esa-sceva/satcom-chunk-collection huggingface dataset:
# Molecular Basis of Irisin Regulating the Effects of Exercise on Insulin Resistance
## 2 Biology and Physiology of FNDC5/Irisin  
### Summary Profile of FNDC5/Irisin  
Two decades ago, a new gene was identified by two independent groups, which is widely distributed in the heart, skeletal muscle, brain, as well as other tissues of mice [15; 16]. After a decade, the gene was named fibronectin type III domain-containing protein 5 (FNDC5). It is one of the most important target genes of peroxisome proliferator-activated receptor \(\gamma\) coactivator 1\(\alpha\) (PGC1\(\alpha\)) which is a transcriptional coactivator, frequently produced in exercised skeletal muscle [10]. Mice either overexpressing PGC-1\(\alpha\) or subjected to endurance exercise highly upregulated FNDC5 gene expression, and conditioned medium (from FNDC5 overexpression mice) cultivated myocytes caused adipocytes to upregulate uncoupling protein 1 (UCP1), a key gene leading to brown adipose tissues (BAT). Meanwhile, a new myokine-irisin has been discovered; it contains 112 amino acids (aa) and is formed from skeletal muscle FNDC5 after being cleaved at the N-terminus [10]. The released irisin, according to this study, may be responsible for several of the positive benefits of physical activity on energy consumption via adipose tissue browning [10].
# A Hybrid Scheme for Disaster-Monitoring Applications in Wireless Sensor Networks
## 2 Related Work
### Clustering Methods  
The nodes of wireless sensor networks are equipped with limited battery power, and perform the task of real-time surveillance in the disaster-monitoring system. Node-clustering is a promising method for maximizing their lifetime and satisfying the delay requirement. Numerous clustering methods have been investigated for homogenous sensor networks, such as LEACH [39] and DWEHC [40]. E-CERP clusters sensor nodes based on an adaptive sailfish optimization algorithm with K-medoids, and can attain a high network lifetime [41]. Heterogeneous clustering methods support networks with sensor nodes of different abilities. A stable election protocol (SEP) has been proposed for the heterogeneous routing protocol [42]. In this protocol, nodes are classified into normal and advanced nodes according to the initial energy and equipment; thus, different types of nodes gain different probabilities of becoming the cluster head. SEP does not use the residual energy of nodes to balance the energy consumption. Based on SEP, DEEC is a combination clustering method of centralization and distribution for heterogeneous networks [43]. DEEC selects the cluster head based on the initial and residual energy level of the nodes. To regulate the energy consumption of the adaptive approach, DEEC utilizes the average energy of the network to select the cluster head for attaining the long lifetime of networks. However, the base station of DEEC owns global knowledge of the networks, and we have not taken full advantage of this aspect. BIDRP is a dynamic routing protocol based on centralized clustering algorithms for heterogeneous wireless sensor networks [44]. In this protocol, nodes with more energy, computation capability, and location awareness are selected as the cluster head. In a cluster, transmission between the cluster head and sensing nodes occurs in a single hop, whereas transmission between different cluster heads occurs in multiple hops. However, the transmission of multiple hops is hardly to support real-time applications.  
In this study, we propose a hybrid superior node token-ring MAC scheme, HT-MAC, for disaster-monitoring applications, such as the rapid reporting of earthquake information. Alerts and seismic information play important roles in disaster monitoring. Therefore, the delay and throughput of WSNs for alerts and seismic data must be guaranteed. HT-MAC operates in the duty cycle mode. Owing to the relatively short active state of the sensor nodes, low-traffic alert data are transmitted with a low-power listening approach during the sleep state to satisfy the delay requirements. By setting up a polling approach for superior nodes, this scheme can support low-delay transmissions in various traffic scenarios and provide flexible adaptation to traffic changes. This satisfies the delay and throughput requirements for disaster monitoring. Reliability is an important aspect of disaster monitoring, and WSNs should provide reliable transmission even in abnormal situations.
# Enhancing Indoor Localization with Room-to-Room Transition Time: A Multi-Dataset Study
## 3 Material and Method
### Offline (Data-Collection) Phase
#### 3.1.1 Hardware Setup for Automated Wi-Fi Data Collection  
To measure Wi-Fi signal strength, a hardware setup comprising an ESP32-DevKitC (ESP32) module [43], a MicroSD card module, and a portable power bank was employed. The ESP32-based Wi-Fi signal-collection module was assembled on a breadboard and strategically positioned at predetermined fixed locations within each room. This module was programmed to record Wi-Fi signal strength at 5 min intervals, storing the data on the MicroSD card. Over a single session, this configuration facilitated the collection of up to 500 data points per room, allowing for continuous data collection for approximately 41.6 h  
Figure 1: Sketch of the house used for Dataset 2, where one of the datasets was collected. Additional datasets were obtained from two other houses with different spatial layouts.  
(around two days). This setup ensured efficient and automated data acquisition, reducing manual intervention and maintaining high reliability in signal strength measurement.  
The circuit diagram of the hardware setup is shown in Figure 3. The ESP32 development board [43] featured a dual-core Xtensa(r) 32-bit LX6 processor running at 240 MHz and 520 kilobytes of Static Random-Access Memory (SRAM), along with integrated Wi-Fi and Bluetooth modules. The development board was connected to an external SD card module via the Serial Peripheral Interface (SPI) for data logging.  
To maintain uninterrupted operation, the study utilized two power banks in an alternating manner. While one power bank powered the ESP32 module during data collection, the other was recharged, ensuring continuous functionality. The ESP32 module was programmed to signal the completion of data collection via a blinking red LED indicator. Upon activation of the indicator, the device was disconnected from the power source, and the collected data, stored in CSV format on the SD card, were transferred to a computer for subsequent processing.  
Following the data-collection phase, three raw datasets were generated, each corresponding to a different residential environment. In total, 3000 data points were collected for each dataset. Dataset 1 contained 58 unique BSSIDs, Dataset 2 included 63 unique BSSIDs, and Dataset 3 comprised 41 unique BSSIDs, reflecting the variations in Wi-Fi signal availability across different environments.  
Figure 2: Offline data-collection phase of the proposed system.
# Modeling and Analysis of Wearable Antennas
## 4 Conclusion and Future Prospects
There are abundant uses such as listening to music simply by placing a portable audio terminal and a speaker on the sheet-like waveguide, synchronizing data just by similarly placing a notebook PC, and charging by simply placing a mobile terminal. As an example, outside the office, medical applications may be considered. It is not desirable to attach a sensor connected to the cable to the body for a long time for testing. By laying a sheet waveguide on the bed, information from sensors installed on the back, for example, can also be transmitted. In the sheet-like waveguide, since the accessible range is limited, there is no problem of interference with other sensors. Research on Body Centric Networks such as wearable networks and human body communication has been drawing attention recently [32; 33]. There is research on wristband type electrodes for human body communication that communicate with people along the body, etc. However, in the high-frequency band such as 2.4 GHz, radiation to the outside of the body is larger than the propagation of electromagnetic waves along the body. In applications where it is permissible to wear a sheet-like waveguide made of wearable material, efficient transmission becomes possible. A smart suit made of conductive cloth is excellent as an auxiliary transmission line of BAN since various sensors can be attached to a human body, meaning power supply and information acquisition from a sensor can be performed at the same time. This becomes a possible means of information transmission for disaster relief and some military applications [34; 35]. In addition, by wearing a sheet-like waveguide fabricated from a flexible material in a robot wearing various sensors including GPS, it is possible to remove the cable between the sensors wound around the robot and measure the size of the robot, an application that gives a conceivable degree of freedom of movement. Studies on wearable antennas using conductive fabrics and the like are actively conducted, and the technology can be applied [34; 35]. There is also no need to wear an antenna at the bottom of the shoe to take out card-type electronic money, but it is possible to ride a train by stepping on the vicinity of the ticket gate, or an application that charges the card by just standing up. As described above, various application fields are expected for the wearable waveguide composed of the sheet waveguide and the flexible material, and it is desirable for use in the future. AcknowledgmentsThis work was supported by the Basic Science Research Program through the National Research Foundation (NRF) of Korea funded by the Ministry of Education (grant number: NRF-2017R1D1A1B03028350).
## 5 Detection of Side-Delaminations  
In this section, the nonlinear response of side-delaminations (see Figure 2b) is investigated.  
Time-frequency filtering (see Section 3.1) is used to extract the linear and the second higher harmonic responses from the measurement data. Next, the \(DBR\) curves are calculated (see Equation (5) for these two filtered signals. Both curves are shown in Figure 7a. All side-delaminations are located at a depth equal to or bigger than half the thickness of the CFRP sample. As a result, no LDR behavior is detected in the linear velocity component (see Figure 7b,c) and the \(DBR^{lin}\) curve fluctuates around 1. Also, in the corresponding band power map (see Figure 7d), the absence of an increased linear response is observed. As such, this linear component is not efficient for the detection of these relatively deep defects.  
The \(DBR\) curve corresponding to the second harmonic component reveals an increased nonlinear activity at the defect. The maximum \(DBR\) is reached at 76 kHz (i.e., \(2\times 38\) kHz) and the corresponding amplitude map (Figure 7e) shows the nonlinear source behavior of the delaminated edge. While at 76 kHz the main nonlinear response is found at the middle of the top edge, other frequencies reveal other parts of the damage. As an additional example, the amplitude map of \(HH2\) at \(2\times 45\) kHz = 90 kHz (Figure 7f) reveals the nonlinear behavior of the deeper side-delamination more towards the left of the top edge.  
The \(DBR\) curve of the second harmonic component (see Figure 7a) indicates that the defect behaves as a source of second harmonics for the majority of the frequencies. To make this more clear, the area  
Figure 7: Side-delamination response analysis: (**a**) defect-to-background ratio of linear and second harmonic component, (**b**-**d**) linear response amplitude at 38 kHz, at 45 kHz and band power map, (**e**-**g**) second harmonic response amplitude at \(2\times 38\) kHz, at \(2\times 45\) kHz and band power map.  
beneath the curve is colored green when there is an increased second harmonic component at the defect compared to the damage-free material, i.e., \(DBR^{HH2}(f)>1\), and it is colored red when \(DBR^{HH2}(f)<1\). This observation is exploited in the calculation of the nonlinear band power map. Indeed, a strong increase in the band power is observed for the second harmonic component (see Figure 7g). The band power map is dominated by the nonlinear source behavior of the side-delamination at the middle of the top edge. Note that the band power of the second harmonic also indicates that the excitation PZT itself induces a limited amount of nonlinearity in the system.  
The linear and nonlinear response of the side-delaminations match well with the response of the artificial backside-delamination discussed in the previous section. In both cases, defect detection is possible by searching for an increased higher harmonic activity at specific frequencies at which LDR is induced, or simply by calculating the nonlinear band power over the total excitation bandwidth.
# Enhancing Industrial Processes Through Augmented Reality: A Scoping Review
## 5 Conclusions
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline
**Title** & **Before AR Implementation** & **After AR Implementation** & **Percentage of Optimisation (\%)** \\ \hline Evaluating Digital Work Instructions with Augmented Reality Versus Paper-based Documents for Manual, Object-specific Repair Tasks in a Case Study with Experienced Workers [32] & The time to repair with paper guide is 466.1 s & The repair time using AR CAS guide is 367.3 s & The time is reduced by 21.19\% using the AR CAS guide. \\ \hline Hologivalay: an Augmented Reality System to Support Assembly Operations in the Railway Industry [48] & The overall average time in assembly by traditional method is 108.37 s and the number of errors is 17 & The overall average time in assembly by AR method is 56.5 s and the number of errors is 2 & By using AR in the assembly, the time is reduced by 52.13\% and the errors decreased by 88\%. \\ \hline Improving Efficiency of Industrial Maintenance with Context-aware Adaptive Audoring in Augmented Reality [23] & The time to perform maintenance tasks with manual paper-based procedure is approximately 13 min & The time to perform maintenance tasks with the use of AR is 7 min & A reduction in time to perform the maintenance activity of about 46\% is observed. \\ \hline Integrated Augmented and Virtual Reality Technologies for Realistic Fire Drill Training [49] & According to [58], the time to complete the fire training was 6 min with VR. & The training lasted 5 min, during which time participants were asked to simulate AR/VR firefighting training using the system. \\ \hline MANTRA: An Effective System Augmented Reality and Infrared Thermography for Industrial Maintenance [50] & The time to perform the diagnostic task in minutes with detailed guidance is 6.74 and the number of errors is 3.60 & Time to perform the diagnostic task in minutes with AR is 6.00 and the number of errors is 3.33 \\ \hline MARMA: A Mobile Augmented Reality Maintenance Assistant for Fast-Track Repair Procedures in the Context of Industry 4.0 [24] & Compressor repair is performed with paper-based procedure. & Compressor repair is performed with MarakMA procedure, an AR system. & Experts mentioned that MARMA can reduce the total compressor repair time by 30\% compared to the paper-based procedure. \\ \hline Multimodal Mixed-Reality Impact on a Hand-Guiding Task with a Holographic Cobot [51] & The time spent during robot execution without (V) haptic feedback is 26.614 s & The time taken during robot execution with (V + H) with haptic feedback is 24.027 s & Cobot execution time with (V+ H) is reduced by 9.72 with respect to (V). \\ \hline \end{tabular}
\end{table} 
Table 30: Cost.
# The Applications and Challenges of Digital Twin Technology in Smart Grids: A Comprehensive Review
## 5 Challenges and Limitations
### Interoperability and Standardization in Digital Twins
#### 5.2.2 Proposed Solutions  
Several specific methods can be implemented to overcome these limitations.  
* Adoption of Open Architectures:  
To address interoperability challenges, organizations should implement open architectures that enhance data transparency and modularity. This approach allows for the integration of various systems, as seen in the AECO sector, where poor interoperability has significant economic impacts due to fragmented data exchange. The adoption of common data models, such as the Industry Foundation Class (IFC), can facilitate seamless communication across platforms [59]. Collaborations with standardization bodies like ISO and IEEE are crucial for developing these models, as demonstrated by the Smart Interoperability Architecture (SINA), which promotes decentralized data management and enhances trust among users [105]. Furthermore, the use of standards like OPC UA can optimize data-driven decision-making and improve system integration [47].  
API Integration and Middleware Solutions:  
API integration and middleware solutions play a crucial role in connecting legacy systems with modern Digital Twins (DTs). These technologies enable real-time data sharing, facilitating communication between platforms not initially designed to interact. Middleware serves as an abstraction layer, allowing the seamless integration of existing infrastructures with minimal disruption, thereby improving the interoperability [48]. Key approaches include middleware strategies that manage virtual replicas of real-world entities, such as YANG-powered DT middleware, which enhances communication in IoT networks by reducing latency and resource consumption [49]. API integration, particularly through Data Link architecture, merges dispersed data from different systems, supporting the creation and operation of Digital Twins while streamlining data management and control systems [29].  
Standardized Communication Protocols:  
The standardization of communication protocols is crucial for efficient data transfer and real-time information exchange between Digital Twin systems, ensuring scalability and interoperability. Common protocols like MQTT, OPC UA, and RESTful APIs support seamless communication across diverse platforms. Standardization is vital for digital transformation and promotes the effective implementation of Digital Twins [106; 107]. MQTT and RESTful APIs offer lightweight communication, while OPC UA ensures secure data exchange in industrial settings [108].
## 5 Numerical Study
### Uncertainty Analysis
#### 5.2.1 Characteristics of HDB under Uncertainty  
As the characteristics of HDB vary with the eccentricity ratio, the probability distribution and mean values of static characteristics \(\{p_{\max},\
abla_{0},S\}\) at \(\varepsilon=\{0.2,0.4,0.6,0.8\}\) are evaluated by the gPC expansion, as shown in Figure 8. The results obtained by the MC simulation are plotted in the same graph. It is observed that the distributions vary with the eccentricity ratio. The values corresponding to the maximum probability for each variable differ from their mean values. Some results show a limited agreement with the MC simulation, such as the PDF of \(p_{\max}\) at \(\varepsilon=0.2\) and \(\Phi_{0}\) at \(\varepsilon=0.8\). This phenomenon results from the numerical iteration [53]. For a random system with an analytic expression avoiding the iterative approach, the consistency will be better. Generally, the PDF and mean values obtained by gPC expansion display good accordance with those obtained by the MC simulation. This indicates that the constructed third-order gPC expansion is accurate enough to approximate the random outputs. Furthermore, it is clearly known that the number of collocation points required for gPC expansion is much smaller than the number of sampling points required for MC simulation of similar accuracy.  
Figure 9 shows the mean values of each variable with different eccentricity ratios, for the dimensionless dynamic coefficients. To illustrate the influence of random parameters, the values corresponding to the 95% confidence interval are also plotted. It is revealed, for small eccentricity ratios, that the confidence intervals have a wider band for \(K_{yx},D_{yy}\) and large ratios for \(K_{yy}\). This is because the sharp change in these dynamic coefficients for these regions makes them more sensitive to the eccentricity ratio.  
Figure 8: _Cont._  
Figure 7: Convergence of MC simulation.
## 5 Conclusions  
This study fully used the direct relationship between florescence spectral information and rice yield, analyzed the sensitive spectral bands to rice yield, and optimized multi-stage VIs-based yield estimation models. The following conclusions can be drawn:  
1. For the first derivative florescence ratio reflectance, the sensitive bands to yield were centered at green bands 560 nm-568 nm, yellow bands 576 nm-588 nm, red-edge bands 680 nm-760 nm and NIR bands around 840 nm; the highly sensitive bands (top 50% of the sensitive bands) were located at 704 nm-760 nm (red-edge). For the first derivative florescence difference reflectance, the sensitive bands were yellow bands 580 nm-596 nm, red bands 664 nm, red-edge bands 688 nm-696 nm, 724 nm-768 nm, the NIR region 840 nm-864 nm, and the highly sensitive bands (top 50% of the sensitive bands) were 590 nm (yellow), 690 nm and 736 nm-748 nm, and 760 nm-768 nm.
2. The useful florescence spectral indices, FDI and FRI, were mainly constructed with NIR bands, red-edge bands and yellow bands as denominators and bands from those that closely relate to the yield as numerators.
3. The best date for monitoring the spectral information of rice flowering was at around the seventh day of the flowering stage.
4. The introduction of florescence information into VIs-based models could not only improve the accuracy of yield estimation, but is also helpful in realizing rice yield estimations at an earlier stage.  
In addition, this study was carried out using the data of a two-year experiment with specific rice varieties, so data of more years and about more rice varieties are needed to verify the effectiveness of models proposed in the study. The monitoring of spectral changes at the flowering stage requires remote sensing data with temporal resolution at a day level, which is difficult to be met by satellite data at present. Besides, the proposed method is waiting to be tested using other crop species and other remotely-sensed data. In the future, with the development of high spatial-temporal resolution technology of satellites, this method is expected to realize the application of large-scale yield estimation based on satellite data.  
Conceptualization, F.W.; data curation, X.Y.; funding acquisition, F.W.; investigation, L.X., J.Z. and T.X.; resources, F.W.; writing--original draft, X.Y.; writing--review and editing, F.W., L.X., J.Z. and T.X. All authors will be informed about each step of manuscript process, including submission, revision, revision reminder, etc. via emails from our system or assigned Assistant Editor. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the \"National Key Research and Development Plan of China\" (2016 YFD0300601) and the \"National Natural Science Foundation of China\" (41871328).  
**Institutional Review Board Statement:** Not applicable.