esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Testing of a Wearable System for Monitoring Car Drivers
## 3 Results
### Test Results  
The acquired data from both the wristband and the upper body device in both test modes in Figure 5 were analyzed to evaluate calm and aggressive driving behavior.  
Referring to the calm driving in the first test mode, the results are shown in Figure 6, indicating that none of the threshold values of the aggressive driving criterion in Table 1 were exceeded, resulting in an evaluation of the criterion for non-aggressive driving. Sensor data from the MAX30102 heart rate sensor, Grove GSR, and IMUs were recorded as reported in Figure 6a with a regular time without exceeding thresholds, confirming the effectiveness of the system in detecting calm driving. Figure 6b shows the acceleration magnitudes of the torso and neck with time evolution and values well below the threshold for aggressive driving according to the aggressive driving criterion in Table 1. The torso acceleration is quite constant, indicating that the driver did not move much during the test, while the neck acceleration, recorded by the head IMU sensor, shows motions that can be related to turning the head when looking around during driving or to following the motion of steering when turning the car on the road.  
Referring to the aggressive driving in the second test mode, the results are shown in Figure 7, indicating that several acquired data points exceed the threshold values. The results in Figure 7a for heart rate (in pulse/s) and skin conductance (in mS) do not show significant variation and have low values, possibly due to failure in the sensors' acquisition or driver acclimatization to aggressive driving. However, both neck and wrist acceleration in Figure 7b surpassed their thresholds, as the red circle highlights, meeting the criterion evaluation for aggressive driving.  
It is worth noting the similarity of the behavior in the experienced test mode in terms of the shape of the time evolution of the acquired data; however, the different car driving attitudes result mainly in a difference in the numerical values of the acquired data, as highlighted in terms of the acceleration of the neck and head of the car driver.  
In summary, while the IMU data effectively distinguish between calm and aggressive driving, data from the heart rate and skin conductance sensors do not show such a significant detection as expected, very likely due to physiological limitations, such as the need for more long-time monitoring and better sensor capabilities.
## 2 Experimental Study on Seismic Performance of SRHPC Frame Columns
### Quantization of SRHPC Framework Seismic Performance Index
#### 2.3.1 The Performance Index Quantization of SRHPC Frame Structures  
Based on the results shown in Table 2 and certain adjustment principles, the inter-story drift of SRHPC frame columns was adjusted to obtain the quantization value for the different seismic performance levels. The adjustment principles are as follows: (1) at the \"no damage\" performance level, the columns of the SRHPC frame structures should be crackless, and the allowable cracking degree of the infilled wall and the damage of non-structural components should be taken into consideration; (2) under the \"repairable\" performance level, the tensile reinforcement is yielded, the cover concrete of the frame columns partially spalls off, and core concrete does not crush; the shear crack width is less than 2 mm, and the residual deformation is no more than 1/400 [24]; (3) at the \"serious damage\" performance level, the building is on the edge of collapse. Therefore, protecting safety must be recognized as the main goal, and all factors that threaten safety should be considered.  
Based on the discussion above, the seismic performance objectives of SRHPC frame structures and corresponding quantitative values were obtained and are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Failure Stage**} & \multicolumn{6}{c}{**Failure Patterns**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Bending Failure**} & \multicolumn{2}{c}{**Shear Bond Failure**} & \multicolumn{2}{c}{**Shear Diagonal Compression Failure**} \\ \cline{2-7}  & \(\mathbf{I}\) & \(\mathbf{m}\) & \(\mathbf{I}\) & \(\mathbf{m}\) & \(\mathbf{I}\) & \(\mathbf{m}\) \\ \hline component cracking & \([1/300,1/190]\) & \(1/242\) & \([1/205,1/101]\) & \(1/151\) & \([1/135,1/110]\) & \(1/123\) \\ component yielded & \([1/160,1/115]\) & \(1/141\) & \([1/115,1/78]\) & \(1/110\) & \([1/90,1/60]\) & \(1/71\) \\ ultimate load & \([1/108,1/60]\) & \(1/78\) & \([1/83,1/51]\) & \(1/63\) & \([1/59,1/31]\) & \(1/35\) \\ component failure & \([1/55,1/32]\) & \(1/39\) & \([1/38,1/21]\) & \(1/30\) & \([1/25,1/18]\) & \(1/21\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The distribution value of inter-story drift of steel reinforced high strength high performance concrete (SRHPC) frame columns under different failure patterns.
## 3 Monitoring System and Optimized Building Operation
### Prediction Approach
#### 3.1.2 User-Driven Demand  
The prediction of the household electricity demand can contribute to an increase in the efficiency and as well to an optimized operational sequence of the buildings' systems [37; 38]. In this context, the extent to which the use of machine learning approaches can contribute to the prediction of household electrical power demand is investigated. The methods used are kNN and DTR.  
Regression using the kNN method is one of the fastest and easiest tools for machine learning. One reason for this is the low complexity of the model. While many other methods of machine learning train a function, the kNN regression stores the individual data points of a selected training data set. In a query, the input data is compared with the training data, and a point that comes as close as possible to the input is delivered as output. This point represents the nearest neighbor. It is possible to select several neighbors, whereby in a very simple model, the mean value of the Euclidean distance is delivered as the output point. However, it is also possible to use other parameters that allow certain data points to have a greater influence on the result than others.  
For the parameterization of the method, the number of neighbors, the weighting of the data points, and the algorithm to detect the nearest neighbors, as well as the regularization can be used [39; 30; 40].  
As already mentioned above, the model is a very simple one, which can produce relatively good predictions with a small number of characteristics (input data such as temperature, pressure,and irradiation). Furthermore, trends can often already be identified with this method, which is why this algorithm can be a suitable tool for generating initial results due to its fast programming [41].
## 2 Method and Principles
\(A\) is the energy modulation amplitude normalized to the initial energy spread of electron beam, which can be calculated by  
\[A = k_{L}KL_{u}[JJ]_{1}\sqrt{\frac{Z_{0}P_{L}}{\pi k_{L}Z_{R}(mc^{2}/e)^{2}}} /\gamma, \tag{8}\]  
where \(k_{L}\) is the wave number of the seed laser, \(K\) is the modulator dimensionless parameter, \(L_{u}\) is the period of undulator, \([JJ]_{1}\) is the polarization modification factor for a linearly polarization planar undulator, \(Z_{0}=377\)\(\Omega\) is the vacuum impedence, \(P_{L}\) is the laser power, \(Z_{R}\) is the Rayleigh length of the seed laser, and \(\gamma\) is the relative beam energy. After the energy modulation, the electron beam passes through a magnetic chicane with the dispersion strength \(R_{56}\) to convert the energy modulation into density modulation, and the longitudinal position of electron becomes  
\[\phi(\omega)=\phi(\omega_{0})+\tau_{0}(\omega-\omega_{0})+\frac{(\omega-\omega _{0})^{2}}{2\mu}+\ldots\, \tag{1}\]0  
The magnetic strength of chicane is required to be optimized to obtain sufficient large bunching factors at fundamental and high harmonics of the beating frequency. Here, we consider a typical high gain FEL to generate THz radiation using the bunched electron beam, the pre-bunched electron beam (\(\lambda=c/f\)) is sent into an undulator, which is resonant at the fundamental or high harmonics of the beating frequency. The electron beam will initiate coherent radiation and amplify it to saturation when passing through  
\[\phi(\omega)=\phi(\omega_{0})+\tau_{0}(\omega-\omega_{0})+\frac{(\omega-\omega _{0})^{2}}{2\mu}+\ldots\, \tag{1}\]1 The relation of the calculated beating frequency \(f\) (THz) (based on Equation (4) with the time delay \(\tau\) (ps) and the linear chirp rate \(\mu\) (\((\mathrm{rad/s})^{2}\)). the undulator. Using a combined pulse with a beating frequency of 30 THz, radiation with a frequency of 30 THz and 60 THz can be generated by amplifying the bunching signals at fundamental and second harmonic of beating frequency respectively. To cover the frequency range from 0.1 to 60 THz, the electron beam energy needs to be changed and variable gap radiators with different periods are required.
# A Cross-Medium Uplink Communication Model Merging Acoustic and Millimeter Waves
## 2 Model of Cross-Medium Communication Channel
### SNR Model  
The initial step was to capture background noise information. Since waves possess mechanical properties, they can be viewed as structural disturbances under similar conditions. As a result, the background noise could be collected in advance and incorporated into the model as additive noise \(N(t)\). To assess the total energy \(E_{N}\) of the background noise, a Fourier transform was applied to \(N(t)\) and a spectral analysis was conducted.  
\[F(\omega)=\int_{-\infty}^{\infty}N(t)e^{-j\omega t}dt \tag{10}\]  
\[E=\sum F(\omega) \tag{11}\]  
The acoustic signal emitted by the speaker was a single-frequency signal. As observed in [28], a significant amplitude was only observed in water surface waves with the same frequency as the vibrating point source. The energy of the surface wave can be derived from the energy of a single-frequency signal using the following formula:  
\[E_{s}=\frac{1}{2}\delta^{2}T \tag{12}\]  
where \(T\) is the time of acquisition, and the expression for \(SNR\) is deduced as:  
\[SNR=10 lg(\frac{P_{s}}{P_{n}}) \tag{13}\]  
\[SNR=10 lg(\frac{\zeta^{2}\Big{(}\frac{2p_{i}}{\omega\rho v}+\eta p_{i}^{\ 2} \Big{)}^{2}T+2\varepsilon}{2E_{N}}) \tag{14}\]  
where \(P_{s}\) stands for the power of the signal and \(P_{n}\) is the power of the noise. By substituting the system parameters into the SNR calculation Formula (13), we can derive the SNR model as expressed in Formula (14). In addition, \(\zeta\) represents the correction coefficient for underwater acoustic interference caused by underwater multipath attenuation; \(\varepsilon\) denotes the correction coefficient for the signal gain resulting from the energy component of the box vibration at the target frequency, which is very small; \(\eta\) is the nonlinear correction coefficient for the linear fluctuation theory.  
In constructing this model, some fundamental assumptions were made. We assumed that the water surface waves collected by the radar were accurate and unaffected by other interference factors. Moreover, we assumed that the underwater acoustic waves were transmitted in the form of spherical waves, aiding in simplifying the model and enhancing the accuracy of predictions. This channel model differs significantly from existing models in several key aspects. By introducing correction coefficients, our model optimized the influence of environmental factors to accurately reflect the actual communication environment. Unlike the linear waveforms commonly used in existing models, we carried out nonlinear optimization on the waveforms of the water surface waves, aiding in describing and predicting communication performance more accurately. Through these optimizations, our model is capable of providing more intuitive and practical communication performance metrics, offering valuable references for practical applications.
# Model-Free-Based Single-Dimension Fuzzy SMC Design for Underactuated Quadrotor UAV
## 6 Hardware Configuration and Experiment
### Error Rates and Flight Mode of Underactuated Quadrotor  
The Figure 24 shows the performance of proposed control algorithm when implemented in practical scenario. One may see the underactuated quadrotor while maneuvering produces the same position error rates as expected in a closed environment. The most prominent guidance is involved here by using GPS module. This is because this module further comprises of several core processing units and sensors such as optical-odometer, 3D sensing and obstacle sensing images. Every unit facilitates in tracking the helical trajectory so that optical odometer performs the measurements related to the speed of the proposed underactuated quadrotor, and 3D sensing helps in sensing the three-dimensional space. Obstacles are also tackled properly along with precise vision-based positioning system.  
There is a minor time delay while executing the control inputs after the execution of experimental setup in open environment only. This can be improved using Pade approximation method and opting for better wireless remote controller but is not needed in closed environment, as demonstrated in this paper. It should be noted that the experiment was performed in a closed jurisdiction by keeping the same conditions within the project lab. The flight performed by quadrotor was the same helical path within the closed jurisdiction, as shown in the Figure 25.  
Figure 24: Position error rates while maneuvering using MFSDF-SMC during hardware implementation.
# Polarization Sensitive Optical Coherence Tomography: A Review of Technology and Applications
## 3 PS-OCT Applications
### PS-OCT in the Eye
Finally, PS-OCT also proved useful to enhance contrast for imaging pathologic structures in less common retinal diseases such as macular telangiectasia and Stargardt disease [163; 164]. PS-OCT of the anterior eye markedly improves the contrast for birefringent, collagenous tissues such as the cornea, sclera, and tendons as well as for the trabecular meshwork [40; 84; 94; 127]. The additional contrast has been exploited for automated, feature-based tissue discrimination [126]. Substantial changes in the birefringent appearance of the cornea can be observed in keratoconus as shown in Figure 6a-e, such that PS-OCT was proposed as a diagnostic method for this disease [165; 128]. Since corneal birefringence depends on the microstructure, PS-OCT was also proposed for imaging changes during corneal crosslinking therapy [166]. After trabeculectomy, which is a surgical procedure for glaucoma treatment, the evolution of filtering blebs was monitored by PS-OCT (Figure 6f) [167; 168; 169]. In the sclera, PS-OCT was used to image necrotizing scleritis [170] and to study birefringence changes related to increased intraocular pressure [148; 171]. _Appl. Sci._**2017**, 7, 474  
Figure 5: PS-OCT in the posterior eye. (**a–d**) PS-OCT of a patient with late-stage AMD (adapted with permission from [120], Optical Society of America, 2014). (**a**) Reflectivity B-scan; (**b**) DOPU B-scan (color map: 0–1); (**c**) Depolarization in the retinal pigment epithelium (RPE, red) and choroid (green) overlaid on reflectivity image; (**d**) Fundus map indicating thickness of depolarizing pixels at the level of the RPE; (**e**) Relation of DOPU and melanin density assessed by histology in RPE/choroid of rat eyes (adapted with permission from [26], ARVO, 2015). (**f,g**) PS-OCT based layer segmentation in an AMD patient with drusen (adapted with permission from [121], SPIE, 2010). (**f**) Reflectivity B-scan with segmented inner limiting membrane (blue), RPE (red), and Bruch’s membrane (green). Drusen can be observed as bumpy elevations of the retina at the RPE level; (**g**) Drusen thickness map; (**h**) Drusen characteristics assessed by PS-OCT (courtesy by Dr. [PERSON], Medical University of Vienna, Austria). Depolarizing pixels are marked in red. (**i,j**) Birefringence in the retinal nerve fiber layer (RNFL) imaged by PS-OCT (adapted with permission from [18], Optical Society of America, 2014). (**i**) Fundus reflectivity map; (**j**) Fundus map showing increased phase retardation caused by the RNFL around the optic nerve head and by [PERSON]’s fibers around the fovea.
# A Comprehensive Survey of Recent Routing Protocols for Underwater Acoustic Sensor Networks
## 3 Routing Protocols for UWSNs
### Re-Pbr  
RE-PBR [56] is a depth-based routing protocol. This scheme proposes the use of a triangle metric to determine the quality of links. The triangle metric determines link quality not only based on successfully received packets, but also takes into account the packets that are not received successfully. An information dissemination scheme is proposed to share information among neighboring nodes. This information is used to select a forwarder during the data transmission phase. Data forwarding decisions are made based on three parameters, which are: (1) Link quality between the sender and the candidate forwarder. This parameter ensures the reliable delivery of packets. (2) Residual energy of the candidate forwarder, which ensures load balancing; and (3) Depth, which improves end-to-end delay (Equation (18)). RE-PBR uses an implicit Ack mechanism in order to reduce communication overhead and improve reliability. Implicit Ack refers to a mechanism in which the receiving nodes do not send ACKs explicitly. Instead, the sender node assumes successful transmission to the next hop node if it overhears the same packet being retransmitted by another node. Moreover, the use of multi metric data forwarding by RE-PBR improves its overall performance by preventing void creation, balancing energy dissipation, achieving reliable communication and end-to-end delay. A drawback of the proposed scheme is that it is designed for grid networks, and in the case of random network topologies, may result in performance degradation. Moreover, for link quality estimation, it requires transmission of packets other than the usual hello packet, thus increasing communication overhead:  
\[PathCost(x,y)=\left(1-\frac{RE_{y}}{TE}\right)+\left(1-\frac{\Delta d_{(x,y)}} {\Delta d_{max}}\right) \tag{18}\]  
where \(RE_{y}\) and \(TE\) denote the residual energy and total energy of node y respectively. \(\Delta d_{(x,y)}\) denotes the link quality between sender \(x\) and forwarder \(y\). \(\Delta d_{max}\) is a system parameter that is set based on the environment.
## 5 Experimental Evaluation
### Functional Validation
#### 5.2.3 Hybrid UDP/HTTP and MQTT-Based Communications  
In this test case, OBU 1 communicates with the Edge Geoserver using UDP/HTTP protocol, and OBU 2 and the Edge Geoserver use the MQTT protocol to communicate via the Edge MQTT broker. The registration, authentication, and subscription to ROI steps of OBU 1 are identically performed, as described in test case 1. Similarly, the connection of OBU 2 to the Edge MQTT broker and the subscription of OBU 1 to a topic that is based on its location is performed, as described in test case 2. Subsequently, we trigger the transmission of a DENM message from OBU 1 towards the Edge Geoserver, and the Edge Geoserver publishes the DENM towards the Edge MQTT broker, which subsequently distributes the DENM message to OBU 2. In the opposite direction, OBU 2 triggers the transmission of a DENM message towards the Edge MQTT broker, and the Edge MQTT broker distributes the DENM message to the Edge Geoserver, which finally forwards the DENM message to OBU 1 over UDP.
## 3 System Model and Problem Formulation
### Problem Formulation
subject to  
\[\mathbf{L}=[\mathbf{L}_{n}^{U},\mathbf{j},\mathbf{k},\mathbf{A}] \tag{7}\] \[\sum_{t=1}^{T}j_{t}=1,\forall t\in\{1,\dots,T\}\] (8) \[\sum_{t=1}^{T}k_{t}=1,\forall t\in\{1,\dots,T\}\] (9) \[\sum_{n=1}^{N_{t}}\sum_{t=1}^{T}P_{n}^{t}=P^{U},\forall n\in\{1, \dots,N_{t}\},\forall t\in\{1,\dots,T\}\] (10) \[j_{t},k_{t}\in[0,1],\forall n\in\{1,\dots,N_{t}\},\forall t\in \{1,\dots,T\} \tag{11}\]  
In the formula, \(min()\) denotes selecting the minimum value. \(\mathbf{L}=[\mathbf{L}_{n}^{U},\mathbf{j},\mathbf{k},\mathbf{A}]\) represents all the decision variables of this optimization problem. Formulas (8) and (9) indicate that all bandwidth resources will be fully utilized to prevent bandwidth waste. Formula (10) states that the forwarding power of the UAV is fully allocated, with the total power limited to \(P^{U}\). Formula (11) specifies that the distribution ratio value is constrained to be between 0 and 1. The decision variable \(\mathbf{L}\) includes a mix of continuous and discrete elements. Given the nonlinearity of the objective function, this constitutes a mixed-integer nonlinear programming (MINLP) problem, which is non-convex [24; 43]. In the presence of limited resources, the optimization among goals is contradictory and conflicting. To accommodate changes in the needs of IoT devices and facilitate rapid resource allocation adjustments, a set of feasible solutions should be pre-established. Coincidentally, multi-objective evolutionary algorithms excel at solving this type of problem. Therefore, in the next section, the solution using the improved MOEA/D algorithm is introduced.
# Role-Based Access Control Model for Inter-System Cross-Domain in Multi-Domain Environment
## 3 Proposed Model  
### Overview  
In order to solve the problems of role naming conflicts, inter-domain management conflicts and cross-domain sharing difficulties. RBAC-IC divides roles into abstract roles and specific roles. Abstract roles do not need to assign permissions and authorize users. It is to define the responsibilities of positions contained in the system, set inheritance relationships and constraints among roles, and form the mapping various jobs or positions to system roles. Specific roles are an instantiation of an abstract role, and its purpose is to grant it to users to obtain the appropriate permissions. The specific role needs to be associated with an abstract role, and assign corresponding permissions to it according to the requirements. At the same time, the specific role inherits the role inheritance and constraints of the associated abstract role. Thus, RBAC-IC has the characteristics of support role name repetition, inter-domain isolation management and fine-grained cross-domain sharing.  
In order to solve the problem of platform-domain management conflict, RBAC-IC divides platform personnel into three categories: platform administrators, domain administrators and ordinary users (users). The platform administrator is responsible for management operations for platform level such as platform configuration, system development and configuration (creating abstract roles, creating permissions, etc.), and deploying systems for domain servers. The domain administrator is responsible for creating specific roles of the deployed system, assigning permissions to specific roles, and authorizing ordinary users. At the same time, when users in a local domain apply for cross-domain access, the local domain administrator is responsible for sending cross-domain authorization requests to the application domain administrator. Ordinary users are users who use the application system. After granting specific roles, they can execute permissions by establishing sessions. The sessions activate a subset of the specific roles that ordinary users have. The permissions available to ordinary users are the aggregate of the permissions of all roles in activated sessions. Ordinary users need permission discrimination to access resources. After passing the discrimination, they can read or manipulate system data. Thus, RBAC-IC has the characteristics of platform-domain isolation management.  
A role-based access control model for inter-system cross-domain in multi-domain environment (RBAC-IC) is defined in Figure 2.