esa-sceva/satcom-chunk-collection huggingface dataset:
# Toward QoS Monitoring in IoT Edge Devices Driven Healthcare--A Systematic Literature Review
## 7 Responses to Research Questions
### Response to Research Question 2  
QoS is an important aspect of healthcare applications as it ensures that the system can provide a certain level of performance for real-time and mission-critical services. As of pre-2018 publications, QoS in healthcare applications was focused on medical imaging, telemedicine, electronic health records (EHR), and medical devices. In general, QoS in healthcare applications is essential for making sure that patients receive the most effective treatment and that healthcare professionals have the information they need to make wise decisions. Currently, research publications are more focused on network performance, data volume, data complexity, and system performance. Optimizing QoS in smart healthcare applications typically involves identifying and addressing the factors that are causing delays or other issues and implementing strategies to boost the general performance of the communication and data transfer processes. This may include things like optimizing network performance, minimizing data volume and complexity, and improving system performance.
## 2 Materials and Methods
### Dual-Mode HTM Setup and Image Acquisition  
We modified the configuration of the HTM device partially so that the device could detect a fluorescence signal. Two linear stages (M-426A; Newport, Irvine, CA, USA) were cross-connected on an optical breadboard, and 2 linear actuators were connected to the stages to allow motion in the x- and y-directions. An objective lens (NT36-132; Edmund Optics, Barrington, NJ, USA) and a charge-coupled device (CCD) camera (IMB-20 FT; imi tech, Anyang, Korea) were equipped on both sides of a 160 mm scope tube. A brightness-controllable, circular light-emitting diode (LED) was set around the objective. The epi-illumination configuration thus enabled HTM to acquire reflected light images of opaque asbestos samples with good contrast by reducing the background intensity. Several optical filters were added to the basic composition of HTM to allow fluorescence imaging. An emission filter (ET605/70m; Chroma, Bellows Falls, VT, USA) and a dichroic mirror (T565 lpxr; Chroma) were used between the CCD camera and the scope tube, and an excitation filter (ET545/25x; Chroma) was added perpendicularly to the circular LED. A green LED (TouchBright X-3; Live Cell Instrument, Seoul, Korea) was used as a fluorescence light source so that the light filtered through the excitation filter excited the fluorescent dye on the asbestos and the emitted fluorescence could be detected. Figure 2 shows a schematic of our modified HTM device for dual-mode reflection and fluorescence imaging.  
The stages were automatically controlled by an application software, Zaber Console (Zaber Technologies, Vancouver, Canada). We used a trigger system to acquire asbestos sample images automatically according to the motion of the stages, which traveled a preset distance in each direction every second. We used a custom-made signal conductor control box (Board Lab, Incheon, Korea) for this work. The signals from the stage actuators were sent to the CCD camera with 0.5 s delay time after amplified in the control box. The travel distance was set to 650 \(\upmu\)m in the x-direction and 490 \(\upmu\)m in the y-direction in accordance with the area of the display window in an image acquisition software (CamViewer; imi tech). After obtaining 108 reflection images within a slide sample in the area of 5 \(\times\) 5 mm\({}^{2}\), fluorescence images were taken at the same locations with the green LED on.  
Figure 2: A schematic of DM-HTM system for both reflection and fluorescence imaging.
# Multi-Wavelength Path Loss Model for Indoor VLC with Mobile Human Blockage
###### Abstract  
Visible light communication (VLC) is one of the candidate technologies for the sixth generation (6G) networks. The path loss model is particularly important for link budget estimation and network planning in VLC. Due to the wideband nature and the extremely poor diffraction capacity of light, the path loss of the VLC channel is susceptible to wavelength dependence and blockage effect. In this paper, we propose a novel path loss model which can characterize the impact of wavelength dependence combined with mobile human blockage for both the single-LED (light emitting diode) and the multi-LED scenario. When there is no blockage in the channel, the multi-wavelength path loss under free space propagation is modeled with a small standard deviation of 0.262 in the single-LED scenario and a small root mean square error of 0.009 in the multi-LED scenario which indicates the high accuracy of the model. When considering the mobile human blockage, the blockage probability (BP) is modeled with full consideration of realistic human mobility and human body shadowing. The results indicate that the BP in single-LED scenario can reach 0.08, while the BP in multi-LED scenario is 0.022. This demonstrates that the distributed deployment of transmitters can effectively reduce the occurrence of the blockage state in VLC.  
VLC; path loss; human blockage; mobile; wavelength dependence +
Footnote †: journal: _electronics_
## 4 Future Work and Discussion
### Research Challenges and Future Directions for 6G IoT  
In the 6G IoT network, energy consumption of IoT devices is a major issue. All IoT devices connected to the network require significant resources in order to have all data received and transmitted to the registered base stations. Therefore, the 6G IoT network will require thousands of base stations in order to provide the necessary coverage, with each base station consuming about 2.5 up to 4 kW of energy [39]. Using protocols and resources that are energy efficient (such as solar power, thermal power, wind power, or even vibration power) will profit QoS in terms of energy consumption [40]. On the other hand, the lack of standards hinders the deployment of the 6G network as a technology in order to meet the stringent requirements 6G brings. The integration of the MODBUSprotocol [41] for 6G IoT communication will provide enhanced connectivity and speed, supporting the needed data rate and latency to IoT systems.  
The MODBUS protocol is a protocol originally developed in 1979 by Modicon for device communication based on master-slave architecture, where the master initiates and controls the communication and the slave responds back to the master's requests. MODBUS remains one of the fundamental protocols for 6G IoT communication and can be integrated with other modern IoT protocols due to its reliability, high-speed, high-capacity and robustness characteristics. We could consider for instance a smart building scenario where sensors and controllers use the MODBUS protocol using the 6G network. Real-time monitoring, connection security, and edge analysis will be the characteristic the MODBUS protocol can offer. Real-time monitoring will be able to send real-time data for the status of the equipment used, authentication and encryption will ensure reliability and security on the transmitted data, and edge analysis will detect any anomalies, triggering immediate corrections without any latency.  
From the hardware point of view, sensors, memory and network servers are used to support powerful and smart IoT and nano IoT devices and embedded wearables need to be redesigned in order to be energy efficient. From the privacy and security points of view, the growth of IoT device communication based on the 6G network brings new security and privacy challenges concerning risks to thread attacks. Key security aspects that need to be taken into consideration are end-to-end encryption, AI thread detection algorithms, and blockchain technology.  
AI integration, in combination with MODBUS and MQTT-SN, is going to be key task for future work. MODBUS is a protocol with low overheads and compatibility with edge devices, and AI with Raspberry Pi filtering will lead to lower power consumption and bandwidth reduction. MODBUS could offer faster and more structured communication, leading to lower energy consumption, and AI will perform data transmission optimization, sending data only when they are needed or during low-traffic periods. MQTT-SN could be used in combination with MODBUS on the edge layer, improving scalability and low-latency requirements.
# Automated Classification of the Tympanic Membrane Using a Convolutional Neural Network
## 2 Materials and Methods
### Deep Neural Network Model  
The CNN model for detecting the TM side and presence of perforation was built using Python programming language. The model comprises six layers (Figure 1). The convolutional layers contain 32 masks. The kernel size is \(3\times 3\) in all convolutional layers and \(2\times 2\) in all max-pooling layers. The fully connected layer includes 128 nodes, whereas the last layer has only two nodes presented as output probabilities. The available images were randomly assigned to training, validation and testing sets in the ratio of 60%:20%:20%, respectively. Regarding the detailed hyperparameters used in this study, we tried a lot of combinations of hyperparameters to optimize CNN training and finally used hyperparameters are as follows: batch size 32, number of epochs 400, SGD as an optimizer, learning rate 0.0001, momentum 0.9, Nesterov momentum. Training dataset was augmented with sheer range 0.2, rotation range 5, horizontal flip for normal vs. perforation training.  
The CNN was trained in the following two stages: first, to distinguish the TM side regardless of the lesion presence; second, to detect the presence of perforations (Figure 2). In the test phase, a class activation map (CAM) was applied to evaluate feature extraction. This process can effectively visualize feature variations in illumination, normal structures and lesions, which are commonly observed in images. The activation site was divided into eight parts as follows: external auditory canal, TM, tympanic annulus, malleolar short process, malleolar handle, umbo, cone of light, perforation margin and middle ear (Figure 3). The TM and external auditory canal (EAC) were divided into four subparts (anterior-posterior and superior-inferior). The CAM results were analyzed statistically using the Statistical Package for the Social Sciences (SPSS) software, version 22.0 (SPSS Inc., Chicago, IL, USA). Logistic regression was used to evaluate the statistical significance and odds ratio (OR).  
Figure 1: Composition of the CNN model. This model consists of two convolutional layers, two max pooling layers and two connected layers.
# A Snake-Like Robot with Envelope Wheels and Obstacle-Aided Gaits
## 2 Methods
### Control System  
#### 2.3.1 Control Command Code  
A control command designed for the wireless communication module in this paper is shown in Figure 12. A complete control command code consists of 25 bytes, of which 0-4 bytes are the master control and setting, 5 and 6 bytes are the telescopic joint control code, and the others are the rolling and yawing motors control code.  
Since the motor that controls the telescopic joint has only three states of elongation, contracting, and stop, two bits are required to control the state of one telescopic motor. This control system allows the control of 8 telescopic motors at the same time. For example, when byte 5 is \(0\times 01\), it means that the telescopic motor of No. 1 is selected to be activated. Currently, if byte 6 is \(0\times 01\), the telescopic motor rotates forward to elongate. If byte 6 is \(0\times 00\), the motor is reversed to contract.  
Byte 7-24 represents the rolling and yawing motors. Since one byte is 8 bits can represent \(2^{8}=256\) position states, with the angle range of the two motors as \(180^{\circ}\) and \(280^{\circ}\), the control accuracy can reach \(1.09^{\circ}\) and \(0.73^{\circ}\), respectively. It can meet the control needs in the case of low accuracy. In addition, it is feasible to improve accuracy by increasing the length of the control code.
# A Literature Review on Caching Transient Contents in Vehicular Named Data Networking
## 3 Transient Contents in VANETs: An Overview
### Minimizing Age of Information of Safety Information
To further reduce the AoI, the work in Reference [30] proposes a proactive queue management scheme that discards old updates waiting in the queue when a new sample arrives. More advanced scheduling mechanisms are proposed to minimize AoI and, sometimes, jointly satisfy other performance metrics, e.g., limiting the energy consumption or the network congestion [31]. More recently, the notion of AoI has been considered for analyzing ultra-reliable low-latency communications (URLLC) in 5G vehicular networks [32]. The proposal considers both periodic and stochastic arrivals of transient packets and allows vehicles to minimize their transmit power while ensuring URLLC communications based on a probabilistic measure of AoI violation.
# SOC Estimation with an Adaptive Unscented Kalman Filter Based on Model Parameter Optimization
## 2 Establishment of Battery Model and Parameter Identification  
The open-circuit voltage and internal resistance are the most basic components of an equivalent circuit model. All equivalent circuit models add other components on the basis of these two components to improve the model accuracy. Typical equivalent circuit models of lithium-ion batteries include the Rint model, the Thevenin model, the PNGV (Partnership for a New Generation of Vehicles) model and the multi-order RC loop model [22; 23; 24; 25]. For the complicated polarization characteristics of a battery, some studies deduce and suggest that the models with more parallel RC networks connected in series should have a much higher accuracy; however, at the same time, memory is consumed, waste calculations are performed and bad real-time applications are caused.
# Mechanical Property of Sn-58 Bi Solder Paste Strengthened by Resin
## 4 Conclusions  
This study has investigated the influences of the content level of epoxy resin on the wettability, microstructure, and shear strength of Sn-58 Bi solder joints. Based on these experimental results, the conclusions can be drawn as follows:  
1. The Sn-58 Bi solder paste with an appropriate amount epoxy of resin exhibited increased wettability because of the surface tension was reduced by the epoxy resin.
2. After reflow, it was observed that the epoxy resin promoted the reaction between Sn-58 Bi and Cu and improved the excessive enrichment of bismuth near the interface.
3. The addition of epoxy resin increased the shear strength of Sn-58 Bi solder joints; the shear strength of the Sn-58 Bi + 6 wt.% ER solder joint was found to be nearly twice as high as that of the Sn-58 Bi  
Figure 7: SEM images of fracture morphology of the Sn-58 Bi solder joints tested at different epoxy resin contents: (**a**,**a**1,**a**2) Sn-58 Bi, (**b**,**b**1,**b**2) Sn-58B I + 6% ER, (**c**,**c**1,**c**2) Sn-58 Bi + 9% ER, (**d**1) spectrum 5 and (**d**2) spectrum 6 for Figure 7(a1), (**e**) spectrum 7 for Figure 7(c1).  
solder joint. Deformation caused by shear force was observed from the fracture of the flux with epoxy resin, which affected the solder joint shear strength by mechanical resistance.
* Shear test results revealed that all solder joints had brittle fracture behaviors, while the fracture tended to exhibit a toughness and brittleness mixing morphology due to the addition of epoxy resin. However, carbon and oxygen were observed in the holes in the fracture when the epoxy resin was excessive, indicating that excess epoxy resin could decrease the shear strength of solder joints.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed materials and analysis tools; [PERSON] wrote the paper.  
This research was funded by the National Natural Science Foundation of China (grant No. 51675269) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).  
The authors declare no conflict of interest.
# The Impact of Technical Error of Measurement on Somatotype Categorization
## 2 Materials and Methods  
Sixty-eight physically active males (mean [SD] 24.8 [7.9] y; 79.8 [14.4] kg; 1.81 [0.07] m) were recruited to the study from the local community. Participants provided written informed consent prior to participating in the study. The study received approval from the Institutional Human Ethics Committee.  
Participants were recruited initially using purposive sampling for body composition assessment. Participants were tested on one occasion and were instructed to attend the session fully hydrated and having refrained from intense physical activity for the 24 h preceding testing. Anthropometric measures were taken from the participant, with a minimum of two measures taken at each site. If a difference existed between the first two measures of <5% for skinfolds and <1% for all other measures, a third measure was taken [4]. The two closest values were transferred to the TEM calculation.  
Participants' anthropometric profiles were measured by a Level 3 ISAK anthropometrist using ISAK protocols [4], and somatotype calculated in line with the Heath-Carter methods [13]. Mean TEM for skinfolds was 2.2% and for all other measures was 0.2%.  
Technical error of measurement was calculated for each individual anthropometric variable using Equation (1) as follows:  
\[\text{TEM}=\sqrt{\left(\frac{\Sigma(\text{sd})2}{2\text{n}}\right)} \tag{1}\]  
where sd = standard deviation (of two repeat measurements) and n = number of participants measured.  
This was used to calculate 95% confidence intervals (CI) for the individual variables, and for the overall somatotype calculation. Further, TEMs equivalent to those who train to become ISAK Level 1 (7.5% for skinfolds, 1.5% for all other measures) and Level 2/3 (5.0% for skinfolds, 1.0% for all other measures) in the post-course guidelines [4] were calculated and used to calculate equivalent 95% CI's for theoretical operators at the relevant qualification thresholds. Each individual participant was assigned a detailed and a simplified somatotype category. It was further analyzed if they were still assigned to this category based on their 95% CI's from the researcher's TEM (RTEM), a theoretical Level 1 ISAK anthropometrist TEM (L1 TEM) or a theoretical Level 2/3 ISAK anthropometrist TEM (L23 TEM) based on the allowable accreditation thresholds for these levels [4].
# Using Artificial Neural Network Models to Assess Hurricane Damage through Transfer Learning
## 4 Results and Discussion
### Damage Detection
#### 4.4.3 Flood Damage Comparison  
In Table 6, the top predictions for ResNet, MobileNet, and EfficientNet correctly classify the object as _flood damage_, and MobileNet again achieves the highest confidence score of 97.45% for that predicted class. The bounding box location in Figure 8b predicted by MobileNet more accurately encompasses the entire _flood damage_ area in comparison to the bounding box location in Figure 8a predicted by ResNet and Figure 8c predicted by EfficientNet. However, the second and third predictions made by ResNet and MobileNet are inaccurately classified as either a _damaged wall_ or a _damaged roof_, likely due to some features of the building in the background of the image that would be extracted to predict those classes. The same applies to the second prediction made by EfficientNet of the _structural damage_ class.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**ResNet**} & \multicolumn{2}{c}{**MobileNet**} & \multicolumn{2}{c}{**EfficientNet**} \\ \hline  & **Score** & **Type** & **Score** & **Type** & **Score** & **Type** \\ \hline \#1 & 52.46\% & flood damage & 97.45\% & flood damage & 48.79\% & flood damage \\ \#2 & 18.73\% & damaged wall & 24.39\% & damaged wall & 10.45\% & structural damage \\ \#3 & 12.63\% & damaged roof & 6.14\% & damaged roof & 10.30\% & flood damage \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of confidence scores and associated object categories for the top three predictions made on the image of _flood damage_ for each of the three models.  
Figure 7: Inference carried out on the image of a _damaged wall_ for each of the three models. The blue bounding boxes correspond to the correct prediction of a _damaged wall_ from the first row of Table 5.
# CRISPR-Cas Systems: Prospects for Use in Medicine
## 3 Genome Editing
### CRISPR-Cas9 Delivery Systems  
CRISPR-Cas9 can be delivered to the organism as a plasmid harboring genes encoding the entire editing system, or as a Cas9 protein in a complex with a guide RNA (gRNA), and in some cases as mRNA [54].  
Microinjection is suitable for delivering all three forms and is considered the safest and most effective method [55]. The advantage of the method is the lack of restrictions on the size of the injected component, but it is not suitable for in vivo application, since it requires injection directly into the cell.  
Another delivery option, quite versatile like microinjection, is electroporation, which uses an electric pulse to produce pores in the cell membrane. Electroporation is mostly used with bacterial cells, since eukaryotic cells are more sensitive to electric stress [51].  
Viral delivery systems based on adenoviruses, lentivirususes, and adeno-associated viruses (AAV), and phage-delivery systems are also quite popular. Phages are capable of specifically infecting bacteria, including drug-resistant strains, and replicating in them. Adenoviral delivery systems have a number of advantages, such as low immunogenicity, almost no risk of integration into the human genome, but their size imposes certain restrictions on the size of the packaged product. The main tool for genome editing, Cas9, is quite large, which creates certain difficulties when packing into a virus or a phage. Cas9 from _Staphylococcus aureus_ (Cas9\({}_{\text{Sau}}\)) is significantly smaller (by 1000 bp) than Cas9\({}_{\text{Spy}}\), thus it is more convenient for packaging into AAV, while its efficiency is comparable to Cas9\({}_{\text{Spy}}\)[56].  
Other delivery systems also include various organic and inorganic nanoparticles (lipid and gold nanoparticles), mesoporous silica particles. Delivery using nanoparticles has both advantages and disadvantages. Nanoparticles are safer than viral ones, but their efficiency is much lower. In addition, as with viral delivery systems, there is a limit to the size of the \"cargo\" that can be delivered with their help [51].
# LVRT and Stability Enhancement of Grid-Tied Wind Farm Using DFIG-Based Wind Turbine
## 4 Design of the DFIG System
### Grid-Side Converter (GSC) Control Scheme  
Figure 7 also illustrates the GSC controller and compensates for various error signals, and it contains four PI controllers. Additionally, \(d\)-axis (\(I_{gd}\)) and q-axis (\(I_{gd}\)) current components regulated the GSC reactive power (\(Q\)) and DC-link voltage (\(V_{dc}\)), sequentially. Additionally, the reference reactive power was fixed to zero, and the reference DC-link voltage was fixed to 1.2 kV (1.0 pu).