioavailability on top of the engineering properties. In addition, it also includes its elastic modulus and innate anti-microbial mechanisms. Hence, magnesium is mainly used in musculoskeletal and orthopedic applications while extensively being investigated for cardiovascular medicine.  
While its ability to biodegrade is celebrated, the rate at which it biodegrades is problematic. It biodegrades rapidly, and as a result, this impeded magnesium's widespread adoption in both engineering and biomedical applications. Nonetheless, with the ongoing research progress and technological advancements, the mitigation strategies to overcome magnesium's overly rapid degradation are widely investigated.  
Magnesium is a promising material, and market research has demonstrated its projected increased demand. However, today's primary magnesium is energy-intensive and inefficient to produce, which inspired research in additive manufacturing of magnesium alloys and using solar energy to perform the carbothermic reduction. Recycling magnesium is also an option to keep the supply circular, and there are ongoing investigations to determine the most commercially viable and environmentally sound recycling process.  
J.T.: Conceptualization, methodology, formal analysis, investigation, writing--original and revision draft preparations, S.R.: writing--review and editing, resources, supervision. Both authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.
## 3 Learning-Based Action Representation Approach
### Non-Deep Learning-Based Approaches
#### 3.1.1 Dictionary Learning-Based Approaches  
Dictionary learning is a type of representation learning which is generally based on the sparse representation of input data. The sparse representation is suitable for the categorization tasks in images and videos. Dictionary learning-based approaches have been employed in wide range of computer vision applications such as image classification and action recognition [123]. The concept of dictionary learning is similar to BoVW model because both are based on the representative vectors learned from the large number of samples. These representative vectors are called code words, forming a codebook in BoVW model, and dictionary atoms in the context of dictionary learning. One way to get the sparse representation of input data is to learn over-complete basis (dictionary). In [124], three over-complete dictionary learning frameworks were investigated for human action recognition. An over-complete dictionary was constructed from a set of spatio-temporal descriptors, where each descriptor was represented by a linear combination of small number of dictionary elements for compact representation. A supervised dictionary learning-based method was proposed for human action recognition in [125] based on the hierarchical descriptor. Cross-view action recognition problem was addressed by using transferable dictionary pair in [126]. In this approach authors learned the view specific dictionaries where each dictionary corresponds to one camera view. Moreover, authors extended this work and a common dictionary which shares information from different views [127]. The proposed approach outperforms state-of-the-art methods on similar datasets. A weakly supervised cross-domain dictionary learning-based method was proposed for visual recognition in [128]. This method learns discriminative, domain-adaptive, and reconstructive dictionary pair and corresponding classifier parameters without any prior information.  
Dictionary leaning-based methods also use unsupervised learning, for example, [PERSON], [PERSON] et al. [129] proposed an unsupervised approach for cross-view human action recognition. This method does not require target view label information or correspondence annotations for action recognition. The set of low-level trajectory features are coded using locality-constrained linear coding (LLC) [130] to form the  
Figure 7: Learning-based action representation approaches.  
coding descriptors, then peak values are pooled to form a histogram that captures the local structure of each action.
## 4 Core Insights and Critical Reflections
### Critical Commentary
#### 4.6.3 The Imbalanced Cost-Benefit Equation: SfM's Dominance over LiDAR  
Keyword co-occurrence analysis reveals a highly representative phenomenon: in the field of UAV-RS 3D modeling, SfM exhibits significantly higher frequency and network centrality than LiDAR. Although both are core technologies for 3D reconstruction, the disparity in their usage popularity reflects an underlying cost-benefit trade-off in current research. SfM enables image acquisition and point cloud generation using standard RGB cameras, offering clear advantages such as low cost, ease of operation, and broad adaptability [226]. These qualities have made SfM a preferred 3D modeling approach for small- to medium-sized research teams and ecological applications [227]. In contrast, while LiDAR provides higher vertical accuracy and superior penetration in complex terrain [228], its high equipment costs, complex system integration, and stringent platform and power requirements restrict its use to high-budget or precision-critical applications [229]. From the perspective of data processing, SfM workflows are relatively mature, with widely adopted software such as Pix4D and Agisoft Metashape that are easy to use and accessible to non-experts. In contrast, LiDAR data preprocessing, point cloud registration, and filtering typically demand greater technical expertise and currently lack standardized, open-access processing frameworks [230]. As a result, despite the unmatched technical capabilities of LiDAR, SfM continues to dominate due to its superior cost-effectiveness, leading to an imbalance in the current research ecosystem. Moving forward, the development of lightweight LiDAR systems and integrated SfM-LiDAR hybrid modeling frameworks will be essential to simultaneously enhance accuracy and reduce cost-helping to overcome key technical bottlenecks in UAV-based 3D modeling.
# Behavior Composition for Marine Pollution Source Localization Using a Mobile Sensor Network
## 2 Combining Upwind Sprint and GA for CPSL  
### Original GA  
To reveal the adaptations made on the GA, the original form of GA is introduced first. Algorithm 1 shows the pseudocodes of GA. As a population-based algorithm, the population of GA comprises \(m\) individuals, which are encoded into strings of binary numbers. In other words, the size of the GA population is denoted as \(m\), i.e., GA maintains a population of \(m\) chromosomes [19]. At the initialization stage, the \(m\) chromosomes are randomly generated in the whole search space. For traditional function optimization problems, the search space is defined by the argument domains of the fitness function.  
```
1 t = 0
2 Initialize the chromosome population (\(P_{0}\));
3 Evaluate \(P_{v_{i}}\)
4 while termination criterion is not satisfied do
5 selection, crossover, and mutation to form \(Q_{i}\);
6 evaluate \(Q_{i}\);
7 replacement of (\(P_{i}\), \(Q_{i}\)) to form \(P_{v_{i}}\);
8 t = t+1;
9 end while
```  
**Algorithm 1********Original GA [20].  
After initialization, all solutions are evaluated by substituting them into the fitness function and calculating the corresponding fitness values. The GA then enters an iterative loop, in which every iterative cycle has three typical operators: selection, crossover, and mutation. For the crossover operators, the selection operator selects \(m\times p_{c}\) chromosomes by following a fitness-based roulette-wheel selection rule. Chromosomes corresponding to larger fitness values during the selection process are more likely to be selected than those with smaller fitness values. To depreciate the chromosomes with small fitness-values, a novel fitness function was proposed for function minimization problems by [PERSON] et al. [22], as follows:  
\[\text{Fit}(y)=\begin{cases}1-0.5\ \Big{|}\frac{y-b}{a}\Big{|},\text{ when }|y-b|<a\\ \frac{1}{1+\Big{(}\frac{|y-b|}{a}\Big{)}^{2}},\text{ otherwise}\end{cases}, \tag{1}\]  
in which \(y\) is the objective function; \(b\) is the minimum objective value at the \(t\)-th cycle; and  
\[a=\text{max}\bigg{(}0.5,\frac{|b-\text{max}y|}{20}\bigg{)} \tag{2}\]Four typical crossover operators [23] are illustrated in Figure 1, in which alleles in bold squares are changed in the operators. In the uniform crossover, all the alleles are switched at the same probability of 0.5. After the crossover operation, the mutation operation is conducted on the new chromosome. As shown in Figure 1d, one of the alleles in the new chromosome is inverted at a probability of \(p_{m}\)[23].  
After crossover and mutation, the fitness values of new chromosomes are evaluated by decoding and substituting them into the fitness function. If the fitness value of the new chromosome is higher than that of the old chromosome, it is inserted into the population and replaced by the old one. The termination criterion is verified at each iteration loop. If the termination criterion is not satisfied, the iterative loop is repeated. Typically, when \(t\) exceeds a predefined maximum cycle number, GA is terminated.
# Siberia: A Self-Sovereign Identity and Multi-Factor Authentication Framework for Industrial Access
## 3 Results
### Service Access Request  
In order to initiate access to a protected service within the SIBERIA ecosystem, the holder must undergo a multi-factor authentication process that combines traditional and decentralized identity mechanisms. This step is essential to ensure that the individual attempting to access the service is indeed the legitimate owner of the VCs presented.  
The access request process begins with a conventional login procedure, wherein the holder must provide an email (or alternatively, their DID) and a password. These credentials must have been previously registered and stored in the verifier's internal database. This first factor establishes a baseline for identity verification using traditional authentication methods.  
Concurrently, the verifier displays a QR code, which the holder scans using their wallet application. Upon scanning, the holder is prompted to select and share the required VCs, including the mandatory voice-based credential. This sharing is executed through the creation of a Verifiable Presentation (VP), transmitted via a secure HTTP request between the wallet and the SIBERIA API. The VP includes the holder's DID, the selected VCs, and the holder's digital signature, ensuring data integrity and provenance. The structure of the VP conforms to the SIBERIA Verifiable Presentation schema, guaranteeing compatibility with the verification components of the system.  
As a third factor, in accordance with the SASV module's requirements, the holder must record a live voice sample during the access attempt. This sample, captured in real time, is used as a biometric proof and is compared against the encrypted voiceprint included in the voice credential previously shared via the VP.  
Only after successfully completing all three steps--traditional login, verifiable presentation submission, and live voice sample capture--does the access request proceed to the final verification phase. This multi-layered approach significantly enhances the security of the system, ensuring that identity assertions are trustworthy, private, and resistant to impersonation.  
Figure 7: Credential issuance and storage.
## 3 Results
### SIF-EVI and SIF-GPP at a Regional Scale  
To understand the relationships of SIF-EVI and SIF-GPP at the regional scale, the mean values of EVI, GPP, and SIF for the study region were calculated for each 16-day period, and a scatter plot was drawn (Figure 3). Here, the MODIS GPP product was used to represent the true GPP value. The relationship between SIF and GPP (R\({}^{2}\) = 0.74, n = 8, P < 0.01) was closer than that between SIF and EVI (R\({}^{2}\) = 0.57, n = 8, P < 0.05). This shows that, at a regional scale, EVI and GPP had significant correlations with SIF. The slope between SIF and EVI was 2.90, and the value between SIF and GPP was 10.70. However, this result only reflects the relationships at a regional scale. To understand more accurate relationships between vegetation indices and SIF, a more detailed analysis at a local scale is needed.  
Figure 3: Scatter plots of SIF–EVI and SIF–GPP in the study region at a 16-day temporal scale. The blue color indicates SIF–EVI, and the orange color indicates SIF–GPP.  
Figure 2: SIF and EVI changes with time in the growing seasons of 2015–2018.