esa-sceva/satcom-chunk-collection huggingface dataset:
# Applying Deep Learning to Medical Imaging: A Review
## 5 Evaluation Methods and Available Datasets
### Publicly Available Datasets and Competitions  
Publicly available datasets and competitions play a critical role in advancing DL research for medical imaging. These resources provide standardized data and evaluation protocols for comparing different methods and fostering collaboration among researchers. There are various publicly available datasets for different medical imaging modalities, such as the Cancer Imaging Archive (TCIA) for CT and MRI, the Alzheimer's Disease Neuroimaging Initiative (ADNI) for MRI [163], and the Retinal OCT (ORIGA) dataset for OCT [164]. In addition, several competitions have been organized to benchmark the performance of DL methods for medical imaging, such as the International Symposium on Biomedical Imaging (ISBI) challenge [165] and the Medical Segmentation Decathlon [166]. However, the availability and quality of publicly available datasets and competitions can vary across different medical imaging modalities and tasks. Moreover, some datasets may have limited diversity in terms of patient populations and imaging protocols, which can affect the generalizability of the results. To address these issues, it is important to establish standards and guidelines for dataset curation and evaluation protocols. Collaborative efforts among researchers, clinicians, and industry partners are needed to ensure the availability and quality of publicly available datasets and competitions for DL research in medical imaging.
# An Integrated Program to Improve Cognitive and Physical Abilities in Older People
## 2 Literature Review
### Gamification Strategy  
Gamification refers to the application of the mechanics and elements used in games to non-gaming applications [22]. The application of elements of gamification to common tasks can help improve the overall user experience [23], and gamification strategies to increase user engagement and involvement are gaining traction [24].  
Reward, challenge, achievement, competition, relationship, access, and self-expression form part of the gamification strategies that have been used in previous studies to increase users' interest and intention to continue, especially in the healthcare domain, where the use of such gamification strategies can increase users' self-efficacy and sustainability [25]. In addition, older adults are more likely to be interested and motivated to do exercise when it is combined with gamification, and their interest tends to increase when an exercise program has a clearly defined topic, such as the prevention of dementia or falls or building strength [26].  
The following interventions in older people had positive results: a twelve-week program incorporating a rhythmic stepping machine with games improved balance in older people [27], a motion-aware walking game that added a sense of challenge and competition improved physical function [28], a six-week interactive dance game with game elements of difficulty and music had a positive effect on balance ability and motivation [29], and a four-week stepping training program using challenge strategies improved walking ability and increased satisfaction [30].  
While earlier studies apply gamification elements and strategies to the field of healthcare, it is often difficult to find examples of the effectiveness of gamification strategies, and there has been little research demonstrating the development and effectiveness of an integrated program using gamification in an offline environment rather than in a digital one. This study selects the main gamification strategies appropriate to the study from the seven gamification strategies that have been previously identified [25], namely reward, challenge, achievement, competition, relationship, access, and self-expression and investigates whether they have an impact on intention to continue using the program and motivation when in application to an integrated program.
## 3 Results and Discussion
### Binding Kinetics  
The binding kinetics were further studied using an initial BSA concentration of 3 \(\mu\text{mol}\cdot\text{L}^{-1}\). As shown in Figure 8, when PBS (pH 7.0, 10 mM) was employed, the PVC@TAA-MIP exhibited a much stronger binding strength and a binding rate that was more than three times faster than when PBS containing NaCl was used. These results confirmed that the immobilization strategy based on anion-exchange-based affinity interaction could remarkably increase the binding rate, as previously reported by [PERSON] et al. [24]. In addition, as previously suggested by [PERSON] et al. [47], the ultrathin imprinted films prepared using SI-PIMP allowed the formation of homogeneous binding cavities and construction of effective binding sites situated at or close to the layer surface. This resulted in a high imprinting efficiency and fast-mass transfer rate toward the template. The binding kinetic results suggested the potential application of PVC@TAA-MIP for the removal of BSA from complex samples.  
Figure 8: Binding kinetics of PVC@TAA\(-\)MIP and PVC@TAA\(-\)NIP toward BSA in the absence or presence of NaCl.
# Photocatalytic Inactivation as a Method of Elimination of _E. coli_ from Drinking Water
## 1 Introduction
### The Different Strains of Escherichia coli  
_E. coli_ is one of the most studied living organisms due to the enormous clinical and scientific interests in the organism. It has been used as the model organism for studies in a lot of biological research including studies in genetics, molecular biology, and evolution [25]. Depending on the strain, _E. coli_ could either be harmful or harmless. Both the pathogenic and non-pathogenic _E. coli_ behave ecologically and biochemically alike, making it difficult to detect the pathogenic ones among commensal _E. coli_[25; 27]. Meanwhile, the harmful pathogenic strains are responsible for diarrheal and other diseases in humans and animals [27; 28]. The pathogenic strains have virulence factors involved in their pathogenesis and are used to group them into various pathotypes or virotypes [28]. There are two broad groups of pathogenic strains of _E. coli_ depending on the site of infection:  
* Intestinal Pathogenic _E. coli_ (IPEC): Most pathogenic strains of _E. coli_ are Intestinal pathogenic _E. coli_. They are also known as Enteric _E. coli_, and are the group of _E. coli_ that elicit their pathological effects and cause diseases in the gastrointestinal tract [1; 28]. Examples of Intestinal pathogenic _E. coli_ include enteropathogenic _E. coli_ (EPEC), enterohaemorrhagic _E. coli_ (EHEC), enterotoxigenic _E. coli_ (ETEC), enteroaggregative _E. coli_ (EAEC), enteroinvasive _E. coli_ (EIEC) and diffusely adherent _E. coli_ (DAEC).
* Extra-Intestinal Pathogenic _E. coli_ (EXPEC): Any other group of _E. coli_ that cause diseases or exert their pathogenic syndromes in systems other than gastrointestinal tract are known as Extra-intestinal pathogenic _E. coli_[28]. Some of the examples of Extra-intestinal pathogenic _E. coli_ includes sepsis-causing _E. coli_ (SEPEC), uropathogenic _E. coli_ (UPEC) and neonatal meningitis-associated _E. coli_ (NMEC). The genomes of several strains of both pathogenic and non-pathogenic _E. coli_ have been completely sequenced, others are being sequenced while many other strains will be sequenced in the future as new strains emerge through the natural biological process of mutation [1; 25; 27; 28]. However, this review is limited to three of the most common strains of _E. coli_ including: the non-pathogenic commensal, K-12 strain; the enterohemorrhagic O157:H7 strain, and the uropathogenic CFT073 strain.
## 4 Multi-Objective Optimization
### Hydraulic Performance Prediction Model
In this study, MATLAB2018b software was used to write and simulate the RBF neural network program, using the newrb function to establish the network topology in the form shown in the following equation:  
\[net=newrb(P,T,GOAL,SPREAD,MN,DF) \tag{13}\]  
where \(P\) is the input matrix of order \(\mathrm{R}\times\mathrm{Q}\); \(T\) is the output matrix of order \(\mathrm{S}\times\mathrm{Q}\); GOAL is the mean square error target (default = 0); SPREAD is the expansion rate of the radial basis function (default = 1.0); MN is the maximum number of neurons (default Q); and DF is the number of neurons added between adjacent displays (default = 25). In this paper, GOAL is set to 0.001, SPREAD is set to 2, and all other default values are used. The training iteration process is shown in Figure 7. From the diagram, it can be seen that after the iteration to the eighth step, the error of 0.001 has been met and the training is complete. To test the accuracy of the RBF neural network predictions, the sample data was re-entered into the network as test data for prediction, and the corresponding predicted head and predicted efficiency were obtained as shown in Figure 8. As can be seen from the graph, the difference between the CFX calculated values and the RBF neural network predictions is small, with a maximum error of 1.5% for the pump head and 1.9% for efficiency. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample Serial** & **Exit Placement** & **Outlet** & **Blade Wrap** & **Lift** & **Efficiency** \\
**Number** & **Angle \(\beta_{2}\)** & **Width \(b_{2}\)** & **Angle \(\varphi\)** & **Life** & **Efficiency** \\ \hline
27 & 30 & 7.8 & 114 & 20.9093 & 77.4695 \\
28 & 30.5 & 10 & 86 & 24.6849 & 73.2271 \\
29 & 31 & 12.2 & 58 & 27.6284 & 63.4781 \\
30 & 31.5 & 14.4 & 104 & 27.8102 & 71.8061 \\
31 & 32 & 9.2 & 76 & 24.5471 & 72.6405 \\
32 & 32.5 & 11.4 & 122 & 24.3591 & 76.2711 \\
33 & 33 & 13.6 & 94 & 27.5183 & 70.6124 \\
34 & 33.5 & 8.4 & 66 & 24.0505 & 70.2596 \\
35 & 34 & 10.6 & 112 & 24.1895 & 76.5615 \\
36 & 34.5 & 12.8 & 84 & 27.1259 & 68.3395 \\
37 & 35 & 15 & 130 & 27.3392 & 74.5881 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._  
Figure 7: Iterative diagram of neural network training. Three sets of structural parameters are randomly generated to predict and model simulation calculations, respectively, and then error analysis is carried out. The results are shown in Table 8. The maximum error for the pump head is 1.38%, and the maximum error for efficiency is 1.43%, which is within the permissible range of engineering, so the RBF neural network prediction model can be considered feasible and can be used as an adaptation function model for the next multi-objective particle swarm optimization.
# Wireless Mesh Networking: An IoT-Oriented Perspective Survey on Relevant Technologies
## 4 Mesh in Bluetooth/BLE Networks
### Standard
The main aim of the Access layer is to guarantee the correctness of all messages exchange between lower layers and application layer. In [36], an analysis of the performance of a small-scale BLE mesh network (composed by Nordic nRF52832 modules on top of which the Nordic implementation of the Bluetooth Mesh standard is running), using link communication delays as metrics, is presented. Besides SIG, also the IETF has worked on its own BLE mesh standard based on its IPv6-related standards initially published for the IEEE 802.15.4 technology, more specifically IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) and IPv6 over Low-Power Wireless Personal Area Network (6 LoWPAN), this has led to two approaches for adapting 6 LoWPAN aiming at making BLE mesh network supporting IPv6: \"IPv6 Mesh over Bluetooth Low Energy using IPSP\" [37] and \"IPv6 over Bluetooth Low Energy\" [38] specifications. The IETF Bluetooth Mesh protocol is based on the assumption of the existence of connections at link layer among a node \(N\) and other nodes in its neighbourhood (say \(G_{1},\ldots,G_{k}\)) allowing to exchange different IPv6 packets. In turn, these links are established adopting the Internet Protocol Support Profile (IPSP) [39] and using a 6 LoWPAN-based adaptation layer between L2 CAP and IPv6, which in turn provides: (i) an optimized IPv6 neighbor discovery mechanism; (ii) User Datagram Protocol (UDP) and IPv6 header compression mechanisms (thus improving the communication efficiency); and (iii) network configuration mechanisms (especially suitable for constrained devices). Finally, even if the adopted routing protocol is not forced by the draft specification [37], in this case the routing useful to find communication paths from an end device to another is at the IP layer. Lastly, the work described in [40] provides a delivery rate and energy consumption performance analysis of IPv6-based BLE mesh networks with very minimal topologies (at most 3 nodes).
## 2 Structure Configuration and Finite Element Model Validation
### Results and Discussion  
Generally, permanent deformation/failure of the face plates is considered the most important attribute of the structural response, as all other parameters (impulse transfer and energy dissipation) rely on it. From the field blast test, it can be observed that for the 150-mm and 100-mm _SoD_, the face sheets plastically deformed and no failure has occurred. However, for the 50 mm _SoD_, the panel was damaged. Experimental and numerical central deflections of the corrugated core sandwich panels were compared and summarized in Table 5. Figure 4 shows the comparison of the deformation/failure modes of the experimental and numerical models. The comparison was focused on the permanent deflection between simulation and experiments. The differences expressed high accuracy since the standard deviation varied between 1.83% and 11.93%, as shown in Table 5. So, the numerical model was assumed with this accuracy to be able to capture and predict the deformation and failure modes for the field blast test. At this point, the proposed models can be used reliably to investigate the performance of the ribbon topology as new core configuration for sandwich structures subjected to air blast loading.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Unit** & **Value** \\ \hline Reference density & g/cm\({}^{3}\) & 7.85 \\ EOS & - & Linear \\ Bulk modulus & kPa & \(1.67\times 10^{8}\) \\ Strength & - & Johnson Cook \\ Shear modulus & kPa & \(6.69\times 10^{7}\) \\ Yield stress, A & kPa & \(3.10\times 10^{5}\) \\ Hardening constant, B & kPa & \(1.00\times 10^{5}\) \\ Hardening, exponent, n & - & 0.65 \\ Strain rate constant, c & - & 0.07 \\ Ref. strain rate, \(\dot{\epsilon}_{0}\) & s\({}^{-1}\) & 1.00 \\ Thermal softening exponent, m & - & 1.00 \\ Indoor temperature, T\({}_{\text{m}}\) & K & 292 \\ Melting temperature, T\({}_{\text{r}}\) & K & 1672 \\ Failure & - & Plastic strain \\ Plastic strain & - & 0.42 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Constitutive model of 304 stainless steel adopted in Ansys/Autodyn.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Panel** & \multicolumn{2}{c}{**Front-Plate Deflection \(\delta_{f}\) (mm)**} & \multirow{2}{*}{**Discrepancy (\%)**} & \multicolumn{2}{c}{**Back Plate Deflection \(\delta_{b}\) (mm)**} & \multirow{2}{*}{**Discrepancy (\%)**} \\ \cline{2-3} \cline{5-6}  & **Experimental \({}^{*}\)** & & & **Experimental \({}^{*}\)** & **Numerical** \\ \hline TZ-1 & Failure & Failure & - & Failure & - \\ TZ-2 & 28.89 & 29.42 & 1.83 & 14.14 & 14.64 & 3.54 \\ TZ-3 & 18.99 & 20.55 & 8.21 & 7.69 & 8.21 & 6.76 \\ T-1 & Failure & Failure & - & Failure & Failure & - \\ T-2 & 22.71 & 24.70 & 8.76 & 10.31 & 11.54 & 11.93 \\ T-3 & 12.91 & 14.11 & 9.31 & 5.18 & 5.63 & 8.69 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimental and numerical results.
## 4 Discussion
We found that the PI3K/Akt, GSK-3, and MAPK pathways were altered by A\(\beta\) oligomers that could be involved in the cell death, and the ginseng Rh1 was found to attenuate A\(\beta\) oligomers-induced inhibition of phospho-Akt/Akt and phospho-GSK-3\(\beta\)/GSK-3\(\beta\) together with inhibiting the A\(\beta\) oligomers-induced phospho-p38 MAPK/p38-MAPK. Moreover, the PI3K/Akt pathway inhibitor, LY294002, significantly removed the neuroprotective effects of ginseng Rh1, suggesting the role of the PI3K/Akt signaling pathway in Rh1 mediated neuroprotective effects. Ginsen has been widely used in the orient for thousands of years, mainly have prescribed for aging and memory degradation [57]. Because it is mostly converted from Rg1 and Re by intestinal metabolites, the ginseng Rh1 in ginseng root is relatively rare [41]. [PERSON] [53] found that mice's learning and memory ability was significantly improved caused by feeding the Rh1 for a long time. [PERSON] [58] reported that ginseng Rh1 prevents cognitive impairment of the mouse cortex and hippocampus. This study reported only in vitro neuroprotective effects of ginseng Rh1 in human neuroblastoma SH-SY5Y cells. However, to the best of our knowledge, this is the first study describing the protective effect of ginseng Rh1 in A\(\beta\) oligomers-mediated ROS in AD. In this study, A\(\beta\) oligomers inhibited the phospho-Akt/Akt and phospho-GSK-3\(\beta\)/GSK-3\(\beta\) and activated the phospho-p38 MAPK/p38-MAPK pathway in SH-SY5Y cells. However, ginseng Rh1 activated the phospho-Akt/Akt and phospho-GSK-3\(\beta\)/GSK-3\(\beta\). It also inhibited phospho-p38 MAPK/p38-MAPK with attenuating A\(\beta\) oligomers-induced death in SH-SY5Y cells. In conclusion, ginseng Rh1 attenuated cell death and oxidative stress via activation of the PI3K/Akt/GSK-3\(\beta\) pathway. Based on these findings, we suggest that ginsenghole Rh1 might be efficacious as a functional compound for alleviating dementia. Investigation, data arrangement, and writing, M.P. ; Original draft preparation, M.P., S.-H.K., and H.-J.L. ; Conceptualization and supervision, H.-J.L. All authors have read and agreed to the published version of the manuscript. The Cooperative Research Program of the Center for Companion Animal Research (Project No. PJ01476703), Rural Development Administration, Republic of Korea, supported this work.
# Analytical Model of an Energy Detector for Ultra-Wideband Chaotic Communications
## 3 Comparing Analytical and Simulation Probabilities  
Let us compare theoretical estimates (15) and (16) with the results of numerical simulation. In Figure 4, BER values are depicted as functions of SNR. Marked plots represent direct BER calculations. To get these plots for a given value of SNR, we acquired statistics of \(E_{0}\) and \(E_{1}\) for \(10^{6}\)-\(10^{7}\) chaotic radio pulses of base \(B\), then we evaluated the area under the overlapping \"tails\" of the distributions (the shaded regions in Figure 2); cumulative probability distribution function (CPF) was more convenient than PDF (probability distribution function) in this task. Symbol probabilities were equal, i.e., \(p(0)=p(1)=0.5\). The curves running through marked plots depict corresponding theoretical estimates (15).  
As can be seen, for small values of the signal base \(B\) analytical and numerical plots quickly diverge with increasing SNR, which means that for small values of \(B\), the theoretical estimates (15) are not very good.  
However, for large \(B\) the discrepancy is negligible. This discrepancy is explained by the approximation of \(\chi^{2}\)-distribution by the normal distribution, the accuracy of which increases with \(B=n\rightarrow\infty\) (more details in the next Section). The question is when this approximation can be considered effective. As can be seen in Figure 4, beginning from \(B=100\), the SNR difference between the actual and approximate plots becomes acceptable, e.g., for BER = \(10^{-4}\), it is less than half dB.  
To proceed from BER as a function of SNR to a more usual function BER(\(E_{b}/N_{0}\)), one should follow these simple transformations:  
\[SNR\ =\ \frac{\langle P_{chaos}\rangle}{\langle P_{noise}\rangle}\ =\ \frac{E_{b}}{T_{b}}\times\frac{1}{N_{0}\Delta f}\ =\ \left(\frac{E_{b}}{N_{0}}\right)\times\frac{2}{2T_{b}\Delta f}\ =\ \left(\frac{E_{b}}{N_{0}}\right)\times\frac{2}{B}, \tag{17}\]  
where \(E_{b}\) is the average bit energy; \(T_{b}\) is the pulse duration; \(N_{0}\) is the AWGN spectral density; \(\Delta f\) is the UWB signal frequency bandwidth; and \(B=2\Delta f\cdot T_{b}\) is the signal base (processing gain, number of freedom degrees) hence:  
\[\frac{E_{b}}{N_{0}}\ =\ SNR\times\frac{B}{2}. \tag{18}\]  
One should note that expression (18) is derived under the assumption of equal probabilities of binary symbols \"0\" and \"1\", so the average signal energy \(E_{b}\) is half the average pulse energy \(E_{1}\).  
The plot of BER as a function of \(E_{b}/N_{0}\) is presented in Figure 5 (fewer curves, larger scale).  
Thus, for \(B>100\), the presented energy detector model allows an evaluation of the error probability with an accuracy that is sufficient for engineering calculations.
# An Efficient Interface for the Integration of IoT Devices with Smart Grids
## 3 Related Work
This result was possible because CBOR requires fewer bytes than JavaScript Object Notation (JSON) and Extensible Markup Language (XML) and CoAP requires fewer bytes on the network than HTTP. Likewise, the authors of [33] also used the IEC 61850 protocol for SGs, but adopted XMPP for communicating with devices. Besides, the authors took a different approach to the integration between protocols using the packet mapping method. The works described above demonstrate the relevance of adapting CoAP for use in conjunction with SGs systems. According to the reported literature, the integration of a widely used IoT protocol developed for resource-constrained devices allows increasing the insertion of devices into the system. This integration enables a broader range of functionalities to be made available to SGs, as well as adding higher levels of management and automation to consumers and service providers. As observed in the literature, most studies present solutions oriented to the IEC 61850 protocol using URI mapping to adapt this protocol with an IoT protocol. In this article, we propose a solution based on packet mapping to integrate CoAP with OSGP, which is a reference architecture for communication between devices operating over SGs. It is worth noting that the work [33] also applies the packet mapping method employed in this work. This method enables extracting data and addresses from OSGP packets to adapt them to CoAP and vice versa. This OSGP-CoAP mapping is different from the ones presented in the other works, with the vast majority focusing on the CoAP and IEC 61850 protocols.
# Beyond Correlation: Mutual Information to Detect Damage in Nonlinear Systems
## 5 Example Application  
Below, we show how the mutual information (MI)-based damage index compares with the two coherence-based metrics described in Section 2-namely, \(ATC\) and \(TMAC\)-in detecting and quantifying damage in structures. We use a standard dataset produced by Los Alamos National Laboratory (Los Alamos National Laboratory (2018) for this purpose. The same dataset was used by [PERSON] et al. (2018) to investigate the performance of \(ATC\) and \(TMAC\).