esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
In the field of healthcare, different stakeholders prefer metaverse platforms so they can provide different remote medical services to patients. For a better performance of the remote monitoring and telemedicine systems, the medical stakeholders are diving into the healthcare metaverse by using augmented and virtual reality. By using the metaverse platform, doctors can virtually examine their patients without the need for in-person visits. The metaverse is a mixture of virtual and augmented reality, and it uses other technologies such as blockchain, 3D objects' detection, natural language processing, and facial recognition [2; 3]. The metaverse can also help medical experts while performing surgeries. The first surgery in the metaverse environment was performed at Johns Hopkins Hospital back in early 2020. While carrying out this operation, experts made use of AR-based headsets developed by Augmedics [4]. Medical experts can also use the metaverse platform for learning and training purposes. For instance, doctors and students can virtually learn practical procedures. The success of this practice led many medical institutions to shift from manual learning to virtual reality, mixed reality, and AI-supported systems to effectively train their employees and students [5]. In the case of the mirror world, different people sitting in different places can meet and play games together. Following this idea, doctors can suggest different games for their patients to play that are helpful to maintain their mental health. Although many companies are still at the implementation stage of their metaverse platform, it appears that it will be convenient for medical experts in the future as it allows for a full and detailed visualization of the human body, which can be used as a surgical training environment for medical practices [6; 7]. This metaverse environment in which many technologies and tools can be used in combination will improve healthcare services, thus improving the user's life expectancy by facilitating the anti-aging process. In this paper, we present the following contributions:  
* We show the potential of the metaverse in supporting the digital anti-aging process and increasing the life expectancy of patients. * We introduce a technological overview of healthcare services for the metaverse, with emphasis on the eventual opportunities. * We highlight the possible challenges of the integration of healthcare services in the metaverse enviroment. In Section 2, we discuss related work. Then, we discuss the metaverse use case as a state-of-the-art for this paper in Section 3, and we present a technological overview of the metaverse for the healthcare industry in Section 4. Furthermore, in Section 5, we mention the challenges of the metaverse in the healthcare industry, with future directions and a conclusion to the article in Section 6.
## 4 Results
### Influence of Plants on the Temperature and Power Generation Capacity of PV Glass Curtain Walls
#### 4.2.3 Power Generation Capacity of PV Glass Curtain Wall  
During the period from 11:10 a.m. to 2:10 p.m., the average surface temperature of the A\({}_{1}\) photovoltaic glass curtain wall was 50.4 \({}^{\circ}\)C. Its minimum power generation capacity was 100 w, the maximum was 455 w, and the average was 371 w. The power generation capacity experienced sharp drops at three moments: 11:40 a.m., 1:10 p.m., and 2:00 p.m., with relatively large fluctuation ranges. During the testing period of the A\({}_{2}\) photovoltaic glass curtain wall, the average surface temperature was 40.3 \({}^{\circ}\)C. Its minimum power generation capacity was 473 w, the maximum was 542 w, and the average was 505 w. Overall, the fluctuation range in power generation capacity was relatively small (Figure 13). The planting of _Epipremum aureus_ resulted in an increase in the average surface temperature of the A\({}_{2}\) photovoltaic glass curtain wall by 10.1 \({}^{\circ}\)C, with the minimum power generation capacity increasing by 373 w, the maximum power generation capacity increasing by 50 w, and the average power generation capacity increasing by 131 w. Additionally, the performance of its power generation was more stable.  
Figure 12: Surface temperature of A\({}_{1}\) and A\({}_{2}\) PV glass curtain walls.  
In summary, by planting _Epipremum aureus_ on the inner side of the photovoltaic glass curtain wall, the physiological activities of the plants, such as photosynthesis and transpiration, effectively reduced the temperature of the micro-environment and the surface temperature of the photovoltaic glass curtain wall. As the surface temperature of the photovoltaic glass curtain wall decreases, the power generation capacity of the photovoltaic system is significantly enhanced.
## 4 Discussion
It eliminates manual or ad-hoc approaches, enabling faster decision-making and reducing administrative burdens. The system can generate optimized driver schedules, taking into account factors such as transport requirements, driver availability, and regulatory constraints. The system could easily be configured to consider and enforce regulatory requirements such as maximum driving times, rest periods, and other labor regulations [29]. By automating the assignment process, it helps ensure compliance with these regulations, minimizing the risk of violations and associated penalties. Regarding safety, the system can also help manage driver fatigue, ensuring that drivers are adequately rested and capable of safely operating the locomotives. This contributes to maintaining a high level of safety in rail freight operations. Real-time adaptability is another advantage of using an automated optimization system [30]. When disruptions occur, such as driver unavailability due to illness or unexpected events, automated optimization systems can quickly analyze the situation and reassign available drivers to fill the gaps [31]. By identifying the most suitable replacements based on various factors like tiredness score, proximity, and regulatory compliance, the system minimizes the impact of disruptions on the overall schedule. In the event of disruptions along a specific route or at a particular location, the automated optimization system can rapidly reallocate an optimal driver for the alternative routing by analyzing available drivers and resources, ensuring timely delivery, and minimizing the impact of disruptions on the entire transport network.
## 3 Materials and Methods
### CipherML  
The proposed framework uses CipherML (Siemens SRL, Brasov, Romania )--an in-house developed library for privacy-preserving machine learning, which is based on the MORE encryption scheme. The library can be used to train standard neural network models, but also to make inferences (predictions) based on MORE homomorphically encrypted data.  
Neural network models are composed of repetitive blocks of computation based on a small set of simple operations over rational numbers (e.g., multiplication, addition, division, subtraction, exponential and logarithm). By leveraging the MORE scheme's homomorphism and direct support for floating point data, the functionality of neural network models can be formulated to account for operations on ciphertext data (e.g., scalar multiplication becomes matrix multiplication), enabling cloud-based training and inference on homomorphically encrypted data in a manner similar to that of classical neural networks (i.e., standard feed-forward and backpropagation). Hence, CipherML extends the functionality of classical neural network models to be available for operations on MORE homomorphically encrypted data.  
The library provides a high-level Keras-like API [26] that enables quick prototyping of privacy-preserving neural network models with minimal code. The learning-based models can be defined by stacking one building block on top of the other, and then trained on MORE homomorphically encrypted data by simply calling the fit method. Written in the C++ programming language, the CipherML library includes implementations of widely used building blocks in neural networks, such as computational layers, activation functions, objective functions, and optimizers. In addition, the library provides support for convolutional neural networks through temporal and 2D spatial convolution layers. It also includes other common utility layers, such as dropout and average pooling for both 1D temporal data and 2D spatial data. Max pooling cannot be formulated because it is not possible to perform comparison operations on ciphertext data. As for activation functions, the current version of CipherML includes the sigmoid, hyperbolic tangent (tanh), and softmax functions. These non-linear functions are formulated as matrix functions. Stochastic Gradient Descent (SGD) is currently the only optimizer available. Other optimizers (e.g., ADAM [27]) may be integrated to improve performance. More advanced training strategies such as gradient clipping, learning rate decay, or early stopping are not supported because they rely on comparison operations (ciphertext values cannot be used in conditional statements).
# Clustering-Based Segmented Regression for Particulate Matter Sensor Calibration
## 2 Materials and Methods  
### Problem Background  
Nowadays, the sensor-based air pollution sensing system has become a key complement to standardized air pollution monitoring architecture. Compared to conventional technologies with precision equipment, it provides fine-grained information in urban air pollution monitoring because low-cost sensors can support large-scale deployment. Meanwhile, it is acknowledged that low-cost sensors suffer from measurement performance limitations due to varied interference factors including sensor drift [14], working conditions (such as temperature and humidity) and cross-sensitivity [15].  
To guarantee the quality of sensory data quality continuously, kinds of system-level sensor calibration methods are developed like collaborative calibration [16], blind calibration [17] and transfer calibration [18]. Although in-field calibration is a relatively basic technology, it still plays an important role in sensing system deployment because it has access to reliable reference from trusted standard monitoring station.  
For example, as shown in Figure 1, in-field calibration can be conducted when the sensor is deployed in close proximity to the station providing trusted measurements as reference or ground truth. They are in co-location and bear common observation on environmental conditions in the same time period. In-field calibration centers on finding a fitting function or regression model that can estimate the ground truth from sensory data.  
In detail, particulate matter monitoring is now a popular issue in urban atmosphere environment monitoring and PM sensors are widely used for sensing system deployment. Common PM sensors are based on optical principles and suffer from measurement deviations due to relative humidity and temperature. In-field calibration on PM sensors under practical working conditions will enhance sensor measurement performance with efficient deployment compared to pre-deployment calibration conducted under a laboratory setting. In essence, regarding sensory data as a series of input variables, the target of in-field PM sensor calibration is to find a function or model. It is then able to map these input variables to a ground truth estimation given by a standard monitoring station.
# Improving Spatial Reuse of Wireless LAN Uplink Using BSS Color and Proximity Information
## 3 Proposed Method
### Protocol Details
#### 3.2.1 Neighbor Table  
In PSC-UL, APs and stations maintain neighbor tables where the RSSI of neighboring APs are stored. An example neighbor table is shown in Table 1. The neighbor table stores the RSSI of neighboring APs, measured using beacons sent from the APs. Each entry of the table stores BSSID, BSS color, and the RSSI value. The RSSI value of each AP is updated  
Figure 5: A scenario illustrating how a node determines concurrent transmission in the proposed method.  
using EMA (Exponential Moving Average) as shown in Equation (9). In the equation, \(R_{t}\) is the average RSSI at time \(t\), \(S_{t}\) is the RSSI sampled from a beacon at time \(t\), and \(\alpha\) is a value between 0 and 1.  
\[R_{t}=\alpha\cdot S_{t}+(1-\alpha)\cdot R_{t-1} \tag{9}\]  
In Table 1a, node B has four entries in its neighbor table, including its associated AP, AP\({}_{2}\). In Table 1b, AP\({}_{2}\) has four entries from which it can receive beacons. While AP\({}_{4}\) (BSSID: 00:00:00:00:00:04) appears in the neighbor table of AP\({}_{2}\), it is too far from node B such that node B cannot hear its beacon. Periodically, an AP sends its neighbor table to all the associated stations. Thus, the stations have two neighbor tables: one that stores RSSI of APs measured at the node, and one that stores RSSI of APs measured at its AP.
## 6 Discussion and Conclusions
### Graphene/GO (rGO)-Based RF Biosensors  
In passive-type RF biosensors (Figure 5 and Table 3), the possibility of RF biosensors based on GO (rGO) sensing materials using passive RF devices and circuits, e.g., resonator and TL, was demonstrated. They examined the sensing behavior of diverse RF electric parameters, such as resonant frequency, reflection or transmission coefficient, permittivity, resistance, inductance, conductance, and capacitance. However, these studies focused on pilot studies for RF sensing with different analytes and concentrations. Thus, there are many parameters to systematically validate sensing performance, such as the sensitivity, selectivity, and repeatability of proposed RF biosensors with excellent merits, in other words, the direct and straightforward detection of an analyte.  
In recent years, novel graphene-based RF biosensors have been proposed for wireless biosensing platforms. First, graphene-based contact lenses (Figure 6) showed the new paradigm for a real-time and continuous wireless sensing platform. This sensor focused on detecting biomolecules, glucose, and cortisol, through tear fluid. Moreover, the sensing information continuously transfers into an RFID reader and then records real-time data. However, there is a risk of heating the human eye due to the sustainable illumination of the radio-wave. For this reason, it was demonstrated that graphene could absorb the radio wave and then dissipate the heat (Figure 7). This is very vital research for wearable and wireless sensing platforms in and on the human body.  
Next, the graphene-based stress sensor (Figure 8) is a good candidate for a wireless biosensing platform. The chip-level sensor could detect cortisol, a stress biomarker, using sweat and the working electrodes coated with graphene. This sensor can be readily compatible with a smartphone system and also be suitable for POC diagnostics. Finally, the graphene-based wireless bacteria sensor (Figure 9) is another good candidate for a real-time and continuous pathogenic bacterium sensing platform. First, this sensor has great merit in detecting the single pathogenic bacterium, _E. coli_, but needs complex functionalization for specific bacterium detection.
## 3 Controller Design
### Fast-Terminal Super-Twisting Reaching Law  
Although traditional super-twisting reduces chattering compared to the traditional reaching law, the reaching time still needs to be shortened to achieve better performance. In order to improve the performance of the traditional super-twisting reaching law, a new type of FTSTRL is used, which is expressed as  
\[s=-k_{1}|s|^{\frac{1}{2}}\text{sgn}(s)-k_{2}\int\limits_{0}^{t}\text{sgn}(s)d \tau-k_{3}s^{\frac{5}{7}}-k_{4}s. \tag{11}\]  
where \(k_{1},k_{2},k_{3},k_{4},p,q\) are positive scalars and \(p>q\). When the system state \(s\) is far from zero, the convergence time is mainly determined by the terminal attractor \(s^{\frac{4}{7}}\) and \(k_{1}|s|^{\frac{1}{2}}\text{sgn}(s)\). However, when the system state \(s\) is close to the equilibrium state \(s=0\), the convergence time is primarily determined by the equation \(\dot{s}=-k_{4}s\) with \(s\) exhibiting an exponential decay. Therefore, Equation (11) incorporates a terminal attractor, ensuring finite-time convergence of the system state, while retaining the rapidity of linear sliding modes near the equilibrium state. This results in the system state converging quickly and accurately to the equilibrium state. The inclusion of the terminal term \(k_{3}s^{\frac{5}{7}}\) enhances the dynamic response performance of the system when \(|s|\) is relatively large. By selecting a sufficiently small \(q/p\), the system state can reach a sufficiently small area of the sliding surface and then converge to the equilibrium state along the sliding surface.
# A Method for the Design of Concrete with Combined Steel and Basalt Fiber
## 6 Numerical Example  
The efficiency of the proposed methodology is demonstrated in a numerical example aimed at the design of fine-grained HFRC composition. The desired values of compressive and flexural tensile strengths of concrete at 28 days are 70 MPa and 17 MPa, respectively. The aggregates' mix includes crushed granite stone 2-5 mm and sand with real density of 2700 kg/m\({}^{3}\). The density of cement is 3100 kg/m\({}^{3}\), SF 7850 kg/m\({}^{3}\) and BF 2700 kg/m\({}^{3}\). The design process includes the following steps:  
* Selecting the fiber content and W/C for concrete with given strength values (Table 6). In our case SF = 80 to 120 kg/m\({}^{3}\), BF = 0 to 4 kg/m\({}^{3}\), W/C = 0.3 to 0.4;
* Assuming minimal SF content (80 kg/m\({}^{3}\)), the required water-cement ratio and cement content, yielding the desired concrete strengths, are found using the nomogram (blue line in Figure 9);
* Translating the obtained values (W/C = 0.3, C = 507 kg/m\({}^{3}\), SF = 80 kg/m\({}^{3}\), BF = 4 kg/m\({}^{3}\)) in coded form: \[x_{1}=\frac{(\text{C}-500)}{50}=\frac{(507-500)}{50}=0.14,\ x_{2}=\frac{(\text{W}/ \text{C}-0.35)}{0.05}=\frac{(0.3-0.35)}{0.05}=-1\] \[x_{3}=\frac{(\text{SF}-100)}{20}=\frac{(80-100)}{20}=-1,\ x_{4}=\frac{(\text{ BF}-2)}{2}=\frac{(4-2)}{2}=1\]
* Substituting of the coded values in the regression equation (Table 6), calculating the concrete compressive strength \[f_{\text{c}}=78.4+5\cdot 0.13-14.2\cdot(-1)-0.1\cdot(-1)+1\cdot 1-1.1\cdot(0.13)^{2}-0.6\cdot(-1)^{2}-0.7\cdot(-1)^{2}-1.3\cdot 1^{2}-0.1\cdot 0.13\cdot(-1)+0.2 \cdot 0.13\cdot(-1)-0.35\cdot(-1)\cdot(-1)=91.4\text{ MPa}.\] Verifying if the requirement is satisfied: 91.4 \(\geq\) 70 MPa
* Calculating the water demand, corresponding to W/C and cement content using Equation (3): \[\text{W}=\text{C}\cdot\text{W}/\text{C}=507\cdot 0.3=152\text{ kg/m}^{3}.\]
* Calculating the superplasticizer content for known composition parameters: SP = 0.93% of the cement weight.
* Calculating the aggregates content using Equations (4)-(7): \[V_{A}=1-\left(\frac{507}{3100}+\frac{152}{1000}+\frac{80}{7850}+\frac{4}{2700 }\right)=0.673\text{ m}^{3}\] \[m_{A}=V_{A}\,\rho_{A}=0.673\cdot 2700=1817\text{ kg/m}^{3}\] \[m_{S}=m_{A}\cdot 0.45=1817\cdot 0.45=818\text{ kg/m}^{3},\ m_{C,S}=m_{A}\cdot 0.55=1817\cdot 0.55=999\text{ kg/m}^{3}\]  
The final concrete proportioning is: 152 kg/m\({}^{3}\) of water, 507 kg/m\({}^{3}\) of cement, 818 kg/m\({}^{3}\) of sand, 999 kg/m\({}^{3}\) of crushed granite stone fraction 2-5 mm. The Melflux 2651f superplasticizer content is 0.93% by cement weight, SF and BF contents are 80 kg/m\({}^{3}\) and 4 kg/m\({}^{3}\) correspondingly. This mix design method, like any other concrete mix design method,gives a preliminary mix proportion. The calculated values should then be tested and adjusted to get the final mix design values.
## Materials and Methods  
### Materials  
#### 2.1.1 Alkaline Activator  
The alkaline activator used for the AASC in this study was prepared using water glass and sodium hydroxide. The water glass was supplied by Julide Chemical Co., Langfang, China and contained 10.3%, 32.1%, and 57.6% of Na\({}_{2}\)O, SiO\({}_{2}\), and H\({}_{2}\)O, respectively. Sodium hydroxide pellets with a purity of 96% were supplied by Dalu Chemical Reagent Factory, Tianjin, China.
# Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis
## 3 Results
### Common Wearable Sensors Used in Gait, Balance, and RoM Analysis
#### 3.1.2 Smart Devices  
Smart devices, such as smartphones and smart watches, are very popular because of their low cost, high availability, and capability to behave as an IMU device. Smart devices contain similar componentry as IMUs including accelerometers, gyroscopes, and magnetometers. Researchers investigate the potential use of smart devices to assess gait, balance, and RoM to reducethe level of obtrusiveness that using multiple devices can introduce and to increase portability. There are studies that implement smart devices to measure trunk movements and postural stability to assess balance [23; 29; 32; 33; 52; 68]. Other studies quantify gait parameters, including symmetry and variability [35; 36; 65; 66].
# NASA Global Satellite and Model Data Products and Services for Tropical Meteorology and Climatology
## 2 Satellite Missions, Projects and Data
### Datasets for Tropical Meteorology and Climatology

Table 3: NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) satellite and model datasets recommended for tropical meteorology and climatology.