esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Taking this into account, it is essential to devise mechanisms that retrieve logs and operating metrics to discover if an IoMT device is acting abnormally or has even been compromised [13; 14; 15]. The IoMT is characterised by the high interconnectivity of physical objects and devices embedded in sensors, software, and other technologies that enable them to collect and exchange data [16]. To protect patients' security and privacy and safeguard their medical data, healthcare organisations must take measures to secure all mobile devices that have access to PHI [17]. This includes both employee-owned and corporate-owned devices. IoMT devices often collect sensitive health data, making them attractive targets for attackers [18; 19]. Patients' homes are also at risk if they have connected medical devices. According to research (ESET Threat Report T1 2022), the volume of IoT malware in the first six months of 2022 was higher than that recorded in the previous four years. This indicates a significant increase in the number of IoT devices targeted by attackers. Considering the above, the security of the IoT and Android is essential, and research has been performed to enhance network authentication and mitigate attacks, contributing to the security of mobile health systems on 5G networks [20]. Therefore, it is critical to protect healthcare and IoMT devices from potential security threats, as these devices are used to handle sensitive medical and healthcare data. This research paper proposes Chidroid, an Android application that can retrieve, collect, and distribute logs for analysis on the cloud or on-premises servers. Chidroid enables log collection and distribution to the cloud or other endpoints (servers, computers, Android devices) for analysis enabling edge computing. It can run on multiple edge devices, enabling edge computing and distributing the data to multiple endpoints for simultaneous management, allowing remote management of edge devices. Edge computing is a computational paradigm that brings computation and data storage closer to the network, where data are generated and used. Edge computing offers several advantages in the Internet of Medical Things (IoMT). Firstly, it enables the processing of large amounts of health-related data generated by various medical devices in real-time without overwhelming the back-end infrastructure. Secondly, edge computing can provide improved privacy and security as sensitive health information can be processed locally, reducing the risk of data breaches. Finally, edge computing can improve the overall system efficiency by reducing the amount of data transmitted to the cloud, thus reducing latency, bandwidth usage, and energy consumption.
## 3 Bispectrum Analysis of EEG Signals for an Emotion Recognition System  
There is a significant amount of research focusing on human emotion recognition. However, only a few publicly open databases of emotion are available for download, and in this research, we chose the DEAP database ([PERSON] et al., 2016) for two reasons. First, the DEAP database uses carefully-chosen emotional stimuli based on statistical methods from the respondents. To ensure objectivity, the respondents who assessed the emotional stimuli were different from the respondents who recorded the EEG signals. Second, this database provides numerous observations. Using a publicly open database enables us to reproduce and compare our research results with other findings.  
To recognize human emotion automatically, the proposed method employs higher order statistics to the EEG signal to produce 3D bispectrum matrices. Then, the bispectrum matrices are filtered using a 3D pyramid filter to reduce the size of the matrices and to form a feature vector. The resulting feature vectors are used as the input for the BPNN classifier.
# Soft Error-Tolerant and Highly Stable Low-Power SRAM for Satellite Applications
## 2 Proposed RHSP16T Cell  
### Operations of Proposed RHSP16T  
Figure 1 shows the schematic of the proposed RHSP16T cell, and Figure 2 presents its corresponding layout. The proposed 16T cell's bit-cell structure contains four nodes. Among them, two nodes, Q and QB, are storage nodes, while the others, S1 and S0, are internal nodes. The access transistors are denoted as N1, N8, P1, and P8. Transistors P1 and P8 are controlled by the word line (WL), whereas transistors N1 and N8 are regulated by the write word line (WWL). When the WL/WWL are set to GND/VDD, the access transistors connect the nodes to their respective bit lines. Transistors N8 and P8 connect nodes Q and S1 to BL, while transistors N1 and P1 connect nodes QB and S0 to BLB. When the stored bit is '1', nodes Q, QB, S1, and S0 have logical states of '1', '0', '1', and '0', respectively. This paper explains all operations by assuming the proposed cell is in the initial state of '1'.  
Figure 1: Schematic of the proposed RHSP16T cell.  
(1) Hold Operation: The WL/WWL are set to VDD/GND in the hold operation, ensuring that all access transistors connected to the nodes remain off. For the assumed initial state of '1', transistors N2, N4, N5, P3, P5, and P6 are turned on while the other transistors are turned off. Thus, nodes Q and S1 can retain their initial state of '1' because they are connected to VDD through the paths 'Q-N5-P5-VDD' and 'S1-P6-VDD', respectively. Similarly, nodes QB and S0 can retain their initial state of '0' through the paths 'QB-N4-GND' and 'S0-P3-N2-GND', respectively. This ensures the stable operation of the cell in the initial state.  
(2) Read Operation: Bit lines BL and BLB are pre-charged to VDD for the read operation. Subsequently, the WL/WWL are set to GND/GND, ensuring the storage nodes Q and QB are isolated from the bit lines. From the assumed initial state, BLB discharges through the pull-down transistors P3 and N2, while BL remains at the pre-charged VDD value, as P7 and N7 are off. Once the voltage difference between BL and BLB reaches approximately 50 mV, the sense amplifier performs the read operation [3].  
(3) Write Operation: To convert the stored data from '1' to '0' within the cell, bit lines BL and BLB are pre-discharged or pre-charged to GND and VDD, respectively. The WL/WWL are set to GND/VDD, ensuring that all access transistors connected to the nodes are in the on state. The voltages at QB and S0 rise through access transistors N1 and P1, respectively. As the voltages of QB and S0 increase, the pull-up paths to Q and S1 weaken, facilitating the rapid discharge of Q. Once the voltage of Q drops sufficiently, the pull-down transistors N2 and N4, connected to QB and S0, turn off, thereby speeding up the charging process of QB and S0. Thus, the states of all nodes can be changed to the desired values using their respective access transistors. Note that synchronously changing the values of both the storage and internal nodes with both of their respective access transistors enhances the speed of the cell's write operation.
## 3 Experimental Results
### Fixed Infrastructure Density Scenario
#### 3.1.1 Packet Delivery Ratio
Figure 6: PDR simulated for all the investigated modes of the 4G-based and 5G-based C-V2X technologies and traffic intensity of 1250 vehicles per hour.
## 2 Materials and Methods
### Detection of mRNA Expression in Colon Tissue of Mice by qPCR  
The colon tissue of mice was homogenized, and RNA TRIzol Reagent (Invitrogen, Carlsbad, CA, USA) was used to extract the total RNA, which was then diluted to 1 \(\upmu\)g/\(\upmu\)L. The diluted RNA solution of 5 \(\upmu\)L was obtained using a reverse transcription kit and the cDNA template was obtained. Subsequently, 2 \(\upmu\)L of the cDNA template was mixed with 10 \(\upmu\)L SYBR Green PCR (polymerase chain reaction) Master Mix and 1 \(\upmu\)L upstream and downstream primers (Thermo Fisher Scientific, Waltham, MA, USA) (Table 5). A total of 40 cycles were conducted after which the reaction conditions were as follows: 95 \({}^{\circ}\)C, 60 s; 95 \({}^{\circ}\)C, 15 s; 55 \({}^{\circ}\)C, 30 s; and, 7 \({}^{\circ}\)C, 35 s (StepOnePlus Real-Time PCR System, Thermo Fisher Scientific, Waltham, MA, USA). Relative gene expression was calculated using the 2-\({}^{\Delta\Delta\text{CI}}\) method, using glyceraldehyde-3-phosphate dehydrogenase (GAPDH) as internal reference [17].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Gene Name** & **Sequences** \\ \hline nNOS & Forward: 5\({}^{\prime}\)-GAGAG ATT CTG AAG ATG AGG-3\({}^{\prime}\) \\  & Reverse: 5\({}^{\prime}\)-TTG CTA ATG AGG GAG TTG TTC-3\({}^{\prime}\) \\  & Forward: 5\({}^{\prime}\)-TGT TTG TCT GCG G ATG T-3\({}^{\prime}\) \\ eNOS & Reverse: 5\({}^{\prime}\)-GGG TGC GTA TGC GGC TTG TC-3\({}^{\prime}\) \\ iNOS & Forward: 5\({}^{\prime}\)-CAT TGG AAG TGA AGC GTT TCG-3\({}^{\prime}\) \\  & Reverse: 5\({}^{\prime}\)-CAC AGA ACT GAG GGT ACA-3\({}^{\prime}\) \\ c-Kit & Forward: 5\({}^{\prime}\)-CATAAGCCCAGGTAAGCACAAAT-3\({}^{\prime}\) \\  & Reverse: 5\({}^{\prime}\)-GAACACTCCAAGATCGTAACTC-3\({}^{\prime}\) \\ SCF & Forward: 5\({}^{\prime}\)-TCAGGGACTACGCCTGGAAAAG-3\({}^{\prime}\) \\  & Reverse: 5\({}^{\prime}\)-AAGAGCTGCAGGACCGACTCA-3\({}^{\prime}\) \\ IL-8 & Forward: 5\({}^{\prime}\)-CTAGGCATCTTCGCTCC-3\({}^{\prime}\) \\  & Reverse: 5\({}^{\prime}\)-TTGGCCAACAGTAGCCTTG-3\({}^{\prime}\) \\  & Forward: 5\({}^{\prime}\)-TCTGCTACAAACAGGCTGTA-3\({}^{\prime}\) \\  & Reverse: 5\({}^{\prime}\)-GAGTGCATGGACAGACACATC-3\({}^{\prime}\) \\ GAPDH & Forward: 5\({}^{\prime}\)-TGC ACC ACC AAC TGC TTA G-3\({}^{\prime}\) \\  & Reverse: 5\({}^{\prime}\)-GAT GCA GGG ATG ATG TTC-3\({}^{\prime}\) \\ \hline \hline \end{tabular} nNOS: neuronal nitric oxide synthase; eNOS: endothelial nitric oxide synthase; iNOS: inducible nitric oxide synthase; SCF: stem cell factor; IL-8: interleukin 8; CXCR: C-X-C chemokine receptor type 2; GAPDH: glyceraldehyde-3-phosphate dehydrogenase.  
\end{table}
Table 5: Sequences of primers used.
# Coordination of Congestion and Awareness Control in Vehicular Networks
## 2 Congestion and Awareness Control
### Awareness Control  
Awareness control also adapts the vehicles' communication parameters, but with the objective to ensure the capacity of each vehicle to detect (and possibly communicate) with the relevant vehicles and infrastructure nodes present in its local neighborhood, while minimizing/controlling the generated channel load. The awareness control protocol implemented in this study follows the design policy proposed in [11], where each vehicle proactively adapts its communication parameters to the minimum needed to satisfy its individual application requirements. This study considers active safety applications since they usually have more strict requirements. The application requirements are defined in terms of communications range (referred to as coverage range in ETSI) and packet reception frequency (inverse of packet inter-reception time) [24; 25]. An application is satisfactorily supported if the number of packets correctly received per second at the established communications range is higher than the application's packet reception frequency. To this aim, the implemented awareness control protocol (MINT, minimum packet transmission frequency) sets the packet transmission frequency equal to the application's packet reception frequency plus a fixed margin \(\Delta T_{f}=1\) Hz. \(\Delta T_{f}\) is used as a'safety' margin to consider possible unexpected propagation effects and ensure that the packet reception frequency requirements are met at the receiver. The transmission power is then set to the level needed to ensure that the demanded packet reception frequency is guaranteed at the required communications range. To this aim, MINT obtains the transmission power needed from a power-range map taking into account the required communications range. The power-range map constructed is based on the computation of the probability of packet reception as a function of the distance between transmitter and receiver and takes into account the propagation conditions. In particular, the analytical expression for the Packet Delivery Ratio (PDR) at a given distance \(d\) to the transmitter proposed in [26] has been adopted:  
\[PDR(d,m,P_{t})=e^{-m(\frac{d^{2}}{K_{R}T})}\left[1+m\frac{d^{2}}{K_{R}P_{t}}+ \frac{m^{2}}{2}\frac{d^{4}}{K_{R}^{2}P_{t}^{2}}\right], \tag{3}\]  
where \(P_{t}\) represents the transmission power, \(m\) is the Nakagami-\(m\) fading intensity, and \(K_{R}\) is a constant that can be obtained with the following equation:  
\[K_{R}=\frac{\lambda^{2}}{\left(4\pi\right)^{2}}\frac{G_{t}G_{r}}{LR_{Th}}, \tag{4}\]  
with \(\lambda\) being the carrier wavelength, \(G_{t}\) and \(G_{r}\) the transmitter and receiver antenna gains, \(L\) the loss factor (typically \(L=1\)), and \(R_{Th}\) the reception threshold. Using Equations (3) and (4), the transmission power \(P_{t}\) needed to obtain the PDR demanded at distance, \(d\), is obtained.
## 4 Discussion  
This work revealed that the optimization of the proposed structure avoids spurious resonances arising from the coupling of multiple modes since the Rayleigh and Sezawa modes are distant in the frequency domain and do not interfere with each other. For the field of SAW filters, the larger \(k^{2}\) and high \(v_{\rm p}\) achieved by this multilayer structure help to construct high-frequency broadband filters, and the zero _TCF_ helps to improve the operating frequency stability of the filters, which is especially promising for applications in the communications field, where the band spacing is much narrower. Future work will proceed to fabricate and characterize SAW devices based on simulations. Comparative analyses of the simulation and experimental results will be carried out to verify the scientific validity of this study.
## 4 Results and Discussion  
### Execution Time and Model Stability  
Figure 4 shows a comparison of the free surface data from the simulations and the theoretical results, calculated at the inlet boundary of the NWT. The free surface was accurately reproduced by OpenFOAM and the motion of the floating bodies under wave action was validated and corresponded well with the reported experimental data [45; 46]. A similar case to the present simulation was recently presented by [PERSON] et al. [47], who studied the hydrodynamic forces of semi-submerged horizontal, rectangular, and circular prisms. In their validation process, the free surface of theoretical and simulated waves was compared, with a relative variation of 1.08%.  
It can be seen from Figure 4 that there is no significant difference between the theoretical and simulated free surfaces. Hence, the result from this simulation setup is considered to be in agreement with the quality of the mesh. The extra setup of the NWT to assess the set of subsequent cases was not necessary. As previously mentioned, the limitations of a simulated approach obliged us to treat the results of the current study with caution.  
The results, in relation to the average power delivered, show that after a few wave periods, the averages and standard deviations of the model simulations do not change significantly, as shown in Figure 5 (the parameters for each simulation from P1 to P9 are described later in Section 4.2).  
The execution time for the simulation of one hundred waves is presented in Figure 5 and the computation time is resumed in Table 3. It can be seen that the computation time increases as the buoy's size increases. It is worth mentioning that the computation time using the overset grid method is double the runtime of that using the mesh morphing method, making the former considerably more demanding, computationally [24].  
Figure 4: Comparison of the surface wave elevation (\(\eta\)) at the NWT inlet calculated with the theoretical result and simulation: (**a**) for \(T=4\) s and (**b**) for \(T=8\) s, with \(H=1\) m in both cases. The NWT considers the \(R_{2}\) buoy.  
Figure 5: One hundred waves simulated for the buoy with \(R=0.25\) m: (**a**) for \(T=4\) s and (**b**) for \(T=8\) s. Details of P1 to P9 are described later in Section 4.2.
# Applications of Machine Learning Algorithms in Geriatrics
## 5 Discussions
In PubMed, this search yielded 10,332 results, in Scopus 3346, in WOS 1501, and in IEEE 16. High values are identified in PubMed Scopus, and WOS, with IEEE having the lowest values because they are associated with technical publications and include sensors, IoT technology, area-specific wearable device technologies, and technical applications that integrate ML. The fourth search on the \"geriatr\" AND elderl\" query yielded the following results: Scopus generated 26,806 results, PubMed 17,397, WOS 9469, and IEEE 427. These results have high values, which suggests increased interest from researchers in this field. Furthermore, the following searches add specific ML constraints. Thus, the fifth search based on the \"diet AND geriatric\" AND machine learning\" query provides 20 results in PubMed, 15 in WOS, 9 in Scopus, and 1 in IEEE. The sixth search on the \"nutrition AND geriatric\" AND machine learning\" query generated 113 results in PubMed, 34 in Scopus, 34 in WOS, and 6 in IEEE. The seventh search on the \"elderl\" AND geriatric\" AND machine learning\" query yielded 327 papers in Scopus, 234 articles in PubMed, 282 in WOS, and 65 in IEEE. The eighth search on the \"geriatr\" AND machine learning\" query showed 1800 results in PubMed, 788 in WOS, 764 in Scopus, and 234 in IEEE. The ninth search based on the \"geriatr\" AND elderl\" AND nutrition AND machine learning\" query generated 29 papers in PubMed, 18 in Scopus, 12 in WOS, and 2 in IEEE. These results suggest the need to intensify the research in the field of geriatric, specifically regarding ML components, as the number of results remains limited regardless of the database used. Table 10 presents a summary of the studies disseminated in the Multidisciplinary Digital Publishing Institute (MDPI). These studies focus on the field of elderly health through an approach using ML methods. These articles cover issues such as sarcopenia, frailty, delirium, mood disorders, and locomotor syndrome. These works come from open-access journals such as Sensors, Journal of Clinical Medicine, and International Journal of Environmental Research and Public Health. The fact that these articles are disseminated in such journals reflects the trend of combining sensor technology with ML models for assessment, prevention, prediction, and treatment suggestions. A common aspect of these works is the use of moderately sized datasets. Some studies use medical imaging, with an emphasis on non-invasive methods that can be easily implemented in clinical practice. The ML models are predominantly RF and LR. More complex architectures like MLP, CatBoost, or Stacking achieve accuracies exceeding 95%. The reported metrics include, in addition to accuracy, the F1-score, AUC, and specialized metrics such as Hamming Loss for classification or R\({}^{2}\) for regression.
## 4 Conclusions  
A semi-empirical model accounting for hole localization by InGaN composition fluctuations has been extended to QWs of polar and nonpolar orientations. The extended model provides quantitative predictions for radiative and Auger RCs as a function of emission wavelength, which is confirmed by direct comparison with available data. Both RCs are found to decrease with the wavelength due to hole localization enhanced at higher InN fractions in InGaN alloys. However, a greater importance of RRC for the \(Q\)-factor and, hence, for IQE, eventually leads to a substantial efficiency decline with the emission wavelength.  
Breakdown of the carrier losses up to \(\sim\)540 nm made by a comparison of IQEs of polar and nonpolar LED structures attributes \(\sim\)53% of the efficiency decline to carrier localization, \(\sim\)29% to QCSE, and \(\sim\)18% to variation of native material properties with the emission wavelength. This explains observations of pronounced efficiency reduction with the wavelength in nonpolar and semipolar LEDs, where QCSE is eliminated or largely reduced. Since the composition fluctuations are unavoidable in InGaN alloys, the carrier localization seems to be a fundamental factor limiting the efficiency of long-wavelength nitride LEDs.  
Among the unresolved problems, determination of the localized hole density of states seems to be of primary importance. This would allow researchers to predict the dependence of the RRC and ARC on temperature and carrier concentration, which is necessary for the optimization of conventional (large-size) and micro-LEDs made of InGaN compounds. Impact of the hole localization on SRH recombination in InGaN alloys is another subject for future studies, which is of special interest for violet and near-UV LEDs.  
Due to the common nature of carrier localization, application of the semi-empirical model to AlGaN alloys utilized as the materials for active regions of deep-UV LEDs also seems to be promising in order to better understand the factors limiting the efficiency of these devices. For this purpose, however, getting any experimental information on the recombination coefficients of deep-UV LEDs would be quite desirable.  
I would like to thank [PERSON] from Technische Universitat Berlin for providing low-temperature data on recombination coefficients and helpful discussion.  
**Conflicts of Interest:** The author declares no conflict of interest.
## 3 Results and Discussion
### Future Works  
According to Equations (1) and (2), the wavelength of the sensor in its undeformed state is determined by both the incident angle and the diffraction angle. This can affect the initial color of the sensor as well as its subsequent discoloration during deformation. Light intensity also can cause changes in saturation (S). In practical structural applications,  
Figure 9: Comparison of predicted displacement using the trained ViT model on images and the measured displacement.  
environmental factors (e.g., weather, indoor vs. outdoor conditions) and the observer's viewing angle can influence these angles, and thus the sensor's initial color and discoloration behavior may vary accordingly. Thus, it is challenging to generalize the findings of this study for structural health monitoring (SHM) using mechanochromic sensors.  
However, since the sensor's discoloration is periodic, knowing its initial color allows one to infer deformation-induced discoloration [16]. Moreover, the rationale for introducing a deep learning-based prediction method in this study was to gather data on discoloration behavior under diverse conditions and utilize them to predict deformation through data-driven training in future works. In fact, several recent studies have reported successful predictions of deformation using vision-based sensors combined with machine learning techniques [21, 22].  
A key finding of this study is that the discoloration sensor can predict displacement even at the small deformation levels commonly required for structural applications. Future research should focus on collecting discoloration data under various conditions, including different incident angles, viewing angles, and imaging equipment. Subsequent efforts should involve data-driven training, sensitivity analyses with respect to environmental factors, and further exploration of deformation prediction strategies.  
[PERSON] et al. [12] investigated the discoloration characteristics of the sensor after freezing and concluded that it returned to its original color within a few minutes following thawing. However, there is a lack of research examining how prolonged UV exposure and high-temperature conditions affect the initial color and discoloration of mechanochromic sensor. To apply this sensor in structural SHM, it is necessary to overcome their long-term durability issues. This topic also should be explored in future research.