esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Belief Networks (DBN) with IoT-Based Alzheimer's Disease Detection and Classification
###### Abstract  
Dementias that develop in older people test the limits of modern medicine. As far as dementia in older people goes, Alzheimer's disease (AD) is by far the most prevalent form. For over fifty years, medical and exclusion criteria were used to diagnose AD, with an accuracy of only 85 per cent. This did not allow for a correct diagnosis, which could be validated only through postmortem examination. Diagnosis of AD can be sped up, and the course of the disease can be predicted by applying machine learning (ML) techniques to Magnetic Resonance Imaging (MRI) techniques. Dementia in specific seniors could be predicted using data from AD screenings and ML classifiers. Classifier performance for AD subjects can be enhanced by including demographic information from the MRI and the patient's preexisting conditions. In this article, we have used the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. In addition, we proposed a framework for the AD/non-AD classification of dementia patients using longitudinal brain MRI features and Deep Belief Network (DBN) trained with the Mayfly Optimization Algorithm (MOA). An IoT-enabled portable MR imaging device is used to capture real-time patient MR images and identify anomalies in MRI scans to detect and classify AD. Our experiments validate that the predictive power of all models is greatly enhanced by including early information about comorbidities and medication characteristics. The random forest model outclasses other models in terms of precision. This research is the first to examine how AD forecasting can benefit from using multimodal time-series data. The ability to distinguish between healthy and diseased patients is demonstrated by the DBN-MOA accuracy of 97.456%, f-Score of 93.187 %, recall of 95.789 % and precision of 94.621% achieved by the proposed technique. The experimental results of this research demonstrate the efficacy, superiority, and applicability of the DBN-MOA algorithm developed for the purpose of AD diagnosis.  
Deep Belief Network; DBN; machine learning; healthcare; disease detection; alzheimer; IoT 2023
# Soy Isoflavones and PCOS: Role in Hormonal and Metabolic Mechanisms
## 4 Polycystic Ovary Syndrome and Soy Isoflavones
### Metabolic Mechanisms
#### 4.2.2 Insulin Resistance and Hyperinsulinemia  
IR involves reduced insulin sensitivity of target tissues, leading to compensatory hyperinsulinemia and impaired carbohydrate metabolism. IR in PCOS includes both classic insulin target tissues, such as adipose tissue and muscle, and non-classic tissues, such as the ovaries. Androgens affect adipocyte function by disrupting the insulin signalling pathway (including GLUT-4 expression) and promoting visceral fat deposition [153]. In addition, there is increased expression of the enzyme AKR1C3, which catalyses the conversion of androstenedione to testosterone, in the adipose tissue of women with PCOS. AKR1C3 expression is stimulated by insulin, which can increase local androgenenesis and exacerbate IR [154; 155]. IR in PCOS mainly involves the PI3K pathway, which is responsible for the metabolic effects of insulin, while the MAPK pathway remains active [156; 157]. This explains the phenomenon whereby maintained and often increased insulin sensitivity in the ovaries leads to increased androgen production, independent of systemic IR [158; 159]. Insulin enhances the action of LH in fecal cells and lowers SHBG levels, leading to an increase in free androgen levels and exacerbating hyperandrogenism [159; 160; 95]. Adipokines play an important role in the pathogenesis of IR in PCOS--decreased adiponectin and increased leptin levels correlate with IR and inflammation [161; 162]. Regulation of these processes is mediated by nuclear peroxisome proliferator-activated receptors (PPAR), in particular the PPARy isoform, which controls adipogenesis and insulin sensitivity. Reduced PPARy activity and its genetic polymorphisms have been associated with increased IR in PCOS [163; 164; 165]. Long-term hyperinsulin can lead to pancreatic \(\beta\)-cell dysfunction and increase the risk of developing type 2 diabetes. IR is therefore considered to be central to the pathophysiology of PCOS and a key risk factor for metabolic complications. Importantly, hyperandrogenemia, as well as hypogonadism, in women with PCOS are closely associated with an increased risk of developing metabolic dysfunction-associated fatty liver disease (MASLD). Although obesity, especially of the visceral type, further exacerbates this risk, studies show that this association also occurs regardless of age and BMI [166]. The main pathogenetic mechanism is IR, which exacerbates both hyperandrogenism and liver fat accumulation [167].
# Photonic Stopband Filters Based on Graphene-Pair Arrays
###### Abstract  
We investigate the photonic bandgaps in graphene-pair arrays. Graphene sheets are installed in a bulk substrate to form periodical graphene photonic crystal. The compound system approves a photonic band structure as a light impinges on it. Multiple stopbands are induced by changing the incident frequency of light. The stopbands widths and their central frequencies could be modulated through the graphene chemical potential. The number of stopbands decreases with the increase in the spatial period of graphene pairs. Otherwise, two full passbands are realized in the parameter space composed of the incident angle and the light frequency. This investigation has potentials applied in tunable multi-stopbands filters.  
photonic filter; graphene-pair array; photonic crystal; stopband; Bragg grating +
Footnote †: journal: Applied
# Enhancing Street-Crossing Safety for Visually Impaired Pedestrians with Haptic and Visual Feedback
## 3 Study 1: Haptic and Visual Feedback for Visually Impaired Pedestrians Crossing at Traffic Signals
### Results
#### 3.2.1 Cross Rate, Decision Time, and User Behavior Data in Study 1
Post hoc Bonferroni pairwise comparisons showed that the distance of haptic feedback (4.66 cm) was significantly longer than that of visual feedback (2.99 cm) (\(p<0.05\)). For the Y dimension, the main effects were found for feedback (\(\chi^{2}(1)=15.86,p<0.001\)) and frequency (\(\chi^{2}(3)=17.90\), \(p<0.001\)). Post hoc Bonferroni pairwise comparisons showed that the distance of haptic feedback (2.30 cm) was significantly longer than that of visual feedback (1.18 cm) (\(p<0.01\)). The distance of frequency 0.5 Hz (2.34 cm) was significantly longer than that of frequency 1.0 Hz (1.53 cm) (\(p<0.05\)) and frequency 4.0 Hz (1.32 cm) (\(p<0.05\)). For the Z dimension, the main effects were found for feedback (\(\chi^{2}(1)=16.05,p<0.001\)). Post hoc Bonferroni pairwise comparisons showed that the distance of haptic feedback (4.40 cm) was significantly longer than that of visual feedback (3.13 cm) (\(p<0.01\)).
## 3 Energy Efficient Schemes for Green C-IoT
### Fog Computing  
Following the traditional way, the sensed data will be transmitted to the remote server and then processed in the centralized data center. This may cause much more transmit power consumption and lead to unacceptable time delay in hot spots with severe traffic congestion. Therefore, with a high density of MTC devices, the existing paradigm for cloud computing is not suitable for some remote IoT applications. To shed light on this, the fog computing-based architecture is regarded as a new promising communication model [61].  
In the fog computing-based model, fog nodes, distributed at network edges, are equipped with computation and processing capabilities and are able to undertake some computing and caching functions [62]. Then, the transmission and processing tasks can be instantly accomplished by nearby fog nodes, which obviously costs less power consumption and is suitable for time-critical tasks [63]. When considering the dynamic topology of the C-IoT scenario, the mobility of fog nodes should also be considered since the fog nodes should be placed in the hotspots to offload more data traffic.
# Potential Aroma Chemical Fingerprint of Oxidised Coffee Note by HS-SPME-GC-MS and Machine Learning
## 3 Results and Discussion
### Potential Markers of the Oxidised Coffee Note  
Twenty-five highly significant volatiles describing oxidised coffees with a similar trend in all blends and packages studied, and with a CV% of at least 20, were identified as potential markers for oxidised coffees. They are listed in bold in Table 2.  
Most of them are present in oxidised samples in lower amounts, some are already known to decrease over time (in italics in Table 2), and others behave differently than in previous studies. These compounds are highly reactive heterocycles, especially in the presence of humidity and oxygen, which could explain their decrease in oxidised coffees [35]. Four volatile compounds increased in all samples examined, albeit to varying degrees depending on the mixture and packaging: acetic and propionic acids (_pungent_, _sour_, _nancial_), 1-H-pyrrole-2-carboxaldehyde (_musty_), and 5-(hydroxymethyl)-dihydro-2(3H)-furanone.  
Short-chain fatty acids are formed in roasted coffee by the breakdown of polysaccharides during roasting. Their continuous increase during storage could be due to the chemical cleavage of triglycerides (TGA), which contributes to the change in sensory properties [9].  
The typical oxidised note perceived by the olfactory organ is due to the altered balance of the volatiles in oxidised samples and can be associated with rancid, walnut oil, peanut shell, old dried fruit, and \"old\" coffee notes. The coffee volatilome includes both odourant and nonodourant compounds, the latter of which contribute to a synergistic effect in aroma perception [40; 41; 42]. It has highly informative power in describing the potential evolution of aroma over time [7; 15; 21], and is a diagnostic tool for the \"identitation\" of blends/origins and the detection of applied technological processes [43; 44; 45; 46].  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Dimethyl Sulphone**} & \multicolumn{2}{c}{**Dimethyl Trisulphide**} & \multicolumn{2}{c}{**Dimethyl Disulphide**} & \multicolumn{2}{c}{**Methanethiololololololololololololololumn{2}{2}{c}}{**Ratio DMDS/MESH**} \\ \cline{2-11}  & **Good** & **Ox** & **Good** & **Ox** & **Good** & **Ox** & **Good** & **Ox** & **Good** & **Ox** \\ \hline
**M** & 0.05 & 0.15 & 0.05 & 0.00 & 3.53 & \(n.\)d.\({}^{\lx@sectionsign}\) & 0.13 & 0.06 & 26.45 & \(n.\)d. \({}^{\lx@sectionsign}\) \\ \hline
**B** & 0.07 & 0.03 & 0.07 & 0.03 & 2.65 & 5.61 & 0.19 & 0.08 & 14.01 & 74.25 \\ \hline
**P** & 0.07 & 0.07 & 0.10 & 0.02 & 3.18 & 19.09 & 0.16 & 0.12 & 19.74 & 153.94 \\ \hline
**I** & 0.06 & 0.10 & 0.10 & 0.04 & 3.50 & 12.44 & 0.11 & 0.12 & 30.85 & 100.02 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of some sulphur markers from the literature and the DMDS/MeSH ratio in good and oxidised samples. \({}^{\lx@sectionsign}\) Under limit of detection.
## 2 Materials and Methods
### EVAR-eaSE Evaluation
#### 2.2.1 Phantom Tests  
Two CT imaging phantoms, i.e., two solid plastic models of regular shape and geometry responding in a similar manner to human tissues under CT, were provided by the Medical Physics Unit of S. Orsola Hospital, University of Bologna (Figure 6).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Measurement** & **Description** \\ \hline AAA-Total Volume (TV) & Volume (in cm\({}^{3}\)) computed for the segmented total AAA, also including the thrombus. \\ \hline AAA-Lumen Volume (LV) & Volume (in cm\({}^{3}\)) computed for the segmented AAA lumen. \\ \hline AAA-Thrombus Volume (THV) & Volume (in cm\({}^{3}\)) computed for the segmented thrombus, calculated as difference between AAA-Total Volume (TV) and AAA-Lumen Volume (LV). \\ \hline \% Volume Ratio (\%VR) & Percentual volumetric ratio between the thrombus volume (THV) and the total AAA volume, calculated as (THV/TV) *100. \\ \hline Free Volume (FV) & Volume (in cm\({}^{3}\)) computed as the difference between the LV and the estimated endograft volume in the AAA \\ \hline AAA Axis Length & Center line length, i.e., the line passing by the centroid of the AAA along each slice \\ \hline Minimum AAA Length & Straight distance between the first and the last selected slices \\ \hline Max Lumen Diameter & Maximum diameter for the segmented AAA lumen \\ \hline Max AAA Diameter & Maximum diameter for the segmented entire AAA. \\ \hline Subrenal Vessel Diameter & Diameter of the aorta just under the renal arteries, calculated as mean aorta diameter in the first five slices of the vessel, starting from the first selected slice. \\ \hline Number of coils & The computed optimal number of coils, starting from the computed FV. \\ \hline \hline \end{tabular} An example of EVAR-eaSE application to a real patient AAA case is shown in the Supplementary Video Material (Video S1).  
\end{table}
Table 1: Output measurements provided by the EVAR-eaSE software. AAA = abdominal aortic aneurysm.  
The first (Phantom 1), made of acrylic material and having a cylindrical shape, was chosen to simulate a highly-contracted medium, like the vessel after the infusion of the contrast medium. The second (Phantom 2) was a \"helix phantom\" made of Lucite and with a truncated cone shape with lead markers inserted according to a helicoidal pattern. This phantom was selected in order to take into account the effect of possible artifacts from metal objects, like the implanted endograft in the aneurysm. For the two phantoms, the size was measured with a precision gauge, then the volume was assessed (54.29 cm\({}^{3}\) and 110.33 cm\({}^{3}\) for Phantoms 1 and 2, respectively). CT scans of the two phantoms were performed varying the acquisition parameters, i.e., the slice thickness (0.67 mm, 2 mm) and the pitch ratio, PR, (1 = no slide overlap, 0.5 = slice overlap).
# Microgrid Cyber-Security: Review and Challenges toward Resilience
## 4 Perspective-Based Interventions Addressing Cyber Attacks
### Microgrid Control
#### 4.3.1 Control Structure
The discussion on privileging one control method over the other is still questioned by several papers focusing on different aspects [7; 23; 43]. Distributed control was originally proposed as a solution to boost scalability in modern networks by means of facilitating the introduction of supplementary DERs. It splits control tasks between units instead of the substantive upgrading of single excessive computational capacities. Moreover, the sparsity of communication networks utilized in distributed control schemes reduces the infrastructure cost [43]. Not to mention, that is also considered to be more resilient as single-point failure does not lead to cascading failure, unlike centralized or what might be called hierarchical control, in which messages that carry out measurements and instructions from and to all system components should pass by a dedicated central controller. Correspondingly, centralized control schemes have a better understanding of the microgrid functions since it has an embedded version of the system model in the central controller which in turn will trigger an optimal application of EMS objectives including the economic performance simultaneously with satisfying real-time operational constraints. In [7], authors review basic branches of distributed control optimization and their application with a brief reflection on the cybersecurity consideration, promoting distributed control on the bases of mitigation obstacles relevant to communication risks and stakeholders' resistance to sharing critical data. Among distributed optimization methods, consensus control has gained more attention in microgrid's control community recently. The initial notion was inspired by biological phenomena that revolve around providing each unit of vision on the overall objective to a limit where different DERs converge to a single value. Here, decisions are built upon local measurements and peer-to-peer communication, offering this model extra flexibility, adding to the already well established feature in the distributed structure. Cooperative control is also a very feasible solution for stability control in terms of voltage and frequency equilibration and economic control with cost consensus for generation units across the network [23].
## 3 Proposed Distributed Secondary Control Technique
### Fundamentals of a Fuzzy Logic System  
The fuzzy logic approach employs measurements, accumulated experiences, and human expertise to transform intricate and non-linear control operations into straightforward fuzzy rules. The overall structure of fuzzy logic, as depicted in Figure 3, can be categorized into three main levels [34]:  
* Fuzzification: it converts crisp or non-fuzzy input values into fuzzy values. It is the initial step in fuzzy logic, where linguistic variables and their associated membership functions are used to represent and quantify the uncertainty and vagueness of the input data.
* Inference Engine: it comprises a database of input and output membership functions and a knowledge base consisting of fuzzy rules, defined by expert knowledge or learned from data, that have been mapped using linguistic variables. The output generated by the inference engine relies on mapping input fuzzy sets using the _\"If-then\"_ rule. The resulting output is typically a fuzzy set or a membership function representing the degree of membership of a particular value.
* Defuzzification: it converts fuzzy output values into crisp or non-fuzzy values; a precise output value that can be easily interpreted and used for decision making by the system of interest.
# Energy-Saving Traffic Scheduling in Hybrid Software Defined Wireless Reschargeable Sensor Networks
## 2 Related Work
### Energy Saving Studies in WSNs  
The energy limitation is always a key problem for various WSNs. A lot of work has been done to reduce energy consumption and prolong the lifetime of WSNs. These studies usually focus on optimizing network deployment [5; 6; 7; 8], power control or sleeping schemes [9; 10], ameliorating data processing method [11; 12; 13], or improving communication protocol [14; 15; 16; 17; 18; 19]. These studies cannot be directly transplanted into hybrid SDWRSNs, but they provide referable thoughts for the latter.
## 4 Experiments
### Experimental Results
#### 4.2.4 Evaluation of Computational Efficiency and Scalability  
Assessing computational efficiency and scalability is crucial for evaluating the performance of occlusion removal methods. Our proposed model, as detailed in Table 3, demonstrates a balance between complexity and performance, though certain trade-offs are evident. With 52.59 M parameters, our model is larger than [PERSON] et al. (2.7 M) and DeOccNet (39.0 M), while being smaller than larger architectures like LBAM (69.3 M) and ISTY (80.6 M). Notably, despite having more parameters than DeOccNet and [PERSON] et al., our model is still relatively efficient compared to LBAM and ISTY, indicating an optimized approach that achieves comparable or better performance with fewer parameters. In terms  
Figure 7: Analyzing occlusion removal effectiveness across real-world scenes. The first row (bike_01) highlights our method’s success with thin occlusions, achieving higher PSNR and SSIM, as indicated by the red boxes. In contrast, the second row (Trees_Back) reveals limitations, as the method encounters challenges with larger, irregular occlusions, resulting in visible residues.  
of inference time, although models such as DeOccNet (10 ms), LBAM (12 ms), and ISTY (24 ms) process faster, this speed often comes at the expense of reduced feature extraction capacity or lower performance. Our model, with an average inference time of 138.8 ms, strikes a balance between lightweight models like DeOccNet and LBAM and computationally heavier ones like [PERSON] et al. (3050 ms). This demonstrates that, while not the fastest, our model's design enables comprehensive multi-scale feature extraction, enhancing occlusion removal without overwhelming computational resources.  
Regarding scalability, the model's computational demand (138.8 ms) and parameter count (52.59 M) suggest that it remains scalable for higher-resolution tasks or larger datasets without overburdening the system. This is crucial for applications that require a balance between accuracy and performance, particularly in real-time or resource-constrained environments. Although our method introduces a higher computational load compared to simpler architectures, its multi-scale processing capabilities and refinement layer contribute to superior occlusion reconstruction. Overall, the added complexity from CSPDarknet53, six BiFPN layers, and the HiNet refinement module enhances the model's capacity to handle detailed and high-resolution inputs. This leads to superior occlusion region reconstruction, justifying the marginal increase in computational cost. Importantly, the model remains scalable and efficient for more demanding tasks, striking a balance between accuracy and reasonable resource usage.
## 2 Modeling Methodology
### Soil-Pile Foundation Interface Model  
In the event of a strong earthquake, slippage and separation phenomena may occur at the contact between the pile foundation and soil according to the magnitude of the applied load. As such phenomena directly affect the seismic behavior of the pile foundation, it is essential to utilize appropriate soil-pile interface elements for accurate modeling. Therefore, in this study, a soil-pile interface model that considered full contact, slippage, and separation between the pile and soil was applied. The interface model calculated the stiffness of the soil-pile boundary elements using vertical and shear springs at each position, and the value was determined through Equation (7) (Itasca Consulting Group, 2006 [17]). As the shear elastic modulus (G) introduced in Equation (6) considers the nonlinearity of soil obtained from the hysteretic damping model, the applied boundary element model also simulates the nonlinear behavior of soil; it was continuously applied and entered in the vertical and shear directions based on depth through the FISH function.  
\[\mathrm{K_{E}=max\ [(K+(4/3)G)/\Delta z_{min}]} \tag{7}\]  
where \(\mathrm{K_{E}}\) is the equivalent stiffness of the interface spring, K is the bulk modulus, G is the shear elastic modulus, and \(\Delta z_{min}\) is the smallest width of the soil element adjacent to the pile in the vertical direction.  
Further, the yield criterion for the pile-soil boundary was determined using Equation (8), and the three boundary conditions between the pile and soil (full contact, slippage, and separation) were considered depending on the magnitude of the external force.  
\[\mathrm{F_{smax}=cA+tan\Phi(F_{n}-pA)} \tag{8}\]  
Figure 3: Curve fitting of \(G\mathrm{G_{max}}\) curve of model sands.  
where c is the adhesion at the boundary, \(\Phi\) is the friction angle at the boundary, p is the pore pressure, and A is the representative area of the boundary.  
For the internal friction angle at the soil-pile interface, it is generally appropriate to input a value slightly less than the maximum value of far-field soil internal friction angle because dynamic behavior of the pile foundation doesn't have effect on far-field soil. [PERSON] (1990) [37] suggested 70% of far-filed value as the interface value, in addition, [PERSON] et al. (2000) [38] suggested 60% of far-field value for the interface. In this study, calculation basis of the internal friction angle at the soil- pile interface proposed by [PERSON] et al. (1979) [39] and [PERSON] et al. (1994) [40] was applied as Equation (9).  
\[\delta=\Phi_{\max}-5^{\circ} \tag{9}\]  
where \(\delta\) is the internal friction angle of the interface, and \(\Phi_{\max}\) is the maximum internal friction angle of the far-field soil.